diff --git a/.cppcheck_suppressions b/.cppcheck_suppressions index 5b140152e7f08..74e4de15becf3 100644 --- a/.cppcheck_suppressions +++ b/.cppcheck_suppressions @@ -1,21 +1,10 @@ -arrayIndexThenCheck -assignBoolToFloat +*:*/test/* + checkersReport -constParameterPointer constParameterReference -constStatement -constVariable -constVariablePointer constVariableReference -containerOutOfBounds +// cspell: ignore cstyle cstyleCast -ctuOneDefinitionRuleViolation -current_deleted_index -duplicateAssignExpression -duplicateBranch -duplicateBreak -duplicateCondition -duplicateExpression funcArgNamesDifferent functionConst functionStatic @@ -23,37 +12,22 @@ invalidPointerCast knownConditionTrueFalse missingInclude missingIncludeSystem -multiCondition noConstructor noExplicitConstructor noValidConfiguration -obstacle_cruise_planner passedByValue preprocessorErrorDirective -redundantAssignment -redundantContinue -redundantIfRemove redundantInitialization -returnByReference -selfAssignment -shadowArgument shadowFunction shadowVariable -stlFindInsert -syntaxError +// cspell: ignore uninit uninitMemberVar unknownMacro unmatchedSuppression -unpreciseMathCall unreadVariable -unsignedLessThanZero unusedFunction -unusedScopedObject unusedStructMember -unusedVariable useInitializationList useStlAlgorithm -uselessCallsSubstr -uselessOverride variableScope virtualCallInConstructor diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index df8673cd68724..1f25372c14d71 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -1,11 +1,15 @@ ### Automatically generated from package.xml ### common/autoware_ad_api_specs/** isamu.takagi@tier4.jp ryohsuke.mitsudome@tier4.jp common/autoware_auto_common/** opensource@apex.ai satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp tomoya.kimura@tier4.jp +common/autoware_grid_map_utils/** maxime.clement@tier4.jp +common/autoware_motion_utils/** fumiya.watanabe@tier4.jp kosuke.takeuchi@tier4.jp mamoru.sobue@tier4.jp satoshi.ota@tier4.jp taiki.tanaka@tier4.jp takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp tomoya.kimura@tier4.jp common/autoware_overlay_rviz_plugin/autoware_mission_details_overlay_rviz_plugin/** ahmed.ebrahim@leodrive.ai common/autoware_overlay_rviz_plugin/autoware_overlay_rviz_plugin/** khalil@leodrive.ai common/autoware_perception_rviz_plugin/** opensource@apex.ai satoshi.tanaka@tier4.jp shunsuke.miura@tier4.jp taiki.tanaka@tier4.jp takeshi.miura@tier4.jp yoshi.ri@tier4.jp common/autoware_point_types/** taichi.higashide@tier4.jp +common/autoware_test_utils/** kyoichi.sugahara@tier4.jp mamoru.sobue@tier4.jp takamasa.horibe@tier4.jp zulfaqar.azmi@tier4.jp common/autoware_testing/** adam.dabrowski@robotec.ai satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp tomoya.kimura@tier4.jp +common/autoware_universe_utils/** mamoru.sobue@tier4.jp takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp common/bag_time_manager_rviz_plugin/** taiki.tanaka@tier4.jp common/component_interface_specs/** isamu.takagi@tier4.jp yukihiro.saito@tier4.jp common/component_interface_tools/** isamu.takagi@tier4.jp @@ -16,10 +20,8 @@ common/geography_utils/** koji.minoda@tier4.jp common/global_parameter_loader/** ryohsuke.mitsudome@tier4.jp common/glog_component/** takamasa.horibe@tier4.jp common/goal_distance_calculator/** taiki.tanaka@tier4.jp -common/autoware_grid_map_utils/** maxime.clement@tier4.jp common/interpolation/** fumiya.watanabe@tier4.jp takayuki.murooka@tier4.jp common/kalman_filter/** koji.minoda@tier4.jp takeshi.ishita@tier4.jp yukihiro.saito@tier4.jp -common/autoware_motion_utils/** fumiya.watanabe@tier4.jp kosuke.takeuchi@tier4.jp mamoru.sobue@tier4.jp satoshi.ota@tier4.jp taiki.tanaka@tier4.jp takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp tomoya.kimura@tier4.jp common/object_recognition_utils/** satoshi.tanaka@tier4.jp shunsuke.miura@tier4.jp takayuki.murooka@tier4.jp yoshi.ri@tier4.jp common/osqp_interface/** fumiya.watanabe@tier4.jp maxime.clement@tier4.jp satoshi.ota@tier4.jp takayuki.murooka@tier4.jp common/path_distance_calculator/** isamu.takagi@tier4.jp @@ -30,7 +32,6 @@ common/signal_processing/** ali.boyali@tier4.jp fumiya.watanabe@tier4.jp kyoichi common/tensorrt_common/** dan.umeda@tier4.jp manato.hirabayashi@tier4.jp common/tier4_adapi_rviz_plugin/** hiroki.ota@tier4.jp isamu.takagi@tier4.jp kosuke.takeuchi@tier4.jp common/tier4_api_utils/** isamu.takagi@tier4.jp -common/autoware_universe_utils/** mamoru.sobue@tier4.jp takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp common/tier4_camera_view_rviz_plugin/** makoto.ybauta@tier4.jp uken.ryu@tier4.jp common/tier4_datetime_rviz_plugin/** isamu.takagi@tier4.jp common/tier4_localization_rviz_plugin/** isamu.takagi@tier4.jp takamasa.horibe@tier4.jp @@ -50,24 +51,23 @@ control/autoware_external_cmd_selector/** fumiya.watanabe@tier4.jp satoshi.ota@t control/autoware_joy_controller/** fumiya.watanabe@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp taiki.tanaka@tier4.jp takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp tomoya.kimura@tier4.jp control/autoware_lane_departure_checker/** kyoichi.sugahara@tier4.jp makoto.kurihara@tier4.jp zulfaqar.azmi@tier4.jp control/autoware_mpc_lateral_controller/** takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp +control/autoware_operation_mode_transition_manager/** takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp tomoya.kimura@tier4.jp control/autoware_pid_longitudinal_controller/** mamoru.sobue@tier4.jp takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp +control/autoware_pure_pursuit/** takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp +control/autoware_shift_decider/** takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp control/autoware_smart_mpc_trajectory_follower/** kosuke.takeuchi@tier4.jp masayuki.aino@proxima-ai-tech.com takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp +control/autoware_trajectory_follower_base/** takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp +control/autoware_trajectory_follower_node/** takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp control/autoware_vehicle_cmd_gate/** takamasa.horibe@tier4.jp tomoya.kimura@tier4.jp control/control_performance_analysis/** berkay@leodrive.ai fumiya.watanabe@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp taiki.tanaka@tier4.jp takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp tomoya.kimura@tier4.jp control/obstacle_collision_checker/** fumiya.watanabe@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp taiki.tanaka@tier4.jp takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp tomoya.kimura@tier4.jp -control/operation_mode_transition_manager/** takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp tomoya.kimura@tier4.jp control/predicted_path_checker/** berkay@leodrive.ai -control/autoware_smart_mpc_trajectory_follower/** masayuki.aino@proxima-ai-tech.com -control/autoware_trajectory_follower_base/** takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp -control/pure_pursuit/** takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp -control/shift_decider/** takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp -control/autoware_trajectory_follower_node/** takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp evaluator/autoware_control_evaluator/** daniel.sanchez@tier4.jp takayuki.murooka@tier4.jp +evaluator/autoware_planning_evaluator/** kyoichi.sugahara@tier4.jp maxime.clement@tier4.jp evaluator/diagnostic_converter/** kyoichi.sugahara@tier4.jp maxime.clement@tier4.jp takamasa.horibe@tier4.jp evaluator/kinematic_evaluator/** dominik.jargot@robotec.ai fumiya.watanabe@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp tomoya.kimura@tier4.jp evaluator/localization_evaluator/** dominik.jargot@robotec.ai koji.minoda@tier4.jp evaluator/perception_online_evaluator/** fumiya.watanabe@tier4.jp kosuke.takeuchi@tier4.jp kotaro.uetake@tier4.jp kyoichi.sugahara@tier4.jp shunsuke.miura@tier4.jp yoshi.ri@tier4.jp -evaluator/autoware_planning_evaluator/** kyoichi.sugahara@tier4.jp maxime.clement@tier4.jp launch/tier4_autoware_api_launch/** isamu.takagi@tier4.jp ryohsuke.mitsudome@tier4.jp launch/tier4_control_launch/** takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp launch/tier4_localization_launch/** anh.nguyen.2@tier4.jp kento.yabuuchi.2@tier4.jp koji.minoda@tier4.jp masahiro.sakamoto@tier4.jp ryu.yamamoto@tier4.jp shintaro.sakoda@tier4.jp taiki.yamada@tier4.jp yamato.ando@tier4.jp @@ -78,21 +78,20 @@ launch/tier4_sensing_launch/** yukihiro.saito@tier4.jp launch/tier4_simulator_launch/** keisuke.shima@tier4.jp taiki.tanaka@tier4.jp takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp tomoya.kimura@tier4.jp launch/tier4_system_launch/** fumihito.ito@tier4.jp tetsuhiro.kawaguchi@tier4.jp launch/tier4_vehicle_launch/** yukihiro.saito@tier4.jp +localization/autoware_landmark_based_localizer/autoware_ar_tag_based_localizer/** anh.nguyen.2@tier4.jp kento.yabuuchi.2@tier4.jp masahiro.sakamoto@tier4.jp ryu.yamamoto@tier4.jp shintaro.sakoda@tier4.jp taiki.yamada@tier4.jp yamato.ando@tier4.jp +localization/autoware_landmark_based_localizer/autoware_landmark_manager/** anh.nguyen.2@tier4.jp kento.yabuuchi.2@tier4.jp masahiro.sakamoto@tier4.jp ryu.yamamoto@tier4.jp shintaro.sakoda@tier4.jp taiki.yamada@tier4.jp yamato.ando@tier4.jp localization/autoware_pose_covariance_modifier/** melike@leodrive.ai +localization/autoware_stop_filter/** anh.nguyen.2@tier4.jp kento.yabuuchi.2@tier4.jp koji.minoda@tier4.jp masahiro.sakamoto@tier4.jp ryu.yamamoto@tier4.jp shintaro.sakoda@tier4.jp taiki.yamada@tier4.jp yamato.ando@tier4.jp localization/ekf_localizer/** anh.nguyen.2@tier4.jp kento.yabuuchi.2@tier4.jp koji.minoda@tier4.jp masahiro.sakamoto@tier4.jp ryu.yamamoto@tier4.jp shintaro.sakoda@tier4.jp taiki.yamada@tier4.jp takamasa.horibe@tier4.jp takeshi.ishita@tier4.jp yamato.ando@tier4.jp localization/geo_pose_projector/** anh.nguyen.2@tier4.jp kento.yabuuchi.2@tier4.jp koji.minoda@tier4.jp masahiro.sakamoto@tier4.jp ryu.yamamoto@tier4.jp shintaro.sakoda@tier4.jp taiki.yamada@tier4.jp yamato.ando@tier4.jp localization/gyro_odometer/** anh.nguyen.2@tier4.jp kento.yabuuchi.2@tier4.jp masahiro.sakamoto@tier4.jp ryu.yamamoto@tier4.jp shintaro.sakoda@tier4.jp taiki.yamada@tier4.jp yamato.ando@tier4.jp -localization/landmark_based_localizer/ar_tag_based_localizer/** anh.nguyen.2@tier4.jp kento.yabuuchi.2@tier4.jp masahiro.sakamoto@tier4.jp ryu.yamamoto@tier4.jp shintaro.sakoda@tier4.jp taiki.yamada@tier4.jp yamato.ando@tier4.jp -localization/landmark_based_localizer/landmark_manager/** anh.nguyen.2@tier4.jp kento.yabuuchi.2@tier4.jp masahiro.sakamoto@tier4.jp ryu.yamamoto@tier4.jp shintaro.sakoda@tier4.jp taiki.yamada@tier4.jp yamato.ando@tier4.jp localization/localization_error_monitor/** anh.nguyen.2@tier4.jp kento.yabuuchi.2@tier4.jp koji.minoda@tier4.jp masahiro.sakamoto@tier4.jp ryu.yamamoto@tier4.jp shintaro.sakoda@tier4.jp taiki.yamada@tier4.jp yamato.ando@tier4.jp localization/localization_util/** anh.nguyen.2@tier4.jp kento.yabuuchi.2@tier4.jp masahiro.sakamoto@tier4.jp ryu.yamamoto@tier4.jp shintaro.sakoda@tier4.jp taiki.yamada@tier4.jp yamato.ando@tier4.jp localization/ndt_scan_matcher/** anh.nguyen.2@tier4.jp kento.yabuuchi.2@tier4.jp koji.minoda@tier4.jp masahiro.sakamoto@tier4.jp ryu.yamamoto@tier4.jp shintaro.sakoda@tier4.jp taiki.yamada@tier4.jp yamato.ando@tier4.jp localization/pose2twist/** anh.nguyen.2@tier4.jp kento.yabuuchi.2@tier4.jp masahiro.sakamoto@tier4.jp ryu.yamamoto@tier4.jp shintaro.sakoda@tier4.jp taiki.yamada@tier4.jp yamato.ando@tier4.jp -localization/pose_estimator_arbiter/** kento.yabuuchi.2@tier4.jp +localization/pose_estimator_arbiter/** anh.nguyen.2@tier4.jp kento.yabuuchi.2@tier4.jp masahiro.sakamoto@tier4.jp ryu.yamamoto@tier4.jp shintaro.sakoda@tier4.jp taiki.yamada@tier4.jp yamato.ando@tier4.jp localization/pose_initializer/** anh.nguyen.2@tier4.jp isamu.takagi@tier4.jp kento.yabuuchi.2@tier4.jp masahiro.sakamoto@tier4.jp ryu.yamamoto@tier4.jp shintaro.sakoda@tier4.jp taiki.yamada@tier4.jp yamato.ando@tier4.jp localization/pose_instability_detector/** anh.nguyen.2@tier4.jp kento.yabuuchi.2@tier4.jp masahiro.sakamoto@tier4.jp ryu.yamamoto@tier4.jp shintaro.sakoda@tier4.jp taiki.yamada@tier4.jp yamato.ando@tier4.jp -localization/stop_filter/** anh.nguyen.2@tier4.jp kento.yabuuchi.2@tier4.jp koji.minoda@tier4.jp masahiro.sakamoto@tier4.jp ryu.yamamoto@tier4.jp shintaro.sakoda@tier4.jp taiki.yamada@tier4.jp yamato.ando@tier4.jp -localization/tree_structured_parzen_estimator/** anh.nguyen.2@tier4.jp kento.yabuuchi.2@tier4.jp masahiro.sakamoto@tier4.jp ryu.yamamoto@tier4.jp shintaro.sakoda@tier4.jp taiki.yamada@tier4.jp yamato.ando@tier4.jp localization/twist2accel/** anh.nguyen.2@tier4.jp kento.yabuuchi.2@tier4.jp koji.minoda@tier4.jp masahiro.sakamoto@tier4.jp ryu.yamamoto@tier4.jp shintaro.sakoda@tier4.jp taiki.yamada@tier4.jp yamato.ando@tier4.jp localization/yabloc/yabloc_common/** anh.nguyen.2@tier4.jp kento.yabuuchi.2@tier4.jp masahiro.sakamoto@tier4.jp ryu.yamamoto@tier4.jp shintaro.sakoda@tier4.jp taiki.yamada@tier4.jp yamato.ando@tier4.jp localization/yabloc/yabloc_image_processing/** anh.nguyen.2@tier4.jp kento.yabuuchi.2@tier4.jp masahiro.sakamoto@tier4.jp ryu.yamamoto@tier4.jp shintaro.sakoda@tier4.jp taiki.yamada@tier4.jp yamato.ando@tier4.jp @@ -120,7 +119,6 @@ perception/lidar_apollo_instance_segmentation/** yukihiro.saito@tier4.jp perception/lidar_apollo_segmentation_tvm/** ambroise.vincent@arm.com xinyu.wang@tier4.jp perception/lidar_apollo_segmentation_tvm_nodes/** ambroise.vincent@arm.com xinyu.wang@tier4.jp perception/lidar_centerpoint/** kenzo.lobos@tier4.jp koji.minoda@tier4.jp -perception/lidar_centerpoint_tvm/** carl.liu@autocore.ai xinyu.wang@tier4.jp perception/lidar_transfusion/** amadeusz.szymko.2@tier4.jp kenzo.lobos@tier4.jp satoshi.tanaka@tier4.jp perception/multi_object_tracker/** taekjin.lee@tier4.jp yoshi.ri@tier4.jp yukihiro.saito@tier4.jp perception/object_merger/** taekjin.lee@tier4.jp yoshi.ri@tier4.jp yukihiro.saito@tier4.jp @@ -146,71 +144,58 @@ perception/traffic_light_map_based_detector/** shunsuke.miura@tier4.jp tao.zhong perception/traffic_light_multi_camera_fusion/** shunsuke.miura@tier4.jp tao.zhong@tier4.jp perception/traffic_light_occlusion_predictor/** shunsuke.miura@tier4.jp tao.zhong@tier4.jp perception/traffic_light_visualization/** tao.zhong@tier4.jp yukihiro.saito@tier4.jp -planning/autoware_behavior_path_avoidance_by_lane_change_module/** fumiya.watanabe@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp tomohito.ando@tier4.jp tomoya.kimura@tier4.jp zulfaqar.azmi@tier4.jp -planning/autoware_behavior_path_dynamic_obstacle_avoidance_module/** kosuke.takeuchi@tier4.jp satoshi.ota@tier4.jp takayuki.murooka@tier4.jp yuki.takagi@tier4.jp -planning/autoware_behavior_path_external_request_lane_change_module/** fumiya.watanabe@tier4.jp kosuke.takeuchi@tier4.jp shumpei.wakabayashi@tier4.jp tomohito.ando@tier4.jp tomoya.kimura@tier4.jp zulfaqar.azmi@tier4.jp -planning/autoware_behavior_path_goal_planner_module/** daniel.sanchez@tier4.jp kosuke.takeuchi@tier4.jp kyoichi.sugahara@tier4.jp mamoru.sobue@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp tomohito.ando@tier4.jp tomoya.kimura@tier4.jp -planning/autoware_behavior_path_lane_change_module/** fumiya.watanabe@tier4.jp kosuke.takeuchi@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp tomohito.ando@tier4.jp tomoya.kimura@tier4.jp zulfaqar.azmi@tier4.jp -planning/autoware_behavior_path_planner_common/** daniel.sanchez@tier4.jp fumiya.watanabe@tier4.jp go.sakayori@tier4.jp kosuke.takeuchi@tier4.jp mamoru.sobue@tier4.jp maxime.clement@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp tomoya.kimura@tier4.jp zulfaqar.azmi@tier4.jp -planning/autoware_behavior_path_side_shift_module/** fumiya.watanabe@tier4.jp kyoichi.sugahara@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp tomohito.ando@tier4.jp tomoya.kimura@tier4.jp -planning/autoware_behavior_path_start_planner_module/** daniel.sanchez@tier4.jp kosuke.takeuchi@tier4.jp kyoichi.sugahara@tier4.jp mamoru.sobue@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp tomohito.ando@tier4.jp tomoya.kimura@tier4.jp -planning/autoware_behavior_path_static_obstacle_avoidance_module/** fumiya.watanabe@tier4.jp go.sakayori@tier4.jp kyoichi.sugahara@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp takamasa.horibe@tier4.jp tomohito.ando@tier4.jp tomoya.kimura@tier4.jp zulfaqar.azmi@tier4.jp -planning/behavior_velocity_planner/autoware_behavior_velocity_blind_spot_module/** mamoru.sobue@tier4.jp shumpei.wakabayashi@tier4.jp tomoya.kimura@tier4.jp -planning/behavior_velocity_planner/autoware_behavior_velocity_detection_area_module/** kyoichi.sugahara@tier4.jp shumpei.wakabayashi@tier4.jp tomoya.kimura@tier4.jp -planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/** kyoichi.sugahara@tier4.jp mamoru.sobue@tier4.jp shumpei.wakabayashi@tier4.jp takayuki.murooka@tier4.jp tomoya.kimura@tier4.jp -planning/behavior_velocity_planner/autoware_behavior_velocity_occlusion_spot_module/** shumpei.wakabayashi@tier4.jp taiki.tanaka@tier4.jp tomoya.kimura@tier4.jp -planning/behavior_velocity_planner/autoware_behavior_velocity_planner/** kosuke.takeuchi@tier4.jp kyoichi.sugahara@tier4.jp makoto.kurihara@tier4.jp mamoru.sobue@tier4.jp maxime.clement@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp taiki.tanaka@tier4.jp takayuki.murooka@tier4.jp tomohito.ando@tier4.jp tomoya.kimura@tier4.jp -planning/behavior_velocity_planner/autoware_behavior_velocity_planner_common/** fumiya.watanabe@tier4.jp isamu.takagi@tier4.jp mamoru.sobue@tier4.jp shumpei.wakabayashi@tier4.jp tomoya.kimura@tier4.jp -planning/behavior_velocity_planner/autoware_behavior_velocity_run_out_module/** kosuke.takeuchi@tier4.jp makoto.kurihara@tier4.jp shumpei.wakabayashi@tier4.jp takayuki.murooka@tier4.jp tomohito.ando@tier4.jp tomoya.kimura@tier4.jp -planning/behavior_velocity_planner/autoware_behavior_velocity_stop_line_module/** fumiya.watanabe@tier4.jp shumpei.wakabayashi@tier4.jp tomoya.kimura@tier4.jp -planning/behavior_velocity_planner/autoware_behavior_velocity_template_module/** daniel.sanchez@tier4.jp -planning/behavior_velocity_planner/autoware_behavior_velocity_traffic_light_module/** mamoru.sobue@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp tomoya.kimura@tier4.jp -planning/behavior_velocity_planner/autoware_behavior_velocity_virtual_traffic_light_module/** kosuke.takeuchi@tier4.jp shumpei.wakabayashi@tier4.jp tomoya.kimura@tier4.jp -planning/behavior_velocity_planner/autoware_behavior_velocity_walkway_module/** satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp takayuki.murooka@tier4.jp tomoya.kimura@tier4.jp planning/autoware_costmap_generator/** kosuke.takeuchi@tier4.jp takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp planning/autoware_external_velocity_limit_selector/** satoshi.ota@tier4.jp shinnosuke.hirakawa@tier4.jp shumpei.wakabayashi@tier4.jp tomohito.ando@tier4.jp tomoya.kimura@tier4.jp planning/autoware_freespace_planner/** kosuke.takeuchi@tier4.jp takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp planning/autoware_freespace_planning_algorithms/** kosuke.takeuchi@tier4.jp takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp planning/autoware_mission_planner/** isamu.takagi@tier4.jp kosuke.takeuchi@tier4.jp mamoru.sobue@tier4.jp ryohsuke.mitsudome@tier4.jp takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp planning/autoware_objects_of_interest_marker_interface/** fumiya.watanabe@tier4.jp kosuke.takeuchi@tier4.jp zulfaqar.azmi@tier4.jp +planning/autoware_obstacle_cruise_planner/** kosuke.takeuchi@tier4.jp satoshi.ota@tier4.jp takayuki.murooka@tier4.jp yuki.takagi@tier4.jp +planning/autoware_obstacle_stop_planner/** berkay@leodrive.ai bnk@leodrive.ai satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp taiki.tanaka@tier4.jp tomoya.kimura@tier4.jp planning/autoware_path_optimizer/** kosuke.takeuchi@tier4.jp takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp planning/autoware_path_smoother/** maxime.clement@tier4.jp takayuki.murooka@tier4.jp planning/autoware_planning_test_manager/** kyoichi.sugahara@tier4.jp takamasa.horibe@tier4.jp planning/autoware_planning_topic_converter/** kosuke.takeuchi@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp planning/autoware_planning_validator/** kosuke.takeuchi@tier4.jp takamasa.horibe@tier4.jp planning/autoware_remaining_distance_time_calculator/** ahmed.ebrahim@leodrive.ai +planning/autoware_route_handler/** alqudah.mohammad@tier4.jp fumiya.watanabe@tier4.jp go.sakayori@tier4.jp kosuke.takeuchi@tier4.jp mamoru.sobue@tier4.jp takayuki.murooka@tier4.jp zulfaqar.azmi@tier4.jp planning/autoware_rtc_interface/** fumiya.watanabe@tier4.jp kyoichi.sugahara@tier4.jp satoshi.ota@tier4.jp taiki.tanaka@tier4.jp planning/autoware_scenario_selector/** fumiya.watanabe@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp taiki.tanaka@tier4.jp takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp tomoya.kimura@tier4.jp planning/autoware_static_centerline_generator/** kosuke.takeuchi@tier4.jp takayuki.murooka@tier4.jp planning/autoware_surround_obstacle_checker/** go.sakayori@tier4.jp satoshi.ota@tier4.jp planning/autoware_velocity_smoother/** fumiya.watanabe@tier4.jp makoto.kurihara@tier4.jp satoshi.ota@tier4.jp takamasa.horibe@tier4.jp -planning/behavior_path_planner/** fumiya.watanabe@tier4.jp go.sakayori@tier4.jp kosuke.takeuchi@tier4.jp kyoichi.sugahara@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp tomohito.ando@tier4.jp tomoya.kimura@tier4.jp zulfaqar.azmi@tier4.jp -planning/autoware_behavior_path_sampling_planner_module/** daniel.sanchez@tier4.jp maxime.clement@tier4.jp -planning/autoware_behavior_path_side_shift_module/** fumiya.watanabe@tier4.jp kyoichi.sugahara@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp tomohito.ando@tier4.jp tomoya.kimura@tier4.jp -planning/autoware_behavior_path_start_planner_module/** daniel.sanchez@tier4.jp kosuke.takeuchi@tier4.jp kyoichi.sugahara@tier4.jp mamoru.sobue@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp tomohito.ando@tier4.jp tomoya.kimura@tier4.jp -planning/autoware_behavior_path_side_shift_module/** fumiya.watanabe@tier4.jp kyoichi.sugahara@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp tomohito.ando@tier4.jp tomoya.kimura@tier4.jp -planning/behavior_path_start_planner_module/** daniel.sanchez@tier4.jp kosuke.takeuchi@tier4.jp kyoichi.sugahara@tier4.jp mamoru.sobue@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp tomohito.ando@tier4.jp tomoya.kimura@tier4.jp +planning/behavior_path_planner/autoware_behavior_path_avoidance_by_lane_change_module/** fumiya.watanabe@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp tomohito.ando@tier4.jp tomoya.kimura@tier4.jp zulfaqar.azmi@tier4.jp +planning/behavior_path_planner/autoware_behavior_path_dynamic_obstacle_avoidance_module/** kosuke.takeuchi@tier4.jp satoshi.ota@tier4.jp takayuki.murooka@tier4.jp yuki.takagi@tier4.jp +planning/behavior_path_planner/autoware_behavior_path_external_request_lane_change_module/** fumiya.watanabe@tier4.jp kosuke.takeuchi@tier4.jp shumpei.wakabayashi@tier4.jp tomohito.ando@tier4.jp tomoya.kimura@tier4.jp zulfaqar.azmi@tier4.jp +planning/behavior_path_planner/autoware_behavior_path_goal_planner_module/** daniel.sanchez@tier4.jp kosuke.takeuchi@tier4.jp kyoichi.sugahara@tier4.jp mamoru.sobue@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp tomohito.ando@tier4.jp tomoya.kimura@tier4.jp +planning/behavior_path_planner/autoware_behavior_path_lane_change_module/** alqudah.mohammad@tier4.jp fumiya.watanabe@tier4.jp kosuke.takeuchi@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp tomohito.ando@tier4.jp tomoya.kimura@tier4.jp zulfaqar.azmi@tier4.jp +planning/behavior_path_planner/autoware_behavior_path_planner/** fumiya.watanabe@tier4.jp go.sakayori@tier4.jp kosuke.takeuchi@tier4.jp kyoichi.sugahara@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp tomohito.ando@tier4.jp tomoya.kimura@tier4.jp zulfaqar.azmi@tier4.jp +planning/behavior_path_planner/autoware_behavior_path_planner_common/** daniel.sanchez@tier4.jp fumiya.watanabe@tier4.jp go.sakayori@tier4.jp kosuke.takeuchi@tier4.jp mamoru.sobue@tier4.jp maxime.clement@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp tomoya.kimura@tier4.jp zulfaqar.azmi@tier4.jp +planning/behavior_path_planner/autoware_behavior_path_sampling_planner_module/** daniel.sanchez@tier4.jp maxime.clement@tier4.jp +planning/behavior_path_planner/autoware_behavior_path_side_shift_module/** fumiya.watanabe@tier4.jp kyoichi.sugahara@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp tomohito.ando@tier4.jp tomoya.kimura@tier4.jp +planning/behavior_path_planner/autoware_behavior_path_start_planner_module/** daniel.sanchez@tier4.jp kosuke.takeuchi@tier4.jp kyoichi.sugahara@tier4.jp mamoru.sobue@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp tomohito.ando@tier4.jp tomoya.kimura@tier4.jp +planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/** fumiya.watanabe@tier4.jp go.sakayori@tier4.jp kyoichi.sugahara@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp takamasa.horibe@tier4.jp tomohito.ando@tier4.jp tomoya.kimura@tier4.jp zulfaqar.azmi@tier4.jp planning/behavior_velocity_planner/autoware_behavior_velocity_blind_spot_module/** mamoru.sobue@tier4.jp shumpei.wakabayashi@tier4.jp tomoya.kimura@tier4.jp planning/behavior_velocity_planner/autoware_behavior_velocity_crosswalk_module/** kyoichi.sugahara@tier4.jp mamoru.sobue@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp takayuki.murooka@tier4.jp tomoya.kimura@tier4.jp yuki.takagi@tier4.jp -planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/** ahmed.ebrahim@leodrive.ai fumiya.watanabe@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp tomoya.kimura@tier4.jp +planning/behavior_velocity_planner/autoware_behavior_velocity_detection_area_module/** kyoichi.sugahara@tier4.jp shumpei.wakabayashi@tier4.jp tomoya.kimura@tier4.jp +planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/** kyoichi.sugahara@tier4.jp mamoru.sobue@tier4.jp shumpei.wakabayashi@tier4.jp takayuki.murooka@tier4.jp tomoya.kimura@tier4.jp +planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/** ahmed.ebrahim@leodrive.ai fumiya.watanabe@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp takamasa.horibe@tier4.jp takayuki.murooka@tier4.jp tomoya.kimura@tier4.jp planning/behavior_velocity_planner/autoware_behavior_velocity_no_stopping_area_module/** kosuke.takeuchi@tier4.jp shumpei.wakabayashi@tier4.jp tomoya.kimura@tier4.jp planning/behavior_velocity_planner/autoware_behavior_velocity_occlusion_spot_module/** shumpei.wakabayashi@tier4.jp taiki.tanaka@tier4.jp tomoya.kimura@tier4.jp +planning/behavior_velocity_planner/autoware_behavior_velocity_planner/** kosuke.takeuchi@tier4.jp kyoichi.sugahara@tier4.jp makoto.kurihara@tier4.jp mamoru.sobue@tier4.jp maxime.clement@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp taiki.tanaka@tier4.jp takayuki.murooka@tier4.jp tomohito.ando@tier4.jp tomoya.kimura@tier4.jp planning/behavior_velocity_planner/autoware_behavior_velocity_planner_common/** fumiya.watanabe@tier4.jp isamu.takagi@tier4.jp mamoru.sobue@tier4.jp shumpei.wakabayashi@tier4.jp tomoya.kimura@tier4.jp planning/behavior_velocity_planner/autoware_behavior_velocity_run_out_module/** kosuke.takeuchi@tier4.jp makoto.kurihara@tier4.jp shumpei.wakabayashi@tier4.jp takayuki.murooka@tier4.jp tomohito.ando@tier4.jp tomoya.kimura@tier4.jp -planning/behavior_velocity_planner/behavior_velocity_speed_bump_module/** mdogru@leodrive.ai shumpei.wakabayashi@tier4.jp tomoya.kimura@tier4.jp +planning/behavior_velocity_planner/autoware_behavior_velocity_speed_bump_module/** mdogru@leodrive.ai shumpei.wakabayashi@tier4.jp tomoya.kimura@tier4.jp +planning/behavior_velocity_planner/autoware_behavior_velocity_stop_line_module/** fumiya.watanabe@tier4.jp shumpei.wakabayashi@tier4.jp tomoya.kimura@tier4.jp zhe.shen@tier4.jp +planning/behavior_velocity_planner/autoware_behavior_velocity_template_module/** daniel.sanchez@tier4.jp +planning/behavior_velocity_planner/autoware_behavior_velocity_traffic_light_module/** mamoru.sobue@tier4.jp satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp tomoya.kimura@tier4.jp +planning/behavior_velocity_planner/autoware_behavior_velocity_virtual_traffic_light_module/** kosuke.takeuchi@tier4.jp shumpei.wakabayashi@tier4.jp tomoya.kimura@tier4.jp +planning/behavior_velocity_planner/autoware_behavior_velocity_walkway_module/** satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp takayuki.murooka@tier4.jp tomoya.kimura@tier4.jp planning/motion_velocity_planner/autoware_motion_velocity_dynamic_obstacle_stop_module/** mamoru.sobue@tier4.jp maxime.clement@tier4.jp planning/motion_velocity_planner/autoware_motion_velocity_obstacle_velocity_limiter_module/** maxime.clement@tier4.jp planning/motion_velocity_planner/autoware_motion_velocity_out_of_lane_module/** maxime.clement@tier4.jp shumpei.wakabayashi@tier4.jp takayuki.murooka@tier4.jp tomoya.kimura@tier4.jp planning/motion_velocity_planner/autoware_motion_velocity_planner_common/** maxime.clement@tier4.jp planning/motion_velocity_planner/autoware_motion_velocity_planner_node/** maxime.clement@tier4.jp -planning/autoware_objects_of_interest_marker_interface/** fumiya.watanabe@tier4.jp kosuke.takeuchi@tier4.jp zulfaqar.azmi@tier4.jp -planning/autoware_obstacle_cruise_planner/** kosuke.takeuchi@tier4.jp satoshi.ota@tier4.jp takayuki.murooka@tier4.jp yuki.takagi@tier4.jp -planning/obstacle_stop_planner/** berkay@leodrive.ai bnk@leodrive.ai satoshi.ota@tier4.jp shumpei.wakabayashi@tier4.jp taiki.tanaka@tier4.jp tomoya.kimura@tier4.jp -planning/planning_test_utils/** kyoichi.sugahara@tier4.jp mamoru.sobue@tier4.jp takamasa.horibe@tier4.jp zulfaqar.azmi@tier4.jp -planning/planning_validator/** kosuke.takeuchi@tier4.jp takamasa.horibe@tier4.jp -planning/autoware_route_handler/** fumiya.watanabe@tier4.jp go.sakayori@tier4.jp kosuke.takeuchi@tier4.jp mamoru.sobue@tier4.jp takayuki.murooka@tier4.jp zulfaqar.azmi@tier4.jp -planning/rtc_interface/** fumiya.watanabe@tier4.jp kyoichi.sugahara@tier4.jp satoshi.ota@tier4.jp taiki.tanaka@tier4.jp -planning/rtc_replayer/** fumiya.watanabe@tier4.jp taiki.tanaka@tier4.jp planning/sampling_based_planner/autoware_bezier_sampler/** maxime.clement@tier4.jp planning/sampling_based_planner/autoware_frenet_planner/** maxime.clement@tier4.jp planning/sampling_based_planner/autoware_path_sampler/** maxime.clement@tier4.jp @@ -254,9 +239,9 @@ system/system_monitor/** fumihito.ito@tier4.jp tetsuhiro.kawaguchi@tier4.jp system/topic_state_monitor/** ryohsuke.mitsudome@tier4.jp system/velodyne_monitor/** fumihito.ito@tier4.jp tools/reaction_analyzer/** berkay@leodrive.ai -vehicle/autoware_accel_brake_map_calibrator/** taiki.tanaka@tier4.jp takeshi.miura@tier4.jp tomoya.kimura@tier4.jp +vehicle/autoware_accel_brake_map_calibrator/** eiki.nagata.2@tier4.jp taiki.tanaka@tier4.jp takeshi.miura@tier4.jp tomoya.kimura@tier4.jp vehicle/autoware_external_cmd_converter/** eiki.nagata.2@tier4.jp takamasa.horibe@tier4.jp -vehicle/autoware_raw_vehicle_cmd_converter/** makoto.kurihara@tier4.jp taiki.tanaka@tier4.jp takamasa.horibe@tier4.jp +vehicle/autoware_raw_vehicle_cmd_converter/** makoto.kurihara@tier4.jp sho.iwasawa.2@tier4.jp taiki.tanaka@tier4.jp takamasa.horibe@tier4.jp vehicle/autoware_steer_offset_estimator/** taiki.tanaka@tier4.jp vehicle/autoware_vehicle_info_utils/** shumpei.wakabayashi@tier4.jp taiki.tanaka@tier4.jp tomoya.kimura@tier4.jp diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md deleted file mode 100644 index 97b0e95452426..0000000000000 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ /dev/null @@ -1,8 +0,0 @@ -**Note**: Confirm the [contribution guidelines](https://autowarefoundation.github.io/autoware-documentation/main/contributing/) before submitting a pull request. - -Click the `Preview` tab and select a PR template: - -- [Standard change](?expand=1&template=standard-change.md) -- [Small change](?expand=1&template=small-change.md) - -**Do NOT send a PR with this description.** diff --git a/.github/PULL_REQUEST_TEMPLATE/small-change.md b/.github/PULL_REQUEST_TEMPLATE/small-change.md deleted file mode 100644 index e15fdd992d114..0000000000000 --- a/.github/PULL_REQUEST_TEMPLATE/small-change.md +++ /dev/null @@ -1,44 +0,0 @@ -## Description - - - -## Tests performed - - - - -Not applicable. - -## Effects on system behavior - - - -Not applicable. - -## Interface changes - - - -## Pre-review checklist for the PR author - -The PR author **must** check the checkboxes below when creating the PR. - -- [ ] I've confirmed the [contribution guidelines]. -- [ ] The PR follows the [pull request guidelines]. - -## In-review checklist for the PR reviewers - -The PR reviewers **must** check the checkboxes below before approval. - -- [ ] The PR follows the [pull request guidelines]. - -## Post-review checklist for the PR author - -The PR author **must** check the checkboxes below before merging. - -- [ ] There are no open discussions or they are tracked via tickets. - -After all checkboxes are checked, anyone who has write access can merge the PR. - -[contribution guidelines]: https://autowarefoundation.github.io/autoware-documentation/main/contributing/ -[pull request guidelines]: https://autowarefoundation.github.io/autoware-documentation/main/contributing/pull-request-guidelines/ diff --git a/.github/PULL_REQUEST_TEMPLATE/standard-change.md b/.github/PULL_REQUEST_TEMPLATE/standard-change.md deleted file mode 100644 index 391af629751e0..0000000000000 --- a/.github/PULL_REQUEST_TEMPLATE/standard-change.md +++ /dev/null @@ -1,63 +0,0 @@ -## Description - - - -## Related links - - - -## Tests performed - - - -## Notes for reviewers - - - -## Interface changes - - - -### ROS Topic Changes - - - - - - -### ROS Parameter Changes - - - - - -## Effects on system behavior - - - -## Pre-review checklist for the PR author - -The PR author **must** check the checkboxes below when creating the PR. - -- [ ] I've confirmed the [contribution guidelines]. -- [ ] The PR follows the [pull request guidelines]. - -## In-review checklist for the PR reviewers - -The PR reviewers **must** check the checkboxes below before approval. - -- [ ] The PR follows the [pull request guidelines]. -- [ ] The PR has been properly tested. -- [ ] The PR has been reviewed by the code owners. - -## Post-review checklist for the PR author - -The PR author **must** check the checkboxes below before merging. - -- [ ] There are no open discussions or they are tracked via tickets. -- [ ] The PR is ready for merge. - -After all checkboxes are checked, anyone who has write access can merge the PR. - -[contribution guidelines]: https://autowarefoundation.github.io/autoware-documentation/main/contributing/ -[pull request guidelines]: https://autowarefoundation.github.io/autoware-documentation/main/contributing/pull-request-guidelines/ diff --git a/.github/cppcheck-problem-matcher.json b/.github/cppcheck-problem-matcher.json new file mode 100644 index 0000000000000..3b18feccd9bd2 --- /dev/null +++ b/.github/cppcheck-problem-matcher.json @@ -0,0 +1,16 @@ +{ + "problemMatcher": [ + { + "owner": "cppcheck", + "pattern": [ + { + "regexp": "^([^:]+):(\\d+):(\\d*):\\s(style|portability|performance|warning|error):\\s(.*)$", + "file": 1, + "line": 2, + "column": 3, + "message": 5 + } + ] + } + ] +} diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md new file mode 100644 index 0000000000000..fcaf3601fef92 --- /dev/null +++ b/.github/pull_request_template.md @@ -0,0 +1,61 @@ +## Description + +## Related links + +**Parent Issue:** + +- Link + + + +## How was this PR tested? + +## Notes for reviewers + +None. + +## Interface changes + +None. + + + +## Effects on system behavior + +None. diff --git a/.github/sync-files.yaml b/.github/sync-files.yaml index 263df86438787..151890e2783d9 100644 --- a/.github/sync-files.yaml +++ b/.github/sync-files.yaml @@ -7,9 +7,6 @@ - source: .github/ISSUE_TEMPLATE/bug.yaml - source: .github/ISSUE_TEMPLATE/config.yml - source: .github/ISSUE_TEMPLATE/task.yaml - - source: .github/PULL_REQUEST_TEMPLATE.md - - source: .github/PULL_REQUEST_TEMPLATE/small-change.md - - source: .github/PULL_REQUEST_TEMPLATE/standard-change.md - source: .github/dependabot.yaml - source: .github/stale.yml - source: .github/workflows/cancel-previous-workflows.yaml diff --git a/.github/workflows/build-and-test-daily-arm64.yaml b/.github/workflows/build-and-test-daily-arm64.yaml index ee9caae26e5d6..65196f5b77a35 100644 --- a/.github/workflows/build-and-test-daily-arm64.yaml +++ b/.github/workflows/build-and-test-daily-arm64.yaml @@ -37,6 +37,16 @@ jobs: id: get-self-packages uses: autowarefoundation/autoware-github-actions/get-self-packages@v1 + - name: Export CUDA state as a variable for adding to cache key + run: | + build_type_cuda_state=nocuda + if [[ "${{ matrix.container-suffix }}" == "-cuda" ]]; then + build_type_cuda_state=cuda + fi + echo "BUILD_TYPE_CUDA_STATE=$build_type_cuda_state" >> "${GITHUB_ENV}" + echo "::notice::BUILD_TYPE_CUDA_STATE=$build_type_cuda_state" + shell: bash + - name: Build if: ${{ steps.get-self-packages.outputs.self-packages != '' }} uses: autowarefoundation/autoware-github-actions/colcon-build@v1 @@ -44,6 +54,7 @@ jobs: rosdistro: ${{ matrix.rosdistro }} target-packages: ${{ steps.get-self-packages.outputs.self-packages }} build-depends-repos: ${{ matrix.build-depends-repos }} + cache-key-element: ${{ env.BUILD_TYPE_CUDA_STATE }} - name: Test if: ${{ steps.get-self-packages.outputs.self-packages != '' }} @@ -62,6 +73,7 @@ jobs: fail_ci_if_error: false verbose: true flags: total-arm64 + token: ${{ secrets.CODECOV_TOKEN }} - name: Show disk space after the tasks run: df -h diff --git a/.github/workflows/build-and-test-daily.yaml b/.github/workflows/build-and-test-daily.yaml index 50e259053de46..816bcd412bb4c 100644 --- a/.github/workflows/build-and-test-daily.yaml +++ b/.github/workflows/build-and-test-daily.yaml @@ -37,6 +37,16 @@ jobs: id: get-self-packages uses: autowarefoundation/autoware-github-actions/get-self-packages@v1 + - name: Export CUDA state as a variable for adding to cache key + run: | + build_type_cuda_state=nocuda + if [[ "${{ matrix.container-suffix }}" == "-cuda" ]]; then + build_type_cuda_state=cuda + fi + echo "BUILD_TYPE_CUDA_STATE=$build_type_cuda_state" >> "${GITHUB_ENV}" + echo "::notice::BUILD_TYPE_CUDA_STATE=$build_type_cuda_state" + shell: bash + - name: Build if: ${{ steps.get-self-packages.outputs.self-packages != '' }} uses: autowarefoundation/autoware-github-actions/colcon-build@v1 @@ -44,6 +54,7 @@ jobs: rosdistro: ${{ matrix.rosdistro }} target-packages: ${{ steps.get-self-packages.outputs.self-packages }} build-depends-repos: ${{ matrix.build-depends-repos }} + cache-key-element: ${{ env.BUILD_TYPE_CUDA_STATE }} - name: Test if: ${{ steps.get-self-packages.outputs.self-packages != '' }} @@ -62,6 +73,7 @@ jobs: fail_ci_if_error: false verbose: true flags: total + token: ${{ secrets.CODECOV_TOKEN }} - name: Show disk space after the tasks run: df -h diff --git a/.github/workflows/build-and-test-differential-arm64.yaml b/.github/workflows/build-and-test-differential-arm64.yaml index 8f333a3ab22d1..3be2792e0929d 100644 --- a/.github/workflows/build-and-test-differential-arm64.yaml +++ b/.github/workflows/build-and-test-differential-arm64.yaml @@ -51,6 +51,16 @@ jobs: id: get-modified-packages uses: autowarefoundation/autoware-github-actions/get-modified-packages@v1 + - name: Export CUDA state as a variable for adding to cache key + run: | + build_type_cuda_state=nocuda + if [[ "${{ matrix.container-suffix }}" == "-cuda" ]]; then + build_type_cuda_state=cuda + fi + echo "BUILD_TYPE_CUDA_STATE=$build_type_cuda_state" >> "${GITHUB_ENV}" + echo "::notice::BUILD_TYPE_CUDA_STATE=$build_type_cuda_state" + shell: bash + - name: Build if: ${{ steps.get-modified-packages.outputs.modified-packages != '' }} uses: autowarefoundation/autoware-github-actions/colcon-build@v1 @@ -58,6 +68,7 @@ jobs: rosdistro: ${{ matrix.rosdistro }} target-packages: ${{ steps.get-modified-packages.outputs.modified-packages }} build-depends-repos: ${{ matrix.build-depends-repos }} + cache-key-element: ${{ env.BUILD_TYPE_CUDA_STATE }} - name: Test id: test @@ -76,6 +87,7 @@ jobs: fail_ci_if_error: false verbose: true flags: differential-arm64 + token: ${{ secrets.CODECOV_TOKEN }} - name: Show disk space after the tasks run: df -h diff --git a/.github/workflows/build-and-test-differential.yaml b/.github/workflows/build-and-test-differential.yaml index e789b8744f03d..a95a070d19bd4 100644 --- a/.github/workflows/build-and-test-differential.yaml +++ b/.github/workflows/build-and-test-differential.yaml @@ -74,6 +74,16 @@ jobs: run: du -sh ${CCACHE_DIR} && ccache -s shell: bash + - name: Export CUDA state as a variable for adding to cache key + run: | + build_type_cuda_state=nocuda + if [[ "${{ matrix.container-suffix }}" == "-cuda" ]]; then + build_type_cuda_state=cuda + fi + echo "BUILD_TYPE_CUDA_STATE=$build_type_cuda_state" >> "${GITHUB_ENV}" + echo "::notice::BUILD_TYPE_CUDA_STATE=$build_type_cuda_state" + shell: bash + - name: Build if: ${{ steps.get-modified-packages.outputs.modified-packages != '' }} uses: autowarefoundation/autoware-github-actions/colcon-build@v1 @@ -81,6 +91,7 @@ jobs: rosdistro: ${{ matrix.rosdistro }} target-packages: ${{ steps.get-modified-packages.outputs.modified-packages }} build-depends-repos: ${{ matrix.build-depends-repos }} + cache-key-element: ${{ env.BUILD_TYPE_CUDA_STATE }} - name: Show ccache stats after build run: du -sh ${CCACHE_DIR} && ccache -s @@ -103,6 +114,51 @@ jobs: fail_ci_if_error: false verbose: true flags: differential + token: ${{ secrets.CODECOV_TOKEN }} + + - name: Show disk space after the tasks + run: df -h + + clang-tidy-differential: + needs: build-and-test-differential + runs-on: ubuntu-latest + container: ghcr.io/autowarefoundation/autoware:latest-prebuilt-cuda + steps: + - name: Set PR fetch depth + run: echo "PR_FETCH_DEPTH=$(( ${{ github.event.pull_request.commits }} + 1 ))" >> "${GITHUB_ENV}" + + - name: Checkout PR branch and all PR commits + uses: actions/checkout@v4 + with: + ref: ${{ github.event.pull_request.head.sha }} + fetch-depth: ${{ env.PR_FETCH_DEPTH }} + + - name: Show disk space before the tasks + run: df -h + + - name: Remove exec_depend + uses: autowarefoundation/autoware-github-actions/remove-exec-depend@v1 + + - name: Get modified packages + id: get-modified-packages + uses: autowarefoundation/autoware-github-actions/get-modified-packages@v1 + + - name: Get changed files (existing files only) + id: get-changed-files + run: | + echo "changed-files=$(git diff --name-only "origin/${{ github.base_ref }}"...HEAD | grep -E '\.(cpp|hpp)$' | while read -r file; do [ -e "$file" ] && echo -n "$file "; done)" >> $GITHUB_OUTPUT + shell: bash + + - name: Run clang-tidy + if: ${{ steps.get-changed-files.outputs.changed-files != '' }} + uses: autowarefoundation/autoware-github-actions/clang-tidy@v1 + with: + rosdistro: humble + target-packages: ${{ steps.get-modified-packages.outputs.modified-packages }} + target-files: ${{ steps.get-changed-files.outputs.changed-files }} + clang-tidy-config-url: https://raw.githubusercontent.com/autowarefoundation/autoware/main/.clang-tidy-ci + build-depends-repos: build_depends.repos + cache-key-element: cuda - name: Show disk space after the tasks run: df -h diff --git a/.github/workflows/build-and-test.yaml b/.github/workflows/build-and-test.yaml index 00860986824e6..72f8679bff6ef 100644 --- a/.github/workflows/build-and-test.yaml +++ b/.github/workflows/build-and-test.yaml @@ -56,10 +56,26 @@ jobs: restore-keys: | ccache-main-${{ runner.arch }}-${{ matrix.rosdistro }}- + - name: Limit ccache size + run: | + rm -f "${CCACHE_DIR}/ccache.conf" + echo -e "# Set maximum cache size\nmax_size = 600MB" >> "${CCACHE_DIR}/ccache.conf" + shell: bash + - name: Show ccache stats before build run: du -sh ${CCACHE_DIR} && ccache -s shell: bash + - name: Export CUDA state as a variable for adding to cache key + run: | + build_type_cuda_state=nocuda + if [[ "${{ matrix.container-suffix }}" == "-cuda" ]]; then + build_type_cuda_state=cuda + fi + echo "BUILD_TYPE_CUDA_STATE=$build_type_cuda_state" >> "${GITHUB_ENV}" + echo "::notice::BUILD_TYPE_CUDA_STATE=$build_type_cuda_state" + shell: bash + - name: Build if: ${{ steps.get-self-packages.outputs.self-packages != '' }} uses: autowarefoundation/autoware-github-actions/colcon-build@v1 @@ -67,6 +83,7 @@ jobs: rosdistro: ${{ matrix.rosdistro }} target-packages: ${{ steps.get-self-packages.outputs.self-packages }} build-depends-repos: ${{ matrix.build-depends-repos }} + cache-key-element: ${{ env.BUILD_TYPE_CUDA_STATE }} - name: Show ccache stats after build run: du -sh ${CCACHE_DIR} && ccache -s @@ -98,6 +115,7 @@ jobs: fail_ci_if_error: false verbose: true flags: total + token: ${{ secrets.CODECOV_TOKEN }} - name: Show disk space after the tasks run: df -h diff --git a/.github/workflows/clang-tidy-differential.yaml b/.github/workflows/clang-tidy-differential.yaml deleted file mode 100644 index a59287a6d9b37..0000000000000 --- a/.github/workflows/clang-tidy-differential.yaml +++ /dev/null @@ -1,61 +0,0 @@ -name: clang-tidy-differential - -on: - pull_request: - types: - - opened - - synchronize - - reopened - - labeled - -jobs: - make-sure-label-is-present: - uses: autowarefoundation/autoware-github-actions/.github/workflows/make-sure-label-is-present.yaml@v1 - with: - label: tag:run-clang-tidy-differential - - clang-tidy-differential: - needs: make-sure-label-is-present - if: ${{ needs.make-sure-label-is-present.outputs.result == 'true' }} - runs-on: ubuntu-latest - container: ghcr.io/autowarefoundation/autoware:latest-prebuilt-cuda - steps: - - name: Set PR fetch depth - run: echo "PR_FETCH_DEPTH=$(( ${{ github.event.pull_request.commits }} + 1 ))" >> "${GITHUB_ENV}" - - - name: Checkout PR branch and all PR commits - uses: actions/checkout@v4 - with: - ref: ${{ github.event.pull_request.head.sha }} - fetch-depth: ${{ env.PR_FETCH_DEPTH }} - - - name: Show disk space before the tasks - run: df -h - - - name: Remove exec_depend - uses: autowarefoundation/autoware-github-actions/remove-exec-depend@v1 - - - name: Get modified packages - id: get-modified-packages - uses: autowarefoundation/autoware-github-actions/get-modified-packages@v1 - - - name: Get modified files - id: get-modified-files - uses: tj-actions/changed-files@v42 - with: - files: | - **/*.cpp - **/*.hpp - - - name: Run clang-tidy - if: ${{ steps.get-modified-files.outputs.all_changed_files != '' }} - uses: autowarefoundation/autoware-github-actions/clang-tidy@v1 - with: - rosdistro: humble - target-packages: ${{ steps.get-modified-packages.outputs.modified-packages }} - target-files: ${{ steps.get-modified-files.outputs.all_changed_files }} - clang-tidy-config-url: https://raw.githubusercontent.com/autowarefoundation/autoware/main/.clang-tidy - build-depends-repos: build_depends.repos - - - name: Show disk space after the tasks - run: df -h diff --git a/.github/workflows/cppcheck-daily.yaml b/.github/workflows/cppcheck-daily.yaml index 6a18bfb988b08..6defc8de4894a 100644 --- a/.github/workflows/cppcheck-daily.yaml +++ b/.github/workflows/cppcheck-daily.yaml @@ -13,29 +13,17 @@ jobs: - name: Checkout code uses: actions/checkout@v2 - - name: Install dependencies + # cppcheck from apt does not yet support --check-level args, and thus install from snap + - name: Install Cppcheck from snap run: | - sudo apt-get update - sudo apt-get install -y build-essential cmake git libpcre3-dev - - # cppcheck from apt does not yet support --check-level args, and thus install from source - - name: Install Cppcheck from source - run: | - mkdir /tmp/cppcheck - git clone https://github.com/danmar/cppcheck.git /tmp/cppcheck - cd /tmp/cppcheck - git checkout 2.14.1 - mkdir build - cd build - cmake .. - make -j $(nproc) - sudo make install + sudo snap install cppcheck + # cspell: ignore suppr - name: Run Cppcheck on all files continue-on-error: true id: cppcheck run: | - cppcheck --enable=all --inconclusive --check-level=exhaustive --error-exitcode=1 --xml . 2> cppcheck-report.xml + cppcheck --enable=all --inconclusive --check-level=exhaustive --suppress=*:*/test/* --error-exitcode=1 --xml --inline-suppr . 2> cppcheck-report.xml shell: bash - name: Count errors by error ID and severity diff --git a/.github/workflows/cppcheck-differential.yaml b/.github/workflows/cppcheck-differential.yaml index 914abd7df86ea..63f15cb9da319 100644 --- a/.github/workflows/cppcheck-differential.yaml +++ b/.github/workflows/cppcheck-differential.yaml @@ -8,58 +8,88 @@ jobs: runs-on: ubuntu-latest steps: - - name: Checkout code - uses: actions/checkout@v2 + - name: Set PR fetch depth + run: echo "PR_FETCH_DEPTH=$(( ${{ github.event.pull_request.commits }} + 1 ))" >> "${GITHUB_ENV}" + + - name: Checkout PR branch and all PR commits + uses: actions/checkout@v4 + with: + ref: ${{ github.event.pull_request.head.sha }} + fetch-depth: ${{ env.PR_FETCH_DEPTH }} - name: Install dependencies run: | sudo apt-get update - sudo apt-get install -y build-essential cmake git libpcre3-dev + sudo apt-get install -y git - # cppcheck from apt does not yet support --check-level args, and thus install from source - - name: Install Cppcheck from source + - name: Install colcon run: | - mkdir /tmp/cppcheck - git clone https://github.com/danmar/cppcheck.git /tmp/cppcheck - cd /tmp/cppcheck - git checkout 2.14.1 - mkdir build - cd build - cmake .. - make -j $(nproc) - sudo make install - - - name: Get changed files - id: changed-files + sudo sh -c 'echo "deb [arch=amd64,arm64] http://repo.ros2.org/ubuntu/main `lsb_release -cs` main" > /etc/apt/sources.list.d/ros2-latest.list' + curl -s https://raw.githubusercontent.com/ros/rosdistro/master/ros.asc | sudo apt-key add - + sudo apt update + sudo apt install python3-colcon-common-extensions + + # cppcheck from apt does not yet support --check-level args, and thus install from snap + - name: Install Cppcheck from snap run: | - git fetch origin ${{ github.base_ref }} --depth=1 - git diff --name-only FETCH_HEAD ${{ github.sha }} > changed_files.txt - cat changed_files.txt + sudo snap install cppcheck + + - name: Fetch the base branch with enough history for a common merge-base commit + run: git fetch origin ${{ github.base_ref }} + shell: bash + + - name: Get modified packages + id: get-modified-packages + uses: autowarefoundation/autoware-github-actions/get-modified-packages@v1 - - name: Run Cppcheck on changed files + - name: Get full paths of modified packages + id: get-full-paths + run: | + modified_packages="${{ steps.get-modified-packages.outputs.modified-packages }}" + paths="" + for pkg in $modified_packages; do + path=$(colcon list --packages-select $pkg | awk '{print $2}' | xargs realpath) + paths="$paths $path" + done + echo "full-paths=$paths" >> $GITHUB_OUTPUT + + - name: Filter packages with no cpp/hpp files + id: filter-paths-no-cpp-files + run: | + filtered_paths="" + for dir in ${{ steps.get-full-paths.outputs.full-paths }}; do + if [ -d "$dir" ] && find "$dir" -name "*.cpp" -o -name "*.hpp" | grep -q .; then + filtered_paths="$filtered_paths $dir" + fi + done + echo "filtered-full-paths=$filtered_paths" >> $GITHUB_OUTPUT + + # cspell: ignore suppr + - name: Run Cppcheck on modified packages + if: ${{ steps.filter-paths-no-cpp-files.outputs.filtered-full-paths != '' }} continue-on-error: true id: cppcheck run: | - files=$(cat changed_files.txt | grep -E '\.(cpp|hpp)$' || true) - if [ -n "$files" ]; then - echo "Running Cppcheck on changed files: $files" - cppcheck --enable=all --inconclusive --check-level=exhaustive --error-exitcode=1 --suppressions-list=.cppcheck_suppressions $files 2> cppcheck-report.txt - else - echo "No C++ files changed." - touch cppcheck-report.txt - fi + echo "Running Cppcheck on modified packages: ${{ steps.filter-paths-no-cpp-files.outputs.filtered-full-paths }}" + cppcheck --enable=all --inconclusive --check-level=exhaustive --error-exitcode=1 --suppressions-list=.cppcheck_suppressions --inline-suppr ${{ steps.filter-paths-no-cpp-files.outputs.filtered-full-paths }} 2> cppcheck-report.txt shell: bash + - name: Setup Problem Matchers for cppcheck + if: ${{ steps.filter-paths-no-cpp-files.outputs.filtered-full-paths != '' }} + run: echo "::add-matcher::.github/cppcheck-problem-matcher.json" + - name: Show cppcheck-report result + if: ${{ steps.filter-paths-no-cpp-files.outputs.filtered-full-paths != '' }} run: | cat cppcheck-report.txt - name: Upload Cppcheck report + if: ${{ steps.filter-paths-no-cpp-files.outputs.filtered-full-paths != '' }} uses: actions/upload-artifact@v2 with: name: cppcheck-report path: cppcheck-report.txt - name: Fail the job if Cppcheck failed - if: steps.cppcheck.outcome == 'failure' + if: ${{ steps.filter-paths-no-cpp-files.outputs.filtered-full-paths != '' && steps.cppcheck.outcome == 'failure' }} run: exit 1 diff --git a/.gitignore b/.gitignore index 5ce2c268169c8..757d2d8a09bed 100644 --- a/.gitignore +++ b/.gitignore @@ -13,3 +13,6 @@ log/ # Python *.pyc + +#prettier +node_modules/ diff --git a/build_depends.repos b/build_depends.repos index bd93897d81d68..0522862dd0eb7 100644 --- a/build_depends.repos +++ b/build_depends.repos @@ -1,8 +1,21 @@ repositories: # core + # TODO(youtalk): Remove autoware_common when https://github.com/autowarefoundation/autoware/issues/4911 is closed core/autoware_common: type: git url: https://github.com/autowarefoundation/autoware_common.git + version: remove-autoware-cmake-utils + core/autoware_cmake: + type: git + url: https://github.com/autowarefoundation/autoware_cmake.git + version: main + core/autoware_utils: + type: git + url: https://github.com/autowarefoundation/autoware_utils.git + version: main + core/autoware_lanelet2_extension: + type: git + url: https://github.com/autowarefoundation/autoware_lanelet2_extension.git version: main core/autoware.core: type: git diff --git a/codecov.yaml b/codecov.yaml index 8ca2196768dac..ecc77815e97e6 100644 --- a/codecov.yaml +++ b/codecov.yaml @@ -1,11 +1,15 @@ +# https://docs.codecov.com/docs/commit-status#project-status + coverage: status: project: default: - target: auto + target: 0% # Make CI always succeed + threshold: 100% # Make CI always succeed patch: default: - target: auto + target: 0% # Make CI always succeed + threshold: 100% # Make CI always succeed comment: show_carryforward_flags: true @@ -16,7 +20,9 @@ flag_management: statuses: - name_prefix: project- type: project - target: auto + target: 0% # Make CI always succeed + threshold: 100% # Make CI always succeed - name_prefix: patch- type: patch - target: auto + target: 0% # Make CI always succeed + threshold: 100% # Make CI always succeed diff --git a/common/autoware_overlay_rviz_plugin/autoware_overlay_rviz_plugin/assets/images/wheel.png b/common/autoware_overlay_rviz_plugin/autoware_overlay_rviz_plugin/assets/images/wheel.png index 07b1e33132e43..04ea298a46829 100644 Binary files a/common/autoware_overlay_rviz_plugin/autoware_overlay_rviz_plugin/assets/images/wheel.png and b/common/autoware_overlay_rviz_plugin/autoware_overlay_rviz_plugin/assets/images/wheel.png differ diff --git a/common/autoware_overlay_rviz_plugin/autoware_overlay_rviz_plugin/include/signal_display.hpp b/common/autoware_overlay_rviz_plugin/autoware_overlay_rviz_plugin/include/signal_display.hpp index 0831ded468c99..5f5c9cee2da43 100644 --- a/common/autoware_overlay_rviz_plugin/autoware_overlay_rviz_plugin/include/signal_display.hpp +++ b/common/autoware_overlay_rviz_plugin/autoware_overlay_rviz_plugin/include/signal_display.hpp @@ -76,6 +76,7 @@ private Q_SLOTS: rviz_common::properties::IntProperty * property_left_; rviz_common::properties::IntProperty * property_top_; rviz_common::properties::ColorProperty * property_signal_color_; + rviz_common::properties::FloatProperty * property_handle_angle_scale_; std::unique_ptr steering_topic_property_; std::unique_ptr gear_topic_property_; std::unique_ptr speed_topic_property_; diff --git a/common/autoware_overlay_rviz_plugin/autoware_overlay_rviz_plugin/include/steering_wheel_display.hpp b/common/autoware_overlay_rviz_plugin/autoware_overlay_rviz_plugin/include/steering_wheel_display.hpp index dada3cddab911..0e42368f2e017 100644 --- a/common/autoware_overlay_rviz_plugin/autoware_overlay_rviz_plugin/include/steering_wheel_display.hpp +++ b/common/autoware_overlay_rviz_plugin/autoware_overlay_rviz_plugin/include/steering_wheel_display.hpp @@ -36,7 +36,8 @@ class SteeringWheelDisplay { public: SteeringWheelDisplay(); - void drawSteeringWheel(QPainter & painter, const QRectF & backgroundRect); + void drawSteeringWheel( + QPainter & painter, const QRectF & backgroundRect, float handle_angle_scale_); void updateSteeringData(const autoware_vehicle_msgs::msg::SteeringReport::ConstSharedPtr & msg); private: @@ -46,6 +47,7 @@ class SteeringWheelDisplay QImage wheelImage; QImage scaledWheelImage; QImage coloredImage(const QImage & source, const QColor & color); + autoware_vehicle_msgs::msg::SteeringReport::ConstSharedPtr last_msg_ptr_; }; } // namespace autoware_overlay_rviz_plugin diff --git a/common/autoware_overlay_rviz_plugin/autoware_overlay_rviz_plugin/src/signal_display.cpp b/common/autoware_overlay_rviz_plugin/autoware_overlay_rviz_plugin/src/signal_display.cpp index ccfdaa69f5d2a..1fd33a95fb34d 100644 --- a/common/autoware_overlay_rviz_plugin/autoware_overlay_rviz_plugin/src/signal_display.cpp +++ b/common/autoware_overlay_rviz_plugin/autoware_overlay_rviz_plugin/src/signal_display.cpp @@ -49,6 +49,9 @@ SignalDisplay::SignalDisplay() property_signal_color_ = new rviz_common::properties::ColorProperty( "Signal Color", QColor(QString("#00E678")), "Color of the signal arrows", this, SLOT(updateOverlayColor())); + property_handle_angle_scale_ = new rviz_common::properties::FloatProperty( + "Handle Angle Scale", 17.0, "Scale of the steering wheel handle angle", this, + SLOT(updateOverlaySize())); // Initialize the component displays steering_wheel_display_ = std::make_unique(); @@ -285,7 +288,8 @@ void SignalDisplay::drawWidget(QImage & hud) } if (steering_wheel_display_) { - steering_wheel_display_->drawSteeringWheel(painter, backgroundRect); + steering_wheel_display_->drawSteeringWheel( + painter, backgroundRect, property_handle_angle_scale_->getFloat()); } if (speed_display_) { diff --git a/common/autoware_overlay_rviz_plugin/autoware_overlay_rviz_plugin/src/steering_wheel_display.cpp b/common/autoware_overlay_rviz_plugin/autoware_overlay_rviz_plugin/src/steering_wheel_display.cpp index d2390b16e5373..28bd2b5c2a52e 100644 --- a/common/autoware_overlay_rviz_plugin/autoware_overlay_rviz_plugin/src/steering_wheel_display.cpp +++ b/common/autoware_overlay_rviz_plugin/autoware_overlay_rviz_plugin/src/steering_wheel_display.cpp @@ -59,19 +59,17 @@ void SteeringWheelDisplay::updateSteeringData( const autoware_vehicle_msgs::msg::SteeringReport::ConstSharedPtr & msg) { try { - // Assuming msg->steering_angle is the field you're interested in - float steeringAngle = msg->steering_tire_angle; - // we received it as a radian value, but we want to display it in degrees - steering_angle_ = - (steeringAngle * -180 / M_PI) * - 17; // 17 is the ratio between the steering wheel and the steering tire angle i assume + last_msg_ptr_ = msg; + + steering_angle_ = msg->steering_tire_angle; } catch (const std::exception & e) { // Log the error std::cerr << "Error in processMessage: " << e.what() << std::endl; } } -void SteeringWheelDisplay::drawSteeringWheel(QPainter & painter, const QRectF & backgroundRect) +void SteeringWheelDisplay::drawSteeringWheel( + QPainter & painter, const QRectF & backgroundRect, float handle_angle_scale_) { // Enable Antialiasing for smoother drawing painter.setRenderHint(QPainter::Antialiasing, true); @@ -80,7 +78,7 @@ void SteeringWheelDisplay::drawSteeringWheel(QPainter & painter, const QRectF & QImage wheel = coloredImage(scaledWheelImage, gray); // Rotate the wheel - float steeringAngle = steering_angle_; // No need to round here + float steeringAngle = std::round(handle_angle_scale_ * (steering_angle_ / M_PI) * -180.0); // Calculate the position int wheelCenterX = backgroundRect.left() + wheel.width() + 54 + 54; int wheelCenterY = backgroundRect.height() / 2; @@ -99,16 +97,40 @@ void SteeringWheelDisplay::drawSteeringWheel(QPainter & painter, const QRectF & // Draw the rotated image painter.drawImage(drawPoint.x(), drawPoint.y(), rotatedWheel); - QString steeringAngleStringAfterModulo = QString::number(fmod(steeringAngle, 360), 'f', 0); + std::ostringstream steering_angle_ss; + if (last_msg_ptr_) { + steering_angle_ss << std::fixed << std::setprecision(1) << steering_angle_ * 180.0 / M_PI + << "°"; + } else { + steering_angle_ss << "N/A"; + } + + QString steeringAngleString = QString::fromStdString(steering_angle_ss.str()); + // if the string doesn't have a negative sign, add a space to the front of the string + // to align the text in both cases (negative and positive) + if (steeringAngleString[0] != '-') { + steeringAngleString = " " + steeringAngleString; + } // Draw the steering angle text - QFont steeringFont("Quicksand", 9, QFont::Bold); + QFont steeringFont("Quicksand", 8, QFont::Bold); painter.setFont(steeringFont); painter.setPen(QColor(0, 0, 0, 255)); - QRect steeringRect( - wheelCenterX - wheelImage.width() / 2, wheelCenterY - wheelImage.height() / 2, - wheelImage.width(), wheelImage.height()); - painter.drawText(steeringRect, Qt::AlignCenter, steeringAngleStringAfterModulo + "°"); + // QRect steeringRect( + // wheelCenterX - wheelImage.width() / 2 + 1, wheelCenterY - wheelImage.height() / 2, + // wheelImage.width(), wheelImage.height()); + // painter.drawText(steeringRect, Qt::AlignCenter, steeringAngleString); + + // Measure the text + QFontMetrics fontMetrics(steeringFont); + QRect textRect = fontMetrics.boundingRect(steeringAngleString); + + // Center the text + int textX = wheelCenterX - textRect.width() / 2; + int textY = wheelCenterY - textRect.height() / 2; + + QRect steeringRect(textX, textY, textRect.width(), textRect.height()); + painter.drawText(steeringRect, Qt::AlignCenter, steeringAngleString); } QImage SteeringWheelDisplay::coloredImage(const QImage & source, const QColor & color) diff --git a/common/autoware_perception_rviz_plugin/src/object_detection/object_polygon_detail.cpp b/common/autoware_perception_rviz_plugin/src/object_detection/object_polygon_detail.cpp index 1d06454582a2f..910bd6ed815e6 100644 --- a/common/autoware_perception_rviz_plugin/src/object_detection/object_polygon_detail.cpp +++ b/common/autoware_perception_rviz_plugin/src/object_detection/object_polygon_detail.cpp @@ -526,10 +526,7 @@ visualization_msgs::msg::Marker::SharedPtr get_shape_marker_ptr( marker_ptr->scale = shape_msg.dimensions; marker_ptr->color.a = 0.75f; } - } else if (shape_msg.type == Shape::POLYGON) { - marker_ptr->type = visualization_msgs::msg::Marker::LINE_LIST; - calc_polygon_line_list(shape_msg, marker_ptr->points); - } else { + } else { // including shape_msg.type == Shape::POLYGON marker_ptr->type = visualization_msgs::msg::Marker::LINE_LIST; calc_polygon_line_list(shape_msg, marker_ptr->points); } @@ -562,10 +559,7 @@ visualization_msgs::msg::Marker::SharedPtr get_2d_shape_marker_ptr( } else if (shape_msg.type == Shape::CYLINDER) { marker_ptr->type = visualization_msgs::msg::Marker::LINE_LIST; calc_2d_cylinder_bottom_line_list(shape_msg, marker_ptr->points); - } else if (shape_msg.type == Shape::POLYGON) { - marker_ptr->type = visualization_msgs::msg::Marker::LINE_LIST; - calc_2d_polygon_bottom_line_list(shape_msg, marker_ptr->points); - } else { + } else { // including shape_msg.type == Shape::POLYGON marker_ptr->type = visualization_msgs::msg::Marker::LINE_LIST; calc_2d_polygon_bottom_line_list(shape_msg, marker_ptr->points); } diff --git a/common/autoware_point_types/package.xml b/common/autoware_point_types/package.xml index e05b604e90d13..ef19431d86514 100644 --- a/common/autoware_point_types/package.xml +++ b/common/autoware_point_types/package.xml @@ -4,7 +4,8 @@ autoware_point_types 0.1.0 The point types definition to use point_cloud_msg_wrapper - Taichi Higashide + David Wong + Max Schmeller Apache License 2.0 ament_cmake_core diff --git a/common/autoware_test_utils/CMakeLists.txt b/common/autoware_test_utils/CMakeLists.txt index 91adf935445b4..6a12539d06cbf 100644 --- a/common/autoware_test_utils/CMakeLists.txt +++ b/common/autoware_test_utils/CMakeLists.txt @@ -15,11 +15,10 @@ target_link_libraries(mock_data_parser ) if(BUILD_TESTING) - ament_add_ros_isolated_gtest(test_mock_data_parser - test/test_mock_data_parser.cpp) - - target_link_libraries(test_mock_data_parser - mock_data_parser) + ament_auto_add_gtest(test_autoware_test_utils + test/test_mock_data_parser.cpp + test/test_autoware_test_manager.cpp + ) endif() ament_auto_package(INSTALL_TO_SHARE diff --git a/common/autoware_test_utils/include/autoware_test_utils/autoware_test_utils.hpp b/common/autoware_test_utils/include/autoware_test_utils/autoware_test_utils.hpp index 49d2c848ab508..2db4e6a9e9162 100644 --- a/common/autoware_test_utils/include/autoware_test_utils/autoware_test_utils.hpp +++ b/common/autoware_test_utils/include/autoware_test_utils/autoware_test_utils.hpp @@ -16,11 +16,11 @@ #define AUTOWARE_TEST_UTILS__AUTOWARE_TEST_UTILS_HPP_ #include +#include +#include +#include +#include #include -#include -#include -#include -#include #include #include @@ -31,6 +31,7 @@ #include #include #include +#include #include #include #include @@ -48,6 +49,7 @@ #include #include #include +#include #include namespace autoware::test_utils @@ -512,6 +514,81 @@ void publishToTargetNode( autoware::test_utils::spinSomeNodes(test_node, target_node, repeat_count); } +/** + * @brief Manages publishing and subscribing to ROS topics for testing Autoware. + * + * The AutowareTestManager class provides utility functions to facilitate + * the publishing of messages to specified topics and the setting up of + * subscribers to listen for messages on specified topics. This class + * simplifies the setup of test environments in Autoware. + */ +class AutowareTestManager +{ +public: + AutowareTestManager() + { + test_node_ = std::make_shared("autoware_test_manager_node"); + pub_clock_ = test_node_->create_publisher("/clock", 1); + } + + template + void test_pub_msg( + rclcpp::Node::SharedPtr target_node, const std::string & topic_name, MessageType & msg) + { + if (publishers_.find(topic_name) == publishers_.end()) { + auto publisher = test_node_->create_publisher(topic_name, 10); + publishers_[topic_name] = std::static_pointer_cast(publisher); + } + + auto publisher = + std::dynamic_pointer_cast>(publishers_[topic_name]); + + autoware::test_utils::publishToTargetNode(test_node_, target_node, topic_name, publisher, msg); + RCLCPP_INFO(test_node_->get_logger(), "Published message on topic '%s'", topic_name.c_str()); + } + + template + void set_subscriber( + const std::string & topic_name, + std::function callback) + { + if (subscribers_.find(topic_name) == subscribers_.end()) { + std::shared_ptr> subscriber; + autoware::test_utils::createSubscription( + test_node_, topic_name, callback, subscriber); + subscribers_[topic_name] = std::static_pointer_cast(subscriber); + } else { + RCLCPP_WARN(test_node_->get_logger(), "Subscriber %s already set.", topic_name.c_str()); + } + } + + /** + * @brief Publishes a ROS Clock message with the specified time. + * + * This function publishes a ROS Clock message with the specified time. + * Be careful when using this function, as it can affect the behavior of + * the system under test. Consider using ament_add_ros_isolated_gtest to + * isolate the system under test from the ROS clock. + * + * @param time The time to publish. + */ + void jump_clock(const rclcpp::Time & time) + { + rosgraph_msgs::msg::Clock clock; + clock.clock = time; + pub_clock_->publish(clock); + } + +protected: + // Publisher + std::unordered_map> publishers_; + std::unordered_map> subscribers_; + rclcpp::Publisher::SharedPtr pub_clock_; + + // Node + rclcpp::Node::SharedPtr test_node_; +}; // class AutowareTestManager + } // namespace autoware::test_utils #endif // AUTOWARE_TEST_UTILS__AUTOWARE_TEST_UTILS_HPP_ diff --git a/common/autoware_test_utils/package.xml b/common/autoware_test_utils/package.xml index 778b9a42ca207..afcf44296a75d 100644 --- a/common/autoware_test_utils/package.xml +++ b/common/autoware_test_utils/package.xml @@ -16,6 +16,7 @@ autoware_cmake autoware_control_msgs + autoware_lanelet2_extension autoware_map_msgs autoware_perception_msgs autoware_planning_msgs @@ -23,7 +24,6 @@ autoware_vehicle_msgs component_interface_specs component_interface_utils - lanelet2_extension lanelet2_io nav_msgs rclcpp diff --git a/common/autoware_test_utils/src/autoware_test_utils.cpp b/common/autoware_test_utils/src/autoware_test_utils.cpp index 563350dbe53cc..d870bcf9974a1 100644 --- a/common/autoware_test_utils/src/autoware_test_utils.cpp +++ b/common/autoware_test_utils/src/autoware_test_utils.cpp @@ -19,6 +19,8 @@ #include #include +#include + #include namespace autoware::test_utils @@ -54,14 +56,32 @@ lanelet::LaneletMapPtr loadMap(const std::string & lanelet2_filename) lanelet::ErrorMessages errors{}; lanelet::projection::MGRSProjector projector{}; lanelet::LaneletMapPtr map = lanelet::load(lanelet2_filename, projector, &errors); - if (errors.empty()) { - return map; + if (!errors.empty()) { + for (const auto & error : errors) { + RCLCPP_ERROR_STREAM(rclcpp::get_logger("map_loader"), error); + } + return nullptr; } - for (const auto & error : errors) { - RCLCPP_ERROR_STREAM(rclcpp::get_logger("map_loader"), error); + for (lanelet::Point3d point : map->pointLayer) { + if (point.hasAttribute("local_x")) { + point.x() = point.attribute("local_x").asDouble().value(); + } + if (point.hasAttribute("local_y")) { + point.y() = point.attribute("local_y").asDouble().value(); + } } - return nullptr; + + // realign lanelet borders using updated points + for (lanelet::Lanelet lanelet : map->laneletLayer) { + auto left = lanelet.leftBound(); + auto right = lanelet.rightBound(); + std::tie(left, right) = lanelet::geometry::align(left, right); + lanelet.setLeftBound(left); + lanelet.setRightBound(right); + } + + return map; } LaneletMapBin convertToMapBinMsg( diff --git a/common/autoware_test_utils/test/test_autoware_test_manager.cpp b/common/autoware_test_utils/test/test_autoware_test_manager.cpp new file mode 100644 index 0000000000000..688ec9df9ad42 --- /dev/null +++ b/common/autoware_test_utils/test/test_autoware_test_manager.cpp @@ -0,0 +1,71 @@ +// Copyright 2024 TIER IV +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "autoware_test_utils/autoware_test_utils.hpp" + +#include + +#include + +#include + +class RelayNode : public rclcpp::Node +{ +public: + RelayNode() : Node("relay_node") + { + subscription_ = this->create_subscription( + "input_topic", 10, [this](const std_msgs::msg::String::ConstSharedPtr msg) { + RCLCPP_INFO(this->get_logger(), "Received message: %s", msg->data.c_str()); + auto new_msg = std::make_shared(); + new_msg->data = msg->data; + publisher_->publish(*new_msg); + }); + + publisher_ = this->create_publisher("output_topic", 10); + } + +private: + rclcpp::Subscription::SharedPtr subscription_; + rclcpp::Publisher::SharedPtr publisher_; +}; + +TEST(AutowareTestManagerTest, TestRelayNode) +{ + rclcpp::init(0, nullptr); + + const std::string input_topic_name = "input_topic"; + const std::string output_topic_name = "output_topic"; + + // Setup target node and its test manager + auto target_node = std::make_shared(); + auto manager = std::make_shared(); + + // Setup subscriber for test manager + std::string received_msg; + manager->set_subscriber( + "output_topic", + [&received_msg](const std_msgs::msg::String::ConstSharedPtr msg) { received_msg = msg->data; }); + + // Publish a message to the relay node + std_msgs::msg::String msg; + msg.data = "Hello, Relay!"; + manager->test_pub_msg(target_node, input_topic_name, msg); + + // Spin to process callbacks + rclcpp::spin_some(target_node); + + // Check that the message was relayed and received by the test node + EXPECT_EQ(received_msg, "Hello, Relay!"); +} diff --git a/common/geography_utils/package.xml b/common/geography_utils/package.xml index 414364e36ad29..bf2356a7fabf5 100644 --- a/common/geography_utils/package.xml +++ b/common/geography_utils/package.xml @@ -10,10 +10,10 @@ ament_cmake_auto autoware_cmake + autoware_lanelet2_extension geographic_msgs geographiclib geometry_msgs - lanelet2_extension lanelet2_io tier4_map_msgs diff --git a/common/geography_utils/src/lanelet2_projector.cpp b/common/geography_utils/src/lanelet2_projector.cpp index 532606248cd1f..ea32e08cd157a 100644 --- a/common/geography_utils/src/lanelet2_projector.cpp +++ b/common/geography_utils/src/lanelet2_projector.cpp @@ -13,9 +13,9 @@ // limitations under the License. #include +#include +#include #include -#include -#include #include diff --git a/common/geography_utils/src/projection.cpp b/common/geography_utils/src/projection.cpp index ac9ce19052702..d32252c28f5c0 100644 --- a/common/geography_utils/src/projection.cpp +++ b/common/geography_utils/src/projection.cpp @@ -13,9 +13,9 @@ // limitations under the License. #include +#include #include #include -#include namespace geography_utils { diff --git a/common/goal_distance_calculator/include/goal_distance_calculator/goal_distance_calculator_node.hpp b/common/goal_distance_calculator/include/goal_distance_calculator/goal_distance_calculator_node.hpp index 037841a505017..602688ffe51d7 100644 --- a/common/goal_distance_calculator/include/goal_distance_calculator/goal_distance_calculator_node.hpp +++ b/common/goal_distance_calculator/include/goal_distance_calculator/goal_distance_calculator_node.hpp @@ -18,6 +18,7 @@ #include "goal_distance_calculator/goal_distance_calculator.hpp" #include +#include #include #include @@ -44,16 +45,9 @@ class GoalDistanceCalculatorNode : public rclcpp::Node private: // Subscriber - rclcpp::Subscription::SharedPtr sub_initial_pose_; autoware::universe_utils::SelfPoseListener self_pose_listener_; - rclcpp::Subscription::SharedPtr sub_route_; - - // Data Buffer - geometry_msgs::msg::PoseStamped::ConstSharedPtr current_pose_; - autoware_planning_msgs::msg::LaneletRoute::SharedPtr route_; - - // Callback - void onRoute(const autoware_planning_msgs::msg::LaneletRoute::ConstSharedPtr & msg); + autoware::universe_utils::InterProcessPollingSubscriber + sub_route_{this, "/planning/mission_planning/route"}; // Publisher autoware::universe_utils::DebugPublisher debug_publisher_; @@ -61,8 +55,8 @@ class GoalDistanceCalculatorNode : public rclcpp::Node // Timer rclcpp::TimerBase::SharedPtr timer_; - bool isDataReady(); - bool isDataTimeout(); + bool tryGetCurrentPose(geometry_msgs::msg::PoseStamped::ConstSharedPtr current_pose); + bool tryGetRoute(autoware_planning_msgs::msg::LaneletRoute::ConstSharedPtr route); void onTimer(); // Parameter @@ -70,8 +64,6 @@ class GoalDistanceCalculatorNode : public rclcpp::Node Param param_; // Core - Input input_; - Output output_; std::unique_ptr goal_distance_calculator_; }; } // namespace goal_distance_calculator diff --git a/common/goal_distance_calculator/src/goal_distance_calculator_node.cpp b/common/goal_distance_calculator/src/goal_distance_calculator_node.cpp index 8e4e4c90bc470..24d54ee2fcf87 100644 --- a/common/goal_distance_calculator/src/goal_distance_calculator_node.cpp +++ b/common/goal_distance_calculator/src/goal_distance_calculator_node.cpp @@ -33,12 +33,6 @@ GoalDistanceCalculatorNode::GoalDistanceCalculatorNode(const rclcpp::NodeOptions self_pose_listener_(this), debug_publisher_(this, "goal_distance_calculator") { - using std::placeholders::_1; - - static constexpr std::size_t queue_size = 1; - rclcpp::QoS durable_qos(queue_size); - durable_qos.transient_local(); - // Node Parameter node_param_.update_rate = declare_parameter("update_rate"); node_param_.oneshot = declare_parameter("oneshot"); @@ -47,11 +41,6 @@ GoalDistanceCalculatorNode::GoalDistanceCalculatorNode(const rclcpp::NodeOptions goal_distance_calculator_ = std::make_unique(); goal_distance_calculator_->setParam(param_); - // Subscriber - sub_route_ = create_subscription( - "/planning/mission_planning/route", queue_size, - [&](const autoware_planning_msgs::msg::LaneletRoute::SharedPtr msg_ptr) { route_ = msg_ptr; }); - // Wait for first self pose self_pose_listener_.waitForFirstPose(); @@ -62,53 +51,54 @@ GoalDistanceCalculatorNode::GoalDistanceCalculatorNode(const rclcpp::NodeOptions goal_distance_calculator_ = std::make_unique(); } -bool GoalDistanceCalculatorNode::isDataReady() +bool GoalDistanceCalculatorNode::tryGetCurrentPose( + geometry_msgs::msg::PoseStamped::ConstSharedPtr current_pose) { - if (!current_pose_) { - RCLCPP_INFO_THROTTLE( - this->get_logger(), *this->get_clock(), 5000, "waiting for current_pose..."); - return false; - } - - if (!route_) { - RCLCPP_INFO_THROTTLE(this->get_logger(), *this->get_clock(), 5000, "waiting for route msg..."); - return false; - } - + auto current_pose_tmp = self_pose_listener_.getCurrentPose(); + if (!current_pose_tmp) return false; + current_pose = current_pose_tmp; return true; } -bool GoalDistanceCalculatorNode::isDataTimeout() +bool GoalDistanceCalculatorNode::tryGetRoute( + autoware_planning_msgs::msg::LaneletRoute::ConstSharedPtr route) { - constexpr double th_pose_timeout = 1.0; - const auto pose_time_diff = rclcpp::Time(current_pose_->header.stamp) - now(); - if (pose_time_diff.seconds() > th_pose_timeout) { - RCLCPP_WARN_THROTTLE(this->get_logger(), *this->get_clock(), 1000, "pose is timeout..."); - return true; - } - return false; + auto route_tmp = sub_route_.takeData(); + if (!route_tmp) return false; + route = route_tmp; + return true; } void GoalDistanceCalculatorNode::onTimer() { - current_pose_ = self_pose_listener_.getCurrentPose(); + Input input = Input(); - if (!isDataReady()) { + if (!tryGetCurrentPose(input.current_pose)) { + RCLCPP_INFO_THROTTLE( + this->get_logger(), *this->get_clock(), 5000, "waiting for current_pose..."); return; } - if (isDataTimeout()) { + if (!tryGetRoute(input.route)) { + RCLCPP_INFO_THROTTLE(this->get_logger(), *this->get_clock(), 5000, "waiting for route msg..."); return; } - input_.current_pose = current_pose_; - input_.route = route_; + // Check pose timeout + { + constexpr double th_pose_timeout = 1.0; + const auto pose_time_diff = rclcpp::Time(input.current_pose->header.stamp) - now(); + if (pose_time_diff.seconds() > th_pose_timeout) { + RCLCPP_WARN_THROTTLE(this->get_logger(), *this->get_clock(), 1000, "pose is timeout..."); + return; + } + } - output_ = goal_distance_calculator_->update(input_); + Output output = goal_distance_calculator_->update(input); { using autoware::universe_utils::rad2deg; - const auto & deviation = output_.goal_deviation; + const auto & deviation = output.goal_deviation; debug_publisher_.publish( "deviation/lateral", deviation.lateral); diff --git a/common/path_distance_calculator/src/path_distance_calculator.cpp b/common/path_distance_calculator/src/path_distance_calculator.cpp index 47dd8258de5bb..833c806cdd6db 100644 --- a/common/path_distance_calculator/src/path_distance_calculator.cpp +++ b/common/path_distance_calculator/src/path_distance_calculator.cpp @@ -25,15 +25,12 @@ PathDistanceCalculator::PathDistanceCalculator(const rclcpp::NodeOptions & options) : Node("path_distance_calculator", options), self_pose_listener_(this) { - sub_path_ = create_subscription( - "~/input/path", rclcpp::QoS(1), - [this](const autoware_planning_msgs::msg::Path::SharedPtr msg) { path_ = msg; }); pub_dist_ = create_publisher("~/output/distance", rclcpp::QoS(1)); using std::chrono_literals::operator""s; timer_ = rclcpp::create_timer(this, get_clock(), 1s, [this]() { - const auto path = path_; + const auto path = sub_path_.takeData(); const auto pose = self_pose_listener_.getCurrentPose(); if (!pose) { RCLCPP_WARN_THROTTLE(get_logger(), *get_clock(), 5000, "no pose"); diff --git a/common/path_distance_calculator/src/path_distance_calculator.hpp b/common/path_distance_calculator/src/path_distance_calculator.hpp index 8e8fbf8d6a019..6624f316401df 100644 --- a/common/path_distance_calculator/src/path_distance_calculator.hpp +++ b/common/path_distance_calculator/src/path_distance_calculator.hpp @@ -15,6 +15,7 @@ #ifndef PATH_DISTANCE_CALCULATOR_HPP_ #define PATH_DISTANCE_CALCULATOR_HPP_ +#include #include #include @@ -27,11 +28,11 @@ class PathDistanceCalculator : public rclcpp::Node explicit PathDistanceCalculator(const rclcpp::NodeOptions & options); private: - rclcpp::Subscription::SharedPtr sub_path_; + autoware::universe_utils::InterProcessPollingSubscriber + sub_path_{this, "~/input/path"}; rclcpp::Publisher::SharedPtr pub_dist_; rclcpp::TimerBase::SharedPtr timer_; autoware::universe_utils::SelfPoseListener self_pose_listener_; - autoware_planning_msgs::msg::Path::SharedPtr path_; }; #endif // PATH_DISTANCE_CALCULATOR_HPP_ diff --git a/common/tensorrt_common/include/tensorrt_common/tensorrt_common.hpp b/common/tensorrt_common/include/tensorrt_common/tensorrt_common.hpp index aabaea7ca6339..6691c1fb9e97d 100644 --- a/common/tensorrt_common/include/tensorrt_common/tensorrt_common.hpp +++ b/common/tensorrt_common/include/tensorrt_common/tensorrt_common.hpp @@ -15,7 +15,9 @@ #ifndef TENSORRT_COMMON__TENSORRT_COMMON_HPP_ #define TENSORRT_COMMON__TENSORRT_COMMON_HPP_ +#ifndef YOLOX_STANDALONE #include +#endif #include #include @@ -86,6 +88,7 @@ struct BuildConfig profile_per_layer(profile_per_layer), clip_value(clip_value) { +#ifndef YOLOX_STANDALONE if ( std::find(valid_calib_type.begin(), valid_calib_type.end(), calib_type_str) == valid_calib_type.end()) { @@ -95,6 +98,7 @@ struct BuildConfig << "Default calibration type will be used: MinMax" << std::endl; std::cerr << message.str(); } +#endif } }; diff --git a/common/tier4_camera_view_rviz_plugin/src/bird_eye_view_controller.cpp b/common/tier4_camera_view_rviz_plugin/src/bird_eye_view_controller.cpp index bd5c3349d3c35..16c3f4b5def96 100644 --- a/common/tier4_camera_view_rviz_plugin/src/bird_eye_view_controller.cpp +++ b/common/tier4_camera_view_rviz_plugin/src/bird_eye_view_controller.cpp @@ -53,7 +53,7 @@ #include #include #include -#include +#include #include namespace tier4_camera_view_rviz_plugin diff --git a/common/tier4_camera_view_rviz_plugin/src/bird_eye_view_controller.hpp b/common/tier4_camera_view_rviz_plugin/src/bird_eye_view_controller.hpp index 3c0091740bd59..8ca2d27388413 100644 --- a/common/tier4_camera_view_rviz_plugin/src/bird_eye_view_controller.hpp +++ b/common/tier4_camera_view_rviz_plugin/src/bird_eye_view_controller.hpp @@ -46,7 +46,7 @@ #include "rviz_common/frame_position_tracking_view_controller.hpp" #include -#include +#include namespace rviz_common { @@ -72,7 +72,7 @@ class BirdEyeViewController : public rviz_common::FramePositionTrackingViewContr void onInitialize() override; - void handleMouseEvent(rviz_common::ViewportMouseEvent & evt) override; + void handleMouseEvent(rviz_common::ViewportMouseEvent & event) override; void lookAt(const Ogre::Vector3 & point) override; @@ -96,7 +96,7 @@ class BirdEyeViewController : public rviz_common::FramePositionTrackingViewContr void orientCamera(); void setPosition(const Ogre::Vector3 & pos_rel_target); - void move_camera(float x, float y); + void move_camera(float dx, float dy); void updateCamera(); Ogre::SceneNode * getCameraParent(Ogre::Camera * camera); diff --git a/common/tier4_camera_view_rviz_plugin/src/third_person_view_controller.cpp b/common/tier4_camera_view_rviz_plugin/src/third_person_view_controller.cpp index 9f4306a9d982a..ed47a15270c3a 100644 --- a/common/tier4_camera_view_rviz_plugin/src/third_person_view_controller.cpp +++ b/common/tier4_camera_view_rviz_plugin/src/third_person_view_controller.cpp @@ -56,7 +56,7 @@ #include #include #include -#include +#include #include #include diff --git a/common/tier4_camera_view_rviz_plugin/src/third_person_view_controller.hpp b/common/tier4_camera_view_rviz_plugin/src/third_person_view_controller.hpp index fef036ceccda3..d80fa4502a48f 100644 --- a/common/tier4_camera_view_rviz_plugin/src/third_person_view_controller.hpp +++ b/common/tier4_camera_view_rviz_plugin/src/third_person_view_controller.hpp @@ -45,7 +45,7 @@ #include "rviz_default_plugins/view_controllers/orbit/orbit_view_controller.hpp" -#include +#include #include @@ -68,7 +68,7 @@ class ThirdPersonViewController : public rviz_default_plugins::view_controllers: public: void onInitialize() override; - void handleMouseEvent(rviz_common::ViewportMouseEvent & evt) override; + void handleMouseEvent(rviz_common::ViewportMouseEvent & event) override; void lookAt(const Ogre::Vector3 & point) override; diff --git a/common/tier4_perception_rviz_plugin/src/tools/util.hpp b/common/tier4_perception_rviz_plugin/src/tools/util.hpp index 2126af001aeb5..bf951aad5006a 100644 --- a/common/tier4_perception_rviz_plugin/src/tools/util.hpp +++ b/common/tier4_perception_rviz_plugin/src/tools/util.hpp @@ -17,7 +17,7 @@ #include -#include +#include #include diff --git a/common/tier4_planning_rviz_plugin/include/tier4_planning_rviz_plugin/path/display.hpp b/common/tier4_planning_rviz_plugin/include/tier4_planning_rviz_plugin/path/display.hpp index ef5663f024c8d..dc0cb0eb5188a 100644 --- a/common/tier4_planning_rviz_plugin/include/tier4_planning_rviz_plugin/path/display.hpp +++ b/common/tier4_planning_rviz_plugin/include/tier4_planning_rviz_plugin/path/display.hpp @@ -77,7 +77,7 @@ void visualizeBound( const float diff_angle = autoware::universe_utils::normalizeRadian(normal_vector_angle - curr_to_next_angle); - if (diff_angle == 0.0) { + if (diff_angle <= 1e-7 && diff_angle >= -1e-7) { return std::make_pair(normal_vector_angle, width); } diff --git a/common/tier4_traffic_light_rviz_plugin/package.xml b/common/tier4_traffic_light_rviz_plugin/package.xml index b123f8e2bdc3c..8f2b075c8b090 100644 --- a/common/tier4_traffic_light_rviz_plugin/package.xml +++ b/common/tier4_traffic_light_rviz_plugin/package.xml @@ -10,9 +10,9 @@ ament_cmake_auto autoware_cmake + autoware_lanelet2_extension autoware_map_msgs autoware_perception_msgs - lanelet2_extension libqt5-core libqt5-gui libqt5-widgets diff --git a/common/tier4_traffic_light_rviz_plugin/src/traffic_light_publish_panel.cpp b/common/tier4_traffic_light_rviz_plugin/src/traffic_light_publish_panel.cpp index db33abd72283d..2165587493a7a 100644 --- a/common/tier4_traffic_light_rviz_plugin/src/traffic_light_publish_panel.cpp +++ b/common/tier4_traffic_light_rviz_plugin/src/traffic_light_publish_panel.cpp @@ -22,9 +22,9 @@ #include #include #include -#include -#include -#include +#include +#include +#include #include #include diff --git a/common/tier4_vehicle_rviz_plugin/src/tools/velocity_history.hpp b/common/tier4_vehicle_rviz_plugin/src/tools/velocity_history.hpp index 96345205289e6..cb191811ddbc3 100644 --- a/common/tier4_vehicle_rviz_plugin/src/tools/velocity_history.hpp +++ b/common/tier4_vehicle_rviz_plugin/src/tools/velocity_history.hpp @@ -26,7 +26,7 @@ #include #include -#include +#include #include #include diff --git a/common/traffic_light_recognition_marker_publisher/package.xml b/common/traffic_light_recognition_marker_publisher/package.xml index 71a3e5238b388..0fa3959d69c47 100644 --- a/common/traffic_light_recognition_marker_publisher/package.xml +++ b/common/traffic_light_recognition_marker_publisher/package.xml @@ -12,11 +12,11 @@ ament_cmake autoware_cmake + autoware_lanelet2_extension autoware_map_msgs autoware_perception_msgs autoware_universe_utils geometry_msgs - lanelet2_extension rclcpp rclcpp_components visualization_msgs diff --git a/common/traffic_light_recognition_marker_publisher/src/traffic_light_recognition_marker_publisher.hpp b/common/traffic_light_recognition_marker_publisher/src/traffic_light_recognition_marker_publisher.hpp index c1cac302647a1..14bb0e39628f9 100644 --- a/common/traffic_light_recognition_marker_publisher/src/traffic_light_recognition_marker_publisher.hpp +++ b/common/traffic_light_recognition_marker_publisher/src/traffic_light_recognition_marker_publisher.hpp @@ -15,10 +15,10 @@ #ifndef TRAFFIC_LIGHT_RECOGNITION_MARKER_PUBLISHER_HPP_ #define TRAFFIC_LIGHT_RECOGNITION_MARKER_PUBLISHER_HPP_ -#include -#include -#include -#include +#include +#include +#include +#include #include #include diff --git a/common/traffic_light_utils/package.xml b/common/traffic_light_utils/package.xml index 7adb856c3c447..59125d1583732 100644 --- a/common/traffic_light_utils/package.xml +++ b/common/traffic_light_utils/package.xml @@ -16,8 +16,8 @@ ament_lint_auto autoware_lint_common + autoware_lanelet2_extension autoware_perception_msgs - lanelet2_extension tier4_perception_msgs diff --git a/common/tvm_utility/example/yolo_v2_tiny/main.cpp b/common/tvm_utility/example/yolo_v2_tiny/main.cpp index 8fd0cde3c306d..38b3355ca38ba 100644 --- a/common/tvm_utility/example/yolo_v2_tiny/main.cpp +++ b/common/tvm_utility/example/yolo_v2_tiny/main.cpp @@ -120,7 +120,6 @@ class PostProcessorYoloV2Tiny : public tvm_utility::pipeline::PostProcessor. +To prevent the AEB from considering noisy points, euclidean clustering is performed on the filtered point cloud. The points in the point cloud that are not close enough to other points to form a cluster are discarded. Furthermore, each point in a cluster is compared against the `cluster_minimum_height` parameter, if no point inside a cluster has a height/z value greater than `cluster_minimum_height`, the whole cluster of points is discarded. The parameters `cluster_tolerance`, `minimum_cluster_size` and `maximum_cluster_size` can be used to tune the clustering and the size of objects to be ignored, for more information about the clustering method used by the AEB module, please check the official documentation on euclidean clustering of the PCL library: . Furthermore, a 2D convex hull is created around each detected cluster, the vertices of each hull represent the most extreme/outside points of the cluster. These vertices are then checked in the next step. -#### Rigorous filtering +##### Rigorous filtering -After Noise filtering, the module performs a geometric collision check to determine whether the filtered obstacles/hull vertices actually have possibility to collide with the ego vehicle. In this check, the ego vehicle is represented as a rectangle, and the point cloud obstacles are represented as points. Only the vertices with a possibility of collision are kept. Finally, the vertex that is closest to the ego vehicle is chosen as the candidate for collision checking: Since rss distance is used to judge if a collision will happen or not, if the closest vertex to the ego is deemed to be safe, the rest of the vertices (and the points in the clusters) will also be safe. +After Noise filtering, the module performs a geometric collision check to determine whether the filtered obstacles/hull vertices actually have possibility to collide with the ego vehicle. In this check, the ego vehicle is represented as a rectangle, and the point cloud obstacles are represented as points. Only the vertices with a possibility of collision are kept. ![rigorous_filtering](./image/obstacle_filtering_2.drawio.svg) -#### Obstacle velocity estimation +#### Using predicted objects to get target obstacles + +If the `use_predicted_object_data` parameter is set to true, the AEB can use predicted object data coming from the perception modules, to get target obstacle points. This is done by obtaining the 2D intersection points between the ego's predicted footprint path and each of the predicted objects enveloping polygon or bounding box. + +![predicted_object_and_path_intersection](./image/using-predicted-objects.drawio.svg) + +### Finding the closest target obstacle + +Once all target obstacles have been identified, the AEB module chooses the point that is closest to the ego vehicle as the candidate for collision checking. Only the closest point is considered because RSS distance is used to judge if a collision will happen or not, and if the closest vertex to the ego is deemed to be safe from collision, the rest of the target obstacles will also be safe. + +![closest_object](./image/closest-point.drawio.svg) + +### 4. Obstacle velocity estimation Once the position of the closest obstacle/point is determined, the AEB modules uses the history of previously detected objects to estimate the closest object relative speed using the following equations: @@ -110,7 +132,9 @@ Where $t_{1}$ and $t_{0}$ are the timestamps of the point clouds used to detect ![relative_speed](./image/object_relative_speed.drawio.svg) -Finally, the velocity vector is compared against the ego's predicted path to get the longitudinal velocity $v_{obj}$: +Note that, when the closest obstacle/point comes from using predicted object data, $v_{norm}$ is calculated by directly computing the norm of the predicted object's velocity in the x and y axes. + +The velocity vector is then compared against the ego's predicted path to get the longitudinal velocity $v_{obj}$: $$ v_{obj} = v_{norm} * Cos(yaw_{diff}) + v_{ego} @@ -118,21 +142,23 @@ $$ where $yaw_{diff}$ is the difference in yaw between the ego path and the displacement vector $$v_{pos} = o_{pos} - prev_{pos} $$ and $v_{ego}$ is the ego's current speed, which accounts for the movement of points caused by the ego moving and not the object. All these equations are performed disregarding the z axis (in 2D). -Note that, the object velocity is calculated against the ego's current movement direction. If the object moves in the opposite direction to the ego's movement, the object velocity is set to 0 m/s. That is because the RSS distance calculation assumes the ego and the object move in the same direction and it cannot deal with negative velocities. +Note that, the object velocity is calculated against the ego's current movement direction. If the object moves in the opposite direction to the ego's movement, the object velocity will be negative, which will reduce the rss distance on the next step. + +The resulting estimated object speed is added to a queue of speeds with timestamps. The AEB then checks for expiration of past speed estimations and eliminates expired speed measurements from the queue, the object expiration is determined by checking if the time elapsed since the speed was first added to the queue is larger than the parameter `previous_obstacle_keep_time`. Finally, the median speed of the queue is calculated. The median speed will be used to calculate the RSS distance used for collision checking. -### 4. Collision check with target obstacles using RSS distance +### 5. Collision check with target obstacles using RSS distance In the fourth step, it checks the collision with the closest obstacle point using RSS distance. RSS distance is formulated as: $$ -d = v_{ego}*t_{response} + v_{ego}^2/(2*a_{min}) - v_{obj}^2/(2*a_{obj_{min}}) + offset +d = v_{ego}*t_{response} + v_{ego}^2/(2*a_{min}) -(sign(v_{obj})) * v_{obj}^2/(2*a_{obj_{min}}) + offset $$ where $v_{ego}$ and $v_{obj}$ is current ego and obstacle velocity, $a_{min}$ and $a_{obj_{min}}$ is ego and object minimum acceleration (maximum deceleration), $t_{response}$ is response time of the ego vehicle to start deceleration. Therefore the distance from the ego vehicle to the obstacle is smaller than this RSS distance $d$, the ego vehicle send emergency stop signals. This is illustrated in the following picture. ![rss_check](./image/rss_check.drawio.svg) -### 5. Send emergency stop signals to `/diagnostics` +### 6. Send emergency stop signals to `/diagnostics` If AEB detects collision with point cloud obstacles in the previous step, it sends emergency signal to `/diagnostics` in this step. Note that in order to enable emergency stop, it has to send ERROR level emergency. Moreover, AEB user should modify the setting file to keep the emergency level, otherwise Autoware does not hold the emergency state. @@ -195,6 +221,4 @@ When vehicle odometry information is faulty, it is possible that the MPC fails t - The accuracy of the predicted path created from sensor data depends on the accuracy of sensors attached to the ego vehicle. -- Currently, the module calculates thee closest object velocity if it goes in the same direction as the ego vehicle, otherwise the velocity is set to 0 m/s since RSS distance calculation does not use negative velocities. - ![aeb_range](./image/range.drawio.svg) diff --git a/control/autoware_autonomous_emergency_braking/config/autonomous_emergency_braking.param.yaml b/control/autoware_autonomous_emergency_braking/config/autonomous_emergency_braking.param.yaml index 4233f8b6bebcb..482ddc50766f8 100644 --- a/control/autoware_autonomous_emergency_braking/config/autonomous_emergency_braking.param.yaml +++ b/control/autoware_autonomous_emergency_braking/config/autonomous_emergency_braking.param.yaml @@ -3,6 +3,8 @@ # Ego path calculation use_predicted_trajectory: true use_imu_path: false + use_pointcloud_data: true + use_predicted_object_data: true use_object_velocity_calculation: true min_generated_path_length: 0.5 imu_prediction_time_horizon: 1.5 @@ -26,6 +28,7 @@ # Point cloud clustering cluster_tolerance: 0.1 #[m] + cluster_minimum_height: 0.0 minimum_cluster_size: 10 maximum_cluster_size: 10000 diff --git a/control/autoware_autonomous_emergency_braking/image/closest-point.drawio.svg b/control/autoware_autonomous_emergency_braking/image/closest-point.drawio.svg new file mode 100644 index 0000000000000..154bba1fc9152 --- /dev/null +++ b/control/autoware_autonomous_emergency_braking/image/closest-point.drawio.svg @@ -0,0 +1,245 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + -
-
- Closest Point -
-
-
- - - - - - - - - - - - -
-
-
- Filter out points outside clusters and get a 2D convex hull of each cluster -
-
-
- - - - - - - - - - - - -
-
-
- Find the closest vertex of the convex hull(s) -
-
-
- - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+ Filter out points outside clusters and get a 2D convex hull of each cluster +
+
+
+ + + + + + + + + + + + + + + + - - - - - - diff --git a/control/autoware_autonomous_emergency_braking/image/using-predicted-objects.drawio.svg b/control/autoware_autonomous_emergency_braking/image/using-predicted-objects.drawio.svg new file mode 100644 index 0000000000000..244b609c7f009 --- /dev/null +++ b/control/autoware_autonomous_emergency_braking/image/using-predicted-objects.drawio.svg @@ -0,0 +1,114 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+ Ego path footprint and object polygon intersection points +
+
+
+ + + + + + + diff --git a/control/autoware_autonomous_emergency_braking/include/autoware/autonomous_emergency_braking/node.hpp b/control/autoware_autonomous_emergency_braking/include/autoware/autonomous_emergency_braking/node.hpp index 7e06af73b0f11..f6df46440d841 100644 --- a/control/autoware_autonomous_emergency_braking/include/autoware/autonomous_emergency_braking/node.hpp +++ b/control/autoware_autonomous_emergency_braking/include/autoware/autonomous_emergency_braking/node.hpp @@ -23,6 +23,7 @@ #include #include +#include #include #include #include @@ -59,7 +60,6 @@ using nav_msgs::msg::Odometry; using sensor_msgs::msg::Imu; using sensor_msgs::msg::PointCloud2; using PointCloud = pcl::PointCloud; -using autoware::universe_utils::Point2d; using autoware::universe_utils::Polygon2d; using autoware::vehicle_info_utils::VehicleInfo; using diagnostic_updater::DiagnosticStatusWrapper; @@ -68,6 +68,9 @@ using visualization_msgs::msg::Marker; using visualization_msgs::msg::MarkerArray; using Path = std::vector; using Vector3 = geometry_msgs::msg::Vector3; +using autoware_perception_msgs::msg::PredictedObject; +using autoware_perception_msgs::msg::PredictedObjects; + struct ObjectData { rclcpp::Time stamp; @@ -116,12 +119,12 @@ class CollisionDataKeeper return this->checkObjectDataExpired(prev_closest_object_, previous_obstacle_keep_time_); } - ObjectData get() const + [[nodiscard]] ObjectData get() const { return (closest_object_.has_value()) ? closest_object_.value() : ObjectData(); } - ObjectData getPreviousObjectData() const + [[nodiscard]] ObjectData getPreviousObjectData() const { return (prev_closest_object_.has_value()) ? prev_closest_object_.value() : ObjectData(); } @@ -152,20 +155,21 @@ class CollisionDataKeeper }), obstacle_velocity_history_.end()); obstacle_velocity_history_.emplace_back( - std::make_pair(current_object_velocity, current_object_velocity_time_stamp)); + current_object_velocity, current_object_velocity_time_stamp); } - std::optional getMedianObstacleVelocity() const + [[nodiscard]] std::optional getMedianObstacleVelocity() const { if (obstacle_velocity_history_.empty()) return std::nullopt; std::vector raw_velocities; + raw_velocities.reserve(obstacle_velocity_history_.size()); for (const auto & vel_time_pair : obstacle_velocity_history_) { raw_velocities.emplace_back(vel_time_pair.first); } const size_t med1 = (raw_velocities.size() % 2 == 0) ? (raw_velocities.size()) / 2 - 1 - : (raw_velocities.size()) / 2.0; - const size_t med2 = (raw_velocities.size()) / 2.0; + : (raw_velocities.size()) / 2; + const size_t med2 = (raw_velocities.size()) / 2; std::nth_element(raw_velocities.begin(), raw_velocities.begin() + med1, raw_velocities.end()); const double vel1 = raw_velocities.at(med1); std::nth_element(raw_velocities.begin(), raw_velocities.begin() + med2, raw_velocities.end()); @@ -176,6 +180,13 @@ class CollisionDataKeeper std::optional calcObjectSpeedFromHistory( const ObjectData & closest_object, const Path & path, const double current_ego_speed) { + // in case the object comes from predicted objects info, we reuse the speed. + if (std::abs(closest_object.velocity) > std::numeric_limits::epsilon()) { + this->setPreviousObjectData(closest_object); + this->updateVelocityHistory(closest_object.velocity, closest_object.stamp); + return this->getMedianObstacleVelocity(); + } + if (this->checkPreviousObjectDataExpired()) { this->setPreviousObjectData(closest_object); this->resetVelocityHistory(); @@ -207,7 +218,7 @@ class CollisionDataKeeper p_vel * std::cos(p_yaw - traj_yaw) + std::abs(current_ego_speed); // Current RSS distance calculation does not account for negative velocities - return (estimated_velocity > 0.0) ? estimated_velocity : 0.0; + return estimated_velocity; }); if (!estimated_velocity_opt.has_value()) { @@ -243,6 +254,8 @@ class AEB : public rclcpp::Node autoware::universe_utils::InterProcessPollingSubscriber sub_imu_{this, "~/input/imu"}; autoware::universe_utils::InterProcessPollingSubscriber sub_predicted_traj_{ this, "~/input/predicted_trajectory"}; + autoware::universe_utils::InterProcessPollingSubscriber predicted_objects_sub_{ + this, "~/input/objects"}; autoware::universe_utils::InterProcessPollingSubscriber sub_autoware_state_{ this, "/autoware/state"}; // publisher @@ -275,6 +288,10 @@ class AEB : public rclcpp::Node std::vector & objects, const pcl::PointCloud::Ptr obstacle_points_ptr); + void createObjectDataUsingPredictedObjects( + const Path & ego_path, const std::vector & ego_polys, + std::vector & objects); + void cropPointCloudWithEgoFootprintPath( const std::vector & ego_polys, pcl::PointCloud::Ptr filtered_objects); @@ -298,6 +315,7 @@ class AEB : public rclcpp::Node VelocityReport::ConstSharedPtr current_velocity_ptr_{nullptr}; Vector3::SharedPtr angular_velocity_ptr_{nullptr}; Trajectory::ConstSharedPtr predicted_traj_ptr_{nullptr}; + PredictedObjects::ConstSharedPtr predicted_objects_ptr_{nullptr}; AutowareState::ConstSharedPtr autoware_state_{nullptr}; tf2_ros::Buffer tf_buffer_{get_clock()}; @@ -313,6 +331,8 @@ class AEB : public rclcpp::Node bool publish_debug_pointcloud_; bool use_predicted_trajectory_; bool use_imu_path_; + bool use_pointcloud_data_; + bool use_predicted_object_data_; bool use_object_velocity_calculation_; double path_footprint_extra_margin_; double detection_range_min_height_; @@ -327,6 +347,7 @@ class AEB : public rclcpp::Node double a_ego_min_; double a_obj_min_; double cluster_tolerance_; + double cluster_minimum_height_; int minimum_cluster_size_; int maximum_cluster_size_; double imu_prediction_time_horizon_; diff --git a/control/autoware_autonomous_emergency_braking/include/autoware/autonomous_emergency_braking/utils.hpp b/control/autoware_autonomous_emergency_braking/include/autoware/autonomous_emergency_braking/utils.hpp new file mode 100644 index 0000000000000..4a6bc0ce2c11f --- /dev/null +++ b/control/autoware_autonomous_emergency_braking/include/autoware/autonomous_emergency_braking/utils.hpp @@ -0,0 +1,87 @@ +// Copyright 2024 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef AUTOWARE__AUTONOMOUS_EMERGENCY_BRAKING__UTILS_HPP_ +#define AUTOWARE__AUTONOMOUS_EMERGENCY_BRAKING__UTILS_HPP_ + +#include + +#include +#include +#include + +#include + +#include +#ifdef ROS_DISTRO_GALACTIC +#include +#include +#else +#include + +#include +#endif + +#include + +namespace autoware::motion::control::autonomous_emergency_braking::utils +{ +using autoware::universe_utils::Polygon2d; +using autoware_perception_msgs::msg::PredictedObject; +using autoware_perception_msgs::msg::PredictedObjects; +using geometry_msgs::msg::Point; +using geometry_msgs::msg::Pose; +using geometry_msgs::msg::TransformStamped; + +/** + * @brief Apply a transform to a predicted object + * @param input the predicted object + * @param transform_stamped the tf2 transform + */ +PredictedObject transformObjectFrame( + const PredictedObject & input, geometry_msgs::msg::TransformStamped & transform_stamped); + +/** + * @brief Get the predicted objects polygon as a geometry polygon + * @param current_pose the predicted object's pose + * @param obj_shape the object's shape + */ +Polygon2d convertPolygonObjectToGeometryPolygon( + const Pose & current_pose, const autoware_perception_msgs::msg::Shape & obj_shape); + +/** + * @brief Get the predicted objects cylindrical shape as a geometry polygon + * @param current_pose the predicted object's pose + * @param obj_shape the object's shape + */ +Polygon2d convertCylindricalObjectToGeometryPolygon( + const Pose & current_pose, const autoware_perception_msgs::msg::Shape & obj_shape); + +/** + * @brief Get the predicted objects bounding box shape as a geometry polygon + * @param current_pose the predicted object's pose + * @param obj_shape the object's shape + */ +Polygon2d convertBoundingBoxObjectToGeometryPolygon( + const Pose & current_pose, const double & base_to_front, const double & base_to_rear, + const double & base_to_width); + +/** + * @brief Get the predicted object's shape as a geometry polygon + * @param obj the object + */ +Polygon2d convertObjToPolygon(const PredictedObject & obj); +} // namespace autoware::motion::control::autonomous_emergency_braking::utils + +#endif // AUTOWARE__AUTONOMOUS_EMERGENCY_BRAKING__UTILS_HPP_ diff --git a/control/autoware_autonomous_emergency_braking/launch/autoware_autonomous_emergency_braking.launch.xml b/control/autoware_autonomous_emergency_braking/launch/autoware_autonomous_emergency_braking.launch.xml index 75b1a9dcf822d..fe0df41ca89c7 100644 --- a/control/autoware_autonomous_emergency_braking/launch/autoware_autonomous_emergency_braking.launch.xml +++ b/control/autoware_autonomous_emergency_braking/launch/autoware_autonomous_emergency_braking.launch.xml @@ -5,6 +5,7 @@ + @@ -15,5 +16,6 @@ + diff --git a/control/autoware_autonomous_emergency_braking/src/node.cpp b/control/autoware_autonomous_emergency_braking/src/node.cpp index 90cf3f900673e..fb0fc2d043cc5 100644 --- a/control/autoware_autonomous_emergency_braking/src/node.cpp +++ b/control/autoware_autonomous_emergency_braking/src/node.cpp @@ -12,8 +12,8 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "autoware/autonomous_emergency_braking/node.hpp" - +#include +#include #include #include #include @@ -21,7 +21,11 @@ #include #include +#include + #include +#include +#include #include #include @@ -35,6 +39,9 @@ #include #include #include + +#include +#include #ifdef ROS_DISTRO_GALACTIC #include #include @@ -46,6 +53,8 @@ namespace autoware::motion::control::autonomous_emergency_braking { +using autoware::motion::control::autonomous_emergency_braking::utils::convertObjToPolygon; +using autoware::universe_utils::Point2d; using diagnostic_msgs::msg::DiagnosticStatus; namespace bg = boost::geometry; @@ -100,7 +109,7 @@ Polygon2d createPolygon( Polygon2d hull_polygon; bg::convex_hull(polygon, hull_polygon); - + bg::correct(hull_polygon); return hull_polygon; } @@ -124,6 +133,8 @@ AEB::AEB(const rclcpp::NodeOptions & node_options) publish_debug_pointcloud_ = declare_parameter("publish_debug_pointcloud"); use_predicted_trajectory_ = declare_parameter("use_predicted_trajectory"); use_imu_path_ = declare_parameter("use_imu_path"); + use_pointcloud_data_ = declare_parameter("use_pointcloud_data"); + use_predicted_object_data_ = declare_parameter("use_predicted_object_data"); use_object_velocity_calculation_ = declare_parameter("use_object_velocity_calculation"); path_footprint_extra_margin_ = declare_parameter("path_footprint_extra_margin"); detection_range_min_height_ = declare_parameter("detection_range_min_height"); @@ -140,6 +151,7 @@ AEB::AEB(const rclcpp::NodeOptions & node_options) a_obj_min_ = declare_parameter("a_obj_min"); cluster_tolerance_ = declare_parameter("cluster_tolerance"); + cluster_minimum_height_ = declare_parameter("cluster_minimum_height"); minimum_cluster_size_ = declare_parameter("minimum_cluster_size"); maximum_cluster_size_ = declare_parameter("maximum_cluster_size"); @@ -172,6 +184,8 @@ rcl_interfaces::msg::SetParametersResult AEB::onParameter( updateParam(parameters, "publish_debug_pointcloud", publish_debug_pointcloud_); updateParam(parameters, "use_predicted_trajectory", use_predicted_trajectory_); updateParam(parameters, "use_imu_path", use_imu_path_); + updateParam(parameters, "use_pointcloud_data", use_pointcloud_data_); + updateParam(parameters, "use_predicted_object_data", use_predicted_object_data_); updateParam( parameters, "use_object_velocity_calculation", use_object_velocity_calculation_); updateParam(parameters, "path_footprint_extra_margin", path_footprint_extra_margin_); @@ -189,6 +203,7 @@ rcl_interfaces::msg::SetParametersResult AEB::onParameter( updateParam(parameters, "a_obj_min", a_obj_min_); updateParam(parameters, "cluster_tolerance", cluster_tolerance_); + updateParam(parameters, "cluster_minimum_height", cluster_minimum_height_); updateParam(parameters, "minimum_cluster_size", minimum_cluster_size_); updateParam(parameters, "maximum_cluster_size", maximum_cluster_size_); @@ -298,13 +313,31 @@ bool AEB::fetchLatestData() return missing("ego velocity"); } - const auto pointcloud_ptr = sub_point_cloud_.takeData(); - if (!pointcloud_ptr) { - return missing("object pointcloud message"); + if (use_pointcloud_data_) { + const auto pointcloud_ptr = sub_point_cloud_.takeData(); + if (!pointcloud_ptr) { + return missing("object pointcloud message"); + } + + onPointCloud(pointcloud_ptr); + if (!obstacle_ros_pointcloud_ptr_) { + return missing("object pointcloud"); + } + } else { + obstacle_ros_pointcloud_ptr_.reset(); + } + + if (use_predicted_object_data_) { + predicted_objects_ptr_ = predicted_objects_sub_.takeData(); + if (!predicted_objects_ptr_) { + return missing("predicted objects"); + } + } else { + predicted_objects_ptr_.reset(); } - onPointCloud(pointcloud_ptr); - if (!obstacle_ros_pointcloud_ptr_) { - return missing("object pointcloud"); + + if (!obstacle_ros_pointcloud_ptr_ && !predicted_objects_ptr_) { + return missing("object detection method (pointcloud or predicted objects)"); } const auto imu_ptr = sub_imu_.takeData(); @@ -381,27 +414,33 @@ bool AEB::checkCollision(MarkerArray & debug_markers) const auto & path, const colorTuple & debug_colors, const std::string & debug_ns, pcl::PointCloud::Ptr filtered_objects) { - // Crop out Pointcloud using an extra wide ego path - const auto expanded_ego_polys = - generatePathFootprint(path, expand_width_ + path_footprint_extra_margin_); - cropPointCloudWithEgoFootprintPath(expanded_ego_polys, filtered_objects); - // Check which points of the cropped point cloud are on the ego path, and get the closest one - std::vector objects_from_point_clusters; const auto ego_polys = generatePathFootprint(path, expand_width_); - const auto current_time = obstacle_ros_pointcloud_ptr_->header.stamp; - createObjectDataUsingPointCloudClusters( - path, ego_polys, current_time, objects_from_point_clusters, filtered_objects); + auto objects = std::invoke([&]() { + std::vector objects; + // Crop out Pointcloud using an extra wide ego path + if (use_pointcloud_data_) { + const auto expanded_ego_polys = + generatePathFootprint(path, expand_width_ + path_footprint_extra_margin_); + cropPointCloudWithEgoFootprintPath(expanded_ego_polys, filtered_objects); + const auto current_time = obstacle_ros_pointcloud_ptr_->header.stamp; + createObjectDataUsingPointCloudClusters( + path, ego_polys, current_time, objects, filtered_objects); + } + if (use_predicted_object_data_) { + createObjectDataUsingPredictedObjects(path, ego_polys, objects); + } + return objects; + }); // Get only the closest object and calculate its speed const auto closest_object_point = std::invoke([&]() -> std::optional { - const auto closest_object_point_itr = std::min_element( - objects_from_point_clusters.begin(), objects_from_point_clusters.end(), - [](const auto & o1, const auto & o2) { + const auto closest_object_point_itr = + std::min_element(objects.begin(), objects.end(), [](const auto & o1, const auto & o2) { return o1.distance_to_object < o2.distance_to_object; }); - if (closest_object_point_itr == objects_from_point_clusters.end()) { + if (closest_object_point_itr == objects.end()) { return std::nullopt; } const auto closest_object_speed = (use_object_velocity_calculation_) @@ -419,8 +458,8 @@ bool AEB::checkCollision(MarkerArray & debug_markers) { const auto [color_r, color_g, color_b, color_a] = debug_colors; addMarker( - this->get_clock()->now(), path, ego_polys, objects_from_point_clusters, - closest_object_point, color_r, color_g, color_b, color_a, debug_ns, debug_markers); + this->get_clock()->now(), path, ego_polys, objects, closest_object_point, color_r, color_g, + color_b, color_a, debug_ns, debug_markers); } // check collision using rss distance return (closest_object_point.has_value()) @@ -462,9 +501,9 @@ bool AEB::checkCollision(MarkerArray & debug_markers) // Debug print if (!filtered_objects->empty() && publish_debug_pointcloud_) { - const auto filtered_objects_ros_pointcloud_ptr_ = std::make_shared(); - pcl::toROSMsg(*filtered_objects, *filtered_objects_ros_pointcloud_ptr_); - pub_obstacle_pointcloud_->publish(*filtered_objects_ros_pointcloud_ptr_); + const auto filtered_objects_ros_pointcloud_ptr = std::make_shared(); + pcl::toROSMsg(*filtered_objects, *filtered_objects_ros_pointcloud_ptr); + pub_obstacle_pointcloud_->publish(*filtered_objects_ros_pointcloud_ptr); } return has_collision; } @@ -473,9 +512,17 @@ bool AEB::hasCollision(const double current_v, const ObjectData & closest_object { const double & obj_v = closest_object.velocity; const double & t = t_response_; - const double rss_dist = std::abs(current_v) * t + - (current_v * current_v) / (2 * std::fabs(a_ego_min_)) - - obj_v * obj_v / (2 * std::fabs(a_obj_min_)) + longitudinal_offset_; + + const double rss_dist = std::invoke([&]() { + const double pre_braking_covered_distance = std::abs(current_v) * t; + const double braking_distance = (current_v * current_v) / (2 * std::fabs(a_ego_min_)); + const double ego_stopping_distance = pre_braking_covered_distance + braking_distance; + const double obj_braking_distance = (obj_v > 0.0) + ? -(obj_v * obj_v) / (2 * std::fabs(a_obj_min_)) + : (obj_v * obj_v) / (2 * std::fabs(a_obj_min_)); + return ego_stopping_distance + obj_braking_distance + longitudinal_offset_; + }); + if (closest_object.distance_to_object < rss_dist) { // collision happens ObjectData collision_data = closest_object; @@ -503,7 +550,7 @@ Path AEB::generateEgoPath(const double curr_v, const double curr_w) return path; } - constexpr double epsilon = 1e-6; + constexpr double epsilon = std::numeric_limits::epsilon(); const double & dt = imu_prediction_time_interval_; const double & horizon = imu_prediction_time_horizon_; for (double t = 0.0; t < horizon + epsilon; t += dt) { @@ -553,11 +600,11 @@ std::optional AEB::generateEgoPath(const Trajectory & predicted_traj) // create path Path path; - path.resize(predicted_traj.points.size()); + path.reserve(predicted_traj.points.size()); for (size_t i = 0; i < predicted_traj.points.size(); ++i) { geometry_msgs::msg::Pose map_pose; tf2::doTransform(predicted_traj.points.at(i).pose, map_pose, transform_stamped); - path.at(i) = map_pose; + path.push_back(map_pose); if (i * mpc_prediction_time_interval_ > mpc_prediction_time_horizon_) { break; @@ -577,6 +624,93 @@ std::vector AEB::generatePathFootprint( return polygons; } +void AEB::createObjectDataUsingPredictedObjects( + const Path & ego_path, const std::vector & ego_polys, + std::vector & object_data_vector) +{ + if (predicted_objects_ptr_->objects.empty()) return; + + const double current_ego_speed = current_velocity_ptr_->longitudinal_velocity; + const auto & objects = predicted_objects_ptr_->objects; + const auto & stamp = predicted_objects_ptr_->header.stamp; + + // Ego position + const auto current_p = [&]() { + const auto & first_point_of_path = ego_path.front(); + const auto & p = first_point_of_path.position; + return autoware::universe_utils::createPoint(p.x, p.y, p.z); + }(); + + auto get_object_tangent_velocity = + [&](const PredictedObject & predicted_object, const auto & obj_pose) { + const double obj_vel_norm = std::hypot( + predicted_object.kinematics.initial_twist_with_covariance.twist.linear.x, + predicted_object.kinematics.initial_twist_with_covariance.twist.linear.y); + + const auto obj_yaw = tf2::getYaw(obj_pose.orientation); + const auto obj_idx = autoware::motion_utils::findNearestIndex(ego_path, obj_pose.position); + const auto path_yaw = (current_ego_speed > 0.0) + ? tf2::getYaw(ego_path.at(obj_idx).orientation) + : tf2::getYaw(ego_path.at(obj_idx).orientation) + M_PI; + return obj_vel_norm * std::cos(obj_yaw - path_yaw); + }; + + geometry_msgs::msg::TransformStamped transform_stamped{}; + try { + transform_stamped = tf_buffer_.lookupTransform( + "base_link", predicted_objects_ptr_->header.frame_id, stamp, + rclcpp::Duration::from_seconds(0.5)); + } catch (tf2::TransformException & ex) { + RCLCPP_ERROR_STREAM(get_logger(), "[AEB] Failed to look up transform from base_link to map"); + return; + } + + // Check which objects collide with the ego footprints + std::for_each(objects.begin(), objects.end(), [&](const auto & predicted_object) { + // get objects in base_link frame + const auto t_predicted_object = + utils::transformObjectFrame(predicted_object, transform_stamped); + const auto & obj_pose = t_predicted_object.kinematics.initial_pose_with_covariance.pose; + const auto obj_poly = convertObjToPolygon(t_predicted_object); + const double obj_tangent_velocity = get_object_tangent_velocity(t_predicted_object, obj_pose); + + for (const auto & ego_poly : ego_polys) { + // check collision with 2d polygon + std::vector collision_points_bg; + bg::intersection(ego_poly, obj_poly, collision_points_bg); + if (collision_points_bg.empty()) continue; + + // Create an object for each intersection point + bool collision_points_added{false}; + for (const auto & collision_point : collision_points_bg) { + const auto obj_position = + autoware::universe_utils::createPoint(collision_point.x(), collision_point.y(), 0.0); + const double obj_arc_length = + autoware::motion_utils::calcSignedArcLength(ego_path, current_p, obj_position); + if (std::isnan(obj_arc_length)) continue; + + // If the object is behind the ego, we need to use the backward long offset. The + // distance should be a positive number in any case + const bool is_object_in_front_of_ego = obj_arc_length > 0.0; + const double dist_ego_to_object = + (is_object_in_front_of_ego) ? obj_arc_length - vehicle_info_.max_longitudinal_offset_m + : obj_arc_length + vehicle_info_.min_longitudinal_offset_m; + + ObjectData obj; + obj.stamp = stamp; + obj.position = obj_position; + obj.velocity = obj_tangent_velocity; + obj.distance_to_object = std::abs(dist_ego_to_object); + object_data_vector.push_back(obj); + collision_points_added = true; + } + // The ego polygons are in order, so the first intersection points found are the closest + // points. It is not necessary to continue iterating the ego polys for the same object. + if (collision_points_added) break; + } + }); +} + void AEB::createObjectDataUsingPointCloudClusters( const Path & ego_path, const std::vector & ego_polys, const rclcpp::Time & stamp, std::vector & objects, const pcl::PointCloud::Ptr obstacle_points_ptr) @@ -605,9 +739,15 @@ void AEB::createObjectDataUsingPointCloudClusters( PointCloud::Ptr points_belonging_to_cluster_hulls(new PointCloud); for (const auto & indices : cluster_indices) { PointCloud::Ptr cluster(new PointCloud); + bool cluster_surpasses_threshold_height{false}; for (const auto & index : indices.indices) { - cluster->push_back((*obstacle_points_ptr)[index]); + const auto & p = (*obstacle_points_ptr)[index]; + cluster_surpasses_threshold_height = (cluster_surpasses_threshold_height) + ? cluster_surpasses_threshold_height + : (p.z > cluster_minimum_height_); + cluster->push_back(p); } + if (!cluster_surpasses_threshold_height) continue; // Make a 2d convex hull for the objects pcl::ConvexHull hull; hull.setDimension(2); diff --git a/control/autoware_autonomous_emergency_braking/src/utils.cpp b/control/autoware_autonomous_emergency_braking/src/utils.cpp new file mode 100644 index 0000000000000..f3b5192855d84 --- /dev/null +++ b/control/autoware_autonomous_emergency_braking/src/utils.cpp @@ -0,0 +1,149 @@ +// Copyright 2024 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include + +namespace autoware::motion::control::autonomous_emergency_braking::utils +{ +using autoware::universe_utils::Polygon2d; +using autoware_perception_msgs::msg::PredictedObject; +using autoware_perception_msgs::msg::PredictedObjects; +using geometry_msgs::msg::Point; +using geometry_msgs::msg::Pose; +using geometry_msgs::msg::TransformStamped; +using geometry_msgs::msg::Vector3; + +PredictedObject transformObjectFrame( + const PredictedObject & input, geometry_msgs::msg::TransformStamped & transform_stamped) +{ + PredictedObject output = input; + const auto & linear_twist = input.kinematics.initial_twist_with_covariance.twist.linear; + const auto & angular_twist = input.kinematics.initial_twist_with_covariance.twist.angular; + const auto & pose = input.kinematics.initial_pose_with_covariance.pose; + + geometry_msgs::msg::Pose t_pose; + Vector3 t_linear_twist; + Vector3 t_angular_twist; + + tf2::doTransform(pose, t_pose, transform_stamped); + tf2::doTransform(linear_twist, t_linear_twist, transform_stamped); + tf2::doTransform(angular_twist, t_angular_twist, transform_stamped); + + output.kinematics.initial_pose_with_covariance.pose = t_pose; + output.kinematics.initial_twist_with_covariance.twist.linear = t_linear_twist; + output.kinematics.initial_twist_with_covariance.twist.angular = t_angular_twist; + return output; +} + +Polygon2d convertPolygonObjectToGeometryPolygon( + const Pose & current_pose, const autoware_perception_msgs::msg::Shape & obj_shape) +{ + Polygon2d object_polygon; + tf2::Transform tf_map2obj; + fromMsg(current_pose, tf_map2obj); + const auto obj_points = obj_shape.footprint.points; + object_polygon.outer().reserve(obj_points.size() + 1); + for (const auto & obj_point : obj_points) { + tf2::Vector3 obj(obj_point.x, obj_point.y, obj_point.z); + tf2::Vector3 tf_obj = tf_map2obj * obj; + object_polygon.outer().emplace_back(tf_obj.x(), tf_obj.y()); + } + object_polygon.outer().push_back(object_polygon.outer().front()); + boost::geometry::correct(object_polygon); + + return object_polygon; +} + +Polygon2d convertCylindricalObjectToGeometryPolygon( + const Pose & current_pose, const autoware_perception_msgs::msg::Shape & obj_shape) +{ + Polygon2d object_polygon; + + const double obj_x = current_pose.position.x; + const double obj_y = current_pose.position.y; + + constexpr int n = 20; + const double r = obj_shape.dimensions.x / 2; + object_polygon.outer().reserve(n + 1); + for (int i = 0; i < n; ++i) { + object_polygon.outer().emplace_back( + obj_x + r * std::cos(2.0 * M_PI / n * i), obj_y + r * std::sin(2.0 * M_PI / n * i)); + } + + object_polygon.outer().push_back(object_polygon.outer().front()); + boost::geometry::correct(object_polygon); + + return object_polygon; +} + +Polygon2d convertBoundingBoxObjectToGeometryPolygon( + const Pose & current_pose, const double & base_to_front, const double & base_to_rear, + const double & base_to_width) +{ + const auto mapped_point = [](const double & length_scalar, const double & width_scalar) { + tf2::Vector3 map; + map.setX(length_scalar); + map.setY(width_scalar); + map.setZ(0.0); + map.setW(1.0); + return map; + }; + + // set vertices at map coordinate + const tf2::Vector3 p1_map = std::invoke(mapped_point, base_to_front, -base_to_width); + const tf2::Vector3 p2_map = std::invoke(mapped_point, base_to_front, base_to_width); + const tf2::Vector3 p3_map = std::invoke(mapped_point, -base_to_rear, base_to_width); + const tf2::Vector3 p4_map = std::invoke(mapped_point, -base_to_rear, -base_to_width); + + // transform vertices from map coordinate to object coordinate + tf2::Transform tf_map2obj; + tf2::fromMsg(current_pose, tf_map2obj); + const tf2::Vector3 p1_obj = tf_map2obj * p1_map; + const tf2::Vector3 p2_obj = tf_map2obj * p2_map; + const tf2::Vector3 p3_obj = tf_map2obj * p3_map; + const tf2::Vector3 p4_obj = tf_map2obj * p4_map; + + Polygon2d object_polygon; + object_polygon.outer().reserve(5); + object_polygon.outer().emplace_back(p1_obj.x(), p1_obj.y()); + object_polygon.outer().emplace_back(p2_obj.x(), p2_obj.y()); + object_polygon.outer().emplace_back(p3_obj.x(), p3_obj.y()); + object_polygon.outer().emplace_back(p4_obj.x(), p4_obj.y()); + + object_polygon.outer().push_back(object_polygon.outer().front()); + boost::geometry::correct(object_polygon); + + return object_polygon; +} + +Polygon2d convertObjToPolygon(const PredictedObject & obj) +{ + Polygon2d object_polygon{}; + if (obj.shape.type == autoware_perception_msgs::msg::Shape::CYLINDER) { + object_polygon = utils::convertCylindricalObjectToGeometryPolygon( + obj.kinematics.initial_pose_with_covariance.pose, obj.shape); + } else if (obj.shape.type == autoware_perception_msgs::msg::Shape::BOUNDING_BOX) { + const double & length_m = obj.shape.dimensions.x / 2; + const double & width_m = obj.shape.dimensions.y / 2; + object_polygon = utils::convertBoundingBoxObjectToGeometryPolygon( + obj.kinematics.initial_pose_with_covariance.pose, length_m, length_m, width_m); + } else if (obj.shape.type == autoware_perception_msgs::msg::Shape::POLYGON) { + object_polygon = utils::convertPolygonObjectToGeometryPolygon( + obj.kinematics.initial_pose_with_covariance.pose, obj.shape); + } else { + throw std::runtime_error("Unsupported shape type"); + } + return object_polygon; +} +} // namespace autoware::motion::control::autonomous_emergency_braking::utils diff --git a/control/autoware_joy_controller/include/autoware/joy_controller/joy_controller.hpp b/control/autoware_joy_controller/include/autoware/joy_controller/joy_controller.hpp index fd2d89f446dbb..a4dcbc841696e 100644 --- a/control/autoware_joy_controller/include/autoware/joy_controller/joy_controller.hpp +++ b/control/autoware_joy_controller/include/autoware/joy_controller/joy_controller.hpp @@ -33,7 +33,6 @@ #include #include -#include #include #include @@ -110,7 +109,6 @@ class AutowareJoyControllerNode : public rclcpp::Node autoware_control_msgs::msg::Control prev_control_command_; tier4_external_api_msgs::msg::ControlCommand prev_external_control_command_; GearShiftType prev_shift_ = tier4_external_api_msgs::msg::GearShift::NONE; - TurnSignalType prev_turn_signal_ = tier4_external_api_msgs::msg::TurnSignal::NONE; GateModeType prev_gate_mode_ = tier4_control_msgs::msg::GateMode::AUTO; // Timer diff --git a/control/autoware_joy_controller/include/autoware/joy_controller/joy_converter/g29_joy_converter.hpp b/control/autoware_joy_controller/include/autoware/joy_controller/joy_converter/g29_joy_converter.hpp index ea253e86305b7..28c424e7d6452 100644 --- a/control/autoware_joy_controller/include/autoware/joy_controller/joy_converter/g29_joy_converter.hpp +++ b/control/autoware_joy_controller/include/autoware/joy_controller/joy_converter/g29_joy_converter.hpp @@ -17,6 +17,8 @@ #include "autoware/joy_controller/joy_converter/joy_converter_base.hpp" +#include + namespace autoware::joy_controller { class G29JoyConverter : public JoyConverterBase @@ -27,7 +29,7 @@ class G29JoyConverter : public JoyConverterBase float accel() const { constexpr float eps = 0.0000001; - if (std::fabs(AccelPedal()) < eps) { + if (std::abs(AccelPedal()) < eps) { return 0.0f; } return (AccelPedal() + 1.0f) / 2; @@ -36,7 +38,7 @@ class G29JoyConverter : public JoyConverterBase float brake() const { constexpr float eps = 0.0000001; - if (std::fabs(BrakePedal()) < eps) { + if (std::abs(BrakePedal()) < eps) { return 0.0f; } return (BrakePedal() + 1.0f) / 2; diff --git a/control/autoware_joy_controller/include/autoware/joy_controller/joy_converter/joy_converter_base.hpp b/control/autoware_joy_controller/include/autoware/joy_controller/joy_converter/joy_converter_base.hpp index fa6081721511c..6deae0445bf0b 100644 --- a/control/autoware_joy_controller/include/autoware/joy_controller/joy_converter/joy_converter_base.hpp +++ b/control/autoware_joy_controller/include/autoware/joy_controller/joy_converter/joy_converter_base.hpp @@ -17,8 +17,6 @@ #include -#include - namespace autoware::joy_controller { class JoyConverterBase @@ -49,6 +47,8 @@ class JoyConverterBase virtual bool vehicle_engage() const = 0; virtual bool vehicle_disengage() const = 0; + + virtual ~JoyConverterBase() = default; }; } // namespace autoware::joy_controller diff --git a/control/autoware_joy_controller/src/joy_controller_node.cpp b/control/autoware_joy_controller/src/joy_controller_node.cpp index cab15c93d753f..378109eacc574 100644 --- a/control/autoware_joy_controller/src/joy_controller_node.cpp +++ b/control/autoware_joy_controller/src/joy_controller_node.cpp @@ -23,7 +23,6 @@ #include #include #include -#include namespace { @@ -404,8 +403,8 @@ void AutowareJoyControllerNode::sendEmergencyRequest(bool emergency) request->emergency = emergency; client_emergency_stop_->async_send_request( - request, [this, emergency]( - rclcpp::Client::SharedFuture result) { + request, + [this](rclcpp::Client::SharedFuture result) { auto response = result.get(); if (tier4_api_utils::is_success(response->status)) { RCLCPP_INFO(get_logger(), "service succeeded"); diff --git a/control/autoware_lane_departure_checker/package.xml b/control/autoware_lane_departure_checker/package.xml index 2a08db7288568..e0e877e618c65 100644 --- a/control/autoware_lane_departure_checker/package.xml +++ b/control/autoware_lane_departure_checker/package.xml @@ -13,6 +13,7 @@ ament_cmake_auto autoware_cmake + autoware_lanelet2_extension autoware_map_msgs autoware_motion_utils autoware_planning_msgs @@ -22,7 +23,6 @@ diagnostic_updater eigen geometry_msgs - lanelet2_extension nav_msgs rclcpp rclcpp_components diff --git a/control/autoware_lane_departure_checker/src/lane_departure_checker_node/lane_departure_checker_node.cpp b/control/autoware_lane_departure_checker/src/lane_departure_checker_node/lane_departure_checker_node.cpp index dde37b03ffecc..27b636f07d4ef 100644 --- a/control/autoware_lane_departure_checker/src/lane_departure_checker_node/lane_departure_checker_node.cpp +++ b/control/autoware_lane_departure_checker/src/lane_departure_checker_node/lane_departure_checker_node.cpp @@ -17,11 +17,11 @@ #include #include #include -#include -#include -#include -#include -#include +#include +#include +#include +#include +#include #include diff --git a/control/autoware_mpc_lateral_controller/include/autoware/mpc_lateral_controller/mpc.hpp b/control/autoware_mpc_lateral_controller/include/autoware/mpc_lateral_controller/mpc.hpp index 058eb45bfaaff..902790f260e5e 100644 --- a/control/autoware_mpc_lateral_controller/include/autoware/mpc_lateral_controller/mpc.hpp +++ b/control/autoware_mpc_lateral_controller/include/autoware/mpc_lateral_controller/mpc.hpp @@ -271,7 +271,7 @@ class MPC */ std::pair executeOptimization( const MPCMatrix & mpc_matrix, const VectorXd & x0, const double prediction_dt, - const MPCTrajectory & trajectory, const double current_velocity); + const MPCTrajectory & trajectory); /** * @brief Resample the trajectory with the MPC resampling time. @@ -386,8 +386,7 @@ class MPC * @param reference_trajectory The reference trajectory. * @param current_velocity current velocity of ego. */ - VectorXd calcSteerRateLimitOnTrajectory( - const MPCTrajectory & trajectory, const double current_velocity) const; + VectorXd calcSteerRateLimitOnTrajectory(const MPCTrajectory & trajectory) const; //!< @brief logging with warn and return false template diff --git a/control/autoware_mpc_lateral_controller/include/autoware/mpc_lateral_controller/mpc_lateral_controller.hpp b/control/autoware_mpc_lateral_controller/include/autoware/mpc_lateral_controller/mpc_lateral_controller.hpp index 1f16bbc39bbbf..09399d1fa2dce 100644 --- a/control/autoware_mpc_lateral_controller/include/autoware/mpc_lateral_controller/mpc_lateral_controller.hpp +++ b/control/autoware_mpc_lateral_controller/include/autoware/mpc_lateral_controller/mpc_lateral_controller.hpp @@ -22,6 +22,8 @@ #include "autoware/trajectory_follower_base/lateral_controller_base.hpp" #include "rclcpp/rclcpp.hpp" +#include + #include "autoware_control_msgs/msg/lateral.hpp" #include "autoware_planning_msgs/msg/trajectory.hpp" #include "autoware_vehicle_msgs/msg/steering_report.hpp" @@ -52,7 +54,8 @@ class MpcLateralController : public trajectory_follower::LateralControllerBase { public: /// \param node Reference to the node used only for the component and parameter initialization. - explicit MpcLateralController(rclcpp::Node & node); + explicit MpcLateralController( + rclcpp::Node & node, std::shared_ptr diag_updater); virtual ~MpcLateralController(); private: @@ -63,6 +66,9 @@ class MpcLateralController : public trajectory_follower::LateralControllerBase rclcpp::Publisher::SharedPtr m_pub_debug_values; rclcpp::Publisher::SharedPtr m_pub_steer_offset; + std::shared_ptr + diag_updater_{}; // Diagnostic updater for publishing diagnostic data. + //!< @brief parameters for path smoothing TrajectoryFilteringParam m_trajectory_filtering_param; @@ -87,9 +93,16 @@ class MpcLateralController : public trajectory_follower::LateralControllerBase // Flag indicating whether to keep the steering control until it converges. bool m_keep_steer_control_until_converged; + // MPC solver checker. + bool m_is_mpc_solved{true}; + // trajectory buffer for detecting new trajectory std::deque m_trajectory_buffer; + void setStatus(diagnostic_updater::DiagnosticStatusWrapper & stat); + + void setupDiag(); + std::unique_ptr m_mpc; // MPC object for trajectory following. // Check is mpc output converged diff --git a/control/autoware_mpc_lateral_controller/src/mpc.cpp b/control/autoware_mpc_lateral_controller/src/mpc.cpp index 1f3327bf28f2e..ea97e9e6d5f39 100644 --- a/control/autoware_mpc_lateral_controller/src/mpc.cpp +++ b/control/autoware_mpc_lateral_controller/src/mpc.cpp @@ -76,9 +76,8 @@ bool MPC::calculateMPC( const auto mpc_matrix = generateMPCMatrix(mpc_resampled_ref_trajectory, prediction_dt); // solve Optimization problem - const auto [success_opt, Uex] = executeOptimization( - mpc_matrix, x0_delayed, prediction_dt, mpc_resampled_ref_trajectory, - current_kinematics.twist.twist.linear.x); + const auto [success_opt, Uex] = + executeOptimization(mpc_matrix, x0_delayed, prediction_dt, mpc_resampled_ref_trajectory); if (!success_opt) { return fail_warn_throttle("optimization failed. Stop MPC."); } @@ -244,7 +243,7 @@ void MPC::setReferenceTrajectory( void MPC::resetPrevResult(const SteeringReport & current_steer) { - // Consider limit. The prev value larger than limitation brakes the optimization constraint and + // Consider limit. The prev value larger than limitation breaks the optimization constraint and // results in optimization failure. const float steer_lim_f = static_cast(m_steer_lim); m_raw_steer_cmd_prev = std::clamp(current_steer.steering_tire_angle, -steer_lim_f, steer_lim_f); @@ -544,8 +543,7 @@ MPCMatrix MPC::generateMPCMatrix( * [ -au_lim * dt ] < [uN-uN-1] < [ au_lim * dt ] (*N... DIM_U) */ std::pair MPC::executeOptimization( - const MPCMatrix & m, const VectorXd & x0, const double prediction_dt, const MPCTrajectory & traj, - const double current_velocity) + const MPCMatrix & m, const VectorXd & x0, const double prediction_dt, const MPCTrajectory & traj) { VectorXd Uex; @@ -578,7 +576,7 @@ std::pair MPC::executeOptimization( VectorXd ub = VectorXd::Constant(DIM_U_N, m_steer_lim); // max steering angle // steering angle rate limit - VectorXd steer_rate_limits = calcSteerRateLimitOnTrajectory(traj, current_velocity); + VectorXd steer_rate_limits = calcSteerRateLimitOnTrajectory(traj); VectorXd ubA = steer_rate_limits * prediction_dt; VectorXd lbA = -steer_rate_limits * prediction_dt; ubA(0) = m_raw_steer_cmd_prev + steer_rate_limits(0) * m_ctrl_period; @@ -730,8 +728,7 @@ double MPC::calcDesiredSteeringRate( return steer_rate; } -VectorXd MPC::calcSteerRateLimitOnTrajectory( - const MPCTrajectory & trajectory, const double current_velocity) const +VectorXd MPC::calcSteerRateLimitOnTrajectory(const MPCTrajectory & trajectory) const { const auto interp = [&](const auto & steer_rate_limit_map, const auto & current) { std::vector reference, limits; @@ -765,12 +762,6 @@ VectorXd MPC::calcSteerRateLimitOnTrajectory( return reference.back(); }; - // when the vehicle is stopped, no steering rate limit. - const bool is_vehicle_stopped = std::fabs(current_velocity) < 0.01; - if (is_vehicle_stopped) { - return VectorXd::Zero(m_param.prediction_horizon); - } - // calculate steering rate limit VectorXd steer_rate_limits = VectorXd::Zero(m_param.prediction_horizon); for (int i = 0; i < m_param.prediction_horizon; ++i) { diff --git a/control/autoware_mpc_lateral_controller/src/mpc_lateral_controller.cpp b/control/autoware_mpc_lateral_controller/src/mpc_lateral_controller.cpp index 7634ebb74eca7..43acbb862b573 100644 --- a/control/autoware_mpc_lateral_controller/src/mpc_lateral_controller.cpp +++ b/control/autoware_mpc_lateral_controller/src/mpc_lateral_controller.cpp @@ -35,13 +35,16 @@ namespace autoware::motion::control::mpc_lateral_controller { -MpcLateralController::MpcLateralController(rclcpp::Node & node) +MpcLateralController::MpcLateralController( + rclcpp::Node & node, std::shared_ptr diag_updater) : clock_(node.get_clock()), logger_(node.get_logger().get_child("lateral_controller")) { const auto dp_int = [&](const std::string & s) { return node.declare_parameter(s); }; const auto dp_bool = [&](const std::string & s) { return node.declare_parameter(s); }; const auto dp_double = [&](const std::string & s) { return node.declare_parameter(s); }; + diag_updater_ = diag_updater; + m_mpc = std::make_unique(node); m_mpc->m_ctrl_period = node.get_parameter("ctrl_period").as_double(); @@ -152,6 +155,8 @@ MpcLateralController::MpcLateralController(rclcpp::Node & node) m_mpc->setLogger(logger_); m_mpc->setClock(clock_); + + setupDiag(); } MpcLateralController::~MpcLateralController() @@ -227,6 +232,24 @@ std::shared_ptr MpcLateralController::createSteerOffset return steering_offset_; } +void MpcLateralController::setStatus(diagnostic_updater::DiagnosticStatusWrapper & stat) +{ + if (m_is_mpc_solved) { + stat.summary(diagnostic_msgs::msg::DiagnosticStatus::OK, "MPC succeeded."); + } else { + const std::string error_msg = "The MPC solver failed. Call MRM to stop the car."; + stat.summary(diagnostic_msgs::msg::DiagnosticStatus::ERROR, error_msg); + } +} + +void MpcLateralController::setupDiag() +{ + auto & d = diag_updater_; + d->setHardwareID("mpc_lateral_controller"); + + d->add("MPC_solve_checker", [&](auto & stat) { setStatus(stat); }); +} + trajectory_follower::LateralOutput MpcLateralController::run( trajectory_follower::InputData const & input_data) { @@ -255,12 +278,16 @@ trajectory_follower::LateralOutput MpcLateralController::run( const bool is_mpc_solved = m_mpc->calculateMPC( m_current_steering, m_current_kinematic_state, ctrl_cmd, predicted_traj, debug_values); + m_is_mpc_solved = is_mpc_solved; // for diagnostic updater + + diag_updater_->force_update(); + // reset previous MPC result // Note: When a large deviation from the trajectory occurs, the optimization stops and // the vehicle will return to the path by re-planning the trajectory or external operation. // After the recovery, the previous value of the optimization may deviate greatly from // the actual steer angle, and it may make the optimization result unstable. - if (!is_mpc_solved) { + if (!is_mpc_solved || !is_under_control) { m_mpc->resetPrevResult(m_current_steering); } else { setSteeringToHistory(ctrl_cmd); @@ -497,7 +524,7 @@ bool MpcLateralController::isMpcConverged() // Find the maximum and minimum values of the steering angle in the past 1 second. double min_steering_value = m_mpc_steering_history[0].first.steering_tire_angle; - double max_steering_value = m_mpc_steering_history[0].first.steering_tire_angle; + double max_steering_value = min_steering_value; for (size_t i = 1; i < m_mpc_steering_history.size(); i++) { if (m_mpc_steering_history.at(i).first.steering_tire_angle < min_steering_value) { min_steering_value = m_mpc_steering_history.at(i).first.steering_tire_angle; @@ -633,8 +660,8 @@ bool MpcLateralController::isValidTrajectory(const Trajectory & traj) const !isfinite(p.pose.orientation.w) || !isfinite(p.pose.orientation.x) || !isfinite(p.pose.orientation.y) || !isfinite(p.pose.orientation.z) || !isfinite(p.longitudinal_velocity_mps) || !isfinite(p.lateral_velocity_mps) || - !isfinite(p.lateral_velocity_mps) || !isfinite(p.heading_rate_rps) || - !isfinite(p.front_wheel_angle_rad) || !isfinite(p.rear_wheel_angle_rad)) { + !isfinite(p.heading_rate_rps) || !isfinite(p.front_wheel_angle_rad) || + !isfinite(p.rear_wheel_angle_rad)) { return false; } } diff --git a/control/autoware_pid_longitudinal_controller/include/autoware/pid_longitudinal_controller/pid_longitudinal_controller.hpp b/control/autoware_pid_longitudinal_controller/include/autoware/pid_longitudinal_controller/pid_longitudinal_controller.hpp index 9291d538b022f..5805ef7db9f86 100644 --- a/control/autoware_pid_longitudinal_controller/include/autoware/pid_longitudinal_controller/pid_longitudinal_controller.hpp +++ b/control/autoware_pid_longitudinal_controller/include/autoware/pid_longitudinal_controller/pid_longitudinal_controller.hpp @@ -64,7 +64,8 @@ class PidLongitudinalController : public trajectory_follower::LongitudinalContro { public: /// \param node Reference to the node used only for the component and parameter initialization. - explicit PidLongitudinalController(rclcpp::Node & node); + explicit PidLongitudinalController( + rclcpp::Node & node, std::shared_ptr diag_updater); private: struct Motion @@ -236,8 +237,8 @@ class PidLongitudinalController : public trajectory_follower::LongitudinalContro std::shared_ptr m_last_running_time{std::make_shared(clock_->now())}; // Diagnostic - - diagnostic_updater::Updater diagnostic_updater_; + std::shared_ptr + diag_updater_{}; // Diagnostic updater for publishing diagnostic data. struct DiagnosticData { double trans_deviation{0.0}; // translation deviation between nearest point and current_pose diff --git a/control/autoware_pid_longitudinal_controller/src/pid_longitudinal_controller.cpp b/control/autoware_pid_longitudinal_controller/src/pid_longitudinal_controller.cpp index 78c7ccf832514..93496c85c741b 100644 --- a/control/autoware_pid_longitudinal_controller/src/pid_longitudinal_controller.cpp +++ b/control/autoware_pid_longitudinal_controller/src/pid_longitudinal_controller.cpp @@ -27,14 +27,16 @@ namespace autoware::motion::control::pid_longitudinal_controller { -PidLongitudinalController::PidLongitudinalController(rclcpp::Node & node) +PidLongitudinalController::PidLongitudinalController( + rclcpp::Node & node, std::shared_ptr diag_updater) : node_parameters_(node.get_node_parameters_interface()), clock_(node.get_clock()), - logger_(node.get_logger().get_child("longitudinal_controller")), - diagnostic_updater_(&node) + logger_(node.get_logger().get_child("longitudinal_controller")) { using std::placeholders::_1; + diag_updater_ = diag_updater; + // parameters timer m_longitudinal_ctrl_period = node.get_parameter("ctrl_period").as_double(); @@ -432,7 +434,7 @@ trajectory_follower::LongitudinalOutput PidLongitudinalController::run( publishDebugData(ctrl_cmd, control_data); // diagnostic - diagnostic_updater_.force_update(); + diag_updater_->force_update(); return output; } @@ -1150,8 +1152,8 @@ void PidLongitudinalController::updateDebugVelAcc(const ControlData & control_da void PidLongitudinalController::setupDiagnosticUpdater() { - diagnostic_updater_.setHardwareID("autoware_pid_longitudinal_controller"); - diagnostic_updater_.add("control_state", this, &PidLongitudinalController::checkControlState); + diag_updater_->setHardwareID("autoware_pid_longitudinal_controller"); + diag_updater_->add("control_state", this, &PidLongitudinalController::checkControlState); } void PidLongitudinalController::checkControlState( diff --git a/control/autoware_smart_mpc_trajectory_follower/.gitignore b/control/autoware_smart_mpc_trajectory_follower/.gitignore index d914a0aa99e5a..c5b22066b75c1 100644 --- a/control/autoware_smart_mpc_trajectory_follower/.gitignore +++ b/control/autoware_smart_mpc_trajectory_follower/.gitignore @@ -1,3 +1,4 @@ # Files generated when installing smart_mpc_trajectory_follower build/ *.egg-info/ +.cspell.json diff --git a/control/autoware_smart_mpc_trajectory_follower/README.md b/control/autoware_smart_mpc_trajectory_follower/README.md index fd135319ff900..bb31a1be3a8b5 100644 --- a/control/autoware_smart_mpc_trajectory_follower/README.md +++ b/control/autoware_smart_mpc_trajectory_follower/README.md @@ -3,7 +3,7 @@

- + # Smart MPC Trajectory Follower @@ -83,19 +83,29 @@ model_trainer.transform_rosbag_to_csv(rosbag_dir) Here, `rosbag_dir` represents the rosbag directory. At this time, all CSV files in `rosbag_dir` are automatically deleted first. -The paths of the rosbag directories used for training, `dir_0`, `dir_1`, `dir_2`,... and the directory `save_dir` where you save the models, the model can be saved in the python environment as follows: +We move on to an explanation of how the model is trained. +If `trained_model_parameter:memory_for_training:use_memory_for_training` in [trained_model_param.yaml](./autoware_smart_mpc_trajectory_follower/param/trained_model_param.yaml) is set to `true`, training is performed on models that include LSTM, and if it is set to `false`, training is performed on models that do not include LSTM. +When using LSTM, cell states and hidden states are updated based on historical time series data and reflected in the prediction. + +The paths of the rosbag directories used for training and validation, `dir_0`, `dir_1`, `dir_2`,..., `dir_val_0`, `dir_val_1`, `dir_val_2`,... and the directory `save_dir` where you save the models, the model can be saved in the python environment as follows: ```python from autoware_smart_mpc_trajectory_follower.training_and_data_check import train_drive_NN_model model_trainer = train_drive_NN_model.train_drive_NN_model() -model_trainer.add_data_from_csv(dir_0) -model_trainer.add_data_from_csv(dir_1) -model_trainer.add_data_from_csv(dir_2) +model_trainer.add_data_from_csv(dir_0, add_mode="as_train") +model_trainer.add_data_from_csv(dir_1, add_mode="as_train") +model_trainer.add_data_from_csv(dir_2, add_mode="as_train") +... +model_trainer.add_data_from_csv(dir_val_0, add_mode="as_val") +model_trainer.add_data_from_csv(dir_val_1, add_mode="as_val") +model_trainer.add_data_from_csv(dir_val_2, add_mode="as_val") ... model_trainer.get_trained_model() model_trainer.save_models(save_dir) ``` +If `add_mode` is not specified or validation data is not added, the training data is split to be used for training and validation. + After performing the polynomial regression, the NN can be trained on the residuals as follows: ```python @@ -130,6 +140,7 @@ To perform a control test on autoware with the nominal model before training, ma

Record rosbag and train the model in the manner described in "Training of model and reflection in control", and move the generated files `model_for_test_drive.pth` and `polynomial_reg_info.npz` to the home directory. +Sample models, which work under the condition that`trained_model_parameter:memory_for_training:use_memory_for_training` in [trained_model_param.yaml](./autoware_smart_mpc_trajectory_follower/param/trained_model_param.yaml) is set to `true`, can be obtained at [sample_models/wheel_base_changed](./sample_models/wheel_base_changed/). > [!NOTE] > Although the data used for training is small, for the sake of simplicity, we will see how much performance can be improved with this amount of data. @@ -158,58 +169,84 @@ First, to give wheel base 2.79 m in the python simulator, create the following f { "wheel_base": 2.79 } ``` -Next, run the following commands to test the slalom driving on the python simulator with the nominal model: +Next, after moving to `control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator`, run the following commands to test the slalom driving on the python simulator with the nominal control: -```python -import python_simulator -from autoware_smart_mpc_trajectory_follower.training_and_data_check import train_drive_NN_model -initial_error = [0.0, 0.03, 0.01, -0.01, 0.0, 0.0] -save_dir = "test_python_sim" -python_simulator.slalom_drive(save_dir=save_dir,use_trained_model=False,initial_error=initial_error) +```bash +python3 run_python_simulator.py nominal_test ``` -Here, `initial_error` is the initial error from the target trajectory, in the order of x-coordinate, y-coordinate, longitudinal velocity, yaw angle, longitudinal acceleration, and steer angle, -and `save_dir` is the directory where the driving test results are saved. +The result of the driving is stored in `test_python_nominal_sim`. -> [!NOTE] -> The value of `use_trained_model` given as the argument of `python_simulator.slalom_drive` takes precedence over the value of `trained_model_parameter:control_application:use_trained_model` in [trained_model_param.yaml](./autoware_smart_mpc_trajectory_follower/param/trained_model_param.yaml). +The following results were obtained. -Run the following commands to perform training using driving data of the nominal model. +

+ +

-```python -model_trainer = train_drive_NN_model.train_drive_NN_model() -model_trainer.add_data_from_csv(save_dir) -model_trainer.save_train_data(save_dir) -model_trainer.get_trained_model(use_polynomial_reg=True) -model_trainer.save_models(save_dir=save_dir) +The center of the upper row represents the lateral deviation. + +Run the following commands to perform training using figure eight driving data under the control of pure pursuit. + +To perform training using a figure eight driving and driving based on the obtained model, run the following commands: + +```bash +python3 run_python_simulator.py ``` -This way, files `model_for_test_drive.pth` and `polynomial_reg_info.npz` are saved in `save_dir`. -The following results were obtained. +The result of the driving is stored in `test_python_trined_sim`. + +When `trained_model_parameter:memory_for_training:use_memory_for_training` in [trained_model_param.yaml](./autoware_smart_mpc_trajectory_follower/param/trained_model_param.yaml) is set to `true`, the following results were obtained.

- +

-The center of the upper row represents the lateral deviation. +When `trained_model_parameter:memory_for_training:use_memory_for_training` in [trained_model_param.yaml](./autoware_smart_mpc_trajectory_follower/param/trained_model_param.yaml) is set to `false`, the following results were obtained. -Finally, to drive with the training model, run the following commands: +

+ +

-```python -load_dir = save_dir -save_dir = "test_python_trained_sim" -python_simulator.slalom_drive(save_dir=save_dir,load_dir=load_dir,use_trained_model=True,initial_error=initial_error) +It can be seen that the lateral deviation has improved significantly. +However, the difference in driving with and without LSTM is not very apparent. + +To see the difference, for example, we can experiment with parameters such as steer_time_delay. + +First, to restore nominal model settings to default values, set the value of `nominal_parameter:vehicle_info:wheel_base` in [nominal_param.yaml](./autoware_smart_mpc_trajectory_follower/param/nominal_param.yaml) to 2.79, and run the following command: + +```bash +python3 -m smart_mpc_trajectory_follower.clear_pycache ``` -The following results were obtained. +Next, modify `sim_setting.json` as follows: + +```json +{ "steer_time_delay": 1.01 } +``` + +In this way, an experiment is performed when `steer_time_delay` is set to 1.01 sec. + +The result of the driving using the nominal model is as follows:

- +

-It can be seen that the lateral deviation has improved significantly. +The result of the driving using the trained model with LSTM is as follows: -Here we have described wheel base, but the parameters that can be passed to the python simulator are as follows. +

+ +

+ +The result of the driving using the trained model without LSTM is as follows: + +

+ +

+ +It can be seen that the performance with the model that includes LSTM is significantly better than with the model that does not. + +The parameters that can be passed to the python simulator are as follows. | Parameter | Type | Description | | ------------------------ | ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | @@ -226,6 +263,7 @@ Here we have described wheel base, but the parameters that can be passed to the | accel_map_scale | float | Parameter that magnifies the corresponding distortion from acceleration input values to actual acceleration realizations.
Correspondence information is kept in `control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/accel_map.csv`. | | acc_scaling | float | acceleration scaling | | steer_scaling | float | steer scaling | +| vehicle_type | int | Take values from 0 to 4 for pre-designed vehicle types.
A description of each vehicle type is given below. | For example, to give the simulation side 0.01 [rad] of steer bias and 0.001 [rad] of steer dead band, edit the `sim_setting.json` as follows. @@ -233,15 +271,74 @@ For example, to give the simulation side 0.01 [rad] of steer bias and 0.001 [rad { "steer_bias": 0.01, "steer_dead_band": 0.001 } ``` -#### Auto test on python simulator +##### vehicle_type_0 -Here, we describe a method for testing adaptive performance by giving the simulation side a predefined range of model parameters while the control side is given constant model parameters. +This vehicle type matches the default vehicle type used in the control. -First, to restore nominal model settings to default values, set the value of `nominal_parameter:vehicle_info:wheel_base` in [nominal_param.yaml](./autoware_smart_mpc_trajectory_follower/param/nominal_param.yaml) to 2.79, and run the following command: +| Parameter | value | +| ------------------- | ----- | +| wheel_base | 2.79 | +| acc_time_delay | 0.1 | +| steer_time_delay | 0.27 | +| acc_time_constant | 0.1 | +| steer_time_constant | 0.24 | +| acc_scaling | 1.0 | -```bash -python3 -m smart_mpc_trajectory_follower.clear_pycache -``` +##### vehicle_type_1 + +This vehicle type is intended for a heavy bus. + +| Parameter | value | +| ------------------- | ----- | +| wheel_base | 4.76 | +| acc_time_delay | 1.0 | +| steer_time_delay | 1.0 | +| acc_time_constant | 1.0 | +| steer_time_constant | 1.0 | +| acc_scaling | 0.2 | + +##### vehicle_type_2 + +This vehicle type is intended for a light bus. + +| Parameter | value | +| ------------------- | ----- | +| wheel_base | 4.76 | +| acc_time_delay | 0.5 | +| steer_time_delay | 0.5 | +| acc_time_constant | 0.5 | +| steer_time_constant | 0.5 | +| acc_scaling | 0.5 | + +##### vehicle_type_3 + +This vehicle type is intended for a small vehicle. + +| Parameter | value | +| ------------------- | ----- | +| wheel_base | 1.335 | +| acc_time_delay | 0.3 | +| steer_time_delay | 0.3 | +| acc_time_constant | 0.3 | +| steer_time_constant | 0.3 | +| acc_scaling | 1.5 | + +##### vehicle_type_4 + +This vehicle type is intended for a small robot. + +| Parameter | value | +| ------------------- | ----- | +| wheel_base | 0.395 | +| acc_time_delay | 0.2 | +| steer_time_delay | 0.2 | +| acc_time_constant | 0.2 | +| steer_time_constant | 0.2 | +| acc_scaling | 1.0 | + +#### Auto test on python simulator + +Here, we describe a method for testing adaptive performance by giving the simulation side a predefined range of model parameters while the control side is given constant model parameters. To run a driving experiment within the parameter change range set in [run_sim.py](./autoware_smart_mpc_trajectory_follower/python_simulator/run_sim.py), for example, move to `control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator` and run the following command: @@ -251,27 +348,43 @@ python3 run_sim.py --param_name steer_bias Here we described the experimental procedure for steer bias, and the same method can be used for other parameters. -If you want to do it for all parameters at once, run the following command: +To run the test for all parameters except limits at once, run the following command: ```bash -yes | python3 run_sim.py +python3 run_auto_test.py ``` +The results are stored in the `auto_test` directory. +After the executions were completed, the following results were obtained by running [plot_auto_test_result.ipynb](./autoware_smart_mpc_trajectory_follower/python_simulator/plot_auto_test_result.ipynb): + +

+ +

+ +The orange line shows the intermediate model trained using pure pursuit figure eight drive, and the blue line shows the final model trained using data from both the intermediate model and the figure eight drive. +In most cases, sufficient performance is obtained, but for `vehicle_type_1`, which is intended for a heavy bus, a lateral deviation of about 2 m was observed, which is not satisfactory. + In `run_sim.py`, the following parameters can be set: -| Parameter | Type | Description | -| ------------------------- | ---- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| USE_TRAINED_MODEL_DIFF | bool | Whether the derivative of the trained model is reflected in the control | -| DATA_COLLECTION_MODE | str | Which method will be used to collect the training data　
"ff": Straight line driving with feed-forward input
"pp": Figure eight driving with pure pursuit control
"mpc": Slalom driving with mpc | -| USE_POLYNOMIAL_REGRESSION | bool | Whether to perform polynomial regression before NN | -| USE_SELECTED_POLYNOMIAL | bool | When USE_POLYNOMIAL_REGRESSION is True, perform polynomial regression using only some preselected polynomials.
The choice of polynomials is intended to be able to absorb the contribution of some parameter shifts based on the nominal model of the vehicle. | -| FORCE_NN_MODEL_TO_ZERO | bool | Whether to force the NN model to zero (i.e., erase the contribution of the NN model).
When USE_POLYNOMIAL_REGRESSION is True, setting FORCE_MODEL_TO_ZERO to True allows the control to reflect the results of polynomial regression only, without using NN models. | -| FIT_INTERCEPT | bool | Whether to include bias in polynomial regression.
If it is False, perform the regression with a polynomial of the first degree or higher. | -| USE_INTERCEPT | bool | When a polynomial regression including bias is performed, whether to use or discard the resulting bias information.
It is meaningful only if FIT_INTERCEPT is True.
If it is False, discard the bias in the polynomial regression in the hope that the NN model can remove the bias term, even if the polynomial regression is performed with the bias included. | +| Parameter | Type | Description | +| ------------------------- | ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| USE_TRAINED_MODEL_DIFF | bool | Whether the derivative of the trained model is reflected in the control | +| DATA_COLLECTION_MODE | DataCollectionMode | Which method will be used to collect the training data　
"DataCollectionMode.ff": Straight line driving with feed-forward input
"DataCollectionMode.pp": Figure eight driving with pure pursuit control
"DataCollectionMode.mpc": Slalom driving with mpc | +| USE_POLYNOMIAL_REGRESSION | bool | Whether to perform polynomial regression before NN | +| USE_SELECTED_POLYNOMIAL | bool | When USE_POLYNOMIAL_REGRESSION is True, perform polynomial regression using only some preselected polynomials.
The choice of polynomials is intended to be able to absorb the contribution of some parameter shifts based on the nominal model of the vehicle. | +| FORCE_NN_MODEL_TO_ZERO | bool | Whether to force the NN model to zero (i.e., erase the contribution of the NN model).
When USE_POLYNOMIAL_REGRESSION is True, setting FORCE_MODEL_TO_ZERO to True allows the control to reflect the results of polynomial regression only, without using NN models. | +| FIT_INTERCEPT | bool | Whether to include bias in polynomial regression.
If it is False, perform the regression with a polynomial of the first degree or higher. | +| USE_INTERCEPT | bool | When a polynomial regression including bias is performed, whether to use or discard the resulting bias information.
It is meaningful only if FIT_INTERCEPT is True.
If it is False, discard the bias in the polynomial regression in the hope that the NN model can remove the bias term, even if the polynomial regression is performed with the bias included. | > [!NOTE] > When `run_sim.py` is run, the `use_trained_model_diff` set in `run_sim.py` takes precedence over the `trained_model_parameter:control_application:use_trained_model_diff` set in [trained_model_param.yaml](./autoware_smart_mpc_trajectory_follower/param/trained_model_param.yaml). +#### Kernel density estimation of pure pursuit driving data + +The distribution of data obtained from pure pursuit runs can be displayed using Kernel density estimation. To do this, run [density_estimation.ipynb](./autoware_smart_mpc_trajectory_follower/python_simulator/density_estimation.ipynb). + +The correlation between the minimum value of the density estimate and the lateral deviation of the run results is low. A scalar indicator that better predicts the value of lateral deviation is under development. + ## Change of nominal parameters and their reloading The nominal parameters of vehicle model can be changed by editing the file [nominal_param.yaml](./autoware_smart_mpc_trajectory_follower/param/nominal_param.yaml). @@ -293,7 +406,7 @@ The nominal parameters include the following: ## Change of control parameters and their reloading -The control parameters can be changed by editing files [mpc_param.yaml](./smart_mpc_trajectory_follower/param/mpc_param.yaml) and [trained_model_param.yaml](./autoware_smart_mpc_trajectory_follower/param/trained_model_param.yaml). +The control parameters can be changed by editing files [mpc_param.yaml](./autoware_smart_mpc_trajectory_follower/param/mpc_param.yaml) and [trained_model_param.yaml](./autoware_smart_mpc_trajectory_follower/param/trained_model_param.yaml). Although it is possible to reflect parameter changes by restarting autoware, the following command allows us to do so without leaving autoware running: ```bash @@ -304,21 +417,29 @@ The main parameters among the control parameters are as follows. ### `mpc_param.yaml` -| Parameter | Type | Description | -| ------------------------------------ | ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| mpc_parameter:system:mode | str | control mode
"ilqr": iLQR mode
"mppi": MPPI mode
"mppi_ilqr": the initial value of iLQR is given by the MPPI solution. | -| mpc_parameter:cost_parameters:Q | list[float] | Stage cost for states.
List of length 8, in order: straight deviation, lateral deviation, velocity deviation, yaw angle deviation, acceleration deviation, steer deviation, acceleration input deviation, steer input deviation cost weights. | -| mpc_parameter:cost_parameters:Q_c | list[float] | Cost in the horizon corresponding to the following timing_Q_c for the states.
The correspondence of the components of the list is the same as for Q. | -| mpc_parameter:cost_parameters:Q_f | list[float] | Termination cost for the states.
The correspondence of the components of the list is the same as for Q. | -| mpc_parameter:cost_parameters:R | list[float] | A list of length 2 where R[0] is weight of cost for the change rate of acceleration input value and R[1] is weight of cost for the change rate of steer input value. | -| mpc_parameter:mpc_setting:timing_Q_c | list[int] | Horizon numbers such that the stage cost for the states is set to Q_c. | +| Parameter | Type | Description | +| ------------------------------------------ | ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| mpc_parameter:system:mode | str | control mode
"ilqr": iLQR mode
"mppi": MPPI mode
"mppi_ilqr": the initial value of iLQR is given by the MPPI solution. | +| mpc_parameter:cost_parameters:Q | list[float] | Stage cost for states.
List of length 8, in order: straight deviation, lateral deviation, velocity deviation, yaw angle deviation, acceleration deviation, steer deviation, acceleration input deviation, steer input deviation cost weights. | +| mpc_parameter:cost_parameters:Q_c | list[float] | Cost in the horizon corresponding to the following timing_Q_c for the states.
The correspondence of the components of the list is the same as for Q. | +| mpc_parameter:cost_parameters:Q_f | list[float] | Termination cost for the states.
The correspondence of the components of the list is the same as for Q. | +| mpc_parameter:cost_parameters:R | list[float] | A list of length 2 where R[0] is weight of cost for the change rate of acceleration input value and R[1] is weight of cost for the change rate of steer input value. | +| mpc_parameter:mpc_setting:timing_Q_c | list[int] | Horizon numbers such that the stage cost for the states is set to Q_c. | +| mpc_parameter:compensation:acc_fb_decay | float | Coefficient of damping in integrating the error between the observed and predicted acceleration values in the compensator outside the MPC. | +| mpc_parameter:compensation:acc_fb_gain | float | Gain of acceleration compensation. | +| mpc_parameter:compensation:max_error_acc | float | Maximum acceleration compensation (m/s^2) | +| mpc_parameter:compensation:steer_fb_decay | float | Coefficient of damping in integrating the error between the observed and predicted steering values in the compensator outside the MPC. | +| mpc_parameter:compensation:steer_fb_gain | float | Gain of steering compensation. | +| mpc_parameter:compensation:max_error_steer | float | Maximum steering compensation (rad) | ### `trained_model_param.yaml` -| Parameter | Type | Description | -| ------------------------------------------------------------------ | ---- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| trained_model_parameter:control_application:use_trained_model | bool | Whether the trained model is reflected in the control or not. | -| trained_model_parameter:control_application:use_trained_model_diff | bool | Whether the derivative of the trained model is reflected on the control or not.
It is meaningful only when use_trained_model is True, and if False, the nominal model is used for the derivative of the dynamics, and trained model is used only for prediction. | +| Parameter | Type | Description | +| ------------------------------------------------------------------- | ---- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| trained_model_parameter:control_application:use_trained_model | bool | Whether the trained model is reflected in the control or not. | +| trained_model_parameter:control_application:use_trained_model_diff | bool | Whether the derivative of the trained model is reflected on the control or not.
It is meaningful only when use_trained_model is True, and if False, the nominal model is used for the derivative of the dynamics, and trained model is used only for prediction. | +| trained_model_parameter:memory_for_training:use_memory_for_training | bool | Whether to use the model that includes LSTM for learning or not. | +| trained_model_parameter:memory_for_training:use_memory_diff | bool | Whether the derivative with respect to the cell state and hidden state at the previous time of LSTM is reflected in the control or not. | ## Request to release the slow stop mode @@ -336,3 +457,5 @@ ros2 topic pub /pympc_stop_mode_reset_request std_msgs/msg/String "data: ''" --o - It may take some time until the end of the planning to compile numba functions at the start of the first control. - In the stopping action near the goal our control switches to another simple control law. As a result, the stopping action may not work except near the goal. Stopping is also difficult if the acceleration map is significantly shifted. + +- If the dynamics deviates too much from the nominal model, as in `vehicle_type_1`, which is intended for heavy buses, it may not be well controlled. diff --git a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/param/mpc_param.yaml b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/param/mpc_param.yaml index 710d1cc992949..797976994ff7e 100644 --- a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/param/mpc_param.yaml +++ b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/param/mpc_param.yaml @@ -1,11 +1,11 @@ mpc_parameter: system: - mode: mppi_ilqr # option: ilqr, mppi, mppi_ilqr + mode: ilqr # option: ilqr, mppi, mppi_ilqr mpc_setting: ctrl_time_step: 0.03333 mpc_freq: 3 N: 50 - steer_ctrl_queue_size: 50 + steer_ctrl_queue_size: 17 steer_ctrl_queue_size_core: 15 acc_ctrl_queue_size: 12 nx_0: 6 @@ -14,21 +14,21 @@ mpc_parameter: cost_parameters: Q: [0.0, 10.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] Q_c: [1e+2, 1e+8, 1e+6, 1e+3, 1.0, 1.0, 1.0, 1.0] - Q_f: [1e+2, 1e+8, 1e+2, 1e+8, 1.0, 1.0, 1.0, 1.0] + Q_f: [1e+2, 1e+8, 1e+2, 1e+7, 1.0, 1.0, 1.0, 1.0] R: [10.0, 1000.0] acc_lim_weight: 100.0 steer_lim_weight: 100.0 acc_rate_lim_weight: 10000.0 steer_rate_lim_weight: 10000.0 - min_steer_rate_transform_for_start: 0.01 - power_steer_rate_transform_for_start: 5 - coef_steer_rate_transform_for_start: 3.0 - min_loose_lateral_cost: 0.00001 - power_loose_lateral_cost: 10 - threshold_loose_lateral_cost: 0.2 - min_loose_yaw_cost: 0.00001 - power_loose_yaw_cost: 1 - threshold_loose_yaw_cost: 0.1 + vel_steer_cost_coef_table: [0.01, 0.01, 0.08, 0.36, 1.0] + vel_steer_table: [0.01, 0.1, 0.2, 0.27, 0.33] + lateral_cost_coef_table: [0.00001, 0.0005, 0.06, 0.27, 1.0] + lateral_error_table: [0.01, 0.1, 0.15, 0.175, 0.2] + yaw_cost_coef_table: [0.00001, 1.0] + yaw_error_table: [0.00001, 0.1] + steer_rate_cost_table: [10.0, 5.0, 1.0] + curvature_table: [0.01, 0.03, 0.05] + use_max_curvature: true ilqr: ls_step: 0.9 max_iter_ls: 10 @@ -40,17 +40,31 @@ mpc_parameter: max_iter_mppi: 2 sample_num: 100 mppi_tol: 0.5 - mppi_step: 20 + mppi_step: 7 + pure_pursuit: + acc_kp: 0.5 + lookahead_time: 3.0 + min_lookahead: 10.0 + steer_kp_param: 2.0 + steer_kd_param: 2.0 + naive_pure_pursuit: + acc_kp: 0.5 + lookahead_coef: 1.0 + lookahead_intercept: 5.0 preprocessing: reference_horizon: 50 cap_pred_error: [0.5, 2.0] use_sg_for_nominal_inputs: true sg_deg_for_nominal_inputs: 0 sg_window_size_for_nominal_inputs: 10 - to_be_deprecated: - tighten_horizon: 20 - min_tighten_steer_rate: 1.0 - power_tighten_steer_rate_by_lateral_error: 1 - threshold_tighten_steer_rate_by_lateral_error: 0.05 - power_tighten_steer_rate_by_yaw_error: 1 - threshold_tighten_steer_rate_by_yaw_error: 0.05 + compensation: + acc_fb_decay: 0.1 + acc_fb_gain: 1.0 + acc_fb_sec_order_ratio: 1.0 + max_error_acc: 1e-3 + steer_fb_decay: 0.001 + steer_fb_gain: 1.0 + steer_fb_sec_order_ratio: 1.0 + max_error_steer: 1e-2 + limit: + read_limit_file: false diff --git a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/param/trained_model_param.yaml b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/param/trained_model_param.yaml index 6d1973586a5eb..b144bde856016 100644 --- a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/param/trained_model_param.yaml +++ b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/param/trained_model_param.yaml @@ -5,6 +5,8 @@ trained_model_parameter: max_train_data_size: 10000 error_decay: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5] use_trained_model_diff: true + minimum_steer_diff: 0.03 + reflect_only_poly_diff: false use_sg_for_trained_model_diff: true sg_deg_for_trained_model_diff: 0 sg_window_size_for_trained_model_diff: 25 @@ -17,6 +19,12 @@ trained_model_parameter: use_theta_noise: false use_acc_noise: false use_steer_noise: false + memory_for_training: + use_memory_for_training: true + use_memory_diff: true + use_sg_for_memory_diff: true + sg_deg_for_memory_diff: 0 + sg_window_size_for_memory_diff: 10 smoothing: acc_sigma_for_learning: 5.0 steer_sigma_for_learning: 5.0 @@ -28,3 +36,29 @@ trained_model_parameter: theta_out_sigma_for_learning: 10.0 acc_out_sigma_for_learning: 5.0 steer_out_sigma_for_learning: 5.0 + normalize: + vel_normalize: 0.2 + acc_normalize: 1.0 + steer_normalize: 3.0 + weight: + NN_x_weight: 1.0 + NN_y_weight: 1.0 + NN_v_weight: 1.0 + NN_yaw_weight: 2.0 + NN_acc_weight: 1.0 + NN_steer_weight: 2.0 + NN_x_weight_diff: 0.001 + NN_y_weight_diff: 0.01 + NN_v_weight_diff: 0.001 + NN_yaw_weight_diff: 0.1 + NN_acc_weight_diff: 0.1 + NN_steer_weight_diff: 0.1 + NN_x_weight_two_diff: 0.0001 + NN_y_weight_two_diff: 0.001 + NN_v_weight_two_diff: 0.00001 + NN_yaw_weight_two_diff: 0.01 + NN_acc_weight_two_diff: 0.01 + NN_steer_weight_two_diff: 0.01 + finalize_x_weight: 10.0 + finalize_y_weight: 10.0 + finalize_v_weight: 10.0 diff --git a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/.gitignore b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/.gitignore index 9f6b19213255b..5b39e844b3405 100644 --- a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/.gitignore +++ b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/.gitignore @@ -8,6 +8,7 @@ train_drive_*.png test_feedforward_*/ test_pure_pursuit_*/ test_python_*/ +test_*/ python_sim_log_*/ sim_setting.json auto_test_result_*.csv diff --git a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/assets.py b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/assets.py new file mode 100644 index 0000000000000..1bf9c7ac894d3 --- /dev/null +++ b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/assets.py @@ -0,0 +1,261 @@ +# Copyright 2024 Proxima Technology Inc, TIER IV +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from enum import Enum +import os + +import numpy as np + +CHANGE_PARAM_P1 = 0.8 +CHANGE_PARAM_P2 = 1.2 + + +class ControlType(Enum): + ff = "feed_forward" + pp_eight = "pure_pursuit_figure_eight" + pp_straight = "pure_pursuit_straight" + npp_eight = "naive_pure_pursuit_figure_eight" + mpc = "smart_mpc" + + def __str__(self) -> str: + return self.name + + +class DataCollectionMode(Enum): + ff = "feed_forward" + pp = "pure_pursuit" + npp = "naive_pure_pursuit" + mpc = "nominal_mpc" + + def __str__(self) -> str: + return self.name + + def toControlTypes(self) -> list[ControlType]: + if self.name == "pp": + return [ControlType.pp_eight, ControlType.pp_straight] + elif self.name == "npp": + return [ControlType.npp_eight] + elif self.name == "ff": + return [ControlType.ff] + elif self.name == "mpc": + return [ControlType.mpc] + else: + print(f"Error: unexpected DataCollectionMode: {self}") + raise Exception + + +class ChangeParam(Enum): + """Parameters to be changed when running the simulation.""" + + steer_bias = [ + -1.0 * np.pi / 180.0, + -0.8 * np.pi / 180.0, + -0.6 * np.pi / 180.0, + -0.4 * np.pi / 180.0, + -0.2 * np.pi / 180.0, + 0.0, + 0.2 * np.pi / 180.0, + 0.4 * np.pi / 180.0, + 0.6 * np.pi / 180.0, + 0.8 * np.pi / 180.0, + 1.0 * np.pi / 180.0, + ] + """steer midpoint (soft + hard)""" + + steer_rate_lim = [0.020, 0.050, 0.100, 0.150, 0.200, 0.300, 0.400, 0.500] + """Maximum steer angular velocity""" + + vel_rate_lim = [0.5, 1.0, 3.0, 5.0, 7.0, 9.0] + """Maximum acceleration/deceleration""" + + wheel_base = [0.5, 1.0, 1.5, 2.0, 3.0, 5.0, 7.0] + """wheel base""" + + steer_dead_band = [0.0000, 0.0012, 0.0025, 0.0050, 0.01] + """steer dead band""" + + adaptive_gear_ratio_coef = [ + [15.713, 0.053, 0.042, 15.713, 0.053, 0.042], + [15.713, 0.053, 0.042, CHANGE_PARAM_P1 * 15.713, 0.053, 0.042], + [15.713, 0.053, 0.042, CHANGE_PARAM_P2 * 15.713, 0.053, 0.042], + [15.713, 0.053, 0.042, 15.713, CHANGE_PARAM_P1 * 0.053, 0.042], + [15.713, 0.053, 0.042, CHANGE_PARAM_P1 * 15.713, CHANGE_PARAM_P1 * 0.053, 0.042], + [15.713, 0.053, 0.042, CHANGE_PARAM_P2 * 15.713, CHANGE_PARAM_P1 * 0.053, 0.042], + [15.713, 0.053, 0.042, 15.713, CHANGE_PARAM_P2 * 0.053, 0.042], + [15.713, 0.053, 0.042, CHANGE_PARAM_P1 * 15.713, CHANGE_PARAM_P2 * 0.053, 0.042], + [15.713, 0.053, 0.042, CHANGE_PARAM_P2 * 15.713, CHANGE_PARAM_P2 * 0.053, 0.042], + [15.713, 0.053, 0.042, 15.713, 0.053, CHANGE_PARAM_P1 * 0.042], + [15.713, 0.053, 0.042, CHANGE_PARAM_P1 * 15.713, 0.053, CHANGE_PARAM_P1 * 0.042], + [15.713, 0.053, 0.042, CHANGE_PARAM_P2 * 15.713, 0.053, CHANGE_PARAM_P1 * 0.042], + [15.713, 0.053, 0.042, 15.713, CHANGE_PARAM_P1 * 0.053, CHANGE_PARAM_P1 * 0.042], + [ + 15.713, + 0.053, + 0.042, + CHANGE_PARAM_P1 * 15.713, + CHANGE_PARAM_P1 * 0.053, + CHANGE_PARAM_P1 * 0.042, + ], + [ + 15.713, + 0.053, + 0.042, + CHANGE_PARAM_P2 * 15.713, + CHANGE_PARAM_P1 * 0.053, + CHANGE_PARAM_P1 * 0.042, + ], + [15.713, 0.053, 0.042, 15.713, CHANGE_PARAM_P2 * 0.053, CHANGE_PARAM_P1 * 0.042], + [ + 15.713, + 0.053, + 0.042, + CHANGE_PARAM_P1 * 15.713, + CHANGE_PARAM_P2 * 0.053, + CHANGE_PARAM_P1 * 0.042, + ], + [ + 15.713, + 0.053, + 0.042, + CHANGE_PARAM_P2 * 15.713, + CHANGE_PARAM_P2 * 0.053, + CHANGE_PARAM_P1 * 0.042, + ], + [15.713, 0.053, 0.042, 15.713, 0.053, CHANGE_PARAM_P2 * 0.042], + [15.713, 0.053, 0.042, CHANGE_PARAM_P1 * 15.713, 0.053, CHANGE_PARAM_P2 * 0.042], + [15.713, 0.053, 0.042, CHANGE_PARAM_P2 * 15.713, 0.053, CHANGE_PARAM_P2 * 0.042], + [15.713, 0.053, 0.042, 15.713, CHANGE_PARAM_P1 * 0.053, CHANGE_PARAM_P2 * 0.042], + [ + 15.713, + 0.053, + 0.042, + CHANGE_PARAM_P1 * 15.713, + CHANGE_PARAM_P1 * 0.053, + CHANGE_PARAM_P2 * 0.042, + ], + [ + 15.713, + 0.053, + 0.042, + CHANGE_PARAM_P2 * 15.713, + CHANGE_PARAM_P1 * 0.053, + CHANGE_PARAM_P2 * 0.042, + ], + [15.713, 0.053, 0.042, 15.713, CHANGE_PARAM_P2 * 0.053, CHANGE_PARAM_P2 * 0.042], + [ + 15.713, + 0.053, + 0.042, + CHANGE_PARAM_P1 * 15.713, + CHANGE_PARAM_P2 * 0.053, + CHANGE_PARAM_P2 * 0.042, + ], + [ + 15.713, + 0.053, + 0.042, + CHANGE_PARAM_P2 * 15.713, + CHANGE_PARAM_P2 * 0.053, + CHANGE_PARAM_P2 * 0.042, + ], + ] + """velocity-dependent gear ratio""" + + acc_time_delay = [0.00, 0.1, 0.27, 0.40, 0.60, 0.80, 1.01] + """acc time delay""" + + steer_time_delay = [0.00, 0.1, 0.27, 0.40, 0.60, 0.80, 1.02] + """steer time delay""" + + acc_time_constant = [0.01, 0.1, 0.20, 0.24, 0.40, 0.60, 0.80, 1.01] + """time constant""" + + steer_time_constant = [0.01, 0.1, 0.20, 0.24, 0.40, 0.60, 0.80, 1.02] + """time constant""" + + accel_map_scale = [0.2, 0.5, 1.0, 2.0, 3.0, 4.0, 5.0] + """pedal - real acceleration correspondence""" + + acc_scaling = [0.2, 0.5, 1.0, 2.0, 3.0, 4.0, 5.01] + """Acceleration scaling coefficient""" + + steer_scaling = [0.2, 0.5, 1.0, 2.0, 3.0, 4.0, 5.02] + """Steer scaling coefficient""" + + vehicle_type = [0, 1, 2, 3, 4] + """change to other vehicle parameters""" + + test_vehicle = [0, 1, 2, 3, 4, 5, 6, 7] + + +def test_dir_name( + root: str = ".", + data_collection_mode: DataCollectionMode | None = None, + control_type: ControlType | None = None, + trained: bool = False, + change_param: ChangeParam | None = None, + index: int | None = None, + validation_flag: bool = False, + use_memory_diff: bool = False, +) -> str: + """Generate string for directory name.""" + dir_name = root + "/test" + if control_type is not None: + dir_name += f"_{control_type.value}_sim" + elif data_collection_mode is not None: + dir_name += f"_{data_collection_mode}_aided_sim" + else: + dir_name += "_sim" + + if trained: + dir_name += "_trained" + if change_param is not None: + dir_name += f"_{change_param.name}" + if index is not None: + dir_name += f"_{index}th" + if validation_flag: + dir_name += "_val" + if use_memory_diff: + dir_name += "_mem_diff" + return dir_name + + +class DirGenerator: + """Class to store parameters for `test_dir_name`.""" + + def __init__(self, root: str): + # create directory if not exist + if not os.path.isdir(root): + os.mkdir(root) + self.root = root + + def test_dir_name( + self, + data_collection_mode: DataCollectionMode | None = None, + control_type: ControlType | None = None, + trained: bool = False, + change_param: ChangeParam | None = None, + index: int | None = None, + validation_flag: bool = False, + use_memory_diff: bool = False, + ): + return test_dir_name( + root=self.root, + data_collection_mode=data_collection_mode, + control_type=control_type, + trained=trained, + change_param=change_param, + index=index, + validation_flag=validation_flag, + use_memory_diff=use_memory_diff, + ) diff --git a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/data_collection_utils.py b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/data_collection_utils.py new file mode 100644 index 0000000000000..5b681328ec7da --- /dev/null +++ b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/data_collection_utils.py @@ -0,0 +1,617 @@ +# Copyright 2024 Proxima Technology Inc, TIER IV +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import csv + +from autoware_smart_mpc_trajectory_follower.scripts import drive_functions +from numba import njit # type: ignore +import numpy as np +from numpy import arctan +from numpy import cos +from numpy import pi +from numpy import sin + +RANDOM_SEED_STEP_RESPONSE = 42 + + +def get_feedforward_nominal_input(t, trajectory_data): + """Calculate the nominal input for feed-forward driving.""" + total_time = trajectory_data[-1, 0] + t_current = t - (t // total_time) * total_time + nearest_index = np.argmin(np.abs(trajectory_data[:, 0] - t_current)) + return trajectory_data[nearest_index, [5, 6]] + + +def create_additional_sine_data( + seed, + t_range, + acc_amp_range, + acc_period_range, + steer_amp_range, + steer_period_range, + large_steer_amp_range, + large_steer_period_range, + start_large_steer_time, +): + """Create sine wave data to be added randomly to feed-forward runs.""" + np.random.seed(seed=seed) + t_acc = 0.0 + t_steer = 0.0 + t_large_steer = 0.0 + t_acc_list = [] + t_steer_list = [] + t_large_steer_list = [] + t_acc_list.append(t_acc) + t_steer_list.append(t_steer) + t_large_steer_list.append(t_large_steer) + t_large_steer += start_large_steer_time + t_large_steer_list.append(t_large_steer) + amp_acc_list = [] + amp_steer_list = [] + amp_large_steer_list = [] + amp_large_steer_list.append(0) + while True: + if max(t_acc, t_large_steer) >= t_steer: + period = ( + steer_period_range[1] - steer_period_range[0] + ) * np.random.uniform() + steer_period_range[0] + t_steer += period + t_steer_list.append(t_steer) + amp_steer_list.append(steer_amp_range * np.random.uniform()) + elif t_large_steer >= t_acc: + period = ( + acc_period_range[1] - acc_period_range[0] + ) * np.random.uniform() + acc_period_range[0] + t_acc += period + t_acc_list.append(t_acc) + amp_acc_list.append(acc_amp_range * np.random.uniform()) + else: + period = ( + large_steer_period_range[1] - large_steer_period_range[0] + ) * np.random.uniform() + large_steer_period_range[0] + t_large_steer += period + t_large_steer_list.append(t_large_steer) + amp_large_steer_list.append(large_steer_amp_range * np.random.uniform()) + if t_acc >= t_range[1] and t_steer >= t_range[1] and t_large_steer >= t_range[1]: + break + return ( + np.array(t_acc_list), + np.array(amp_acc_list), + np.array(t_steer_list), + np.array(amp_steer_list), + np.array(t_large_steer_list), + np.array(amp_large_steer_list), + ) + + +# cSpell:ignore njit fastmath numba +@njit(cache=False, fastmath=True) +def get_current_additional_sine( + t, + t_acc_array, + amp_acc_array, + t_steer_array, + amp_steer_array, + t_large_steer_array, + amp_large_steer_array, +): + """Calculate current values from already created sine wave data.""" + acc_index = 0 + steer_index = 0 + large_steer_index = 0 + for i in range(t_acc_array.shape[0] - 1): + if t < t_acc_array[i + 1]: + break + acc_index += 1 + for i in range(t_steer_array.shape[0] - 1): + if t < t_steer_array[i + 1]: + break + steer_index += 1 + for i in range(t_large_steer_array.shape[0] - 1): + if t < t_large_steer_array[i + 1]: + break + large_steer_index += 1 + acc = amp_acc_array[acc_index] * np.sin( + 2 + * np.pi + * (t - t_acc_array[acc_index]) + / (t_acc_array[acc_index + 1] - t_acc_array[acc_index]) + ) + steer = amp_steer_array[steer_index] * np.sin( + 2 + * np.pi + * (t - t_steer_array[steer_index]) + / (t_steer_array[steer_index + 1] - t_steer_array[steer_index]) + ) + steer += amp_large_steer_array[large_steer_index] * np.sin( + 2 + * np.pi + * (t - t_large_steer_array[large_steer_index]) + / (t_large_steer_array[large_steer_index + 1] - t_large_steer_array[large_steer_index]) + ) + return np.array([acc, steer]) + + +@njit(cache=False, fastmath=True) +def get_vel_sine(t, v_mid, v_range, period, constant_vel_time): + """Calculate current target velocity values already created sine wave data.""" + if t < period / 4: + vel = v_mid + v_range * np.sin(2 * np.pi * t / period) + elif t < period / 4 + constant_vel_time: + vel = v_mid + v_range + elif t < 3 * period / 4 + constant_vel_time: + vel = v_mid + v_range * np.sin(2 * np.pi * (t - constant_vel_time) / period) + elif t < 3 * period / 4 + 2 * constant_vel_time: + vel = v_mid - v_range + else: + vel = v_mid + v_range * np.sin(2 * np.pi * (t - 2 * constant_vel_time) / period) + + return vel + + +def get_periodic_count(counter, split_size): + return split_size - 0.5 - np.abs(counter % (2 * split_size - 1) - split_size + 0.5) + + +def compute_curvature_radius(trajectory_position_data, trajectory_yaw_data): + d_step = 5 + curvature_radius = [] + for i in range(len(trajectory_position_data)): + tmp_pos = trajectory_position_data[i] + tmp_yaw = trajectory_yaw_data[i] + tmp_computed_flag = False + for j in range(i, len(trajectory_position_data)): + distance = np.sqrt(((tmp_pos[:2] - trajectory_position_data[j, :2]) ** 2).sum()) + if distance >= d_step: + diff_yaw = tmp_yaw - trajectory_yaw_data[j] + if diff_yaw > np.pi: + diff_yaw -= 2 * np.pi + if diff_yaw < -np.pi: + diff_yaw += 2 * np.pi + curvature_radius.append(distance / (1e-12 + np.abs(diff_yaw))) + tmp_computed_flag = True + break + if tmp_computed_flag is False: + curvature_radius.append(1 * curvature_radius[-1]) + curvature_radius = np.array(curvature_radius) + return curvature_radius + + +def compute_curvature_radius_loop_trajectory(trajectory_position_data, trajectory_yaw_data): + data_length = len(trajectory_position_data) + augmented_trajectory_position_data = np.vstack( + [trajectory_position_data, trajectory_position_data[: data_length // 2]] + ) + augmented_trajectory_yaw_data = np.hstack( + [trajectory_yaw_data, trajectory_yaw_data[: data_length // 2]] + ) + return compute_curvature_radius( + augmented_trajectory_position_data, augmented_trajectory_yaw_data + )[:data_length] + + +class StraightLine: + """Straight line target velocity.""" + + def __init__(self, v_min=1.0, v_max=11.0, acc_max=1.2, constant_vel_time=5.0, split_size=5): + self.v_min = v_min + self.v_max = v_max + self.v_mid = 0.5 * (v_min + v_max) + self.period = 2 * np.pi * (v_max - self.v_mid) / acc_max + self.constant_vel_time = constant_vel_time + self.split_size = split_size + self.break_flag = False + + def get_current_velocity(self, t): + index = int(t / (self.period + 2 * self.constant_vel_time)) + t1 = t - (self.period + 2 * self.constant_vel_time) * index + if index < 2 * self.split_size: + adjust = 0.0 + if index >= self.split_size: + adjust = 0.5 + v_range = ( + (self.v_max - self.v_mid) + * (get_periodic_count(index, self.split_size) + 1 - adjust) + / self.split_size + ) + return get_vel_sine(t1, self.v_mid, v_range, self.period, self.constant_vel_time) + else: + if t1 > 2 * self.constant_vel_time: + self.break_flag = True + return self.v_mid + + +class FigureEight: + """Figure eight trajectory.""" + + def __init__( + self, + y_length: float, + x_length: float, + v_min=1.0, + v_max=11.0, + split_size=5, + acc_max=1.2, + constant_vel_time=5.0, + smoothing_trajectory_data_flag=False, + ): + if y_length >= x_length: + raise Exception("invalid argument: y_length must be less than x_length") + self.y_length = y_length + self.x_length = x_length + self.v_min = v_min + self.v_max = v_max + self.split_size = split_size + + self.v_mid = 0.5 * (v_min + v_max) + self.v_start = self.v_mid + + self.period = 2 * np.pi * (v_max - self.v_mid) / acc_max + self.constant_vel_time = constant_vel_time + + self.counter = 0 + self.previous_circle = "left_circle" + self.break_flag = False + self.accel_mode = 1 + self.smoothing_trajectory_data_flag = smoothing_trajectory_data_flag + + @property + def total_distance(self) -> float: + a = self.y_length + b = self.x_length + arc = a * pi + diagonal = 2 * np.sqrt((b - a) ** 2 + a**2) + return arc + diagonal + + def get_trajectory_points(self, step: float): + """Get the position and yaw angle in world coordinates of the figure eight. + + The return value is a 2-dimensional array of positions and a 1-dimensional array of yaw angles corresponding to `t`. + """ + a = self.y_length + b = self.x_length + + t_array = np.arange(start=0.0, stop=self.total_distance, step=step).astype("float") + x = t_array.copy() + y = t_array.copy() + yaw = t_array.copy() + curvature_radius = t_array.copy() + parts = [] + achievement_rates = [] + + # Boundary points between circular and linear trajectory + C = [-(b - a) / 2, -a / 2] + D = [(b - a) / 2, -a / 2] + + R = a / 2 # radius of the circle + OL = [-(b - a) / 2, 0] # center of the left circle + OR = [(b - a) / 2, 0] # center of the right circle + OB = np.sqrt((b - a) ** 2 + a**2) / 2 # half length of the linear trajectory + AD = 2 * OB + θB = arctan(a / (b - a)) # Angle that OB makes with respect to x-axis + BD = pi * a / 2 # the length of arc BD + AC = BD + CO = OB + + i_end = t_array.shape[0] + for i, t in enumerate(t_array): + if t > OB + BD + AD + AC + CO: + i_end = i + break + if 0 <= t and t <= OB: + x[i] = (b - a) * t / (2 * OB) + y[i] = a * t / (2 * OB) + yaw[i] = θB + curvature_radius[i] = 1e12 + parts.append("linear_positive") + achievement_rates.append(t / (2 * OB) + 0.5) + if OB <= t and t <= OB + BD: + t1 = t - OB + t1_rad = t1 / R + x[i] = OR[0] + R * cos(pi / 2 - t1_rad) + y[i] = OR[1] + R * sin(pi / 2 - t1_rad) + yaw[i] = -t1_rad + curvature_radius[i] = R + parts.append("right_circle") + achievement_rates.append(0.0) + if OB + BD <= t and t <= OB + BD + AD: + t2 = t - (OB + BD) + x[i] = D[0] - (b - a) * t2 / (2 * OB) + y[i] = D[1] + a * t2 / (2 * OB) + yaw[i] = pi - θB + curvature_radius[i] = 1e12 + parts.append("linear_negative") + achievement_rates.append(t2 / (2 * OB)) + if OB + BD + AD <= t and t <= OB + BD + AD + AC: + t3 = t - (OB + BD + AD) + t3_rad = t3 / R + x[i] = OL[0] + R * cos(pi / 2 + t3_rad) + y[i] = OL[1] + R * sin(pi / 2 + t3_rad) + yaw[i] = pi + t3_rad + curvature_radius[i] = R + parts.append("left_circle") + achievement_rates.append(0.0) + if OB + BD + AD + AC <= t and t <= OB + BD + AD + AC + CO: + t4 = t - (OB + BD + AD + AC) + x[i] = C[0] + (b - a) * t4 / (2 * OB) + y[i] = C[1] + a * t4 / (2 * OB) + yaw[i] = θB + curvature_radius[i] = 1e12 + parts.append("linear_positive") + achievement_rates.append(t4 / (2 * OB)) + + # drop rest + x = x[:i_end] + y = y[:i_end] + trajectory_position_data = np.array([x, y]).T + trajectory_yaw_data = yaw[:i_end] + curvature_radius = curvature_radius[:i_end] + parts = parts[:i_end] + achievement_rates = achievement_rates[:i_end] + + if self.smoothing_trajectory_data_flag: + window = 1000 + w = np.ones(window) / window + augmented_position_data = np.vstack( + [ + trajectory_position_data[-window:], + trajectory_position_data, + trajectory_position_data[:window], + ] + ) + trajectory_position_data[:, 0] = ( + 1 * np.convolve(augmented_position_data[:, 0], w, mode="same")[window:-window] + ) + trajectory_position_data[:, 1] = ( + 1 * np.convolve(augmented_position_data[:, 1], w, mode="same")[window:-window] + ) + augmented_yaw_data = np.hstack( + [ + trajectory_yaw_data[-window:], + trajectory_yaw_data, + trajectory_yaw_data[:window], + ] + ) + smoothed_trajectory_yaw_data = trajectory_yaw_data.copy() + for i in range(len(trajectory_yaw_data)): + tmp_yaw = trajectory_yaw_data[i] + tmp_data = ( + augmented_yaw_data[window + (i - window // 2) : window + (i + window // 2)] + - tmp_yaw + ) + for j in range(len(tmp_data)): + if tmp_data[j] > np.pi: + tmp_data[j] -= 2 * np.pi + if tmp_data[j] < -np.pi: + tmp_data[j] += 2 * np.pi + tmp_data = np.convolve(tmp_data, w, mode="same") + smoothed_trajectory_yaw_data[i] = ( + tmp_yaw + np.convolve(tmp_data, w, mode="same")[window // 2] + ) + if smoothed_trajectory_yaw_data[i] > np.pi: + smoothed_trajectory_yaw_data[i] -= 2 * np.pi + if smoothed_trajectory_yaw_data[i] < -np.pi: + smoothed_trajectory_yaw_data[i] += 2 * np.pi + + trajectory_yaw_data = smoothed_trajectory_yaw_data.copy() + + return ( + trajectory_position_data, + trajectory_yaw_data, + curvature_radius, + parts, + np.array(achievement_rates), + ) + + def get_current_velocity(self, t): + index = int(t / (self.period + 2 * self.constant_vel_time)) + t1 = t - (self.period + 2 * self.constant_vel_time) * index + if index < 2 * self.split_size: + adjust = 0.0 + if index >= self.split_size: + adjust = 0.5 + v_range = ( + (self.v_max - self.v_mid) + * (get_periodic_count(index, self.split_size) + 1 - adjust) + / self.split_size + ) + return get_vel_sine(t1, self.v_mid, v_range, self.period, self.constant_vel_time) + else: + self.break_flag = True + return self.v_mid + + +def get_pure_pursuit_info(x_current, trajectory_position_data, trajectory_yaw_data, previous_index): + """Calculate the target position and yaw angle required for pure pursuit.""" + search_range = ( + np.arange( + previous_index - trajectory_position_data.shape[0] // 4, + previous_index + trajectory_position_data.shape[0] // 4, + ) + % trajectory_position_data.shape[0] + ) + nearest_index = np.argmin( + ((trajectory_position_data[search_range] - x_current[:2].reshape(1, 2)) ** 2).sum(axis=1) + ) + return ( + trajectory_position_data[search_range[nearest_index]], + trajectory_yaw_data[search_range[nearest_index]], + search_range[nearest_index], + ) + + +def get_naive_pure_pursuit_info( + x_current, trajectory_position_data, trajectory_yaw_data, previous_index +): + """Calculate the target position and yaw angle required for naive pure pursuit.""" + search_range = ( + np.arange( + previous_index - trajectory_position_data.shape[0] // 4, + previous_index + trajectory_position_data.shape[0] // 4, + ) + % trajectory_position_data.shape[0] + ) + nearest_index = np.argmin( + ((trajectory_position_data[search_range] - x_current[:2].reshape(1, 2)) ** 2).sum(axis=1) + ) + lookahead_distance = ( + drive_functions.naive_pure_pursuit_lookahead_coef * x_current[2] + + drive_functions.naive_pure_pursuit_lookahead_intercept + ) + + aug_trajectory_position_data = np.vstack([trajectory_position_data, trajectory_position_data]) + i = 0 + while True: + tmp_distance = np.sqrt( + ( + (aug_trajectory_position_data[search_range[nearest_index] + i] - x_current[:2]) ** 2 + ).sum() + ) + if tmp_distance > lookahead_distance: + break + if (search_range[nearest_index] + i) == (len(aug_trajectory_position_data) - 1): + break + i += 1 + return ( + trajectory_position_data[search_range[nearest_index]], + trajectory_yaw_data[search_range[nearest_index]], + search_range[nearest_index], + aug_trajectory_position_data[search_range[nearest_index] + i], + ) + + +def restrict_target_vel(delta, v_m=2.0, v_M=15.0, steer_m=0.01, steer_M=0.3): + if np.abs(delta) < steer_m: + return v_M + elif np.abs(delta) < steer_M: + return (-(v_M - v_m) * delta + (v_M * steer_M - v_m * steer_m)) / (steer_M - steer_m) + else: + return v_m + + +def step_response( + t: float, + start_time: float, + interval: float, + max_input: float, + max_length: float, + min_length: float, +) -> float: + """Calculate the value of the step response.""" + if t < start_time: + return 0.0 + + step = int((t - start_time) // interval) + step_start_time = step * interval + start_time + + np.random.seed(seed=step + RANDOM_SEED_STEP_RESPONSE) + + if max_length > interval: + print(f"warning: max_length = {max_length} > interval = {interval}") + + length = np.random.uniform(min_length, min(max_length, interval)) + input_u = np.random.uniform(-max_input, max_input) + + if (t - step_start_time) >= length: + return 0.0 + + return input_u + + +class driving_log_updater: + """Class for updating logs when driving on the Python simulator.""" + + def __init__(self): + self.X_history = [] + self.U_history = [] + self.control_cmd_time_stamp_list = [] + self.control_cmd_steer_list = [] + self.control_cmd_acc_list = [] + self.kinematic_state_list = [] + self.acceleration_list = [] + self.steering_status_list = [] + self.control_cmd_orig_list = [] + self.operation_mode_list = [] + + def update(self, t_current, x_current, u_current): + """Update logs.""" + self.X_history.append(x_current) + self.U_history.append(u_current) + self.control_cmd_time_stamp_list.append(t_current) + self.control_cmd_steer_list.append(u_current[1]) + self.control_cmd_acc_list.append(u_current[0]) + if self.control_cmd_time_stamp_list[-1] - self.control_cmd_time_stamp_list[0] > 3.0: + self.control_cmd_time_stamp_list.pop(0) + self.control_cmd_steer_list.pop(0) + self.control_cmd_acc_list.pop(0) + t_sec = int(t_current) + t_n_sec = int(1e9 * (t_current - t_sec)) + kinematic_state = np.zeros(7) + acceleration = np.zeros(4) + steering_status = np.zeros(3) + control_cmd_orig = np.zeros(17) + operation_mode = np.zeros(3) + kinematic_state[0] = t_sec + kinematic_state[1] = t_n_sec + kinematic_state[2] = x_current[0] + kinematic_state[3] = x_current[1] + kinematic_state[4] = np.sin(0.5 * x_current[3]) + kinematic_state[5] = np.cos(0.5 * x_current[3]) + kinematic_state[6] = x_current[2] + self.kinematic_state_list.append(kinematic_state) + acceleration[0] = t_sec + acceleration[1] = t_n_sec + acceleration[3] = x_current[4] + self.acceleration_list.append(acceleration) + steering_status[0] = t_sec + steering_status[1] = t_n_sec + steering_status[2] = x_current[5] + self.steering_status_list.append(steering_status) + control_cmd_orig[0] = t_sec + control_cmd_orig[1] = t_n_sec + control_cmd_orig[8] = u_current[1] + control_cmd_orig[16] = u_current[0] + self.control_cmd_orig_list.append(control_cmd_orig) + operation_mode[0] = t_sec + operation_mode[1] = t_n_sec + operation_mode[2] = 2.0 + self.operation_mode_list.append(operation_mode) + + def save(self, save_dir): + """Save logs in csv format.""" + kinematic_states = np.zeros((len(self.kinematic_state_list), 48)) + kinematic_states[:, [0, 1, 4, 5, 9, 10, 47]] = np.array(self.kinematic_state_list) + np.savetxt(save_dir + "/kinematic_state.csv", kinematic_states, delimiter=",") + np.savetxt(save_dir + "/acceleration.csv", np.array(self.acceleration_list), delimiter=",") + np.savetxt( + save_dir + "/steering_status.csv", + np.array(self.steering_status_list), + delimiter=",", + ) + np.savetxt( + save_dir + "/control_cmd_orig.csv", + np.array(self.control_cmd_orig_list), + delimiter=",", + ) + np.savetxt( + save_dir + "/system_operation_mode_state.csv", + np.array(self.operation_mode_list), + delimiter=",", + ) + with open(save_dir + "/system_operation_mode_state.csv", "w") as f: + writer = csv.writer(f) + for i in range(len(self.operation_mode_list)): + operation_mode_plus_true = self.operation_mode_list[i].tolist() + operation_mode_plus_true.append("True") + writer.writerow(operation_mode_plus_true) diff --git a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/density_estimation.ipynb b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/density_estimation.ipynb new file mode 100644 index 0000000000000..34e5fa94349f2 --- /dev/null +++ b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/density_estimation.ipynb @@ -0,0 +1,635 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# cSpell:ignore codemirror ipython nbconvert pygments nbformat kernelspec #" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from density_estimation import load_kinematic_states, KinematicStates\n", + "from assets import ControlType, ChangeParam\n", + "import json\n", + "\n", + "def load_kinematic_states_and_result(\n", + " load_dir: str, mem_diff=False, max_lateral_deviation: float = 5.0\n", + ") -> tuple[KinematicStates, float, float]:\n", + " \"\"\"Return the KinematicStates object and the lateral error of the simulation result.\n", + "\n", + " This read the given directory (the directory where the kinematic states are stored)\n", + " To read the lateral deviation, however, it goes to read the appropriate directory next to the given directory.\n", + "\n", + " Returns:\n", + " * KinematicStates: KinematicStates object\n", + " * float: the lateral error of the simulation result. this is not for trained data\n", + " * float: the lateral error of the simulation result. this is for trained data\n", + " \"\"\"\n", + " kinematic_states = load_kinematic_states(\n", + " control_type=None,\n", + " change_param=None,\n", + " index=None,\n", + " load_dir=load_dir,\n", + " )\n", + "\n", + " load_dir_for_result = load_dir.replace(ControlType.pp_eight.value, \"pp_aided\")\n", + " if mem_diff:\n", + " load_dir_for_result += \"_mem_diff\"\n", + " load_dir_for_trained_result = load_dir_for_result.replace(\"_aided_sim_\", \"_aided_sim_trained_\")\n", + "\n", + " with open(f\"{load_dir_for_result}/auto_test_performance_result.json\", \"r\") as f:\n", + " data = json.load(f)\n", + "\n", + " with open(f\"{load_dir_for_trained_result}/auto_test_performance_result.json\", \"r\") as f:\n", + " data_trained = json.load(f)\n", + "\n", + " total_abs_max_lateral_deviation = min(data.get(\"total_abs_max_lateral_deviation\"), max_lateral_deviation)\n", + " trained_total_abs_max_lateral_deviation = min(data_trained.get(\"total_abs_max_lateral_deviation\"), max_lateral_deviation)\n", + " return kinematic_states, total_abs_max_lateral_deviation, trained_total_abs_max_lateral_deviation\n", + "\n", + "\n", + "load_kinematic_states_and_result(\n", + " \"test_run_sim_20240607_163543/test_pure_pursuit_figure_eight_sim_steer_scaling_0th\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from typing import Callable\n", + "from density_estimation import Shape, calc_minimum_density_point, ScottCoef\n", + "from typing import Literal\n", + "\n", + "\n", + "def calc_scalar_indexes(\n", + " kinematic_states: KinematicStates,\n", + " fst: Literal[\"speed\", \"acc\", \"steer\"],\n", + " snd: Literal[\"speed\", \"acc\", \"steer\"],\n", + " fst_range: list[float],\n", + " snd_range: list[float],\n", + " bandwidths: list[float] | list[ScottCoef],\n", + " shape: Callable[..., bool] = Shape.trivial,\n", + ") -> np.ndarray:\n", + " \"\"\"Return an ndarray of scalar indexes calculated by the kernel density estimation.\n", + "\n", + " This takes as a parameter the method (the minimum value in a particular region) used to compute the scalar index of kernel density.\n", + " \"\"\"\n", + " array = np.array([])\n", + "\n", + " for bandwidth in bandwidths:\n", + " min_point, min_val = calc_minimum_density_point(\n", + " kinematic_states=kinematic_states,\n", + " fst=fst,\n", + " snd=snd,\n", + " fst_range=fst_range,\n", + " snd_range=snd_range,\n", + " shape=shape,\n", + " bandwidth=bandwidth,\n", + " )\n", + " array = np.append(array, min_val)\n", + "\n", + " return array\n", + "\n", + "\n", + "kinematic_states_steer_scaling_0th = load_kinematic_states(\n", + " ControlType.pp_eight, ChangeParam.steer_scaling, 0\n", + ")\n", + "\n", + "calc_scalar_indexes(\n", + " kinematic_states=kinematic_states_steer_scaling_0th,\n", + " fst=\"speed\",\n", + " snd=\"acc\",\n", + " fst_range=[0.0, 12.0],\n", + " snd_range=[-1.5, 1.5],\n", + " bandwidths= [ScottCoef(i) for i in np.arange(0.5, 5.5, 0.5)],\n", + " shape=lambda x, y: Shape.ellipse(\n", + " Shape.ELLIPSE_CENTER, Shape.ELLIPSE_WIDTH, Shape.ELLIPSE_HEIGHT, x, y\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def correlation_between_scalar_indexes_and_lateral_errors(\n", + " load_dir_list: list[str],\n", + " fst: Literal[\"speed\", \"acc\", \"steer\"],\n", + " snd: Literal[\"speed\", \"acc\", \"steer\"],\n", + " fst_range: list[float],\n", + " snd_range: list[float],\n", + " bandwidths: list[float] | list[ScottCoef],\n", + " shape: Callable[..., bool] = Shape.trivial,\n", + ") -> np.ndarray:\n", + " accumulation = np.array([])\n", + " for load_dir in load_dir_list:\n", + " # The one that was additionally learned is named \"trained\"\n", + " kinematic_states, result, trained_result = load_kinematic_states_and_result(load_dir)\n", + " scalar_indexes = calc_scalar_indexes(\n", + " kinematic_states=kinematic_states,\n", + " fst=fst,\n", + " snd=snd,\n", + " fst_range=fst_range,\n", + " snd_range=snd_range,\n", + " bandwidths=bandwidths,\n", + " shape=shape,\n", + " )\n", + "\n", + " # cSpell:ignore corcoef #\n", + " # Add lateral errors to the end of the vector of scalar indexes for calculating the correlation coefficient with numpy.corcoef\n", + " scalar_indexes = np.append(arr=scalar_indexes, values=[result, trained_result])\n", + "\n", + " if accumulation.size == 0:\n", + " accumulation = accumulation.reshape(scalar_indexes.shape[0], -1)\n", + " accumulation = np.hstack((accumulation, scalar_indexes.reshape(-1,1)))\n", + "\n", + " return np.corrcoef(accumulation)[-1,:-2]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "def list_of_kde_and_error(\n", + " load_dir_list: list[str],\n", + " fst: Literal[\"speed\", \"acc\", \"steer\"],\n", + " snd: Literal[\"speed\", \"acc\", \"steer\"],\n", + " fst_range: list[float],\n", + " snd_range: list[float],\n", + " bandwidth: float | ScottCoef,\n", + " shape: Callable[..., bool] = Shape.trivial,\n", + ") -> np.ndarray:\n", + " accumulation = np.array([])\n", + " for load_dir in load_dir_list:\n", + " # The one that was additionally learned is named \"trained\"\n", + " kinematic_states, result, trained_result = load_kinematic_states_and_result(\n", + " load_dir\n", + " )\n", + " min_point, min_val = calc_minimum_density_point(\n", + " kinematic_states=kinematic_states,\n", + " fst=fst,\n", + " snd=snd,\n", + " fst_range=fst_range,\n", + " snd_range=snd_range,\n", + " shape=shape,\n", + " bandwidth=bandwidth,\n", + " )\n", + " accumulation = np.append(accumulation, [trained_result, min_val])\n", + " return accumulation.reshape(-1, 2)\n", + "\n", + "def plot_scatter_acc(bandwidth : float | ScottCoef) -> None:\n", + " data = list_of_kde_and_error (\n", + " load_dir_list=[\n", + " f\"test_param_search_{i}_test_vehicle/test_pure_pursuit_figure_eight_sim_test_vehicle_3th\"\n", + " for i in range(5, 35)\n", + " ],\n", + " fst=\"speed\",\n", + " snd=\"acc\",\n", + " fst_range=[0.0, 12.0],\n", + " snd_range=[-1.5, 1.5],\n", + " bandwidth=bandwidth,\n", + " shape=lambda x, y: Shape.ellipse(\n", + " Shape.ELLIPSE_CENTER, Shape.ELLIPSE_WIDTH, Shape.ELLIPSE_HEIGHT, x, y\n", + " ),\n", + " )\n", + " plt.scatter(data[:, 0], data[:, 1])\n", + "\n", + " plt.xlabel('Trained lateral error')\n", + " plt.ylabel('minimum value of KDE')\n", + "\n", + " plt.title('Kernel density and lateral error')\n", + "\n", + " plt.show()\n", + "\n", + "plot_scatter_acc(0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_scatter_steer(bandwidth : float | ScottCoef) -> None:\n", + " coef = 4.76/2.79\n", + " POINTS = np.array(\n", + " [\n", + " [1.0+0.3*5, coef * (0.15)],\n", + " [4.0, coef * (0.15)],\n", + " [11.0, 0.0],\n", + " [1.0+0.3*5, coef * -(0.15)],\n", + " [4, coef * -(0.15)],\n", + " ]\n", + " )\n", + " data = list_of_kde_and_error (\n", + " load_dir_list=[\n", + " f\"test_param_search_{i}_test_vehicle/test_pure_pursuit_figure_eight_sim_test_vehicle_2th\"\n", + " for i in range(5, 35)\n", + " ],\n", + " fst=\"speed\",\n", + " snd=\"steer\",\n", + " fst_range=[0.0, 12.0],\n", + " snd_range=[-0.3, 0.3],\n", + " bandwidth=bandwidth,\n", + " shape=lambda x, y: Shape.convex_hull(POINTS, x, y),\n", + " )\n", + " plt.scatter(data[:, 0], data[:, 1])\n", + "\n", + " plt.xlabel('Trained lateral error')\n", + " plt.ylabel('minimum value of KDE')\n", + "\n", + " plt.title('Kernel density and lateral error')\n", + "\n", + " plt.show()\n", + "\n", + "plot_scatter_steer(0.3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "correlation_between_scalar_indexes_and_lateral_errors(\n", + " load_dir_list=[\n", + " f\"test_param_search_{i}_test_vehicle/test_pure_pursuit_figure_eight_sim_test_vehicle_3th\" for i in range(5, 35)\n", + " ],\n", + " fst=\"speed\",\n", + " snd=\"acc\",\n", + " fst_range=[0.0, 12.0],\n", + " snd_range=[-1.5, 1.5],\n", + " bandwidths=np.arange(0.5, 5.5, 0.5),\n", + " shape=lambda x, y: Shape.ellipse(\n", + " Shape.ELLIPSE_CENTER, Shape.ELLIPSE_WIDTH, Shape.ELLIPSE_HEIGHT, x, y\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(correlation_between_scalar_indexes_and_lateral_errors(\n", + " load_dir_list=[\n", + " f\"test_param_search_{i}_test_vehicle/test_pure_pursuit_figure_eight_sim_test_vehicle_3th\" for i in range(5, 35)\n", + " ],\n", + " fst=\"speed\",\n", + " snd=\"acc\",\n", + " fst_range=[0.0, 12.0],\n", + " snd_range=[-1.5, 1.5],\n", + " bandwidths=np.arange(0.1, 1.1, 0.1),\n", + " shape=lambda x, y: Shape.ellipse(\n", + " Shape.ELLIPSE_CENTER, Shape.ELLIPSE_WIDTH, Shape.ELLIPSE_HEIGHT, x, y\n", + " ),\n", + "))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(correlation_between_scalar_indexes_and_lateral_errors(\n", + " load_dir_list=[\n", + " f\"test_param_search_{i}_test_vehicle/test_pure_pursuit_figure_eight_sim_test_vehicle_2th\" for i in range(5, 35)\n", + " ],\n", + " fst=\"speed\",\n", + " snd=\"acc\",\n", + " fst_range=[0.0, 12.0],\n", + " snd_range=[-1.5, 1.5],\n", + " bandwidths=np.arange(0.1, 1.1, 0.1),\n", + " shape=lambda x, y: Shape.ellipse(\n", + " Shape.ELLIPSE_CENTER, Shape.ELLIPSE_WIDTH, Shape.ELLIPSE_HEIGHT, x, y\n", + " ),\n", + "))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(correlation_between_scalar_indexes_and_lateral_errors(\n", + " load_dir_list=[\n", + " f\"test_param_search_{i}_test_vehicle/test_pure_pursuit_figure_eight_sim_test_vehicle_3th\" for i in range(5, 35)\n", + " ],\n", + " fst=\"speed\",\n", + " snd=\"acc\",\n", + " fst_range=[0.0, 12.0],\n", + " snd_range=[-1.5, 1.5],\n", + " bandwidths=[ScottCoef(i) for i in np.arange(0.5, 5.5, 0.5)],\n", + " shape=lambda x, y: Shape.ellipse(\n", + " Shape.ELLIPSE_CENTER, Shape.ELLIPSE_WIDTH, Shape.ELLIPSE_HEIGHT, x, y\n", + " ),\n", + "))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.arange(1.5, 1.8, 0.1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "coef = 4.76/2.79 #get_estimated_wheel_base_coef(\"test_param_search_0_test_vehicle/test_pure_pursuit_figure_eight_sim_test_vehicle_2th\")\n", + "for i in range(1):\n", + " POINTS = np.array(\n", + " [\n", + " [1.0+0.3*5, coef * (0.15-0.01*i)],\n", + " [4.0, coef * (0.15-0.01*i)],\n", + " [11.0, 0.0],\n", + " [1.0+0.3*5, coef * -(0.15-0.01*i)],\n", + " [4, coef * -(0.15-0.01*i)],\n", + " ]\n", + " )\n", + "\n", + " corrcoef_steer = correlation_between_scalar_indexes_and_lateral_errors(\n", + " load_dir_list=[\n", + " f\"test_param_search_{i}_test_vehicle/test_pure_pursuit_figure_eight_sim_test_vehicle_3th\" for i in range(5, 35)\n", + " ],\n", + " fst=\"speed\",\n", + " snd=\"steer\",\n", + " fst_range=[0.0, 12.0],\n", + " snd_range=[-0.3, 0.3],\n", + " bandwidths=np.arange(0.1, 1.1, 0.1),\n", + " shape=lambda x, y: Shape.convex_hull(POINTS, x, y),\n", + " )\n", + " print(i,corrcoef_steer)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "coef = 4.76/2.79 #get_estimated_wheel_base_coef(\"test_param_search_0_test_vehicle/test_pure_pursuit_figure_eight_sim_test_vehicle_2th\")\n", + "for i in range(1):\n", + " POINTS = np.array(\n", + " [\n", + " [1.0+0.3*5, coef * (0.15-0.01*i)],\n", + " [4.0, coef * (0.15-0.01*i)],\n", + " [11.0, 0.0],\n", + " [1.0+0.3*5, coef * -(0.15-0.01*i)],\n", + " [4, coef * -(0.15-0.01*i)],\n", + " ]\n", + " )\n", + "\n", + " corrcoef_steer = correlation_between_scalar_indexes_and_lateral_errors(\n", + " load_dir_list=[\n", + " f\"test_param_search_{i}_test_vehicle/test_pure_pursuit_figure_eight_sim_test_vehicle_2th\" for i in range(5, 35)\n", + " ],\n", + " fst=\"speed\",\n", + " snd=\"steer\",\n", + " fst_range=[0.0, 12.0],\n", + " snd_range=[-0.3, 0.3],\n", + " bandwidths=np.arange(0.1, 1.1, 0.1),\n", + " shape=lambda x, y: Shape.convex_hull(POINTS, x, y),\n", + " )\n", + " print(i,corrcoef_steer)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from density_estimation import visualize_speed_acc, visualize_speed_steer, ScottCoef\n", + "\n", + "# cSpell:ignore nrows ncols #\n", + "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(18, 12), tight_layout=True)\n", + "\n", + "ax1 : plt.Axes = axes[0]\n", + "ax2 : plt.Axes = axes[1]\n", + "\n", + "fig, ax1 = visualize_speed_acc(fig=fig, ax=ax1, kinematic_states=kinematic_states_steer_scaling_0th, bandwidth=ScottCoef(0.6))\n", + "ax1.plot()\n", + "\n", + "fig, ax2 = visualize_speed_steer(fig=fig, ax=ax2, kinematic_states=kinematic_states_steer_scaling_0th, bandwidth=ScottCoef(1.3))\n", + "ax2.plot()\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from density_estimation import KinematicStates\n", + "\n", + "kinematic_states_steer_scaling_0th.plot(\"speed\", \"acc\", \"steer\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from density_estimation import Shape\n", + "\n", + "fig = kinematic_states_steer_scaling_0th.plot(\"speed\", \"acc\")\n", + "print(type(fig))\n", + "\n", + "ellipse_center = [6, 0]\n", + "ellipse_width = 6\n", + "ellipse_height = 2\n", + "\n", + "# cSpell:ignore yref showarrow arrowcolor #\n", + "fig.add_shape(\n", + " type=\"circle\",\n", + " xref=\"x\", yref=\"y\",\n", + " x0=ellipse_center[0] - ellipse_width / 2,\n", + " y0=ellipse_center[1] - ellipse_height / 2,\n", + " x1=ellipse_center[0] + ellipse_width / 2,\n", + " y1=ellipse_center[1] + ellipse_height / 2,\n", + " line=dict(color=\"RoyalBlue\"),\n", + " fillcolor=\"LightSkyBlue\",\n", + " opacity=0.5,\n", + " layer=\"below\"\n", + ")\n", + "\n", + "min_element, min_val = calc_minimum_density_point(\n", + " kinematic_states=kinematic_states_steer_scaling_0th,\n", + " bandwidth=0.50,\n", + " fst=\"speed\",\n", + " snd=\"acc\",\n", + " fst_range=[4, 8],\n", + " snd_range=[-1.0, 1.0],\n", + " shape=lambda x, y : Shape.ellipse(ellipse_center, ellipse_width, ellipse_height, x, y),\n", + ")\n", + "\n", + "fig.add_annotation(\n", + " x=min_element[0],\n", + " y=min_element[1],\n", + " text=f\"min val: {round(min_val, 4)} at {min_element}\",\n", + " showarrow=True,\n", + " arrowhead=1,\n", + " font=dict(\n", + " family=\"Arial, sans-serif\",\n", + " size=12,\n", + " color=\"blue\"\n", + " ),\n", + " align=\"center\",\n", + " arrowcolor=\"blue\",\n", + ")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kinematic_states_steer_scaling_0th.plot(\"acc\", \"steer\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from density_estimation import plot_kernel_density\n", + "\n", + "kinematic_states_steer_scaling_0th.plot(\"speed\", \"acc\").show()\n", + "plot_kernel_density(kinematic_states_steer_scaling_0th, \"speed\", \"acc\", [2, 10], [-2, 2])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "calc_minimum_density_point(\n", + " kinematic_states=kinematic_states_steer_scaling_0th,\n", + " bandwidth=0.01,\n", + " fst=\"speed\",\n", + " snd=\"steer\",\n", + " fst_range=[2, 10],\n", + " snd_range=[-1.0, 1.0],\n", + " shape=lambda x, y : Shape.triangle([4, -0.2], [4, 0.2], [10, 0], x, y),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kinematic_states_steer_scaling_0th.plot(\"speed\", \"steer\").show()\n", + "plot_kernel_density(\n", + " kinematic_states=kinematic_states_steer_scaling_0th,\n", + " bandwidth=0.15,\n", + " fst=\"speed\",\n", + " snd=\"steer\",\n", + " fst_range=[2, 10],\n", + " snd_range=[-0.4, 0.2],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "kinematic_states_vehicle = load_kinematic_states(\n", + " ControlType.pp_eight, ChangeParam.vehicle_type, 0\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kinematic_states_vehicle.plot(\"speed\", \"acc\", \"steer\").show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kinematic_states_vehicle.plot(\"speed\", \"steer\").show()\n", + "plot_kernel_density(\n", + " kinematic_states=kinematic_states_vehicle,\n", + " fst=\"speed\",\n", + " snd=\"steer\",\n", + " fst_range=[2, 10],\n", + " snd_range=[-0.3, 0.4],\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/density_estimation.py b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/density_estimation.py new file mode 100644 index 0000000000000..0e5f58f352f5d --- /dev/null +++ b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/density_estimation.py @@ -0,0 +1,468 @@ +# Copyright 2024 Proxima Technology Inc, TIER IV +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Callable +from typing import Literal + +from assets import ChangeParam # type: ignore +from assets import ControlType +from assets import test_dir_name +from matplotlib.patches import Ellipse # type: ignore +import matplotlib.pyplot as plt # type: ignore +import numpy as np +import plotly.express as px # type: ignore +import plotly.graph_objs as go # type: ignore +from scipy.spatial import ConvexHull # type: ignore +from scipy.spatial import Delaunay +from sklearn.neighbors import KernelDensity # type: ignore +from sklearn.preprocessing import StandardScaler # type: ignore + + +class KinematicStates: + """The class for kinematic states, which includes speed, acceleration, and steering angle.""" + + def __init__(self, speed: np.ndarray, acc: np.ndarray, steer: np.ndarray): + self.speed = speed + self.acc = acc + self.steer = steer + + def to_ndarray(self, field_list: list[str] = ["speed", "acc", "steer"]) -> np.ndarray: + return np.stack( + arrays=[getattr(self, field) for field in field_list], + axis=1, + ) + + def plot( + self, + x_axis: Literal["speed", "acc", "steer"], + y_axis: Literal["speed", "acc", "steer"], + z_axis: Literal["speed", "acc", "steer"] | None = None, + ) -> go.Figure: + """Plot interactive scatter image of kinematic states in 2D or 3D.""" + if z_axis is None: + fig = px.scatter(x=getattr(self, x_axis), y=getattr(self, y_axis)) + fig.update_layout(xaxis_title=x_axis, yaxis_title=y_axis) + else: + fig = px.scatter_3d( + x=getattr(self, x_axis), + y=getattr(self, y_axis), + z=getattr(self, z_axis), + ) + fig.update_layout( + scene={"xaxis_title": x_axis, "yaxis_title": y_axis, "zaxis_title": z_axis} + ) + fig.update_traces(marker={"size": 1}) + return fig + + +def load_kinematic_states( + control_type: ControlType | None = None, + change_param: ChangeParam | None = None, + index: int | None = None, + load_dir: str | None = None, + verbose: bool = False, +) -> KinematicStates: + """Load kinematic states from the specified directory.""" + if load_dir is None: + load_dir = test_dir_name( + control_type=control_type, + change_param=change_param, + index=index, + ) + if verbose: + print(f"loaded directory: {load_dir}") + + # cSpell:ignore usecols + observed_speed_array = np.loadtxt( + fname=load_dir + "/kinematic_state.csv", delimiter=",", usecols=47 + ) + observed_acc_array = np.loadtxt( + fname=load_dir + "/acceleration.csv", + delimiter=",", + usecols=3, + ) + observed_steer_array = np.loadtxt( + fname=load_dir + "/steering_status.csv", + delimiter=",", + usecols=2, + ) + kinematic_states = KinematicStates( + speed=observed_speed_array, acc=observed_acc_array, steer=observed_steer_array + ) + return kinematic_states + + +class Shape: + """The class for shape functions, just a namespace.""" + + # config for ellipse + ELLIPSE_CENTER = [6.0, 0.0] + + ELLIPSE_WIDTH = 10.0 + + ELLIPSE_HEIGHT = 2.0 + + @staticmethod + def trivial(_x: float, _y: float) -> bool: + """Trivial shape function.""" + return True + + @staticmethod + def ellipse(center: list[float], width: float, hight: float, x: float, y: float) -> bool: + """Represent the interior of an ellipse. + + Args: + center: the coordinates of the center of the ellipse + width: the length of the ellipse along the x-axis + height: the length of the ellipse along the y-axis + """ + return (x - center[0]) ** 2 / (width / 2) ** 2 + (y - center[1]) ** 2 / ( + hight / 2 + ) ** 2 <= 1 + + @staticmethod + def triangle(A: list[float], B: list[float], C: list[float], x: float, y: float) -> bool: + """Represent the interior of a triangle. + + Determine if the given point is inside the triangle formed by connecting the given three points. + """ + + def sign(p, q, r): + """Check relation of `p`, `q`, `r`.""" + return (p[0] - r[0]) * (q[1] - r[1]) - (q[0] - r[0]) * (p[1] - r[1]) + + P = [x, y] + b1 = sign(P, A, B) < 0 + b2 = sign(P, B, C) < 0 + b3 = sign(P, C, A) < 0 + + # P is inside triangle ABC if all sides are on the same side + return b1 == b2 == b3 + + @staticmethod + def convex_hull(points: np.ndarray, x: float, y: float) -> bool: + """Determine if a point is within the convex hull. + + Parameters: + points (ndarray): An array of points forming the convex hull (Nx2). + x (float): The x-coordinate of the point to be checked. + y (float): The y-coordinate of the point to be checked. + + Returns: + bool: True if the point is within the convex hull, False otherwise. + """ + hull = ConvexHull(points) + delaunay = Delaunay(points[hull.vertices]) + return delaunay.find_simplex([x, y]) >= 0 + + +def get_search_points( + n_points: int, + x_range: list[float], + y_range: list[float], + z_range: list[float] = [], +): + """Help for density estimation.""" + x = np.linspace(x_range[0], x_range[1], num=n_points) + y = np.linspace(y_range[0], y_range[1], num=n_points) + if z_range != []: + z = np.linspace(z_range[0], z_range[1], num=n_points) + return np.array(np.meshgrid(x, y, z)).T.reshape(-1, 3) + return np.array(np.meshgrid(x, y)).T.reshape(-1, 2) + + +class ScottCoef: + """Multiple coefficient for bandwidth of Scott's algorithm.""" + + def __init__(self, val: float): + self.val = val + + +# cSpell:ignore silverman +def kde_score_func( + kinematic_states: KinematicStates, + fst: Literal["speed", "acc", "steer"], + snd: Literal["speed", "acc", "steer"], + bandwidth: float | Literal["scott", "silverman"] | ScottCoef = "scott", + verbose: bool = False, +) -> Callable[..., float]: + """Return the function which is a variation of `KernelDensity.score_samples` including step of standardization.""" + # standardize the kinematic states + scaler = StandardScaler() + scaler.fit(kinematic_states.to_ndarray([fst, snd])) + standardized_2d_kinematic_states = scaler.transform(kinematic_states.to_ndarray([fst, snd])) + + if not isinstance(bandwidth, ScottCoef): + # perform density estimation with standardization + if verbose: + print(f"info: bandwidth is set to {bandwidth}") + standardized_kde = KernelDensity(kernel="gaussian", bandwidth=bandwidth).fit( + standardized_2d_kinematic_states + ) + else: + scott_kde = KernelDensity(kernel="gaussian", bandwidth="scott").fit( + standardized_2d_kinematic_states + ) + scott_bandwidth = scott_kde.bandwidth_ + if verbose: + print(f"info: bandwidth is set to {scott_bandwidth * bandwidth.val}") + standardized_kde = KernelDensity( + kernel="gaussian", bandwidth=scott_bandwidth * bandwidth.val + ).fit(standardized_2d_kinematic_states) + + return lambda arr: standardized_kde.score_samples(scaler.transform(arr)) + + +def plot_kernel_density( + kinematic_states: KinematicStates, + fst: Literal["speed", "acc", "steer"], + snd: Literal["speed", "acc", "steer"], + fst_range: list[float], + snd_range: list[float], + point_number: int = 30, + bandwidth: float | Literal["scott", "silverman"] | ScottCoef = "scott", +) -> None: + kde_score = kde_score_func( + kinematic_states=kinematic_states, + fst=fst, + snd=snd, + bandwidth=bandwidth, + ) + grids = get_search_points(point_number, fst_range, snd_range) + kernel_densities = kde_score(grids) + Z = np.exp(kernel_densities).reshape(point_number, point_number).T + + x_range = np.linspace(fst_range[0], fst_range[1], point_number) + y_range = np.linspace(snd_range[0], snd_range[1], point_number) + fig = go.Figure(data=[go.Surface(z=Z, x=x_range, y=y_range)]) + + # cSpell:ignore zaxis + fig.update_layout( + title="3D Surface Plot of Kernel Density", + scene={ + "xaxis": {"title": fst}, + "yaxis": {"title": snd}, + "zaxis": {"title": "kernel density"}, + }, + ) + fig.show() + + +def calc_minimum_density_point( + kinematic_states: KinematicStates, + fst: Literal["speed", "acc", "steer"], + snd: Literal["speed", "acc", "steer"], + fst_range: list[float], + snd_range: list[float], + shape: Callable[..., bool] = Shape.trivial, + point_number: int = 30, + bandwidth: float | Literal["scott", "silverman"] | ScottCoef = "scott", +) -> tuple[np.ndarray, float]: + kde_score = kde_score_func( + kinematic_states=kinematic_states, + fst=fst, + snd=snd, + bandwidth=bandwidth, + ) + grids = get_search_points(point_number, fst_range, snd_range) + filtered_grids = np.array([point for point in grids if shape(point[0], point[1])]) + kernel_densities = kde_score(filtered_grids) + + min_value: float = np.exp(kernel_densities).min() + min_index = kernel_densities.argmin() # type: ignore + min_element = filtered_grids[min_index] + return min_element.reshape(1, -1)[0], min_value + + +def kde_data( + kinematic_states: KinematicStates, + fst: Literal["speed", "acc", "steer"], + snd: Literal["speed", "acc", "steer"], + fst_range: list[float], + snd_range: list[float], + n_points: int = 100, + bandwidth: float | Literal["scott", "silverman"] | ScottCoef = "scott", +) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + kde_score = kde_score_func( + kinematic_states=kinematic_states, + fst=fst, + snd=snd, + bandwidth=bandwidth, + ) + + x = np.linspace(fst_range[0], fst_range[1], n_points) + y = np.linspace(snd_range[0], snd_range[1], n_points) + X, Y = np.meshgrid(x, y) + + grids = np.column_stack((X.flatten(), Y.flatten())).reshape((n_points**2, 2)) + Z = np.exp(kde_score(grids)).reshape(n_points, n_points) + return X, Y, Z + + +def visualize_speed_acc( + fig: plt.Figure, + ax: plt.Axes, + kinematic_states: KinematicStates, + bandwidth: float | Literal["scott", "silverman"] | ScottCoef = "scott", +) -> tuple[plt.Figure, plt.Axes]: + """Visualizes the speed and acceleration of kinematic states using kernel density estimation. + + This function plots a heatmap of the kernel density estimation of speed and acceleration + values from the given kinematic states. It also overlays an ellipse and marks the minimum + density point within the elliptical region. + + Parameters: + fig (plt.Figure): The figure object to be used for plotting. + ax (plt.Axes): The axes object to be used for plotting. + kinematic_states (KinematicStates): The kinematic states containing speed and acceleration data. + + Returns: + tuple[plt.Figure, plt.Axes]: The modified figure and axes objects. + """ + # config for kernel density estimation + fst: Literal["speed", "acc", "steer"] = "speed" + snd: Literal["speed", "acc", "steer"] = "acc" + fst_range = [0.0, 12.0] + snd_range = [-1.5, 1.5] + + ellipse: Ellipse = Ellipse( + xy=Shape.ELLIPSE_CENTER, + width=Shape.ELLIPSE_WIDTH, + height=Shape.ELLIPSE_HEIGHT, + angle=0, + edgecolor="orange", + fc="None", + lw=2, + ) + + # kernel density estimation + X, Y, Z = kde_data( + kinematic_states=kinematic_states, + fst=fst, + snd=snd, + fst_range=fst_range, + snd_range=snd_range, + n_points=100, + bandwidth=bandwidth, + ) + + # plot heatmap + # cSpell:ignore pcolormesh viridis + mesh_acc = ax.pcolormesh(X, Y, Z, cmap="viridis", shading="auto") + fig.colorbar(mesh_acc, ax=ax) + + # plot ellipse + ax.add_patch(ellipse) + + # Find the minimum value of kernel density in the elliptic region + min_element, min_val = calc_minimum_density_point( + kinematic_states=kinematic_states, + fst=fst, + snd=snd, + fst_range=fst_range, + snd_range=snd_range, + shape=lambda x, y: Shape.ellipse( + Shape.ELLIPSE_CENTER, Shape.ELLIPSE_WIDTH, Shape.ELLIPSE_HEIGHT, x, y + ), + bandwidth=bandwidth, + ) + rounded_min_val = round(min_val, 4) + rounded_min_element = [round(min_element[0], 4), round(min_element[1], 4)] + + # plot the minimum point as a white point + ax.plot(min_element[0], min_element[1], marker="o", color="white", markersize=2) + + ax.set_xlabel(fst, fontsize=12) + ax.set_ylabel(snd, fontsize=12) + ax.set_title(f"kernel density: min_density={rounded_min_val} at {rounded_min_element}") + + return fig, ax + + +def visualize_speed_steer( + fig: plt.Figure, + ax: plt.Axes, + kinematic_states: KinematicStates, + bandwidth: float | Literal["scott", "silverman"] | ScottCoef = "scott", +) -> tuple[plt.Figure, plt.Axes]: + """Visualizes the speed and steering angle of kinematic states using kernel density estimation. + + This function plots a heatmap of the kernel density estimation of speed and steering angle + values from the given kinematic states. It also overlays the convex hull and marks the minimum + density point within the convex hull. + + Parameters: + fig (plt.Figure): The figure object to be used for plotting. + ax (plt.Axes): The axes object to be used for plotting. + kinematic_states (KinematicStates): The kinematic states containing speed and steering angle data. + + Returns: + tuple[plt.Figure, plt.Axes]: The modified figure and axes objects. + """ + # vertexes of convex hull + POINTS = np.array( + [ + [1.0, 0.15], + [4.0, 0.15], + [11.0, 0.0], + [1.0, -0.15], + [4, -0.15], + ] + ) + + fst: Literal["speed", "acc", "steer"] = "speed" + snd: Literal["speed", "acc", "steer"] = "steer" + fst_range = [0.0, 12.0] + snd_range = [-0.3, 0.3] + + # calculate convex hull + hull = ConvexHull(POINTS) + + X_STEER, Y_STEER, Z_STEER = kde_data( + kinematic_states=kinematic_states, + fst=fst, + snd=snd, + fst_range=fst_range, + snd_range=snd_range, + n_points=200, + bandwidth=bandwidth, + ) + + # plot heatmap + mesh_steer = ax.pcolormesh(X_STEER, Y_STEER, Z_STEER, cmap="viridis", shading="auto") + fig.colorbar(mesh_steer, ax=ax) + + # plot edges of the convex hull + for simplex in hull.simplices: + ax.plot(POINTS[simplex, 0], POINTS[simplex, 1], color="orange") + + # calculate the minimum value of kernel density in the convex hull + min_element, min_val = calc_minimum_density_point( + kinematic_states=kinematic_states, + fst=fst, + snd=snd, + fst_range=fst_range, + snd_range=snd_range, + shape=lambda x, y: Shape.convex_hull(POINTS, x, y), + bandwidth=bandwidth, + ) + rounded_min_element = [round(min_element[0], 4), round(min_element[1], 4)] + + # plot the minimum point as a white point + ax.plot(min_element[0], min_element[1], marker="o", color="white", markersize=2) + + ax.set_xlabel(fst, fontsize=12) + ax.set_ylabel(snd, fontsize=12) + ax.set_title(f"kernel density: min_density={min_val: .4f} at {rounded_min_element}") + return fig, ax diff --git a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/get_estimated_wheel_base.py b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/get_estimated_wheel_base.py new file mode 100644 index 0000000000000..752740787d8ab --- /dev/null +++ b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/get_estimated_wheel_base.py @@ -0,0 +1,23 @@ +# Copyright 2024 Proxima Technology Inc, TIER IV +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from autoware_smart_mpc_trajectory_follower.scripts import drive_functions +import numpy as np + + +def get_estimated_wheel_base_coef(dir_name: str) -> float: + A = np.load(dir_name + "/polynomial_reg_info.npz")["A"] + estimated_wheel_base = 1 / (1 / drive_functions.L + A[3, 11]) + print("estimated wheel base:", estimated_wheel_base) + return estimated_wheel_base / drive_functions.L diff --git a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/plot_auto_test_result.ipynb b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/plot_auto_test_result.ipynb index dd4dc9ae25cb2..b876a635ef94a 100644 --- a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/plot_auto_test_result.ipynb +++ b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/plot_auto_test_result.ipynb @@ -2,16 +2,18 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "e78db57e-3218-4515-9ae5-4458ea071a1c", "metadata": {}, "outputs": [], "source": [ - "# cspell: ignore kernelspec ipykernel codemirror ipython nbconvert pygments nbformat \n", + "# cspell: ignore kernelspec ipykernel codemirror ipython nbconvert pygments nbformat #\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "import pandas as pd" + "import pandas as pd\n", + "import os\n", + "import json" ] }, { @@ -21,6 +23,7 @@ "metadata": {}, "outputs": [], "source": [ + "from autoware_smart_mpc_trajectory_follower.scripts import drive_functions\n", "import python_simulator" ] }, @@ -33,31 +36,28 @@ "source": [ "# header of auto_test_result_*.csv\n", "header = [\n", - " \"param_name\", # 0\n", - " \"param_val\", # 1\n", - " \"max_abs_lateral_error\", # 2\n", - " \"max_abs_vel_error\", # 3\n", - " # \"max_abs_yaw_error\", # 4\n", - " # \"max_abs_acc_error\", # 5\n", - " # \"max_abs_steer_error\" # 6\n", + " \"max_abs_lateral_error\", \n", + " \"straight_line_abs_max_lateral_error\",\n", + " \"max_abs_vel_error\", \n", "]\n", "\n", "\n", "plot_param_list = [\n", " # [label, header_index, unit_for_plot, nominal_value]\n", - " [\"steer_bias\", 2, \"[deg]\", 0.0] ,\n", - " [\"steer_rate_lim\", 2, \"[rad/s]\", python_simulator.steer_rate_lim],\n", - " [\"vel_rate_lim\", 3, \"[m/ss]\", python_simulator.vel_rate_lim],\n", - " [\"steer_dead_band\", 2, \"[rad]\", python_simulator.steer_dead_band],\n", - " [\"wheel_base\",2, \"[m]\", python_simulator.L],\n", + " [\"steer_bias\", 0, \"[deg]\", 0.0] ,\n", + " #[\"steer_rate_lim\", 0, \"[rad/s]\", python_simulator.steer_rate_lim],\n", + " #[\"vel_rate_lim\", 2, \"[m/ss]\", python_simulator.vel_rate_lim],\n", + " [\"steer_dead_band\", 0, \"[rad]\", python_simulator.steer_dead_band],\n", + " [\"wheel_base\",0, \"[m]\", drive_functions.L],\n", " # [\"adaptive_gear_ratio_coef\", 2,\"[-]\", None],\n", - " [\"acc_time_delay\",3, \"[s]\", python_simulator.acc_time_delay],\n", - " [\"steer_time_delay\",2, \"[s]\", python_simulator.steer_time_delay],\n", - " [\"acc_time_constant\",3, \"[s]\", python_simulator.acc_time_constant],\n", - " [\"steer_time_constant\",2, \"[s]\", python_simulator.steer_time_constant],\n", - " [\"accel_map_scale\",3, \"[-]\", None],\n", - " [\"acc_scaling\",3, \"[-]\", 1],\n", - " [\"steer_scaling\",2, \"[-]\", 1],\n", + " [\"acc_time_delay\",2, \"[s]\", drive_functions.acc_time_delay],\n", + " [\"steer_time_delay\",0, \"[s]\", drive_functions.steer_time_delay],\n", + " [\"acc_time_constant\",2, \"[s]\", drive_functions.acc_time_constant],\n", + " [\"steer_time_constant\",0, \"[s]\", drive_functions.steer_time_constant],\n", + " [\"accel_map_scale\",2, \"[-]\", None],\n", + " [\"acc_scaling\",2, \"[-]\", 1],\n", + " [\"steer_scaling\",0, \"[-]\", 1],\n", + " [\"vehicle_type\", 0, \"[-]\",0],\n", "]\n", "print(plot_param_list)\n", "print(\"len(plot_param_list)\",len(plot_param_list))\n" @@ -72,85 +72,78 @@ }, "outputs": [], "source": [ + "mem_diff = True\n", + "root_dir = \"auto_test\"\n", + "\n", + "def get_dir_name(param_name, index, intermediate):\n", + " dir_name = root_dir + \"/test_pp_aided_sim_\"\n", + " if not intermediate:\n", + " dir_name += \"trained_\"\n", + " \n", + " dir_name += param_name + \"_\" + str(index) + \"th\"\n", + " if mem_diff:\n", + " dir_name += \"_mem_diff\"\n", + " return dir_name\n", "fig = plt.figure(figsize=(30, 18), tight_layout=True)\n", "\n", "ax_list = []\n", "for i in range(len(plot_param_list)):\n", - " ax_list.append(plt.subplot(4, 3, i+1))\n", + " ax_list.append(plt.subplot(4, 4, i+1))\n", "\n", - "# ff_data_result_csv = \"auto_test_result_final_model_control_trained_with_data_collected_by_ff_control.csv\"\n", - "nominal_data_result_csv = \"auto_test_result_final_model_control_trained_with_data_collected_by_nominal_control.csv\"\n", "\n", "df_info_list = [ \n", - " {\"file_name\": \"nominal/auto_test_result_nominal_model_control.csv\", \n", - " \"label_name\": \"nominal\"},\n", - "\n", - " # {\"file_name\": \"poly(selected) (data using FF)/\"+ff_data_result_csv, \n", - " # \"label_name\" : \"poly(selected) (data using FF)\"}, \n", - " # # {\"file_name\": \"poly(full) (data using FF)/\"+ff_data_result_csv, \n", - " # # \"label_name\" : \"poly(full) (data using FF)\"}, \n", - " # {\"file_name\": \"poly(selected)+NN (data using FF)/\"+ff_data_result_csv, \n", - " # \"label_name\" : \"poly(selected)+NN (data using FF)\"}, \n", - " # # {\"file_name\": \"poly(full)+NN (data using FF)/\"ff_data_result_csv, \n", - " # # \"label_name\" : \"poly(full)+NN (data using FF)\"},\n", - " # {\"file_name\": \"NN (data using FF)/\"+ff_data_result_csv, \n", - " # \"label_name\" : \"NN (data using FF)\"},\n", - " \n", - " {\"file_name\": \"poly(selected) (nominal control data)/\"+nominal_data_result_csv, \n", - " \"label_name\" : \"poly(selected) (nominal control data)\"}, \n", - " # {\"file_name\": \"poly(full) (nominal control data)/\"+nominal_data_result_csv, \n", - " # \"label_name\" : \"poly(full) (nominal control data)\"},\n", - " {\"file_name\": \"poly(selected)+NN (nominal control data)/\"+nominal_data_result_csv, \n", - " \"label_name\" : \"poly(selected)+NN (nominal control data)\"}, \n", - " # {\"file_name\": \"poly(full)+NN (nominal control data)/\"+nominal_data_result_csv, \n", - " # \"label_name\" : \"poly(full)+NN (nominal control data)\"},\n", - " {\"file_name\": \"NN (nominal control data)/\"+nominal_data_result_csv, \n", - " \"label_name\" : \"NN (nominal control data)\"}, \n", + " { \n", + " \"intermediate\" : False, \n", + " \"label_name\" : \"poly(selected)+NN (final model)\"},\n", + " { \n", + " \"intermediate\" : True, \n", + " \"label_name\" : \"poly(selected)+NN (intermediate model)\"},\n", "]\n", "\n", + "plot_intermediate = []\n", + "\n", "cmap = plt.get_cmap(\"tab10\") \n", "for l in range(len(df_info_list)):\n", - " df = pd.read_csv(df_info_list[l][\"file_name\"], header=None,sep=\",\")\n", + " #df = pd.read_csv(df_info_list[l][\"file_name\"], header=None,sep=\",\")\n", " for i in range(len(plot_param_list)):\n", - " index_bool = df[0].values==plot_param_list[i][0]\n", - " if any(index_bool):\n", - " x = df[1].values[index_bool]\n", - " \n", - " if plot_param_list[i][0]==\"adaptive_gear_ratio_coef\":\n", - " x = np.array(range(len(x)))\n", - " x = np.array([float(x[j]) for j in range(len(x))])\n", + " index = 0\n", + " x = []\n", + " y = []\n", + " while os.path.exists(get_dir_name(plot_param_list[i][0],index,df_info_list[l][\"intermediate\"])):\n", + " with open(get_dir_name(plot_param_list[i][0],index,df_info_list[l][\"intermediate\"])+\"/auto_test_performance_result.json\",\"r\") as f:\n", + " data=json.load(f)\n", + " x.append(list(list(data.values())[7].values())[0])\n", + " y.append(list(data.values())[plot_param_list[i][1]])\n", + " index += 1\n", + " if len(x) > 0:\n", + " x = np.array(x)\n", + " y = np.array(y)\n", " if plot_param_list[i][0]==\"steer_bias\":\n", " x *= 180.0/np.pi\n", - " \n", - " \n", - " y = df[plot_param_list[i][1]].values[index_bool]\n", + " if i==0:\n", " ax_list[i].plot(x,y,\"o-\",label=df_info_list[l][\"label_name\"],color=cmap(l))\n", " else:\n", - " pass\n", - " if i==0:\n", - " ax_list[i].plot([0],[0],\"o-\",label=df_info_list[l][\"label_name\"],color=cmap(l))\n", - "\n", + " ax_list[i].plot(x,y,\"o-\",label=df_info_list[l][\"label_name\"],color=cmap(l))\n", "\n", - "for i in range(len(plot_param_list)): \n", - " #ax_list[i].set_title(header[plot_param_list[i][1]]+\" / \"+plot_param_list[i][0], fontsize=16)\n", + "for i in range(len(plot_param_list)):\n", " ax_list[i].set_xlabel(plot_param_list[i][0] + \" \" + plot_param_list[i][2],fontsize=20)\n", " ax_list[i].set_ylabel(header[plot_param_list[i][1]],fontsize=20)\n", " ax_list[i].grid()\n", "\n", " if plot_param_list[i][3] is not None:\n", - " ax_list[i].plot([plot_param_list[i][3],plot_param_list[i][3]],[-0.1,2],\"k--\")\n", - " \n", + " ax_list[i].plot([plot_param_list[i][3],plot_param_list[i][3]],[-0.1,3],\"k--\")\n", + "\n", " # lateral_error\n", " if 2==plot_param_list[i][1]:\n", - " ax_list[i].set_ylim([-0.01,1.0])\n", + " ax_list[i].set_ylim([-0.01,2.5])\n", " # vel_error\n", " if 3==plot_param_list[i][1]:\n", " ax_list[i].set_ylim([-0.01,1.0])\n", "\n", - " # if(y.max()>2.0):\n", - " # ax_list[i].set_ylim([-0.1,2.0])\n", " if i==0:\n", " ax_list[i].legend(fontsize=13)\n", + " if plot_param_list[i][0]==\"vehicle_type\":\n", + " ax_list[i].text(0.5, 0.8, \"0: nominal vehicle\\n1: heavy-weight bus\\n2: light-weight bus\\n3: small vehicle\\n4: small robot\", va='center', ha='center', fontsize=15, transform=ax_list[i].transAxes)\n", "plt.savefig(\"auto_test_result.png\")\n", "plt.show()" ] diff --git a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/pure_pursuit_gain_updater.py b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/pure_pursuit_gain_updater.py new file mode 100644 index 0000000000000..6e6e4c76c6054 --- /dev/null +++ b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/pure_pursuit_gain_updater.py @@ -0,0 +1,198 @@ +# Copyright 2024 Proxima Technology Inc, TIER IV +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import warnings + +from autoware_smart_mpc_trajectory_follower.scripts import drive_functions +import numpy as np +from sklearn import linear_model + +warnings.simplefilter("ignore") + + +class pure_pursuit_gain_updater: + def __init__(self, max_time=10.0, max_input_delay=1.5): + self.vel_queue = [] + self.yaw_queue = [] + self.acc_queue = [] + self.steer_queue = [] + self.acc_input_queue = [] + self.steer_input_queue = [] + self.max_queue_size = round(max_time / drive_functions.ctrl_time_step) + self.max_input_delay_size = round(max_input_delay / drive_functions.ctrl_time_step) + self.A_wheel_base = np.zeros((2, 2)) + self.B_wheel_base = np.zeros(2) + + def state_queue_updater(self, vel, yaw, acc, steer): + self.vel_queue.append(vel) + self.yaw_queue.append(yaw) + self.acc_queue.append(acc) + self.steer_queue.append(steer) + if len(self.vel_queue) > self.max_queue_size: + self.vel_queue.pop(0) + self.yaw_queue.pop(0) + self.acc_queue.pop(0) + self.steer_queue.pop(0) + if len(self.vel_queue) > 1: + numerator = self.vel_queue[-2] * np.tan(self.steer_queue[-2]) + yaw_dot_error = ( + self.yaw_queue[-1] - self.yaw_queue[-2] + ) / drive_functions.ctrl_time_step + yaw_dot_error -= numerator / drive_functions.L + + X_wheel_base = np.array([[1.0, numerator]]) + self.A_wheel_base += X_wheel_base.T @ X_wheel_base + 1e-5 * np.eye(2) + self.B_wheel_base += yaw_dot_error * X_wheel_base[0] + + def input_queue_updater(self, acc_input, steer_input): + self.acc_input_queue.append(acc_input) + self.steer_input_queue.append(steer_input) + if len(self.acc_input_queue) > self.max_queue_size - 1: + self.acc_input_queue.pop(0) + self.steer_input_queue.pop(0) + + def get_acc_gain_scaling(self): + if len(self.vel_queue) < self.max_queue_size: + return 1.0 + acc_array = np.array(self.acc_queue) + acc_input_array = np.array(self.acc_input_queue) + + acc_dot = (acc_array[1:] - acc_array[:-1]) / drive_functions.ctrl_time_step + acc_estimation_error = np.inf + best_acc_coef = np.array( + [ + -drive_functions.ctrl_time_step / drive_functions.acc_time_constant, + drive_functions.ctrl_time_step / drive_functions.acc_time_constant, + ] + ) + for i in range(self.max_input_delay_size): + clf_acc = linear_model.ElasticNet( + fit_intercept=False, alpha=1e-10, l1_ratio=0.5, max_iter=100000 + ) + clf_acc.fit( + np.stack( + ( + acc_array[self.max_input_delay_size - 1 : -1], + acc_input_array[ + self.max_input_delay_size - 1 - i : len(acc_input_array) - i + ], + ), + 1, + ), + acc_dot[self.max_input_delay_size - 1 :], + ) + acc_error = ( + ( + clf_acc.coef_[0] * acc_array[self.max_input_delay_size - 1 : -1] + + clf_acc.coef_[1] + * acc_input_array[self.max_input_delay_size - 1 - i : len(acc_input_array) - i] + - acc_dot[self.max_input_delay_size - 1 :] + ) + ** 2 + ).sum() + + if acc_error < acc_estimation_error and clf_acc.coef_[0] < 0 and clf_acc.coef_[1] > 0: + acc_estimation_error = acc_error + best_acc_coef = clf_acc.coef_ + X_acc = np.stack( + ( + acc_array[self.max_input_delay_size - 1 : -1], + acc_input_array[self.max_input_delay_size - 1 - i : len(acc_input_array) - i], + ), + 1, + ) + X_acc = np.hstack((np.ones((X_acc.shape[0], 1)), X_acc)) + + A = X_acc.T @ X_acc + 1e-15 * X_acc.shape[0] * np.eye(3) + + B = X_acc.T @ acc_dot[self.max_input_delay_size - 1 :] + + coef = np.linalg.solve(A, B) + acc_error = ( + ( + coef[1] * acc_array[self.max_input_delay_size - 1 : -1] + + coef[2] + * acc_input_array[self.max_input_delay_size - 1 - i : len(acc_input_array) - i] + + coef[0] + - acc_dot[self.max_input_delay_size - 1 :] + ) + ** 2 + ).sum() + + if acc_error < acc_estimation_error and coef[1] < 0 and coef[2] > 0: + acc_estimation_error = acc_error + best_acc_coef = coef[[1, 2]] + estimated_acc_scaling = np.clip(-best_acc_coef[1] / best_acc_coef[0], 0.1, 10.0) + + return 1 / estimated_acc_scaling + + def get_steer_gain_scaling(self): + if len(self.vel_queue) < self.max_queue_size: + return 1.0 + + steer_array = np.array(self.steer_queue) + steer_input_array = np.array(self.steer_input_queue) + + estimated_wheel_base_scaling = 1.0 / np.clip( + 1 + np.linalg.solve(self.A_wheel_base, self.B_wheel_base)[1] * drive_functions.L, + 0.1, + 1.0, + ) + + steer_dot = (steer_array[1:] - steer_array[:-1]) / drive_functions.ctrl_time_step + + steer_estimation_error = np.inf + best_steer_coef = np.array( + [ + -drive_functions.ctrl_time_step / drive_functions.steer_time_constant, + drive_functions.ctrl_time_step / drive_functions.steer_time_constant, + ] + ) + for i in range(self.max_input_delay_size): + X_steer = np.stack( + ( + steer_array[self.max_input_delay_size - 1 : -1], + steer_input_array[ + self.max_input_delay_size - 1 - i : len(steer_input_array) - i + ], + ), + 1, + ) + X_steer = np.hstack((np.ones((X_steer.shape[0], 1)), X_steer)) + + A = X_steer.T @ X_steer + 1e-15 * X_steer.shape[0] * np.eye(3) + + B = X_steer.T @ steer_dot[self.max_input_delay_size - 1 :] + + coef = np.linalg.solve(A, B) + steer_error = ( + ( + coef[1] * steer_array[self.max_input_delay_size - 1 : -1] + + coef[2] + * steer_input_array[ + self.max_input_delay_size - 1 - i : len(steer_input_array) - i + ] + + coef[0] + - steer_dot[self.max_input_delay_size - 1 :] + ) + ** 2 + ).sum() + + if steer_error < steer_estimation_error and coef[1] < 0 and coef[2] > 0: + steer_estimation_error = steer_error + best_steer_coef = coef[[1, 2]] + + estimated_steer_scaling = np.clip(-best_steer_coef[1] / best_steer_coef[0], 0.1, 10.0) + + return np.clip(estimated_wheel_base_scaling / estimated_steer_scaling, 0.1, 10.0) diff --git a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/python_simulator.py b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/python_simulator.py index 0eaa6ce1c0fbf..78ccc0dea3922 100644 --- a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/python_simulator.py +++ b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/python_simulator.py @@ -12,23 +12,30 @@ # See the License for the specific language governing permissions and # limitations under the License. -# cspell: ignore numba njit simplejson fastmath interp suptitle fftfreq basefmt markerfmt - -import csv import datetime import os +from assets import ControlType # type: ignore from autoware_smart_mpc_trajectory_follower.scripts import drive_controller from autoware_smart_mpc_trajectory_follower.scripts import drive_functions -import matplotlib.pyplot as plt -from numba import njit +import data_collection_utils # type: ignore +from density_estimation import KinematicStates # type: ignore +from density_estimation import visualize_speed_acc +from density_estimation import visualize_speed_steer +import matplotlib.pyplot as plt # type: ignore +from numba import njit # type: ignore import numpy as np -import pandas as pd -import scipy.interpolate -import simplejson as json +import pandas as pd # type: ignore +import pure_pursuit_gain_updater # type: ignore +import scipy.interpolate # type: ignore +import simplejson as json # type: ignore +# cSpell:ignore numba simplejson print("\n\n### import python_simulator.py ###") +ACTIVATE_LIMITS = False +UPDATE_PP_GAIN = True + ctrl_time_step = drive_functions.ctrl_time_step sim_dt = 0.003333 # Simulation time step @@ -46,8 +53,12 @@ L = drive_functions.L N = drive_functions.N -steer_rate_lim = 0.35 -vel_rate_lim = 7.0 +if ACTIVATE_LIMITS: + steer_rate_lim = 0.35 + vel_rate_lim = 7.0 +else: + steer_rate_lim = 10.35 + vel_rate_lim = 107.0 mpc_freq = drive_functions.mpc_freq @@ -114,7 +125,7 @@ 1 * vgr_coef_a2, 1 * vgr_coef_b2, 1 * vgr_coef_c2, - ] + ] # type: ignore vgr_coef_a1 = sim_setting_dict["adaptive_gear_ratio_coef"][0] vgr_coef_b1 = sim_setting_dict["adaptive_gear_ratio_coef"][1] vgr_coef_c1 = sim_setting_dict["adaptive_gear_ratio_coef"][2] @@ -171,7 +182,7 @@ print("perturbed steer_time_constant", steer_time_constant) if "accel_map_scale" in sim_setting_dict.keys(): - nominal_setting_dict_display["accel_map_scale"] = None + nominal_setting_dict_display["accel_map_scale"] = None # type: ignore use_accel_map = True accel_map_scale = sim_setting_dict["accel_map_scale"] df = pd.read_csv("accel_map.csv", header=None) @@ -244,8 +255,9 @@ def accel_map_f(x, y): pass elif sim_setting_dict["vehicle_type"] == 1: # heavy-weight bus - steer_rate_lim = 5.0 - vel_rate_lim = 7.0 + if ACTIVATE_LIMITS: + steer_rate_lim = 5.0 + vel_rate_lim = 7.0 L = 4.76 acc_delay_step_sim = round(1.0 / sim_dt) steer_delay_step_sim = round(1.0 / sim_dt) @@ -254,8 +266,9 @@ def accel_map_f(x, y): acc_scaling = 0.2 elif sim_setting_dict["vehicle_type"] == 2: # light-weight bus - steer_rate_lim = 5.0 - vel_rate_lim = 7.0 + if ACTIVATE_LIMITS: + steer_rate_lim = 5.0 + vel_rate_lim = 7.0 L = 4.76 acc_delay_step_sim = round(0.5 / sim_dt) steer_delay_step_sim = round(0.5 / sim_dt) @@ -264,8 +277,9 @@ def accel_map_f(x, y): acc_scaling = 0.5 elif sim_setting_dict["vehicle_type"] == 3: # small vehicle - steer_rate_lim = 5.0 - vel_rate_lim = 7.0 + if ACTIVATE_LIMITS: + steer_rate_lim = 5.0 + vel_rate_lim = 7.0 L = 1.335 acc_delay_step_sim = round(0.3 / sim_dt) steer_delay_step_sim = round(0.3 / sim_dt) @@ -274,8 +288,9 @@ def accel_map_f(x, y): acc_scaling = 1.5 elif sim_setting_dict["vehicle_type"] == 4: # small robot - steer_rate_lim = 60.0 * (np.pi / 180.0) - vel_rate_lim = 3.0 + if ACTIVATE_LIMITS: + steer_rate_lim = 60.0 * (np.pi / 180.0) + vel_rate_lim = 3.0 L = 0.395 acc_delay_step_sim = round(0.2 / sim_dt) steer_delay_step_sim = round(0.2 / sim_dt) @@ -308,6 +323,109 @@ def accel_map_f(x, y): ) print("acc_scaling", nominal_setting_dict_display["acc_scaling"], acc_scaling) + if "test_vehicle" in sim_setting_dict.keys(): + nominal_setting_dict_display["steer_rate_lim"] = 1 * steer_rate_lim + nominal_setting_dict_display["vel_rate_lim"] = 1 * vel_rate_lim + nominal_setting_dict_display["wheel_base"] = 1 * L + nominal_setting_dict_display["acc_time_delay"] = sim_dt * acc_delay_step_sim + nominal_setting_dict_display["steer_time_delay"] = sim_dt * steer_delay_step_sim + nominal_setting_dict_display["acc_time_constant"] = 1 * acc_time_constant + nominal_setting_dict_display["steer_time_constant"] = 1 * steer_time_constant + nominal_setting_dict_display["acc_scaling"] = 1 * acc_scaling + if sim_setting_dict["test_vehicle"] == 0: + L = 5.76 + acc_delay_step_sim = round(0.2 / sim_dt) + steer_delay_step_sim = round(0.9 / sim_dt) + acc_time_constant = 0.4 + steer_time_constant = 0.8 + steer_scaling = 3.0 + acc_scaling = 0.5 + steer_dead_band = 0.002 + measurement_steer_bias = 0.2 * np.pi / 180.0 + elif sim_setting_dict["test_vehicle"] == 1: + L = 1.76 + acc_delay_step_sim = round(0.7 / sim_dt) + steer_delay_step_sim = round(0.8 / sim_dt) + acc_time_constant = 0.2 + steer_time_constant = 0.9 + steer_scaling = 0.3 + acc_scaling = 3.0 + steer_dead_band = 0.001 + measurement_steer_bias = 0.3 * np.pi / 180.0 + elif sim_setting_dict["test_vehicle"] == 2: + L = 4.56 + acc_delay_step_sim = round(0.4 / sim_dt) + steer_delay_step_sim = round(0.8 / sim_dt) + acc_time_constant = 0.4 + steer_time_constant = 0.9 + steer_scaling = 0.6 + acc_scaling = 0.3 + steer_dead_band = 0.004 + measurement_steer_bias = 0.7 * np.pi / 180.0 + elif sim_setting_dict["test_vehicle"] == 3: + # heavy-weight bus + L = 4.76 + acc_delay_step_sim = round(1.0 / sim_dt) + steer_delay_step_sim = round(1.0 / sim_dt) + acc_time_constant = 1.0 + steer_time_constant = 1.0 + acc_scaling = 0.2 + elif sim_setting_dict["test_vehicle"] == 4: + L = 3.56 + acc_delay_step_sim = round(0.3 / sim_dt) + steer_delay_step_sim = round(0.8 / sim_dt) + acc_time_constant = 0.3 + steer_time_constant = 0.9 + steer_scaling = 0.3 + acc_scaling = 0.3 + steer_dead_band = 0.003 + measurement_steer_bias = 0.3 * np.pi / 180.0 + elif sim_setting_dict["test_vehicle"] == 5: + L = 4.76 + acc_delay_step_sim = round(1.0 / sim_dt) + steer_delay_step_sim = round(0.7 / sim_dt) + acc_time_constant = 1.0 + steer_time_constant = 0.7 + acc_scaling = 0.2 + elif sim_setting_dict["test_vehicle"] == 6: + L = 4.76 + acc_delay_step_sim = round(1.0 / sim_dt) + steer_delay_step_sim = round(0.6 / sim_dt) + acc_time_constant = 1.0 + steer_time_constant = 0.6 + acc_scaling = 0.2 + elif sim_setting_dict["test_vehicle"] == 7: + L = 4.76 + acc_delay_step_sim = round(1.0 / sim_dt) + steer_delay_step_sim = round(0.5 / sim_dt) + acc_time_constant = 1.0 + steer_time_constant = 0.5 + acc_scaling = 0.2 + print("steer_rate_lim", nominal_setting_dict_display["steer_rate_lim"], steer_rate_lim) + print("vel_rate_lim", nominal_setting_dict_display["vel_rate_lim"], vel_rate_lim) + print("wheel_base", nominal_setting_dict_display["wheel_base"], L) + print( + "acc_time_delay", + nominal_setting_dict_display["acc_time_delay"], + sim_dt * acc_delay_step_sim, + ) + print( + "steer_time_delay", + nominal_setting_dict_display["steer_time_delay"], + sim_dt * steer_delay_step_sim, + ) + print( + "acc_time_constant", + nominal_setting_dict_display["acc_time_constant"], + acc_time_constant, + ) + print( + "steer_time_constant", + nominal_setting_dict_display["steer_time_constant"], + steer_time_constant, + ) + print("acc_scaling", nominal_setting_dict_display["acc_scaling"], acc_scaling) + # dynamics # @njit(cache=False, fastmath=True) # Commented out because we want to use scipy.interpolate2d @@ -336,13 +454,13 @@ def f_sim( else: delta_diff = 0.0 - actual_alpha = alpha + actual_alpha_input = acc_scaling * inputs[0] actual_delta = delta - measurement_steer_bias # Acceleration input value -> Actual acceleration input value distortion applied if use_accel_map: - if actual_alpha > 0: - actual_alpha = accel_map_f(v, actual_alpha)[0] # vel, acc_cmd + if actual_alpha_input > 0: + actual_alpha_input = accel_map_f(v, actual_alpha_input)[0] # vel, acc_cmd # Tire angle input value -> Actual tire angle input value distortion application steer_tire_angle_cmd = 1 * delta @@ -360,9 +478,9 @@ def f_sim( states_dot = np.zeros(6) states_dot[0] = v * np.cos(theta) states_dot[1] = v * np.sin(theta) - states_dot[2] = actual_alpha + states_dot[2] = alpha states_dot[3] = v * np.tan(actual_delta) / L - states_dot[4] = (acc_scaling * inputs[0] - alpha) / acc_time_constant + states_dot[4] = (actual_alpha_input - alpha) / acc_time_constant states_dot[5] = delta_diff / steer_time_constant return states_dot @@ -383,6 +501,7 @@ def F_sim( return states_next +# cSpell:ignore njit fastmath @njit(cache=False, fastmath=True) def d_inputs_to_inputs(u_old, d_inputs): """Compute the sequence of input values from the sequence of input change rates.""" @@ -469,360 +588,42 @@ def get_mpc_trajectory(x_current, trajectory_data, trajectory_interpolator_list) return X_des, U_des, x_current[:6] - trajectory_data[nearest_index, 1:7], break_flag -def get_feedforward_nominal_input(t, trajectory_data): - """Calculate the nominal input for feed-forward driving.""" - total_time = trajectory_data[-1, 0] - t_current = t - (t // total_time) * total_time - nearest_index = np.argmin(np.abs(trajectory_data[:, 0] - t_current)) - return trajectory_data[nearest_index, [5, 6]] - - -def create_additional_sine_data( - seed, - t_range, - acc_width_range, - acc_period_range, - steer_width_range, - steer_period_range, - large_steer_width_range, - large_steer_period_range, - start_large_steer_time, -): - """Create sine wave data to be added randomly to feed-forward runs.""" - np.random.seed(seed=seed) - t_acc = 0.0 - t_steer = 0.0 - t_large_steer = 0.0 - t_acc_list = [] - t_steer_list = [] - t_large_steer_list = [] - t_acc_list.append(t_acc) - t_steer_list.append(t_steer) - t_large_steer_list.append(t_large_steer) - t_large_steer += start_large_steer_time - t_large_steer_list.append(t_large_steer) - width_acc_list = [] - width_steer_list = [] - width_large_steer_list = [] - width_large_steer_list.append(0) - while True: - if max(t_acc, t_large_steer) >= t_steer: - period = ( - steer_period_range[1] - steer_period_range[0] - ) * np.random.uniform() + steer_period_range[0] - t_steer += period - t_steer_list.append(t_steer) - width_steer_list.append(steer_width_range * np.random.uniform()) - elif t_large_steer >= t_acc: - period = ( - acc_period_range[1] - acc_period_range[0] - ) * np.random.uniform() + acc_period_range[0] - t_acc += period - t_acc_list.append(t_acc) - width_acc_list.append(acc_width_range * np.random.uniform()) - else: - period = ( - large_steer_period_range[1] - large_steer_period_range[0] - ) * np.random.uniform() + large_steer_period_range[0] - t_large_steer += period - t_large_steer_list.append(t_large_steer) - width_large_steer_list.append(large_steer_width_range * np.random.uniform()) - if t_acc >= t_range[1] and t_steer >= t_range[1] and t_large_steer >= t_range[1]: - break - return ( - np.array(t_acc_list), - np.array(width_acc_list), - np.array(t_steer_list), - np.array(width_steer_list), - np.array(t_large_steer_list), - np.array(width_large_steer_list), - ) - - -@njit(cache=False, fastmath=True) -def get_current_additional_sine( - t, - t_acc_array, - width_acc_array, - t_steer_array, - width_steer_array, - t_large_steer_array, - width_large_steer_array, -): - """Calculate current values from already created sine wave data.""" - acc_index = 0 - steer_index = 0 - large_steer_index = 0 - for i in range(t_acc_array.shape[0] - 1): - if t < t_acc_array[i + 1]: - break - acc_index += 1 - for i in range(t_steer_array.shape[0] - 1): - if t < t_steer_array[i + 1]: - break - steer_index += 1 - for i in range(t_large_steer_array.shape[0] - 1): - if t < t_large_steer_array[i + 1]: - break - large_steer_index += 1 - acc = width_acc_array[acc_index] * np.sin( - 2 - * np.pi - * (t - t_acc_array[acc_index]) - / (t_acc_array[acc_index + 1] - t_acc_array[acc_index]) - ) - steer = width_steer_array[steer_index] * np.sin( - 2 - * np.pi - * (t - t_steer_array[steer_index]) - / (t_steer_array[steer_index + 1] - t_steer_array[steer_index]) - ) - steer += width_large_steer_array[large_steer_index] * np.sin( - 2 - * np.pi - * (t - t_large_steer_array[large_steer_index]) - / (t_large_steer_array[large_steer_index + 1] - t_large_steer_array[large_steer_index]) - ) - return np.array([acc, steer]) - - -def create_vel_sine_data(seed, t_range, vel_width_range, vel_period_range): - """Create sine wave data for target velocity.""" - np.random.seed(seed=seed) - t_vel = 0.0 - t_vel_list = [] - t_vel_list.append(t_vel) - width_vel_list = [] - while True: - period = ( - vel_period_range[1] - vel_period_range[0] - ) * np.random.uniform() + vel_period_range[0] - t_vel += period - t_vel_list.append(t_vel) - width_vel_list.append(vel_width_range * np.random.uniform()) - if t_vel >= t_range[1]: - break - return ( - np.array(t_vel_list), - np.array(width_vel_list), - ) - - -@njit(cache=False, fastmath=True) -def get_current_vel_sine(t, t_vel_array, width_vel_array, v_mid): - """Calculate current target velocity values from already created sine wave data.""" - vel_index = 0 - for i in range(t_vel_array.shape[0] - 1): - if t < t_vel_array[i + 1]: - break - vel_index += 1 - vel = v_mid + width_vel_array[vel_index] * np.sin( - 2 - * np.pi - * (t - t_vel_array[vel_index]) - / (t_vel_array[vel_index + 1] - t_vel_array[vel_index]) - ) - return vel - - -def get_figure_eight_point(t, circle_radius): - """ - Get the position and yaw angle in world coordinates of the figure eight given the circle radius. - - Here t is a 1-dimensional array of numpy and each t[i] represents the distance traveled. - The return value is a 2-dimensional array of positions and a 1-dimensional array of yaw angles corresponding to t. - """ - sign = -2 * (np.floor(t / (2 * np.pi * circle_radius)) % 2).astype(int) + 1 - x = circle_radius * np.sin(t / circle_radius) - y = sign * circle_radius * (1 - np.cos(t / circle_radius)) - yaw = ( - sign * ((t / circle_radius) % (2 * np.pi) - np.pi) + np.pi - ) # if sign == 1, then yaw = (t/circle_radius)%(2*np.pi). Else, yaw = 2*np.pi - (t/circle_radius)%(2*np.pi). - return np.array([x, y]).T, yaw - - -def pure_pursuit( - x_current, - target_position, - target_vel, - target_yaw, - acc_lim=7.0, - steer_lim=1.0, - wheel_base=drive_functions.L, - lookahead_time=3.0, - min_lookahead=3.0, - acc_kp=0.5, -): - """Calculate acceleration and steer angle input values based on pure pursuit.""" - present_position = x_current[:2] - present_longitudinal_velocity = x_current[2] - present_point_yaw = x_current[3] - longitudinal_vel_err = present_longitudinal_velocity - target_vel - acc_kp = 0.5 - acc_cmd = np.clip(-acc_kp * longitudinal_vel_err, -acc_lim, acc_lim) - - # compute steer cmd - cos_yaw = np.cos(target_yaw) - sin_yaw = np.sin(target_yaw) - diff_position = present_position - target_position - lat_err = -sin_yaw * diff_position[0] + cos_yaw * diff_position[1] - yaw_err = present_point_yaw - target_yaw - while True: - if yaw_err > np.pi: - yaw_err -= 2.0 * np.pi - if yaw_err < (-np.pi): - yaw_err += 2.0 * np.pi - if np.abs(yaw_err) < np.pi: - break - - lookahead = min_lookahead + lookahead_time * np.abs(present_longitudinal_velocity) - steer_kp = 2.0 * wheel_base / (lookahead * lookahead) - steer_kd = 2.0 * wheel_base / lookahead - steer_cmd = np.clip(-steer_kp * lat_err - steer_kd * yaw_err, -steer_lim, steer_lim) - return np.array([acc_cmd, steer_cmd]) - - -def get_pure_pursuit_info(x_current, trajectory_position_data, trajectory_yaw_data, previous_index): - """Calculate the target position and yaw angle required for pure pursuit.""" - search_range = ( - np.arange( - previous_index - trajectory_position_data.shape[0] // 4, - previous_index + trajectory_position_data.shape[0] // 4, - ) - % trajectory_position_data.shape[0] - ) - nearest_index = np.argmin( - ((trajectory_position_data[search_range] - x_current[:2].reshape(1, 2)) ** 2).sum(axis=1) - ) - return ( - trajectory_position_data[search_range[nearest_index]], - trajectory_yaw_data[search_range[nearest_index]], - search_range[nearest_index], - ) - - -class driving_log_updater: - """Class for updating logs when driving on the Python simulator.""" - - def __init__(self): - self.X_history = [] - self.U_history = [] - self.control_cmd_time_stamp_list = [] - self.control_cmd_steer_list = [] - self.control_cmd_acc_list = [] - self.kinematic_state_list = [] - self.acceleration_list = [] - self.steering_status_list = [] - self.control_cmd_orig_list = [] - self.operation_mode_list = [] - - def update(self, t_current, x_current, u_current): - """Update logs.""" - self.X_history.append(x_current) - self.U_history.append(u_current) - self.control_cmd_time_stamp_list.append(t_current) - self.control_cmd_steer_list.append(u_current[1]) - self.control_cmd_acc_list.append(u_current[0]) - if self.control_cmd_time_stamp_list[-1] - self.control_cmd_time_stamp_list[0] > 3.0: - self.control_cmd_time_stamp_list.pop(0) - self.control_cmd_steer_list.pop(0) - self.control_cmd_acc_list.pop(0) - t_sec = int(t_current) - t_n_sec = int(1e9 * (t_current - t_sec)) - kinematic_state = np.zeros(7) - acceleration = np.zeros(4) - steering_status = np.zeros(3) - control_cmd_orig = np.zeros(10) - operation_mode = np.zeros(3) - kinematic_state[0] = t_sec - kinematic_state[1] = t_n_sec - kinematic_state[2] = x_current[0] - kinematic_state[3] = x_current[1] - kinematic_state[4] = np.sin(0.5 * x_current[3]) - kinematic_state[5] = np.cos(0.5 * x_current[3]) - kinematic_state[6] = x_current[2] - self.kinematic_state_list.append(kinematic_state) - acceleration[0] = t_sec - acceleration[1] = t_n_sec - acceleration[3] = x_current[4] - self.acceleration_list.append(acceleration) - steering_status[0] = t_sec - steering_status[1] = t_n_sec - steering_status[2] = x_current[5] - self.steering_status_list.append(steering_status) - control_cmd_orig[0] = t_sec - control_cmd_orig[1] = t_n_sec - control_cmd_orig[4] = u_current[1] - control_cmd_orig[9] = u_current[0] - self.control_cmd_orig_list.append(control_cmd_orig) - operation_mode[0] = t_sec - operation_mode[1] = t_n_sec - operation_mode[2] = 2.0 - self.operation_mode_list.append(operation_mode) - - def save(self, save_dir): - """Save logs in csv format.""" - kinematic_states = np.zeros((len(self.kinematic_state_list), 48)) - kinematic_states[:, [0, 1, 4, 5, 9, 10, 47]] = np.array(self.kinematic_state_list) - np.savetxt(save_dir + "/kinematic_state.csv", kinematic_states, delimiter=",") - np.savetxt(save_dir + "/acceleration.csv", np.array(self.acceleration_list), delimiter=",") - np.savetxt( - save_dir + "/steering_status.csv", - np.array(self.steering_status_list), - delimiter=",", - ) - np.savetxt( - save_dir + "/control_cmd_orig.csv", - np.array(self.control_cmd_orig_list), - delimiter=",", - ) - np.savetxt( - save_dir + "/system_operation_mode_state.csv", - np.array(self.operation_mode_list), - delimiter=",", - ) - with open(save_dir + "/system_operation_mode_state.csv", "w") as f: - writer = csv.writer(f) - for i in range(len(self.operation_mode_list)): - operation_mode_plus_true = self.operation_mode_list[i].tolist() - operation_mode_plus_true.append("True") - writer.writerow(operation_mode_plus_true) - - -def slalom_drive( +def drive_sim( save_file=True, save_dir=None, load_dir=drive_functions.load_dir, visualize=True, use_trained_model=False, use_trained_model_diff=None, - control_type="mpc", # feedforward_test=False, - straight_line_test=False, + use_memory_diff=None, + control_type: ControlType = ControlType.mpc, initial_error=np.zeros(6), t_range=[0, 100], seed=1, - acc_width_range=0.005, + acc_amp_range=0.05, acc_period_range=[5.0, 20.0], - steer_width_range=0.005, - steer_period_range=[5.0, 20.0], - large_steer_width_range=0.00, + steer_amp_range=0.005, + steer_period_range=[5.0, 30.0], + large_steer_amp_range=0.00, large_steer_period_range=[5.0, 20.0], start_large_steer_time=40.0, - vel_width_range=5.0, - vel_period_range=[5.0, 20.0], - v_mid=6.0, - circle_radius=30.0, + acc_max=1.2, + constant_vel_time=5.0, + split_size=5, + y_length=60.0, + x_length=120.0, + step_response_max_input=0.01, + step_response_max_length=1.5, + step_response_start_time_ratio=0.0, + step_response_interval=5.0, + step_response_min_length=0.5, + smoothing_trajectory_data_flag=True, ): """Perform a slalom driving simulation.""" - if control_type == "pp": - print("\n[run figure_eight_drive]\n") - elif control_type == "ff": - print("\n[run feedforward_drive]\n") + if control_type != ControlType.mpc: + print(f"\n[run {control_type.value}]\n") else: - if not straight_line_test: - print("\n[run slalom_drive]\n") - else: - print("\n[straight_line_test]\n") + print("\n[run slalom_drive]\n") if save_file: if save_dir is None: save_dir_ = "python_sim_log_" + str(datetime.datetime.now()) @@ -830,40 +631,41 @@ def slalom_drive( save_dir_ = save_dir if not os.path.isdir(save_dir_): os.mkdir(save_dir_) - controller = drive_controller.drive_controller( - model_file_name=(load_dir + "/model_for_test_drive.pth"), - load_GP_dir=load_dir, - load_polynomial_reg_dir=load_dir, - use_trained_model=use_trained_model, - use_trained_model_diff=use_trained_model_diff, - load_train_data_dir=load_dir, - ) - mode = controller.mode - if control_type == "mpc": + if control_type == ControlType.mpc: + controller = drive_controller.drive_controller( + model_file_name=(load_dir + "/model_for_test_drive.pth"), + load_GP_dir=load_dir, + load_polynomial_reg_dir=load_dir, + use_trained_model=use_trained_model, + use_trained_model_diff=use_trained_model_diff, + use_memory_diff=use_memory_diff, + load_train_data_dir=load_dir, + ) + mode = controller.mode print("mode:", mode) + # cSpell:ignore interp plt.rcParams["figure.figsize"] = (8, 8) - t_eval = np.arange(*t_range, sim_dt) - if not straight_line_test: - trajectory_data = np.loadtxt("slalom_course_data.csv", delimiter=",") - else: - # Test by straight_line. To run, the following create_straight_line_test_csv() must be executed - trajectory_data = np.loadtxt("straight_line.csv", delimiter=",") + t_eval = np.arange(t_range[0], t_range[1], sim_dt) + trajectory_data = np.loadtxt("slalom_course_data.csv", delimiter=",") trajectory_interpolator_list = [ scipy.interpolate.interp1d(trajectory_data[:, 0], trajectory_data[:, 1 + i]) for i in range(trajectory_data.shape[1] - 1) ] - x_init = trajectory_data[0, 1:7] + initial_error - - x_current = x_init.copy() + x_init = trajectory_data[0, 1:7].copy() acc_des_queue = [0] * acc_delay_step_sim - steer_des_queue = [0] * steer_delay_step_sim + + initial_steer_input = ( + measurement_steer_bias + np.sign(measurement_steer_bias) * steer_dead_band + ) / steer_scaling + steer_des_queue = [initial_steer_input] * steer_delay_step_sim calculated = 0 break_flag = False tracking_error_list = [] + target_vel_list = [] total_abs_max_lateral_deviation = -1 straight_line_abs_max_lateral_deviation = -1 @@ -872,48 +674,80 @@ def slalom_drive( total_abs_max_acc_error = -1 total_abs_max_steer_error = -1 prev_u_actual_input = np.zeros(2) - log_updater = driving_log_updater() + log_updater = data_collection_utils.driving_log_updater() + pp_gain_updater = pure_pursuit_gain_updater.pure_pursuit_gain_updater() + acc_gain_scaling = 1.0 + steer_gain_scaling = 1.0 # L/2.79 + + acc_gain_scaling_decay = 0.9 + steer_gain_scaling_decay = 0.9 + + # used for "pp_eight" previous_pp_index = 0 - if control_type != "mpc": # feedforward_test: + if control_type != ControlType.mpc: # feedforward_test: ( t_acc_array, - width_acc_array, + amp_acc_array, t_steer_array, - width_steer_array, + amp_steer_array, t_large_steer_array, - width_large_steer_array, - ) = create_additional_sine_data( + amp_large_steer_array, + ) = data_collection_utils.create_additional_sine_data( seed, t_range, - acc_width_range, + acc_amp_range, acc_period_range, - steer_width_range, + steer_amp_range, steer_period_range, - large_steer_width_range, + large_steer_amp_range, large_steer_period_range, start_large_steer_time, ) - if control_type == "pp": - t_figure_eight = np.arange(*[0, 4 * np.pi * circle_radius], 0.01) - trajectory_position_data, trajectory_yaw_data = get_figure_eight_point( - t_figure_eight, circle_radius - ) - # plt.plot(trajectory_position_data[:,0],trajectory_position_data[:,1]) - # plt.show() - t_vel_array, width_vel_array = create_vel_sine_data( - seed, t_range, vel_width_range, vel_period_range - ) + if control_type in [ControlType.pp_eight, ControlType.pp_straight, ControlType.npp_eight]: + if control_type in [ControlType.pp_eight, ControlType.npp_eight]: + figure_eight = data_collection_utils.FigureEight( + y_length, + x_length, + acc_max=acc_max, + constant_vel_time=constant_vel_time, + split_size=split_size, + smoothing_trajectory_data_flag=smoothing_trajectory_data_flag, + ) + ( + trajectory_position_data, + trajectory_yaw_data, + curvature_radius, + parts, + achievement_rates, + ) = figure_eight.get_trajectory_points(0.01) + + x_init[:2] = trajectory_position_data[0] + x_init[3] = trajectory_yaw_data[0] + x_init[2] = figure_eight.v_start + else: + straight_line = data_collection_utils.StraightLine( + acc_max=acc_max, constant_vel_time=constant_vel_time, split_size=split_size + ) + x_init[2] = straight_line.v_mid + + x_init += initial_error + + x_current = x_init.copy() for i in range(t_eval.size): if i % ctrl_freq == 0: # update u_opt - if control_type == "mpc": # not feedforward_test: + if control_type in [ControlType.pp_eight, ControlType.pp_straight]: + pp_gain_updater.state_queue_updater( + x_current[2], x_current[3], x_current[4], x_current[5] + ) + + if control_type == ControlType.mpc: # not feedforward_test: X_des, U_des, tracking_error, break_flag = get_mpc_trajectory( x_current, trajectory_data, trajectory_interpolator_list ) tracking_error_list.append(tracking_error.copy()) - u_opt = controller.update_input_queue_and_get_optimal_control( log_updater.control_cmd_time_stamp_list, log_updater.control_cmd_acc_list, @@ -921,38 +755,177 @@ def slalom_drive( x_current, X_des, U_des, + t_eval[i], + t_eval[i], ) - elif control_type == "ff": - u_opt = get_current_additional_sine( + if t_eval[i] < 5.0: + u_opt[1] = initial_steer_input + elif control_type == ControlType.ff: + u_opt = data_collection_utils.get_current_additional_sine( t_eval[i], t_acc_array, - width_acc_array, + amp_acc_array, t_steer_array, - width_steer_array, + amp_steer_array, t_large_steer_array, - width_large_steer_array, + amp_large_steer_array, ) - u_opt[0] += get_feedforward_nominal_input(t_eval[i], trajectory_data)[0] - elif control_type == "pp": - target_position, target_yaw, previous_pp_index = get_pure_pursuit_info( + u_opt[0] += data_collection_utils.get_feedforward_nominal_input( + t_eval[i], trajectory_data + )[0] + + elif control_type == ControlType.pp_eight: + ( + target_position, + target_yaw, + previous_pp_index, + ) = data_collection_utils.get_pure_pursuit_info( x_current, trajectory_position_data, trajectory_yaw_data, previous_pp_index ) - target_vel = get_current_vel_sine(t_eval[i], t_vel_array, width_vel_array, v_mid) - u_opt = pure_pursuit(x_current, target_position, target_vel, target_yaw) - u_opt += get_current_additional_sine( + target_vel = figure_eight.get_current_velocity(t_eval[i]) + target_vel_list.append(np.array([t_eval[i], target_vel])) + u_opt = drive_functions.pure_pursuit_control( + pos_xy_obs=x_current[:2], + pos_yaw_obs=x_current[3], + longitudinal_vel_obs=x_current[2], + pos_xy_ref=target_position[:2], + pos_yaw_ref=target_yaw, + longitudinal_vel_ref=target_vel, + acc_gain_scaling=acc_gain_scaling, + steer_gain_scaling=steer_gain_scaling, + ) + u_opt += data_collection_utils.get_current_additional_sine( + t_eval[i], + t_acc_array, + amp_acc_array, + t_steer_array, + amp_steer_array, + t_large_steer_array, + amp_large_steer_array, + ) + u_opt[1] += data_collection_utils.step_response( + t_eval[i], + step_response_start_time_ratio * t_range[1], + step_response_interval, + step_response_max_input, + step_response_max_length, + step_response_min_length, + ) + break_flag = figure_eight.break_flag + elif control_type == ControlType.pp_straight: + target_vel = straight_line.get_current_velocity(t_eval[i]) + target_vel_list.append(np.array([t_eval[i], target_vel])) + target_position = np.array([x_current[0], 0.0]) + target_yaw = 0.0 + u_opt = drive_functions.pure_pursuit_control( + pos_xy_obs=x_current[:2], + pos_yaw_obs=x_current[3], + longitudinal_vel_obs=x_current[2], + pos_xy_ref=target_position[:2], + pos_yaw_ref=target_yaw, + longitudinal_vel_ref=target_vel, + acc_gain_scaling=acc_gain_scaling, + steer_gain_scaling=steer_gain_scaling, + ) + u_opt += data_collection_utils.get_current_additional_sine( t_eval[i], t_acc_array, - width_acc_array, + amp_acc_array, t_steer_array, - width_steer_array, + amp_steer_array, t_large_steer_array, - width_large_steer_array, + amp_large_steer_array, + ) + u_opt[1] += data_collection_utils.step_response( + t_eval[i], + step_response_start_time_ratio * t_range[1], + step_response_interval, + step_response_max_input, + step_response_max_length, + step_response_min_length, ) + break_flag = straight_line.break_flag + + elif control_type == ControlType.npp_eight: + ( + target_position, + target_yaw, + previous_pp_index, + target_position_ahead, + ) = data_collection_utils.get_naive_pure_pursuit_info( + x_current, trajectory_position_data, trajectory_yaw_data, previous_pp_index + ) + target_vel = figure_eight.get_current_velocity(t_eval[i]) + + applying_velocity_limit_by_lateral_acc_flag = True + if applying_velocity_limit_by_lateral_acc_flag: + max_lateral_accel = 0.3 + velocity_limit_by_lateral_acc = np.sqrt( + max_lateral_accel * curvature_radius[previous_pp_index] + ) + target_vel = min(target_vel, velocity_limit_by_lateral_acc) + + applying_velocity_limit_by_tracking_error_flag = True + if applying_velocity_limit_by_tracking_error_flag: + lateral_error = np.sqrt(((x_current[:2] - target_position[:2]) ** 2).sum()) + yaw_error = x_current[3] - target_yaw + if yaw_error > np.pi: + yaw_error -= 2 * np.pi + if yaw_error < -np.pi: + yaw_error += 2 * np.pi + + safety_velocity = 3.0 + lateral_error_threshold = 2.0 + yaw_error_threshold = 0.5 + if lateral_error_threshold < lateral_error or yaw_error_threshold < np.abs( + yaw_error + ): + target_vel = safety_velocity + + target_vel_list.append(np.array([t_eval[i], target_vel])) + u_opt = drive_functions.naive_pure_pursuit_control( + pos_xy_obs=x_current[:2], + pos_yaw_obs=x_current[3], + longitudinal_vel_obs=x_current[2], + pos_xy_ref_target=target_position_ahead[:2], + longitudinal_vel_ref_nearest=target_vel, + ) + u_opt += data_collection_utils.get_current_additional_sine( + t_eval[i], + t_acc_array, + amp_acc_array, + t_steer_array, + amp_steer_array, + t_large_steer_array, + amp_large_steer_array, + ) + u_opt[1] += data_collection_utils.step_response( + t_eval[i], + step_response_start_time_ratio * t_range[1], + step_response_interval, + step_response_max_input, + step_response_max_length, + step_response_min_length, + ) + break_flag = figure_eight.break_flag calculated += 1 log_updater.update(t_eval[i], x_current, u_opt) - if ( - visualize and (control_type in ["ff", "pp"]) and (i == t_eval.size - 1 or break_flag) - ): # feedforward_test and (i == t_eval.size - 1 or break_flag): + if control_type in [ControlType.pp_eight, ControlType.pp_straight]: + pp_gain_updater.input_queue_updater(u_opt[0], u_opt[1]) + if i % (100 * ctrl_freq) == 0 and UPDATE_PP_GAIN: # update u_opt + # pp_gain_updater.get_acc_gain_scaling() + acc_gain_scaling = ( + acc_gain_scaling_decay * acc_gain_scaling + + (1 - acc_gain_scaling_decay) * pp_gain_updater.get_acc_gain_scaling() + ) + if control_type == ControlType.pp_eight: + steer_gain_scaling = ( + steer_gain_scaling_decay * steer_gain_scaling + + (1 - steer_gain_scaling_decay) + * pp_gain_updater.get_steer_gain_scaling() + ) + + if visualize and (control_type != ControlType.mpc) and (i == t_eval.size - 1 or break_flag): X = np.array(log_updater.X_history) U = np.array(log_updater.U_history) @@ -967,11 +940,11 @@ def slalom_drive( + ", nominal_model: " + str(nominal_setting_dict_display) ) - fig = plt.figure(figsize=(18, 12), tight_layout=True) + # cSpell:ignore nrows ncols suptitle + fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(18, 12), tight_layout=True) fig.suptitle(title_str) - plt.subplot(231) - ax1 = plt.subplot(2, 3, 1) + ax1: plt.Axes = axes[0, 0] ax1.plot(X[:, 0], X[:, 1], label="trajectory") ax1.legend() ax1.set_xlabel("x_position [m]") @@ -980,13 +953,13 @@ def slalom_drive( time_normalize_1 = ctrl_freq * sim_dt f_s = int(1.0 / time_normalize_1) - ax2 = plt.subplot(2, 3, 2) + # cSpell:ignore numba simplejson fftfreq basefmt markerfmt + ax2: plt.Axes = axes[0, 1] X_acc = np.fft.fft(U[:, 0]) / len(U[:, 0]) # Fourier transform of waveforms freqs = np.fft.fftfreq(len(U[:, 0])) * f_s ax2.stem( freqs, np.abs(X_acc), - use_line_collection=True, basefmt="k-", markerfmt="cx", label="acc input", @@ -996,13 +969,12 @@ def slalom_drive( ax2.set_xlabel("freq") ax2.set_ylabel("amplitude") - ax3 = plt.subplot(2, 3, 3) + ax3: plt.Axes = axes[0, 2] X_steer = np.fft.fft(U[:, 1]) / len(U[:, 1]) # Fourier transform of waveforms freqs = np.fft.fftfreq(len(U[:, 1])) * f_s ax3.stem( freqs, np.abs(X_steer), - use_line_collection=True, basefmt="k-", markerfmt="cx", label="steer input", @@ -1012,27 +984,55 @@ def slalom_drive( ax3.set_xlabel("freq") ax3.set_ylabel("amplitude") - ax5 = plt.subplot(2, 3, 5) + if control_type in [ + ControlType.pp_eight, + ControlType.pp_straight, + ControlType.npp_eight, + ]: + ax4: plt.Axes = axes[1, 0] + ax4.plot( + np.array(target_vel_list)[:, 0], + np.array(target_vel_list)[:, 1], + label="vel target", + ) + ax4.plot(time_normalize_1 * np.arange(X.shape[0]), X[:, 2], label="vel") + ax4.legend() + + ax5: plt.Axes = axes[1, 1] ax5.plot(time_normalize_1 * np.arange(U.shape[0]), U[:, 0], label="acc input") ax5.legend() - ax6 = plt.subplot(2, 3, 6) + ax6: plt.Axes = axes[1, 2] ax6.plot(time_normalize_1 * np.arange(U.shape[0]), U[:, 1], label="steer input") ax6.legend() + kinematic_states = KinematicStates( + speed=X[:, 2], + acc=X[:, 4], + steer=X[:, 5], + ) + + ax7: plt.Axes = axes[2, 0] + fig, ax7 = visualize_speed_acc(fig=fig, ax=ax7, kinematic_states=kinematic_states) + ax7.plot() + + ax8: plt.Axes = axes[2, 1] + fig, ax8 = visualize_speed_steer(fig=fig, ax=ax8, kinematic_states=kinematic_states) + ax8.plot() + + ax9: plt.Axes = axes[2, 2] + ax9.axis("off") + if save_file: png_save_dir = save_dir_ else: png_save_dir = "." - if control_type == "ff": - plt.savefig(png_save_dir + "/python_simulator_feedforward_drive.png") - elif control_type == "pp": - plt.savefig(png_save_dir + "/python_simulator_pure_pursuit_drive.png") + plt.savefig(f"{png_save_dir}/python_simulator_{control_type.value}_drive.png") plt.close() if ( visualize - and (control_type == "mpc") + and (control_type == ControlType.mpc) and (i % 1000 * ctrl_freq == 999 * ctrl_freq or i == t_eval.size - 1 or break_flag) ): fig = plt.figure(figsize=(24, 15), tight_layout=True) @@ -1049,7 +1049,7 @@ def slalom_drive( ) fig.suptitle(title_str) - plt.subplot(331) + plt.subplot(3, 3, 1) ax1 = plt.subplot(3, 3, 1) X = np.array(log_updater.X_history) @@ -1064,7 +1064,7 @@ def slalom_drive( c="orange", label="reference_trajectory", ) - ax1.plot(X[:, 0], X[:, 1], label="trajectory") + ax1.plot(X[:, 0], X[:, 1], label="trajectory", color="tab:blue") if mode != "mppi": ax1.scatter( controller.nominal_traj_ilqr[:, 0], @@ -1112,51 +1112,15 @@ def slalom_drive( lateral_deviation, label="lateral_deviation", ) - ax2.plot( - time_normalize_1 * np.arange(tracking_error_array.shape[0]), - np.zeros(tracking_error_array.shape[0]), - linestyle="dashed", - ) - ax2.plot( - time_normalize_1 * np.arange(tracking_error_array.shape[0]), - 0.05 * np.ones(tracking_error_array.shape[0]), - linestyle="dashed", - ) - ax2.plot( - time_normalize_1 * np.arange(tracking_error_array.shape[0]), - -0.05 * np.ones(tracking_error_array.shape[0]), - linestyle="dashed", - ) - ax2.plot( - time_normalize_1 * np.arange(tracking_error_array.shape[0]), - 0.1 * np.ones(tracking_error_array.shape[0]), - linestyle="dashed", - ) - ax2.plot( - time_normalize_1 * np.arange(tracking_error_array.shape[0]), - -0.1 * np.ones(tracking_error_array.shape[0]), - linestyle="dashed", - ) - ax2.plot( - time_normalize_1 * np.arange(tracking_error_array.shape[0]), - 0.15 * np.ones(tracking_error_array.shape[0]), - linestyle="dashed", - ) - ax2.plot( - time_normalize_1 * np.arange(tracking_error_array.shape[0]), - -0.15 * np.ones(tracking_error_array.shape[0]), - linestyle="dashed", - ) - ax2.plot( - time_normalize_1 * np.arange(tracking_error_array.shape[0]), - 0.2 * np.ones(tracking_error_array.shape[0]), - linestyle="dashed", - ) - ax2.plot( - time_normalize_1 * np.arange(tracking_error_array.shape[0]), - -0.2 * np.ones(tracking_error_array.shape[0]), - linestyle="dashed", - ) + + ax2_coef = [0.00, 0.05, -0.05, 0.10, -0.10, 0.15, -0.15, 0.20, -0.20] + for coe in ax2_coef: + ax2.plot( + time_normalize_1 * np.arange(tracking_error_array.shape[0]), + coe * np.ones(tracking_error_array.shape[0]), + linestyle="dashed", + ) + ax2.set_xlabel("Time [s]") ax2.set_ylabel("Lateral deviation [m]") ax2.legend() @@ -1177,14 +1141,23 @@ def slalom_drive( ) ax3 = plt.subplot(3, 3, 4) - ax3.plot(time_normalize_1 * np.arange(X.shape[0]), X[:, 2], label="velocity") ax3.plot( - time_normalize_1 * np.arange(X.shape[0]), X_des_hist[:, 2], label="velocity_target" + time_normalize_1 * np.arange(X.shape[0]), + X[:, 2], + label="velocity", + color="tab:blue", + ) + ax3.plot( + time_normalize_1 * np.arange(X.shape[0]), + X_des_hist[:, 2], + label="velocity_target", + color="orange", ) ax3.plot( time_normalize_1 * np.arange(tracking_error_array.shape[0]), -tracking_error_array[:, 2], label="velocity_error", + color="lightgrey", ) if mode != "mppi": ax3.scatter( @@ -1193,6 +1166,7 @@ def slalom_drive( true_prediction[:, 2], s=4, label="velocity_true_prediction", + c="green", ) ax3.scatter( time_normalize_1 * (X.shape[0] - 1) @@ -1200,11 +1174,13 @@ def slalom_drive( controller.nominal_traj_ilqr[:, 2], s=4, label="velocity_prediction_ilqr", + c="red", ) ax3.plot( time_normalize_1 * np.arange(tracking_error_array.shape[0]), np.zeros(tracking_error_array.shape[0]), linestyle="dashed", + color="darkred", ) ax3.set_xlabel("Time [s]") ax3.set_ylabel("velocity [m/s]") @@ -1222,12 +1198,20 @@ def slalom_drive( ) ax4 = plt.subplot(3, 3, 5) - ax4.plot(time_normalize_1 * np.arange(X.shape[0]), X[:, 3], label="yaw") - ax4.plot(time_normalize_1 * np.arange(X.shape[0]), X_des_hist[:, 3], label="yaw_target") + ax4.plot( + time_normalize_1 * np.arange(X.shape[0]), X[:, 3], label="yaw", color="tab:blue" + ) + ax4.plot( + time_normalize_1 * np.arange(X.shape[0]), + X_des_hist[:, 3], + label="yaw_target", + color="orange", + ) ax4.plot( time_normalize_1 * np.arange(tracking_error_array.shape[0]), -tracking_error_array[:, 3], label="yaw_error", + color="lightgrey", ) if mode != "mppi": ax4.scatter( @@ -1236,6 +1220,7 @@ def slalom_drive( true_prediction[:, 3], s=4, label="yaw_true_prediction", + c="green", ) ax4.scatter( time_normalize_1 * (X.shape[0] - 1) @@ -1243,11 +1228,13 @@ def slalom_drive( controller.nominal_traj_ilqr[:, 3], s=4, label="yaw_prediction_ilqr", + c="red", ) ax4.plot( time_normalize_1 * np.arange(tracking_error_array.shape[0]), np.zeros(tracking_error_array.shape[0]), linestyle="dashed", + c="darkred", ) ax4.set_xlabel("Time [s]") ax4.set_ylabel("yaw [rad]") @@ -1265,13 +1252,23 @@ def slalom_drive( ) ax5 = plt.subplot(3, 3, 7) - ax5.plot(time_normalize_1 * np.arange(X.shape[0]), X[:, 4], label="acc") - ax5.plot(time_normalize_1 * np.arange(X.shape[0]), U[:, 0], label="acc_input") - ax5.plot(time_normalize_1 * np.arange(X.shape[0]), X_des_hist[:, 4], label="acc_target") + ax5.plot( + time_normalize_1 * np.arange(X.shape[0]), X[:, 4], label="acc", color="tab:blue" + ) + ax5.plot( + time_normalize_1 * np.arange(X.shape[0]), U[:, 0], label="acc_input", color="violet" + ) + ax5.plot( + time_normalize_1 * np.arange(X.shape[0]), + X_des_hist[:, 4], + label="acc_target", + color="orange", + ) ax5.plot( time_normalize_1 * np.arange(tracking_error_array.shape[0]), -tracking_error_array[:, 4], label="acc_error", + color="lightgrey", ) if mode != "mppi": ax5.scatter( @@ -1280,6 +1277,7 @@ def slalom_drive( true_prediction[:, 4], s=4, label="acc_true_prediction", + c="green", ) ax5.scatter( time_normalize_1 * (X.shape[0] - 1) @@ -1287,6 +1285,7 @@ def slalom_drive( controller.nominal_traj_ilqr[:, 4], s=4, label="acc_prediction_ilqr", + c="red", ) ax5.scatter( time_normalize_1 * (X.shape[0] - 1) @@ -1294,11 +1293,13 @@ def slalom_drive( nominal_inputs[:, 0], s=4, label="acc_input_schedule", + c="plum", ) ax5.plot( time_normalize_1 * np.arange(tracking_error_array.shape[0]), np.zeros(tracking_error_array.shape[0]), linestyle="dashed", + color="darkred", ) ax5.set_xlabel("Time [s]") ax5.set_ylabel("acc [m/s^2]") @@ -1316,15 +1317,26 @@ def slalom_drive( ) ax6 = plt.subplot(3, 3, 8) - ax6.plot(time_normalize_1 * np.arange(X.shape[0]), X[:, 5], label="steer") - ax6.plot(time_normalize_1 * np.arange(X.shape[0]), U[:, 1], label="steer_input") ax6.plot( - time_normalize_1 * np.arange(X.shape[0]), X_des_hist[:, 7], label="steer_mpc_target" + time_normalize_1 * np.arange(X.shape[0]), X[:, 5], label="steer", color="tab:blue" + ) + ax6.plot( + time_normalize_1 * np.arange(X.shape[0]), + U[:, 1], + label="steer_input", + color="violet", + ) + ax6.plot( + time_normalize_1 * np.arange(X.shape[0]), + X_des_hist[:, 7], + label="steer_mpc_target", + color="orange", ) ax6.plot( time_normalize_1 * np.arange(tracking_error_array.shape[0]), -tracking_error_array[:, 5], label="steer_error", + color="lightgrey", ) if mode != "mppi": ax6.scatter( @@ -1333,6 +1345,7 @@ def slalom_drive( true_prediction[:, 5], s=4, label="steer_true_prediction", + c="green", ) ax6.scatter( time_normalize_1 * (X.shape[0] - 1) @@ -1340,6 +1353,7 @@ def slalom_drive( controller.nominal_traj_ilqr[:, 5], s=4, label="steer_prediction_ilqr", + c="red", ) ax6.scatter( time_normalize_1 * (X.shape[0] - 1) @@ -1347,11 +1361,13 @@ def slalom_drive( nominal_inputs[:, 1], s=4, label="steer_input_schedule", + c="plum", ) ax6.plot( time_normalize_1 * np.arange(tracking_error_array.shape[0]), np.zeros(tracking_error_array.shape[0]), linestyle="dashed", + color="darkred", ) ax6.set_xlabel("Time [s]") ax6.set_ylabel("steer [rad]") @@ -1377,7 +1393,6 @@ def slalom_drive( ax7.stem( freqs, np.abs(X_steer) / len(steer_state), - use_line_collection=True, basefmt="k-", markerfmt="rx", label="steer_state", @@ -1397,7 +1412,6 @@ def slalom_drive( ax7.stem( freqs, np.abs(X_lateral_acc) / len(lateral_acc), - use_line_collection=True, basefmt="k-", markerfmt="gx", label="lateral_acc", @@ -1408,7 +1422,6 @@ def slalom_drive( ax7.stem( freqs, np.abs(X_lateral_jerk) / len(lateral_jerk), - use_line_collection=True, basefmt="k-", markerfmt="bx", label="lateral_jerk", @@ -1467,13 +1480,14 @@ def slalom_drive( x_current = x_next.copy() prev_u_actual_input = u_actual_input.copy() if break_flag: - controller.send_initialize_input_queue() - controller.stop_model_update() + if control_type == ControlType.mpc: + controller.send_initialize_input_queue() + controller.stop_model_update() break if save_file: log_updater.save(save_dir_) - if control_type == "mpc" and perturbed_sim_flag: + if control_type == ControlType.mpc and perturbed_sim_flag: auto_test_performance_result_dict = { "total_abs_max_lateral_deviation": total_abs_max_lateral_deviation, "straight_line_abs_max_lateral_deviation": straight_line_abs_max_lateral_deviation, @@ -1505,89 +1519,3 @@ def slalom_drive( print("how_many_time_controlled", calculated) return auto_test_performance_result_list - - -def create_straight_line_test_csv( - jerk=0.3, starting_vel=5.0, interval_1=2.0, interval_2=3.0, interval_3=10.0 -): - """Generate data for a straight line driving test.""" - t_1 = 10.0 - t_2 = t_1 + interval_1 # Increasing acceleration. - t_3 = t_2 + interval_2 # Constant acceleration - t_4 = t_3 + interval_1 # Decreasing acceleration. - t_5 = t_4 + interval_3 # acceleration = 0 - t_6 = t_5 + interval_1 # Decreasing acceleration. - t_7 = t_6 + interval_2 # Constant acceleration - t_8 = t_7 + interval_1 # Decreasing acceleration. - vel_t1 = starting_vel - x_t1 = starting_vel * t_1 - vel_t2 = vel_t1 + 0.5 * jerk * interval_1 * interval_1 - x_t2 = x_t1 + vel_t1 * interval_1 + 0.5 * jerk * interval_1 * interval_1 * interval_1 / 3 - vel_t3 = vel_t2 + jerk * interval_1 * interval_2 - x_t3 = x_t2 + vel_t2 * interval_2 + 0.5 * jerk * interval_1 * interval_2 * interval_2 - vel_t4 = vel_t3 + 0.5 * jerk * interval_1 * interval_1 - x_t4 = x_t3 + vel_t4 * interval_1 - 0.5 * jerk * interval_1 * interval_1 * interval_1 / 3 - vel_t5 = vel_t4 - x_t5 = x_t4 + vel_t4 * interval_3 - vel_t6 = vel_t5 - 0.5 * jerk * interval_1 * interval_1 - x_t6 = x_t5 + vel_t5 * interval_1 - 0.5 * jerk * interval_1 * interval_1 * interval_1 / 3 - vel_t7 = vel_t6 - jerk * interval_1 * interval_2 - x_t7 = x_t6 + vel_t6 * interval_2 - 0.5 * jerk * interval_1 * interval_2 * interval_2 - vel_t8 = vel_t7 - 0.5 * jerk * interval_1 * interval_1 - x_t8 = x_t7 + vel_t8 * interval_1 + 0.5 * jerk * interval_1 * interval_1 * interval_1 / 3 - - def calc_longitudinal_state(t): - if t < t_1: - return starting_vel * t, starting_vel, 0.0 - elif t < t_2: - return ( - x_t1 + vel_t1 * (t - t_1) + 0.5 * jerk * (t - t_1) * (t - t_1) * (t - t_1) / 3, - vel_t1 + 0.5 * jerk * (t - t_1) * (t - t_1), - jerk * (t - t_1), - ) - elif t < t_3: - return ( - x_t2 + vel_t2 * (t - t_2) + 0.5 * jerk * (t_2 - t_1) * (t - t_2) * (t - t_2), - vel_t2 + jerk * (t_2 - t_1) * (t - t_2), - jerk * (t_2 - t_1), - ) - elif t < t_4: - return ( - x_t4 - vel_t4 * (t_4 - t) + 0.5 * jerk * (t_4 - t) * (t_4 - t) * (t_4 - t) / 3, - vel_t4 - 0.5 * jerk * (t_4 - t) * (t_4 - t), - jerk * (t_4 - t), - ) - elif t < t_5: - return x_t4 + vel_t4 * (t - t_4), vel_t4, 0.0 - elif t < t_6: - return ( - x_t5 + vel_t5 * (t - t_5) - 0.5 * jerk * (t - t_5) * (t - t_5) * (t - t_5) / 3, - vel_t5 - 0.5 * jerk * (t - t_5) * (t - t_5), - -jerk * (t - t_5), - ) - elif t < t_7: - return ( - x_t6 + vel_t6 * (t - t_6) - 0.5 * jerk * (t_6 - t_5) * (t - t_6) * (t - t_6), - vel_t6 - jerk * (t_6 - t_5) * (t - t_6), - -jerk * (t_6 - t_5), - ) - elif t < t_8: - return ( - x_t8 - vel_t8 * (t_8 - t) - 0.5 * jerk * (t_8 - t) * (t_8 - t) * (t_8 - t) / 3, - vel_t8 + 0.5 * jerk * (t_8 - t) * (t_8 - t), - -jerk * (t_8 - t), - ) - else: - return x_t8 + vel_t8 * (t - t_8), vel_t8, 0.0 - - t_range = [0, 100] - time_stamps = np.arange(*t_range, 0.001) - trajectory_data = np.zeros((time_stamps.shape[0], 9)) - trajectory_data[:, 0] = time_stamps - for i in range(time_stamps.shape[0]): - ( - trajectory_data[i, 1], - trajectory_data[i, 3], - trajectory_data[i, 5], - ) = calc_longitudinal_state(time_stamps[i]) - np.savetxt("straight_line.csv", trajectory_data, delimiter=",") diff --git a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/run_auto_test.py b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/run_auto_test.py new file mode 100644 index 0000000000000..86ed6470dc14d --- /dev/null +++ b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/run_auto_test.py @@ -0,0 +1,33 @@ +# Copyright 2024 Proxima Technology Inc, TIER IV +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import subprocess + +if __name__ == "__main__": + params = [ + "steer_bias", + "steer_dead_band", + "wheel_base", + "acc_time_delay", + "steer_time_delay", + "acc_time_constant", + "steer_time_constant", + "accel_map_scale", + "acc_scaling", + "steer_scaling", + "vehicle_type", + ] + for param in params: + str_run = "python3 run_sim.py " + f" --param_name {param}" + " --root auto_test" + subprocess.run(str_run, shell=True) diff --git a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/run_figure_eight_param_search.py b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/run_figure_eight_param_search.py new file mode 100644 index 0000000000000..9e1ee6f53e16f --- /dev/null +++ b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/run_figure_eight_param_search.py @@ -0,0 +1,63 @@ +# Copyright 2024 Proxima Technology Inc, TIER IV +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import subprocess + +import numpy as np + + +def print_progress(msg: str): + print("______________________________") + print("start running:", msg) + print("______________________________") + + +SEED = 44 +if __name__ == "__main__": + for k in range(100): + np.random.seed(seed=k + SEED) + acc_amp_range = np.random.uniform(0.005, 0.1) + acc_period_min = np.random.uniform(2.0, 10.0) + acc_period_max = acc_period_min + np.random.uniform(5.0, 30.0) + steer_amp_range = np.random.uniform(0.005, 0.1) + steer_period_min = np.random.uniform(2.0, 10.0) + steer_period_max = steer_period_min + np.random.uniform(5.0, 30.0) + step_response_max_input = np.random.uniform(0.001, 0.1) + step_response_min_length = np.random.uniform(0.02, 1.0) + step_response_max_length = step_response_min_length + np.random.uniform(0.03, 2.0) + step_response_interval = step_response_max_length + np.random.uniform(1.0, 20.0) + constant_vel_time = np.random.uniform(0.5, 20.0) + + dir_name = "test_param_search_" + str(k) + "_test_vehicle" + + for memory in range(2): + use_memory_diff_flag = " " + ("--use_memory_diff" if memory == 1 else "") + str_run = ( + "python3 test_figure_eight_param.py " + + f" --acc_amp_range {acc_amp_range}" + + f" --acc_period_min {acc_period_min}" + + f" --acc_period_max {acc_period_max}" + + f" --steer_amp_range {steer_amp_range}" + + f" --steer_period_min {steer_period_min}" + + f" --steer_period_max {steer_period_max}" + + f" --step_response_max_input {step_response_max_input}" + + f" --step_response_min_length {step_response_min_length}" + + f" --step_response_max_length {step_response_max_length}" + + f" --step_response_interval {step_response_interval}" + + f" --constant_vel_time {constant_vel_time}" + + f" --dir_name {dir_name}" + + use_memory_diff_flag + ) + print_progress(str_run) + subprocess.run(str_run, shell=True) diff --git a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/run_python_simulator.py b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/run_python_simulator.py index 68d8a9f64e73f..739d8ea70a656 100644 --- a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/run_python_simulator.py +++ b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/run_python_simulator.py @@ -12,6 +12,9 @@ # See the License for the specific language governing permissions and # limitations under the License. +import sys + +from assets import ControlType from autoware_smart_mpc_trajectory_follower.training_and_data_check import train_drive_NN_model import numpy as np import python_simulator @@ -20,17 +23,40 @@ [0.001, 0.03, 0.01, -0.001, 0, 2 * python_simulator.measurement_steer_bias] ) -save_dir = "test_python_sim" -python_simulator.slalom_drive(save_dir=save_dir, initial_error=initial_error) -model_trainer = train_drive_NN_model.train_drive_NN_model() -model_trainer.add_data_from_csv(save_dir) -model_trainer.save_train_data(save_dir) -model_trainer.get_trained_model() -model_trainer.save_models(save_dir=save_dir) +if len(sys.argv) > 1: + if sys.argv[1] == "nominal_test": + save_dir = "test_python_nominal_sim" + python_simulator.drive_sim(save_dir=save_dir, initial_error=initial_error) + else: + print("Invalid argument") + sys.exit(1) +else: + train_dir = "test_python_pure_pursuit_train" + val_dir = "test_python_pure_pursuit_val" -save_dir = "test_python_trained_sim" -load_dir = "test_python_sim" -python_simulator.slalom_drive( - save_dir=save_dir, load_dir=load_dir, use_trained_model=True, initial_error=initial_error -) + python_simulator.drive_sim( + seed=0, + t_range=[0, 900.0], + control_type=ControlType.pp_eight, + save_dir=train_dir, + initial_error=initial_error, + ) + python_simulator.drive_sim( + seed=1, + t_range=[0, 900.0], + control_type=ControlType.pp_eight, + save_dir=val_dir, + initial_error=initial_error, + ) + + model_trainer = train_drive_NN_model.train_drive_NN_model() + model_trainer.add_data_from_csv(train_dir, add_mode="as_train") + model_trainer.add_data_from_csv(val_dir, add_mode="as_val") + model_trainer.get_trained_model(use_polynomial_reg=True) + model_trainer.save_models(save_dir=train_dir) + + save_dir = "test_python_trained_sim" + python_simulator.drive_sim( + save_dir=save_dir, load_dir=train_dir, use_trained_model=True, initial_error=initial_error + ) diff --git a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/run_sim.py b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/run_sim.py index d2bb4ea26f0a6..f6e7337997edf 100644 --- a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/run_sim.py +++ b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/run_sim.py @@ -11,11 +11,8 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - -# cspell: ignore oneline - import argparse -from enum import Enum +import datetime from importlib import reload as ir import json import os @@ -23,19 +20,17 @@ import traceback from typing import Dict +from assets import ChangeParam # type: ignore +from assets import ControlType +from assets import DataCollectionMode +from assets import DirGenerator from autoware_smart_mpc_trajectory_follower.training_and_data_check import train_drive_NN_model import numpy as np -import python_simulator - -parser = argparse.ArgumentParser() -parser.add_argument("--param_name", default=None) -args = parser.parse_args() - -P1 = 0.8 -P2 = 1.2 +import python_simulator # type: ignore USE_TRAINED_MODEL_DIFF = True -DATA_COLLECTION_MODE = "pp" # option: "pp": pure_pursuit, "ff": feed_forward, "mpc": smart_mpc +DATA_COLLECTION_MODE = DataCollectionMode.pp +CONTROL_TYPE_TO_SKIP = [ControlType.pp_straight] USE_POLYNOMIAL_REGRESSION = True USE_SELECTED_POLYNOMIAL = True FORCE_NN_MODEL_TO_ZERO = False @@ -45,93 +40,30 @@ USE_INTERCEPT = False # Should be True if FORCE_NN_MODEL_TO_ZERO is True -class ChangeParam(Enum): - """Parameters to be changed when running the simulation.""" - - steer_bias = [ - -1.0 * np.pi / 180.0, - -0.8 * np.pi / 180.0, - -0.6 * np.pi / 180.0, - -0.4 * np.pi / 180.0, - -0.2 * np.pi / 180.0, - 0.0, - 0.2 * np.pi / 180.0, - 0.4 * np.pi / 180.0, - 0.6 * np.pi / 180.0, - 0.8 * np.pi / 180.0, - 1.0 * np.pi / 180.0, - ] - """steer midpoint (soft + hard)""" - - steer_rate_lim = [0.020, 0.050, 0.100, 0.150, 0.200, 0.300, 0.400, 0.500] - """Maximum steer angular velocity""" - - vel_rate_lim = [0.5, 1.0, 3.0, 5.0, 7.0, 9.0] - """Maximum acceleration/deceleration""" - - wheel_base = [0.5, 1.0, 1.5, 2.0, 3.0, 5.0, 7.0] - """wheel base""" - - steer_dead_band = [0.0000, 0.0012, 0.0025, 0.0050, 0.01] - """steer dead band""" - - adaptive_gear_ratio_coef = [ - [15.713, 0.053, 0.042, 15.713, 0.053, 0.042], - [15.713, 0.053, 0.042, P1 * 15.713, 0.053, 0.042], - [15.713, 0.053, 0.042, P2 * 15.713, 0.053, 0.042], - [15.713, 0.053, 0.042, 15.713, P1 * 0.053, 0.042], - [15.713, 0.053, 0.042, P1 * 15.713, P1 * 0.053, 0.042], - [15.713, 0.053, 0.042, P2 * 15.713, P1 * 0.053, 0.042], - [15.713, 0.053, 0.042, 15.713, P2 * 0.053, 0.042], - [15.713, 0.053, 0.042, P1 * 15.713, P2 * 0.053, 0.042], - [15.713, 0.053, 0.042, P2 * 15.713, P2 * 0.053, 0.042], - [15.713, 0.053, 0.042, 15.713, 0.053, P1 * 0.042], - [15.713, 0.053, 0.042, P1 * 15.713, 0.053, P1 * 0.042], - [15.713, 0.053, 0.042, P2 * 15.713, 0.053, P1 * 0.042], - [15.713, 0.053, 0.042, 15.713, P1 * 0.053, P1 * 0.042], - [15.713, 0.053, 0.042, P1 * 15.713, P1 * 0.053, P1 * 0.042], - [15.713, 0.053, 0.042, P2 * 15.713, P1 * 0.053, P1 * 0.042], - [15.713, 0.053, 0.042, 15.713, P2 * 0.053, P1 * 0.042], - [15.713, 0.053, 0.042, P1 * 15.713, P2 * 0.053, P1 * 0.042], - [15.713, 0.053, 0.042, P2 * 15.713, P2 * 0.053, P1 * 0.042], - [15.713, 0.053, 0.042, 15.713, 0.053, P2 * 0.042], - [15.713, 0.053, 0.042, P1 * 15.713, 0.053, P2 * 0.042], - [15.713, 0.053, 0.042, P2 * 15.713, 0.053, P2 * 0.042], - [15.713, 0.053, 0.042, 15.713, P1 * 0.053, P2 * 0.042], - [15.713, 0.053, 0.042, P1 * 15.713, P1 * 0.053, P2 * 0.042], - [15.713, 0.053, 0.042, P2 * 15.713, P1 * 0.053, P2 * 0.042], - [15.713, 0.053, 0.042, 15.713, P2 * 0.053, P2 * 0.042], - [15.713, 0.053, 0.042, P1 * 15.713, P2 * 0.053, P2 * 0.042], - [15.713, 0.053, 0.042, P2 * 15.713, P2 * 0.053, P2 * 0.042], - ] - """velocity-dependent gear ratio""" - - acc_time_delay = [0.00, 0.1, 0.27, 0.40, 0.60, 0.80, 1.01] - """acc time delay""" - - steer_time_delay = [0.00, 0.1, 0.27, 0.40, 0.60, 0.80, 1.02] - """steer time delay""" - - acc_time_constant = [0.01, 0.1, 0.20, 0.24, 0.40, 0.60, 0.80, 1.01] - """time constant""" - - steer_time_constant = [0.01, 0.1, 0.20, 0.24, 0.40, 0.60, 0.80, 1.02] - """time constant""" - - accel_map_scale = [0.2, 0.5, 1.0, 2.0, 3.0, 4.0, 5.0] - """pedal - real acceleration correspondence""" - - acc_scaling = [0.2, 0.5, 1.0, 2.0, 3.0, 4.0, 5.01] - """Acceleration scaling coefficient""" - - steer_scaling = [0.2, 0.5, 1.0, 2.0, 3.0, 4.0, 5.02] - """Steer scaling coefficient""" - - vehicle_type = [1, 2, 3, 4] - """change to other vehicle parameters""" - - -def run_simulator(change_param: ChangeParam): +def run_simulator( + change_param: ChangeParam, + root: str = ".", + batch_size=5, + patience_1=10, + patience_2=10, + acc_amp_range=0.05, + acc_period_range=[5.0, 20.0], + hidden_layer_sizes=(16, 16), + hidden_layer_lstm=8, + steer_amp_range=0.05, + steer_period_range=[5.0, 30.0], + acc_max=1.2, + constant_vel_time=5.0, + split_size=5, + step_response_max_input=0.01, + step_response_max_length=1.5, + step_response_start_time_ratio=0.0, + step_response_interval=5.0, + step_response_min_length=0.5, + use_memory_diff=True, # False, + skip_data_collection=False, + smoothing_trajectory_data_flag=True, +): """Run the simulator.""" # initialize parameters print("reset sim_setting_json") @@ -142,6 +74,8 @@ def run_simulator(change_param: ChangeParam): param_val_list = change_param.value start_time = time.time() + dir_generator = DirGenerator(root=root) + for j in range(len(param_val_list)): i = j + 0 with open("sim_setting.json", "r") as f: @@ -151,28 +85,18 @@ def run_simulator(change_param: ChangeParam): with open("sim_setting.json", "w") as f: json.dump(data, f) ir(python_simulator) + training_data_dirs = [] + val_data_dirs = [] try: initial_error = np.array( - [0.001, 0.03, 0.01, -0.001, 0, 2 * python_simulator.measurement_steer_bias] + [0.001, 0.03, 0.01, 0.0, 0, python_simulator.measurement_steer_bias] ) - if DATA_COLLECTION_MODE in ["ff", "pp"]: - if DATA_COLLECTION_MODE == "ff": - save_dir = "test_feedforward_sim" + change_param.name + str(i) - else: - save_dir = "test_pure_pursuit_sim" + change_param.name + str(i) - python_simulator.slalom_drive( - save_dir=save_dir, - control_type=DATA_COLLECTION_MODE, - t_range=[0, 200.0], - acc_width_range=0.005, - acc_period_range=[5.0, 20.0], - steer_width_range=0.005, - steer_period_range=[5.0, 20.0], - large_steer_width_range=0.05, - large_steer_period_range=[10.0, 20.0], - start_large_steer_time=150.0, - ) + if DATA_COLLECTION_MODE in [ + DataCollectionMode.ff, + DataCollectionMode.pp, + DataCollectionMode.npp, + ]: if FORCE_NN_MODEL_TO_ZERO: model_trainer = train_drive_NN_model.train_drive_NN_model() else: @@ -181,51 +105,108 @@ def run_simulator(change_param: ChangeParam): alpha_2_for_polynomial_regression=0.1**5, ) - model_trainer.add_data_from_csv(save_dir) - start_time_learning = time.time() - model_trainer.get_trained_model( - use_polynomial_reg=USE_POLYNOMIAL_REGRESSION, - use_selected_polynomial=USE_SELECTED_POLYNOMIAL, - force_NN_model_to_zero=FORCE_NN_MODEL_TO_ZERO, - fit_intercept=FIT_INTERCEPT, - use_intercept=USE_INTERCEPT, - ) - learning_computation_time = time.time() - start_time_learning - model_trainer.plot_trained_result(save_dir=save_dir) - model_trainer.save_models(save_dir=save_dir) + for control_type in DATA_COLLECTION_MODE.toControlTypes(): + if control_type in CONTROL_TYPE_TO_SKIP: + continue + add_mode = ["as_val", "as_train"] + y_length = 60.0 + x_length = 120.0 + initial_error_diff = np.zeros(6) + if control_type == ControlType.pp_eight: + initial_error_diff[0] = -(x_length - y_length) / 4.0 + initial_error_diff[1] = -y_length / 4.0 + else: + initial_error_diff[1] = -0.05 + for k in range(2): + save_dir = dir_generator.test_dir_name( + control_type=control_type, + change_param=change_param, + index=i, + validation_flag=(1 - k), + ) + if not skip_data_collection: + python_simulator.drive_sim( + save_dir=save_dir, + control_type=control_type, + seed=k + 1, + t_range=[0, 900.0], + acc_amp_range=acc_amp_range, + acc_period_range=acc_period_range, + steer_amp_range=steer_amp_range, + steer_period_range=steer_period_range, + large_steer_amp_range=0.0, + large_steer_period_range=[10.0, 20.0], + start_large_steer_time=150.0, + acc_max=acc_max, + constant_vel_time=constant_vel_time, + split_size=split_size, + y_length=(0.9 ** (1 - k)) * y_length, + x_length=(0.9 ** (1 - k)) * x_length, + step_response_max_input=step_response_max_input, + step_response_max_length=step_response_max_length, + step_response_start_time_ratio=step_response_start_time_ratio, + step_response_interval=step_response_interval, + step_response_min_length=step_response_min_length, + initial_error=initial_error + (1 - k) * initial_error_diff, + smoothing_trajectory_data_flag=smoothing_trajectory_data_flag, + ) + model_trainer.add_data_from_csv(save_dir, add_mode=add_mode[k]) + if k == 0: + val_data_dirs.append(save_dir) + else: + training_data_dirs.append(save_dir) + + if not skip_data_collection: + start_time_learning = time.time() + model_trainer.get_trained_model( + use_polynomial_reg=USE_POLYNOMIAL_REGRESSION, + use_selected_polynomial=USE_SELECTED_POLYNOMIAL, + force_NN_model_to_zero=FORCE_NN_MODEL_TO_ZERO, + fit_intercept=FIT_INTERCEPT, + use_intercept=USE_INTERCEPT, + hidden_layer_sizes=hidden_layer_sizes, + hidden_layer_lstm=hidden_layer_lstm, + batch_size=batch_size, + patience=patience_1, + ) + learning_computation_time = time.time() - start_time_learning + model_trainer.plot_trained_result(save_dir=save_dir) + model_trainer.save_models(save_dir=save_dir) + print("learning_computation_time:", learning_computation_time) + f = open( + save_dir + + f"/computational_time_learning_from_{DATA_COLLECTION_MODE}_data.txt", + "w", + ) + f.write(str(learning_computation_time)) + f.close() load_dir = save_dir - if DATA_COLLECTION_MODE == "ff": - save_dir = "test_python_ff_aided_sim_" + change_param.name + str(i) - elif DATA_COLLECTION_MODE == "pp": - save_dir = "test_python_pp_aided_sim_" + change_param.name + str(i) - auto_test_performance_result_list = python_simulator.slalom_drive( + save_dir = dir_generator.test_dir_name( + data_collection_mode=DATA_COLLECTION_MODE, + change_param=change_param, + index=i, + use_memory_diff=use_memory_diff, + ) + auto_test_performance_result_list = python_simulator.drive_sim( load_dir=load_dir, save_dir=save_dir, use_trained_model=True, use_trained_model_diff=USE_TRAINED_MODEL_DIFF, initial_error=initial_error, + use_memory_diff=use_memory_diff, ) + training_data_dirs.append(save_dir) - print("learning_computation_time:", learning_computation_time) - if DATA_COLLECTION_MODE == "ff": - f = open(save_dir + "/computational_time_learning_from_ff_data.txt", "w") - f.write(str(learning_computation_time)) - f.close() - f = open( - "auto_test_result_intermediate_model_control_trained_with_data_collected_by_ff_control.csv", - mode="a", - ) - elif DATA_COLLECTION_MODE == "pp": - f = open(save_dir + "/computational_time_learning_from_pp_data.txt", "w") - f.write(str(learning_computation_time)) - f.close() - f = open( - "auto_test_result_intermediate_model_control_trained_with_data_collected_by_pp_control.csv", - mode="a", - ) + f = open( + f"auto_test_result_intermediate_model_control_trained_with_data_collected_by_{control_type}_control.csv", + mode="a", + ) else: - save_dir = "test_python_sim_" + change_param.name + str(i) - auto_test_performance_result_list = python_simulator.slalom_drive( + save_dir = dir_generator.test_dir_name( + change_param=change_param, + index=i, + ) + auto_test_performance_result_list = python_simulator.drive_sim( save_dir=save_dir, initial_error=initial_error, ) @@ -236,7 +217,7 @@ def run_simulator(change_param: ChangeParam): str(param_val_list[j]).replace(",", "_"), *auto_test_performance_result_list, sep=",", - file=f + file=f, ) f.close() @@ -246,10 +227,16 @@ def run_simulator(change_param: ChangeParam): if not skip_learning_for_developing_testcase: ir(train_drive_NN_model) model_trainer = train_drive_NN_model.train_drive_NN_model() - learning_computation_time = None - if DATA_COLLECTION_MODE in ["ff", "pp"]: - model_trainer.add_data_from_csv(load_dir) - model_trainer.add_data_from_csv(save_dir) + learning_computation_time = None # type: ignore + if DATA_COLLECTION_MODE in [ + DataCollectionMode.ff, + DataCollectionMode.pp, + DataCollectionMode.npp, + ]: + for dir_name in val_data_dirs: + model_trainer.add_data_from_csv(dir_name, add_mode="as_val") + for dir_name in training_data_dirs: + model_trainer.add_data_from_csv(dir_name, add_mode="as_train") start_time_learning = time.time() model_trainer.update_saved_trained_model( path=load_dir + "/model_for_test_drive.pth", @@ -258,16 +245,25 @@ def run_simulator(change_param: ChangeParam): force_NN_model_to_zero=FORCE_NN_MODEL_TO_ZERO, fit_intercept=FIT_INTERCEPT, use_intercept=USE_INTERCEPT, + batch_size=batch_size, + patience=patience_2, ) learning_computation_time = time.time() - start_time_learning model_trainer.plot_trained_result(save_dir=save_dir) model_trainer.save_models(save_dir=save_dir) - if DATA_COLLECTION_MODE == "ff": - load_dir = "test_python_ff_aided_sim_" + change_param.name + str(i) - save_dir = "test_python_ff_aided_sim_trained_" + change_param.name + str(i) - elif DATA_COLLECTION_MODE == "pp": - load_dir = "test_python_pp_aided_sim_" + change_param.name + str(i) - save_dir = "test_python_pp_aided_sim_trained_" + change_param.name + str(i) + load_dir = dir_generator.test_dir_name( + data_collection_mode=DATA_COLLECTION_MODE, + change_param=change_param, + index=i, + use_memory_diff=use_memory_diff, + ) + save_dir = dir_generator.test_dir_name( + data_collection_mode=DATA_COLLECTION_MODE, + trained=True, + change_param=change_param, + index=i, + use_memory_diff=use_memory_diff, + ) else: model_trainer.add_data_from_csv(save_dir) start_time_learning = time.time() @@ -277,20 +273,35 @@ def run_simulator(change_param: ChangeParam): force_NN_model_to_zero=FORCE_NN_MODEL_TO_ZERO, fit_intercept=FIT_INTERCEPT, use_intercept=USE_INTERCEPT, + hidden_layer_sizes=hidden_layer_sizes, + hidden_layer_lstm=hidden_layer_lstm, + batch_size=batch_size, + patience=patience_1, ) learning_computation_time = time.time() - start_time_learning model_trainer.plot_trained_result(save_dir=save_dir) model_trainer.save_models(save_dir=save_dir) - load_dir = "test_python_sim_" + change_param.name + str(i) - save_dir = "test_python_sim_trained_" + change_param.name + str(i) - auto_test_performance_result_list = python_simulator.slalom_drive( + load_dir = dir_generator.test_dir_name( + change_param=change_param, + index=i, + use_memory_diff=use_memory_diff, + ) + save_dir = dir_generator.test_dir_name( + trained=True, + change_param=change_param, + index=i, + use_memory_diff=use_memory_diff, + ) + auto_test_performance_result_list = python_simulator.drive_sim( load_dir=load_dir, use_trained_model=True, use_trained_model_diff=USE_TRAINED_MODEL_DIFF, save_dir=save_dir, initial_error=initial_error, + use_memory_diff=use_memory_diff, ) + # cSpell:ignore oneline print("learning_computation_time: ", learning_computation_time) f = open(save_dir + "/test_info.txt", "w") f.write("commit id: " + str(os.popen("git log --oneline -1").read()) + "\n") @@ -307,55 +318,33 @@ def run_simulator(change_param: ChangeParam): f.write("USE_INTERCEPT: " + str(USE_INTERCEPT) + "\n") f.close() - if DATA_COLLECTION_MODE == "ff": - f = open( - "auto_test_result_final_model_control_trained_with_data_collected_by_ff_control.csv", - mode="a", - ) - elif DATA_COLLECTION_MODE == "pp": - f = open( - "auto_test_result_final_model_control_trained_with_data_collected_by_pp_control.csv", - mode="a", - ) - else: - f = open( - "auto_test_result_final_model_control_trained_with_data_collected_by_nominal_control.csv", - mode="a", - ) + f = open( + f"auto_test_result_final_model_control_trained_with_data_collected_by_{DATA_COLLECTION_MODE}_control.csv", + mode="a", + ) print( change_param.name, str(param_val_list[j]).replace(",", "_"), *auto_test_performance_result_list, sep=",", - file=f + file=f, ) f.close() print("experiment success") except Exception: print("# Catch Exception #") - print(traceback.print_exc()) + traceback.print_exc() auto_test_performance_result_list = [1e16] * 11 - if DATA_COLLECTION_MODE == "ff": - f = open( - "auto_test_result_final_model_control_trained_with_data_collected_by_ff_control.csv", - mode="a", - ) - elif DATA_COLLECTION_MODE == "pp": - f = open( - "auto_test_result_final_model_control_trained_with_data_collected_by_pp_control.csv", - mode="a", - ) - else: - f = open( - "auto_test_result_final_model_control_trained_with_data_collected_by_nominal_control.csv", - mode="a", - ) + f = open( + f"auto_test_result_final_model_control_trained_with_data_collected_by_{DATA_COLLECTION_MODE}_control.csv", + mode="a", + ) print( change_param.name, str(param_val_list[j]).replace(",", "_"), *auto_test_performance_result_list, sep=",", - file=f + file=f, ) f.close() print("experiment failure") @@ -373,20 +362,29 @@ def yes_no_input(): if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--param_name", default=None) + parser.add_argument("--root", default=".") + args = parser.parse_args() + + if args.root == "time": + args.root = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") + args.root = "test_run_sim_" + args.root + if args.param_name is None: print("Do you want to run the simulation for all parameters at once?") if yes_no_input(): for _, change_param in ChangeParam.__members__.items(): - run_simulator(change_param) + run_simulator(change_param, root=args.root) else: print("Enter the name of the parameter for which the simulation is to be run.") input_string = input() for name, change_param in ChangeParam.__members__.items(): if name == input_string: - run_simulator(change_param) + run_simulator(change_param, root=args.root) break else: for name, change_param in ChangeParam.__members__.items(): if name == args.param_name: - run_simulator(change_param) + run_simulator(change_param, root=args.root) break diff --git a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/test_figure_eight_param.py b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/test_figure_eight_param.py new file mode 100644 index 0000000000000..eaaeeaf8351b5 --- /dev/null +++ b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/python_simulator/test_figure_eight_param.py @@ -0,0 +1,112 @@ +# Copyright 2024 Proxima Technology Inc, TIER IV +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import argparse +import os + +from assets import ChangeParam # type: ignore +import numpy as np +import run_sim # type: ignore + +if __name__ == "__main__": + test_NN_size = [(16, 16), (8, 0)] + batch_size = 5 + + parser = argparse.ArgumentParser(description="Process some integers.") + parser.add_argument("--acc_amp_range", type=float) + parser.add_argument("--acc_period_min", type=float) + parser.add_argument("--acc_period_max", type=float) + parser.add_argument("--steer_amp_range", type=float) + parser.add_argument("--steer_period_min", type=float) + parser.add_argument("--steer_period_max", type=float) + parser.add_argument("--step_response_max_input", type=float) + parser.add_argument("--step_response_max_length", type=float) + parser.add_argument("--step_response_interval", type=float) + parser.add_argument("--step_response_min_length", type=float) + parser.add_argument("--constant_vel_time", type=float) + parser.add_argument("--use_memory_diff", action="store_true", help="Use memory diff.") + parser.add_argument("--dir_name", type=str, default="test_vehicles") + + args = parser.parse_args() + + acc_amp_range = args.acc_amp_range + acc_period_min = args.acc_period_min + acc_period_max = args.acc_period_max + steer_amp_range = args.steer_amp_range + steer_period_min = args.steer_period_min + steer_period_max = args.steer_period_max + step_response_max_input = args.step_response_max_input + step_response_max_length = args.step_response_max_length + step_response_interval = args.step_response_interval + step_response_min_length = args.step_response_min_length + constant_vel_time = args.constant_vel_time + use_memory_diff: bool = args.use_memory_diff + dir_name = args.dir_name + + if not os.path.isdir(dir_name): + os.mkdir(dir_name) + condition = np.array( + [ + acc_amp_range, + acc_period_min, + acc_period_max, + steer_amp_range, + steer_period_min, + steer_period_max, + step_response_max_input, + step_response_max_length, + step_response_interval, + step_response_min_length, + constant_vel_time, + ] + ) + print("condition: ", condition) + np.savetxt(dir_name + "/figure_eight_param.csv", condition, delimiter=",") + + if use_memory_diff: + run_sim.run_simulator( + change_param=ChangeParam.test_vehicle, + root=dir_name, + batch_size=batch_size, + hidden_layer_sizes=test_NN_size[0], + hidden_layer_lstm=test_NN_size[1][0], + acc_amp_range=acc_amp_range, + acc_period_range=[acc_period_min, acc_period_max], + steer_amp_range=steer_amp_range, + steer_period_range=[steer_period_min, steer_period_max], + step_response_max_input=step_response_max_input, + step_response_max_length=step_response_max_length, + step_response_interval=step_response_interval, + step_response_min_length=step_response_min_length, + constant_vel_time=constant_vel_time, + use_memory_diff=True, + skip_data_collection=True, + ) + else: + run_sim.run_simulator( + change_param=ChangeParam.test_vehicle, + root=dir_name, + batch_size=batch_size, + hidden_layer_sizes=test_NN_size[0], + hidden_layer_lstm=test_NN_size[1][0], + acc_amp_range=acc_amp_range, + acc_period_range=[acc_period_min, acc_period_max], + steer_amp_range=steer_amp_range, + steer_period_range=[steer_period_min, steer_period_max], + step_response_max_input=step_response_max_input, + step_response_max_length=step_response_max_length, + step_response_interval=step_response_interval, + step_response_min_length=step_response_min_length, + constant_vel_time=constant_vel_time, + use_memory_diff=False, + ) diff --git a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/scripts/drive_NN.py b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/scripts/drive_NN.py index f13aa07b0f795..84a0486b1e074 100644 --- a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/scripts/drive_NN.py +++ b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/scripts/drive_NN.py @@ -25,6 +25,37 @@ dim_acc_layer_1 = 16 dim_acc_layer_2 = 16 +loss_weights = torch.tensor( + [ + drive_functions.NN_x_weight, + drive_functions.NN_y_weight, + drive_functions.NN_yaw_weight, + drive_functions.NN_v_weight, + drive_functions.NN_acc_weight, + drive_functions.NN_steer_weight, + ] +) +loss_weights_diff = torch.tensor( + [ + drive_functions.NN_x_weight_diff, + drive_functions.NN_y_weight_diff, + drive_functions.NN_yaw_weight_diff, + drive_functions.NN_v_weight_diff, + drive_functions.NN_acc_weight_diff, + drive_functions.NN_steer_weight_diff, + ] +) +loss_weights_two_diff = torch.tensor( + [ + drive_functions.NN_x_weight_two_diff, + drive_functions.NN_y_weight_two_diff, + drive_functions.NN_yaw_weight_two_diff, + drive_functions.NN_v_weight_two_diff, + drive_functions.NN_acc_weight_two_diff, + drive_functions.NN_steer_weight_two_diff, + ] +) + def nominal_model_input(Var, lam, step): """Calculate prediction with nominal model that takes into account time constants for the first order and time delays related to the input.""" @@ -43,13 +74,41 @@ def nominal_model_input(Var, lam, step): def loss_fn_plus_tanh(loss_fn, pred, Y, tanh_gain, tanh_weight): """Compute the loss function to be used in the training.""" - loss = loss_fn(pred, Y) + loss = loss_fn(pred * loss_weights, Y * loss_weights) loss += tanh_weight * loss_fn( torch.tanh(tanh_gain * (pred[:, -1] - Y[:, -1])), torch.zeros(Y.shape[0]) ) return loss +def loss_fn_plus_tanh_with_memory( + loss_fn, pred, Y, tanh_gain, tanh_weight, first_order_weight, second_order_weight +): + """Compute the loss function to be used in the training.""" + loss = loss_fn(pred * loss_weights, Y * loss_weights) + loss += ( + first_order_weight + * loss_fn( + (pred[:, 1:] - pred[:, :-1]) * loss_weights_diff, + (Y[:, 1:] - Y[:, :-1]) * loss_weights_diff, + ) + / drive_functions.ctrl_time_step + ) + loss += ( + second_order_weight + * loss_fn( + (pred[:, 2:] + pred[:, :-2] - 2 * pred[:, 1:-1]) * loss_weights_two_diff, + (Y[:, 2:] + Y[:, :-2] - 2 * Y[:, 1:-1]) * loss_weights_two_diff, + ) + / (drive_functions.ctrl_time_step * drive_functions.ctrl_time_step) + ) + loss += tanh_weight * loss_fn( + torch.tanh(tanh_gain * (pred[:, :, -1] - Y[:, :, -1])), + torch.zeros((Y.shape[0], Y.shape[1])), + ) + return loss + + # example usage: `model = DriveNeuralNetwork(params...).to("cpu")` class DriveNeuralNetwork(nn.Module): """Define the neural net model to be used in vehicle control.""" @@ -80,8 +139,6 @@ def __init__( self.steer_input_index = np.concatenate( ([2], np.arange(steer_queue_size_core) + acc_queue_size + 3) ) - self.acc_input_index_ = np.arange(acc_queue_size) + 3 - self.steer_input_index_ = np.arange(steer_queue_size_core) + acc_queue_size + 3 self.steer_input_index_full = np.arange(steer_queue_size) + acc_queue_size + 3 self.acc_layer_1 = nn.Sequential( nn.Linear(self.acc_input_index.shape[0], dim_acc_layer_1), @@ -102,12 +159,12 @@ def __init__( nn.init.uniform_(self.steer_layer_1_tail[0].weight, a=lb, b=ub) nn.init.uniform_(self.steer_layer_1_tail[0].bias, a=lb, b=ub) - self.acc_layer_2 = nn.Sequential(nn.Linear(dim_acc_layer_1, dim_acc_layer_2), nn.Tanh()) + self.acc_layer_2 = nn.Sequential(nn.Linear(dim_acc_layer_1, dim_acc_layer_2), nn.ReLU()) nn.init.uniform_(self.acc_layer_2[0].weight, a=lb, b=ub) nn.init.uniform_(self.acc_layer_2[0].bias, a=lb, b=ub) self.steer_layer_2 = nn.Sequential( - nn.Linear(dim_steer_layer_1_head + dim_steer_layer_1_tail, dim_steer_layer_2), nn.Tanh() + nn.Linear(dim_steer_layer_1_head + dim_steer_layer_1_tail, dim_steer_layer_2), nn.ReLU() ) nn.init.uniform_(self.steer_layer_2[0].weight, a=lb, b=ub) nn.init.uniform_(self.steer_layer_2[0].bias, a=lb, b=ub) @@ -132,22 +189,148 @@ def __init__( self.steer_dropout = nn.Dropout(steer_drop_out) def forward(self, x): - acc_layer_1 = self.acc_layer_1(x[:, self.acc_input_index]) + acc_layer_1 = self.acc_layer_1(drive_functions.acc_normalize * x[:, self.acc_input_index]) steer_layer_1 = torch.cat( ( - self.steer_layer_1_head(x[:, self.steer_input_index]), - self.steer_layer_1_tail(x[:, self.steer_input_index_full]), + self.steer_layer_1_head( + drive_functions.steer_normalize * x[:, self.steer_input_index] + ), + self.steer_layer_1_tail( + drive_functions.steer_normalize * x[:, self.steer_input_index_full] + ), ), dim=1, ) acc_layer_2 = self.acc_layer_2(self.acc_dropout(acc_layer_1)) steer_layer_2 = self.steer_layer_2(self.steer_dropout(steer_layer_1)) - pre_pred = self.linear_relu_stack(torch.cat((x[:, [0]], acc_layer_2, steer_layer_2), dim=1)) + pre_pred = self.linear_relu_stack( + torch.cat( + (drive_functions.vel_normalize * x[:, [0]], acc_layer_2, steer_layer_2), dim=1 + ) + ) pred = self.finalize(torch.cat((pre_pred, acc_layer_2, steer_layer_2), dim=1)) return pred +class DriveNeuralNetworkWithMemory(nn.Module): + """Define the neural net model with memory to be used in vehicle control.""" + + def __init__( + self, + hidden_layer_sizes=(32, 16), + hidden_layer_lstm=64, + randomize=0.01, + acc_drop_out=0.0, + steer_drop_out=0.0, + acc_delay_step=drive_functions.acc_delay_step, + steer_delay_step=drive_functions.steer_delay_step, + acc_time_constant_ctrl=drive_functions.acc_time_constant, + steer_time_constant_ctrl=drive_functions.steer_time_constant, + acc_queue_size=drive_functions.acc_ctrl_queue_size, + steer_queue_size=drive_functions.steer_ctrl_queue_size, + steer_queue_size_core=drive_functions.steer_ctrl_queue_size_core, + ): + super().__init__() + self.acc_time_constant_ctrl = acc_time_constant_ctrl + self.acc_delay_step = acc_delay_step + self.steer_time_constant_ctrl = steer_time_constant_ctrl + self.steer_delay_step = steer_delay_step + + lb = -randomize + ub = randomize + self.acc_input_index = np.concatenate(([1], np.arange(acc_queue_size) + 3)) + self.steer_input_index = np.concatenate( + ([2], np.arange(steer_queue_size_core) + acc_queue_size + 3) + ) + self.steer_input_index_full = np.arange(steer_queue_size) + acc_queue_size + 3 + self.acc_layer_1 = nn.Sequential( + nn.Linear(self.acc_input_index.shape[0], dim_acc_layer_1), + nn.ReLU(), + ) + nn.init.uniform_(self.acc_layer_1[0].weight, a=lb, b=ub) + nn.init.uniform_(self.acc_layer_1[0].bias, a=lb, b=ub) + self.steer_layer_1_head = nn.Sequential( + nn.Linear(self.steer_input_index.shape[0], dim_steer_layer_1_head), + nn.ReLU(), + ) + nn.init.uniform_(self.steer_layer_1_head[0].weight, a=lb, b=ub) + nn.init.uniform_(self.steer_layer_1_head[0].bias, a=lb, b=ub) + + self.steer_layer_1_tail = nn.Sequential( + nn.Linear(self.steer_input_index_full.shape[0], dim_steer_layer_1_tail), nn.ReLU() + ) + nn.init.uniform_(self.steer_layer_1_tail[0].weight, a=lb, b=ub) + nn.init.uniform_(self.steer_layer_1_tail[0].bias, a=lb, b=ub) + + self.acc_layer_2 = nn.Sequential(nn.Linear(dim_acc_layer_1, dim_acc_layer_2), nn.ReLU()) + nn.init.uniform_(self.acc_layer_2[0].weight, a=lb, b=ub) + nn.init.uniform_(self.acc_layer_2[0].bias, a=lb, b=ub) + + self.steer_layer_2 = nn.Sequential( + nn.Linear(dim_steer_layer_1_head + dim_steer_layer_1_tail, dim_steer_layer_2), nn.ReLU() + ) + nn.init.uniform_(self.steer_layer_2[0].weight, a=lb, b=ub) + nn.init.uniform_(self.steer_layer_2[0].bias, a=lb, b=ub) + + self.lstm = nn.LSTM( + 1 + dim_acc_layer_2 + dim_steer_layer_2, hidden_layer_lstm, batch_first=True + ) + + nn.init.uniform_(self.lstm.weight_hh_l0, a=lb, b=ub) + nn.init.uniform_(self.lstm.weight_ih_l0, a=lb, b=ub) + nn.init.uniform_(self.lstm.bias_hh_l0, a=lb, b=ub) + nn.init.uniform_(self.lstm.bias_ih_l0, a=lb, b=ub) + + self.linear_relu_stack_1 = nn.Sequential( + nn.Linear(1 + dim_acc_layer_2 + dim_steer_layer_2, hidden_layer_sizes[0]), + nn.ReLU(), + ) + nn.init.uniform_(self.linear_relu_stack_1[0].weight, a=lb, b=ub) + nn.init.uniform_(self.linear_relu_stack_1[0].bias, a=lb, b=ub) + + self.linear_relu_stack_2 = nn.Sequential( + nn.Linear(hidden_layer_lstm + hidden_layer_sizes[0], hidden_layer_sizes[1]), + nn.ReLU(), + ) + nn.init.uniform_(self.linear_relu_stack_2[0].weight, a=lb, b=ub) + nn.init.uniform_(self.linear_relu_stack_2[0].bias, a=lb, b=ub) + + self.finalize = nn.Linear(hidden_layer_sizes[1] + dim_acc_layer_2 + dim_steer_layer_2, 6) + + nn.init.uniform_(self.finalize.weight, a=lb, b=ub) + nn.init.uniform_(self.finalize.bias, a=lb, b=ub) + + self.acc_dropout = nn.Dropout(acc_drop_out) + self.steer_dropout = nn.Dropout(steer_drop_out) + + def forward(self, x): + acc_layer_1 = self.acc_layer_1( + drive_functions.acc_normalize * x[:, :, self.acc_input_index] + ) + steer_layer_1 = torch.cat( + ( + self.steer_layer_1_head( + drive_functions.steer_normalize * x[:, :, self.steer_input_index] + ), + self.steer_layer_1_tail( + drive_functions.steer_normalize * x[:, :, self.steer_input_index_full] + ), + ), + dim=2, + ) + acc_layer_2 = self.acc_layer_2(self.acc_dropout(acc_layer_1)) + steer_layer_2 = self.steer_layer_2(self.steer_dropout(steer_layer_1)) + h_1 = torch.cat( + (drive_functions.vel_normalize * x[:, :, [0]], acc_layer_2, steer_layer_2), dim=2 + ) + pre_pred, _ = self.lstm(h_1) + pre_pred = torch.cat((pre_pred, self.linear_relu_stack_1(h_1)), dim=2) + pre_pred = self.linear_relu_stack_2(pre_pred) + pred = self.finalize(torch.cat((pre_pred, acc_layer_2, steer_layer_2), dim=2)) + return pred + + class EarlyStopping: """Class for early stopping in NN training.""" @@ -180,68 +363,110 @@ def __init__( A_for_linear_reg, b_for_linear_reg, deg, - acc_time_constant, - acc_delay_step, - steer_time_constant, - steer_delay_step, - acc_queue_size, - steer_queue_size, - steer_queue_size_core, + acc_delay_step=drive_functions.acc_delay_step, + steer_delay_step=drive_functions.steer_delay_step, + acc_queue_size=drive_functions.acc_ctrl_queue_size, + steer_queue_size=drive_functions.steer_ctrl_queue_size, + steer_queue_size_core=drive_functions.steer_ctrl_queue_size_core, + vel_normalize=drive_functions.vel_normalize, + acc_normalize=drive_functions.acc_normalize, + steer_normalize=drive_functions.steer_normalize, + ): + self.transform = proxima_calc.transform_model_to_eigen() + self.transform.set_params( + model.acc_layer_1[0].weight.detach().numpy().astype(np.float64), + model.steer_layer_1_head[0].weight.detach().numpy().astype(np.float64), + model.steer_layer_1_tail[0].weight.detach().numpy().astype(np.float64), + model.acc_layer_2[0].weight.detach().numpy().astype(np.float64), + model.steer_layer_2[0].weight.detach().numpy().astype(np.float64), + model.linear_relu_stack[0].weight.detach().numpy().astype(np.float64), + model.linear_relu_stack[2].weight.detach().numpy().astype(np.float64), + model.finalize.weight.detach().numpy().astype(np.float64), + model.acc_layer_1[0].bias.detach().numpy().astype(np.float64), + model.steer_layer_1_head[0].bias.detach().numpy().astype(np.float64), + model.steer_layer_1_tail[0].bias.detach().numpy().astype(np.float64), + model.acc_layer_2[0].bias.detach().numpy().astype(np.float64), + model.steer_layer_2[0].bias.detach().numpy().astype(np.float64), + model.linear_relu_stack[0].bias.detach().numpy().astype(np.float64), + model.linear_relu_stack[2].bias.detach().numpy().astype(np.float64), + model.finalize.bias.detach().numpy().astype(np.float64), + A_for_linear_reg, + b_for_linear_reg, + deg, + acc_delay_step, + steer_delay_step, + acc_queue_size, + steer_queue_size, + steer_queue_size_core, + vel_normalize, + acc_normalize, + steer_normalize, + ) + self.pred = self.transform.rot_and_d_rot_error_prediction + self.pred_only_state = self.transform.rotated_error_prediction + self.pred_with_diff = self.transform.rot_and_d_rot_error_prediction_with_diff + self.pred_with_poly_diff = self.transform.rot_and_d_rot_error_prediction_with_poly_diff + self.Pred = self.transform.Rotated_error_prediction + + +class transform_model_with_memory_to_c: + """Pass the necessary information to the C++ program to call the trained model at high speed.""" + + def __init__( + self, + model, + A_for_linear_reg, + b_for_linear_reg, + deg, + acc_delay_step=drive_functions.acc_delay_step, + steer_delay_step=drive_functions.steer_delay_step, + acc_queue_size=drive_functions.acc_ctrl_queue_size, + steer_queue_size=drive_functions.steer_ctrl_queue_size, + steer_queue_size_core=drive_functions.steer_ctrl_queue_size_core, + vel_normalize=drive_functions.vel_normalize, + acc_normalize=drive_functions.acc_normalize, + steer_normalize=drive_functions.steer_normalize, ): - transformer = proxima_calc.transform_model_to_eigen() - - numpy_weight_list = [] - numpy_bias_list = [] - for i, w in enumerate(model.parameters()): - if i % 2 == 0: - numpy_weight_list.append(w.detach().numpy().astype(np.float64)) - else: - numpy_bias_list.append(w.detach().numpy().astype(np.float64)) - array_weight_list_0 = numpy_weight_list[0] - array_bias_list_0 = numpy_bias_list[0] - array_weight_list_1 = numpy_weight_list[1] - array_bias_list_1 = numpy_bias_list[1] - array_weight_list_2 = numpy_weight_list[2] - array_bias_list_2 = numpy_bias_list[2] - array_weight_list_3 = numpy_weight_list[3] - array_bias_list_3 = numpy_bias_list[3] - array_weight_list_4 = numpy_weight_list[4] - array_bias_list_4 = numpy_bias_list[4] - array_weight_list_5 = numpy_weight_list[5] - array_bias_list_5 = numpy_bias_list[5] - array_weight_list_6 = numpy_weight_list[6] - array_bias_list_6 = numpy_bias_list[6] - array_weight_list_7 = numpy_weight_list[7] - array_bias_list_7 = numpy_bias_list[7] - - transformer.set_params( - array_weight_list_0, - array_weight_list_1, - array_weight_list_2, - array_weight_list_3, - array_weight_list_4, - array_weight_list_5, - array_weight_list_6, - array_weight_list_7, - array_bias_list_0, - array_bias_list_1, - array_bias_list_2, - array_bias_list_3, - array_bias_list_4, - array_bias_list_5, - array_bias_list_6, - array_bias_list_7, + self.transform = proxima_calc.transform_model_with_memory_to_eigen() + self.transform.set_params( + model.acc_layer_1[0].weight.detach().numpy().astype(np.float64), + model.steer_layer_1_head[0].weight.detach().numpy().astype(np.float64), + model.steer_layer_1_tail[0].weight.detach().numpy().astype(np.float64), + model.acc_layer_2[0].weight.detach().numpy().astype(np.float64), + model.steer_layer_2[0].weight.detach().numpy().astype(np.float64), + model.lstm.weight_ih_l0.detach().numpy().astype(np.float64), + model.lstm.weight_hh_l0.detach().numpy().astype(np.float64), + model.linear_relu_stack_1[0].weight.detach().numpy().astype(np.float64), + model.linear_relu_stack_2[0].weight.detach().numpy().astype(np.float64), + model.finalize.weight.detach().numpy().astype(np.float64), + model.acc_layer_1[0].bias.detach().numpy().astype(np.float64), + model.steer_layer_1_head[0].bias.detach().numpy().astype(np.float64), + model.steer_layer_1_tail[0].bias.detach().numpy().astype(np.float64), + model.acc_layer_2[0].bias.detach().numpy().astype(np.float64), + model.steer_layer_2[0].bias.detach().numpy().astype(np.float64), + model.lstm.bias_hh_l0.detach().numpy().astype(np.float64), + model.lstm.bias_ih_l0.detach().numpy().astype(np.float64), + model.linear_relu_stack_1[0].bias.detach().numpy().astype(np.float64), + model.linear_relu_stack_2[0].bias.detach().numpy().astype(np.float64), + model.finalize.bias.detach().numpy().astype(np.float64), + ) + self.transform.set_params_res( A_for_linear_reg, b_for_linear_reg, deg, - acc_time_constant, acc_delay_step, - steer_time_constant, steer_delay_step, acc_queue_size, steer_queue_size, steer_queue_size_core, + vel_normalize, + acc_normalize, + steer_normalize, ) - self.pred = transformer.rot_and_d_rot_error_prediction - self.pred_with_diff = transformer.rot_and_d_rot_error_prediction_with_diff - self.Pred = transformer.Rotated_error_prediction + self.pred = self.transform.rot_and_d_rot_error_prediction + self.pred_only_state = self.transform.rotated_error_prediction + self.pred_with_diff = self.transform.rot_and_d_rot_error_prediction_with_diff + self.pred_with_memory_diff = self.transform.rot_and_d_rot_error_prediction_with_memory_diff + self.pred_with_poly_diff = self.transform.rot_and_d_rot_error_prediction_with_poly_diff + self.Pred = self.transform.Rotated_error_prediction + self.test_lstm = self.transform.error_prediction diff --git a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/scripts/drive_controller.py b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/scripts/drive_controller.py index 3877f7177720b..4fb4fe3a951e1 100644 --- a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/scripts/drive_controller.py +++ b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/scripts/drive_controller.py @@ -20,6 +20,7 @@ from importlib import reload as ir import threading import time +from typing import Literal from autoware_smart_mpc_trajectory_follower.scripts import drive_GP from autoware_smart_mpc_trajectory_follower.scripts import drive_NN @@ -57,8 +58,8 @@ class drive_controller: update_trained_model: bool | None """Whether to update the model online with the data under control.""" - mode: str | None - """which algorithm to use, "mppi", "ilqr", or "mppi_ilqr" """ + mode: Literal["pure_pursuit", "naive_pure_pursuit", "mppi", "ilqr", "mppi_ilqr"] | None + """which algorithm to use""" use_x_noise: bool | None """Whether to reflect the uncertainty of the straight-line position when performing iLQG""" @@ -109,6 +110,7 @@ def __init__( self, use_trained_model: bool | None = None, use_trained_model_diff=None, + use_memory_diff=None, update_trained_model=None, mode=None, use_x_noise=None, @@ -138,6 +140,14 @@ def __init__( ir(drive_iLQR) ir(drive_mppi) + if mode is None: + self.mode = drive_functions.mode + else: + self.mode = mode + + if mode == "pure_pursuit" or mode == "naive_pure_pursuit": + use_trained_model = False + if use_trained_model is None: self.use_trained_model = drive_functions.use_trained_model else: @@ -145,6 +155,7 @@ def __init__( if not self.use_trained_model: self.use_trained_model_diff = False self.update_trained_model = False + self.use_memory_diff = False else: if use_trained_model_diff is None: self.use_trained_model_diff = drive_functions.use_trained_model_diff @@ -154,10 +165,10 @@ def __init__( self.update_trained_model = drive_functions.update_trained_model else: self.update_trained_model = update_trained_model - if mode is None: - self.mode = drive_functions.mode - else: - self.mode = mode + if use_memory_diff is None: + self.use_memory_diff = drive_functions.use_memory_diff + else: + self.use_memory_diff = use_memory_diff self.acc_delay_step = drive_functions.acc_delay_step self.steer_delay_step = drive_functions.steer_delay_step @@ -230,6 +241,8 @@ def __init__( self.use_trained_model_diff, ) self.err = 0 + if self.use_trained_model and drive_functions.use_memory_for_training: + print("use_memory_diff", self.use_memory_diff) if self.use_trained_model: self.model = torch.load(model_file_name) polynomial_reg_info = np.load(self.load_polynomial_reg_dir + "/polynomial_reg_info.npz") @@ -237,30 +250,52 @@ def __init__( self.b_for_polynomial_reg = polynomial_reg_info["b"] self.deg = int(polynomial_reg_info["deg"]) self.polynomial_features = PolynomialFeatures(degree=self.deg, include_bias=False) - transform_model = drive_NN.transform_model_to_c( - self.model, - self.A_for_polynomial_reg, - self.b_for_polynomial_reg, - self.deg, - self.acc_time_constant_ctrl, - self.acc_delay_step, - self.steer_time_constant_ctrl, - self.steer_delay_step, - drive_functions.acc_ctrl_queue_size, - drive_functions.steer_ctrl_queue_size, - drive_functions.steer_ctrl_queue_size_core, - ) - pred = transform_model.pred - Pred = transform_model.Pred - self.pred = pred - self.pred_with_diff = transform_model.pred_with_diff + if drive_functions.use_memory_for_training: + self.transform_model = drive_NN.transform_model_with_memory_to_c( + self.model, + self.A_for_polynomial_reg, + self.b_for_polynomial_reg, + self.deg, + self.acc_delay_step, + self.steer_delay_step, + drive_functions.acc_ctrl_queue_size, + drive_functions.steer_ctrl_queue_size, + drive_functions.steer_ctrl_queue_size_core, + ) + self.h = np.zeros(self.model.lstm.weight_hh_l0.shape[1]) + self.c = (self.h).copy() + if self.use_memory_diff: + self.ilqr.receive_memory_diff( + self.transform_model.transform.get_dhc_dx, + self.transform_model.transform.get_dhc_dhc, + self.transform_model.transform.get_dy_dhc, + ) + else: + self.transform_model = drive_NN.transform_model_to_c( + self.model, + self.A_for_polynomial_reg, + self.b_for_polynomial_reg, + self.deg, + self.acc_delay_step, + self.steer_delay_step, + drive_functions.acc_ctrl_queue_size, + drive_functions.steer_ctrl_queue_size, + drive_functions.steer_ctrl_queue_size_core, + ) + self.pred = self.transform_model.pred + if drive_functions.reflect_only_poly_diff: + self.pred_with_diff = self.transform_model.pred_with_poly_diff + elif self.use_memory_diff and drive_functions.use_memory_for_training: + self.pred_with_diff = self.transform_model.pred_with_memory_diff + else: + self.pred_with_diff = self.transform_model.pred_with_diff self.loss_fn = torch.nn.L1Loss() self.drive_optimizer = torch.optim.Adam( params=self.model.parameters(), lr=drive_learning_rate ) self.F_N_initial_diff = partial( drive_functions.F_with_model_initial_diff, - pred=pred, + pred=self.transform_model.pred, i=self.acc_delay_step, j=self.steer_delay_step, acc_time_constant_ctrl=self.acc_time_constant_ctrl, @@ -275,9 +310,17 @@ def __init__( acc_time_constant_ctrl=self.acc_time_constant_ctrl, steer_time_constant_ctrl=self.steer_time_constant_ctrl, ) + self.F_N_only_state = partial( + drive_functions.F_with_model, + pred=self.transform_model.pred_only_state, + i=self.acc_delay_step, + j=self.steer_delay_step, + acc_time_constant_ctrl=self.acc_time_constant_ctrl, + steer_time_constant_ctrl=self.steer_time_constant_ctrl, + ) self.F_N_for_candidates = partial( drive_functions.F_with_model_for_candidates, - Pred=Pred, + Pred=self.transform_model.Pred, i=self.acc_delay_step, j=self.steer_delay_step, acc_time_constant_ctrl=self.acc_time_constant_ctrl, @@ -292,6 +335,13 @@ def __init__( steer_time_constant_ctrl=self.steer_time_constant_ctrl, ) self.F_N_initial_diff = self.F_N_diff + self.F_N_only_state = partial( + drive_functions.F_with_history, + i=self.acc_delay_step, + j=self.steer_delay_step, + acc_time_constant_ctrl=self.acc_time_constant_ctrl, + steer_time_constant_ctrl=self.steer_time_constant_ctrl, + ) self.F_N_for_candidates = partial( drive_functions.F_with_history_for_candidates, i=self.acc_delay_step, @@ -367,12 +417,25 @@ def __init__( self.x_old = np.empty(6) + self.X_queue_for_learning: list[np.ndarray] = [] + self.acc_input_queue_for_learning: list[np.ndarray] = [] + self.steer_input_queue_for_learning: list[np.ndarray] = [] + self.time_stamp_queue_for_learning: list[float] = [] + self.X_smoothed_queue: list[np.ndarray] = [] + + self.pre_X_input_list: list[np.ndarray] = [] + self.pre_Y_output_list: list[np.ndarray] = [] + + self.time_stamp_for_update_lstm: list[float] = [] + self.X_queue_for_update_lstm: list[np.ndarray] = [] + def __del__(self): print("control finished") def get_optimal_control( self, x_current_: np.ndarray, + time_stamp: list[float], X_des_: np.ndarray, U_des: np.ndarray = np.zeros((drive_functions.N, drive_functions.nu_0)), ) -> np.ndarray: @@ -387,32 +450,43 @@ def get_optimal_control( X_des_, ) self.u_opt = X_des[0, drive_functions.nx_0 + np.arange(drive_functions.nu_0)] - self.u_old = self.u_opt.copy() - self.u_old2 = self.u_opt.copy() self.acc_input_queue = self.u_opt[0] * np.ones(drive_functions.acc_ctrl_queue_size) self.steer_input_queue = self.u_opt[1] * np.ones(drive_functions.steer_ctrl_queue_size) - self.nominal_inputs = drive_functions.U_des_from_X_des(self.u_old, X_des, diff_delta) + self.nominal_inputs = drive_functions.U_des_from_X_des(self.u_opt, X_des, diff_delta) self.previous_error = np.zeros(8) - self.X_queue_for_learning: list[np.ndarray] = [] - self.acc_input_queue_for_learning: list[np.ndarray] = [] - self.steer_input_queue_for_learning: list[np.ndarray] = [] - self.time_stamp_queue_for_learning: list[float] = [] - self.X_smoothed_queue: list[np.ndarray] = [] + self.X_queue_for_learning.clear() + self.acc_input_queue_for_learning.clear() + self.steer_input_queue_for_learning.clear() + self.time_stamp_queue_for_learning.clear() + self.X_smoothed_queue.clear() + + self.pre_X_input_list.clear() + self.pre_Y_output_list.clear() - self.pre_X_input_list: list[np.ndarray] = [] - self.pre_Y_output_list: list[np.ndarray] = [] + self.time_stamp_for_update_lstm.clear() + self.X_queue_for_update_lstm.clear() + if self.use_trained_model and drive_functions.use_memory_for_training: + self.h = np.zeros(self.model.lstm.weight_hh_l0.shape[1]) + self.c = (self.h).copy() self.initialize_X_smoothing_time_stamp = True + self.acc_fb_1 = 0.0 + self.acc_fb_2 = 0.0 + self.steer_fb_1 = 0.0 + self.steer_fb_2 = 0.0 + else: x_current = drive_functions.transform_yaw_for_x_current(self.x_old, x_current_) X_des, diff_delta = drive_functions.transform_yaw_for_X_des( x_current, X_des_, ) - self.u_old2 = self.u_old.copy() - self.u_old = self.u_opt.copy() - + if drive_functions.use_max_curvature: + curvature = np.abs(X_des[:, 5]).max() / drive_functions.L + else: + curvature = np.abs(X_des[:, 5]).mean() / drive_functions.L + steer_rate_cost_coef = drive_functions.calc_steer_rate_cost_coef(curvature) if self.init: self.X_current_queue = [] self.init = False @@ -421,6 +495,10 @@ def get_optimal_control( self.X_current = np.concatenate( (x_current, self.acc_input_queue[::-1], self.steer_input_queue[::-1]) ) + self.u_old = np.array([self.acc_input_queue[-1], self.steer_input_queue[-1]]) + if self.use_trained_model and drive_functions.use_memory_for_training: + if len(time_stamp) > 0: + self.update_lstm_info(time_stamp[-1]) self.X_current_queue.append(self.X_current.copy()) if len(self.X_current_queue) > drive_functions.mpc_freq: @@ -429,22 +507,38 @@ def get_optimal_control( self.nominal_inputs = drive_functions.sg_filter_for_nominal_inputs(self.nominal_inputs) if self.mode == "mppi" or self.mode == "mppi_ilqr": # Run mppi self.start_mppi = time.time() - self.mppi.receive_model(self.F_N_for_candidates) - self.nominal_inputs, self.u_opt_dot, nominal_traj = (self.mppi).compute_optimal_control( - self.X_current, - self.nominal_inputs, - X_des, - U_des, - self.previous_error[:6], - ) - self.nominal_traj_mppi = nominal_traj.copy() + proceed = True + for k in range(drive_functions.max_iter_mppi): + if not proceed: + break + if drive_functions.use_memory_for_training and self.use_trained_model: + self.transform_model.transform.set_lstm(self.h, self.c) + self.transform_model.transform.set_lstm_for_candidate( + self.h, self.c, drive_functions.sample_num + ) + self.mppi.receive_model(self.F_N_for_candidates, self.F_N_only_state) + self.nominal_inputs, self.u_opt_dot, nominal_traj, self.mppi_candidates, proceed = ( + self.mppi + ).compute_optimal_control( + self.X_current, + self.nominal_inputs, + X_des, + U_des, + self.previous_error[:6], + ) + self.nominal_traj_mppi = nominal_traj.copy() self.end_mppi = time.time() - if self.mode != "mppi": + if self.mode == "ilqr" or self.mode == "mppi_ilqr": self.start_ilqr = time.time() proceed = True for k in range(drive_functions.max_iter_ilqr): if not proceed: break + if drive_functions.use_memory_for_training and self.use_trained_model: + self.transform_model.transform.set_lstm(self.h, self.c) + self.transform_model.transform.set_lstm_for_candidate( + self.h, self.c, drive_functions.max_iter_ls + 1 + ) self.nominal_inputs = drive_functions.sg_filter_for_nominal_inputs( self.nominal_inputs ) @@ -465,6 +559,7 @@ def get_optimal_control( self.theta_noise, self.acc_noise, self.steer_noise, + steer_rate_cost_coef, ) self.nominal_traj_ilqr = nominal_traj.copy() self.nominal_inputs_ilqr = self.nominal_inputs.copy() @@ -490,7 +585,41 @@ def get_optimal_control( self.time_5 = self.ilqr.time_5 self.end_ilqr = time.time() + if self.mode == "pure_pursuit": + nominal_traj = np.zeros((drive_functions.N + 1, x_current_.shape[0])) + + u_opt = drive_functions.pure_pursuit_control( + pos_xy_obs=x_current_[:2], + pos_yaw_obs=x_current_[3], + longitudinal_vel_obs=x_current_[2], + pos_xy_ref=X_des_[0, :2], + pos_yaw_ref=X_des_[0, 3], + longitudinal_vel_ref=X_des_[0, 2], + ) + self.u_opt_dot = (u_opt - self.u_old) / drive_functions.ctrl_time_step + + if self.mode == "naive_pure_pursuit": + nominal_traj = np.zeros((drive_functions.N + 1, x_current_.shape[0])) + lookahead_distance = ( + drive_functions.naive_pure_pursuit_lookahead_coef * x_current_[2] + + drive_functions.naive_pure_pursuit_lookahead_intercept + ) + targetIndex = np.abs( + ((X_des_[:, :2] - x_current_[:2]) ** 2).sum(axis=1) - lookahead_distance**2 + ).argmin() + + u_opt = drive_functions.naive_pure_pursuit_control( + pos_xy_obs=x_current_[:2], + pos_yaw_obs=x_current_[3], + longitudinal_vel_obs=x_current_[2], + pos_xy_ref_target=X_des_[targetIndex, :2], + longitudinal_vel_ref_nearest=X_des_[0, 2], + ) + self.u_opt_dot = (u_opt - self.u_old) / drive_functions.ctrl_time_step + if self.use_trained_model: + if drive_functions.use_memory_for_training: + self.transform_model.transform.set_lstm(self.h, self.c) self.previous_error = drive_functions.error_decay * self.previous_error + ( 1 - drive_functions.error_decay ) * self.pred(self.X_current) @@ -505,6 +634,41 @@ def get_optimal_control( acc_lim, acc_rate_lim, ) = drive_functions.calc_limits(x_current[2], x_current[4], x_current[5]) + if not self.initialize_input_queue and ( + self.mode != "pure_pursuit" and self.mode != "naive_pure_pursuit" + ): + self.acc_fb_1, self.acc_fb_2 = np.clip( + drive_functions.acc_prediction_error_compensation( + self.X_current, + self.X_old, + self.u_old, + self.previous_error, + self.acc_fb_1, + self.acc_fb_2, + self.acc_time_stamp - self.acc_time_stamp_old, + ), + -drive_functions.max_error_acc, + drive_functions.max_error_acc, + ) + self.steer_fb_1, self.steer_fb_2 = np.clip( + drive_functions.steer_prediction_error_compensation( + self.X_current, + self.X_old, + self.u_old, + self.previous_error, + self.steer_fb_1, + self.steer_fb_2, + self.steer_time_stamp - self.steer_time_stamp_old, + ), + -drive_functions.max_error_steer, + drive_functions.max_error_steer, + ) + self.u_opt[0] += drive_functions.acc_fb_gain * ( + 2 * self.acc_fb_1 - drive_functions.acc_fb_sec_order_ratio * self.acc_fb_2 + ) + self.u_opt[1] += drive_functions.steer_fb_gain * ( + 2 * self.steer_fb_1 - drive_functions.steer_fb_sec_order_ratio * self.steer_fb_2 + ) self.u_opt = drive_functions.u_cut_off( self.u_opt, @@ -517,10 +681,13 @@ def get_optimal_control( ) self.initialize_input_queue = False - self.X_des_history.append(X_des[0]) + self.X_des_history.append(X_des_[0]) self.initial_guess_for_debug = (self.nominal_inputs).copy() self.x_old = x_current + self.X_old = (self.X_current).copy() + self.acc_time_stamp_old = self.acc_time_stamp + self.steer_time_stamp_old = self.steer_time_stamp return self.u_opt def update_input_queue( @@ -529,12 +696,16 @@ def update_input_queue( acc_history: list[float], steer_history: list[float], x_current_: np.ndarray, + acc_time_stamp: float, + steer_time_stamp: float, ) -> None: """Receives the history of the acceleration and steer inputs and their time stamps. And interpolates them to be the history of the time steps used by the controller. The return value can be passed to get_optimal_control. """ + self.acc_time_stamp = acc_time_stamp + self.steer_time_stamp = steer_time_stamp if not self.initialize_input_queue: if len(time_stamp) == 1: self.acc_input_queue[-1] = acc_history[0] @@ -849,6 +1020,39 @@ def update_input_queue( self.steer_input_queue_for_learning.pop(0) self.time_stamp_queue_for_learning.pop(0) + def update_lstm_info(self, time_stamp): + self.transform_model.transform.set_lstm(0 * self.h, 0 * self.c) + self.time_stamp_for_update_lstm.append(time_stamp) + self.X_queue_for_update_lstm.append(self.X_current) + if len(self.X_queue_for_update_lstm) > 1: + while self.time_stamp_for_update_lstm[-1] - self.time_stamp_for_update_lstm[ + 1 + ] > drive_functions.mpc_time_step * (drive_functions.N + 1): + self.time_stamp_for_update_lstm.pop(0) + self.X_queue_for_update_lstm.pop(0) + if ( + self.time_stamp_for_update_lstm[-1] - self.time_stamp_for_update_lstm[0] + > drive_functions.mpc_time_step + ): + horizon_num = min( + int( + (self.time_stamp_for_update_lstm[-1] - self.time_stamp_for_update_lstm[0]) + / drive_functions.mpc_time_step + ), + drive_functions.N, + ) + time_stamp_interp = ( + self.time_stamp_for_update_lstm[-1] + - drive_functions.mpc_time_step * np.arange(1, horizon_num + 1)[::-1] + ) + X_interp = scipy.interpolate.interp1d( + np.array(self.time_stamp_for_update_lstm), + np.array(self.X_queue_for_update_lstm).T, + )(time_stamp_interp) + self.transform_model.transform.update_memory_by_state_history(X_interp) + self.h = self.transform_model.transform.get_h() + self.c = self.transform_model.transform.get_c() + def update_input_queue_and_get_optimal_control( self, time_stamp, @@ -857,10 +1061,14 @@ def update_input_queue_and_get_optimal_control( x_current_, X_des_, U_des=np.zeros((drive_functions.N, drive_functions.nu_0)), + acc_time_stamp=0.0, + steer_time_stamp=0.0, ): """Run update_input_queue and get_optimal_control all at once.""" - self.update_input_queue(time_stamp, acc_history, steer_history, x_current_) - u_opt = self.get_optimal_control(x_current_, X_des_, U_des) + self.update_input_queue( + time_stamp, acc_history, steer_history, x_current_, acc_time_stamp, steer_time_stamp + ) + u_opt = self.get_optimal_control(x_current_, time_stamp, X_des_, U_des) return u_opt def update_model(self): @@ -888,25 +1096,27 @@ def update_model(self): loss.backward() self.drive_optimizer.step() - transform_model = drive_NN.transform_model_to_c( + self.transform_model = drive_NN.transform_model_to_c( self.model, self.A_for_polynomial_reg, self.b_for_polynomial_reg, self.deg, - self.acc_time_constant_ctrl, self.acc_delay_step, - self.steer_time_constant_ctrl, self.steer_delay_step, drive_functions.acc_ctrl_queue_size, drive_functions.steer_ctrl_queue_size, drive_functions.steer_ctrl_queue_size_core, ) - self.pred = transform_model.pred - self.Pred = transform_model.Pred - self.pred_with_diff = transform_model.pred_with_diff + self.pred = self.transform_model.pred + if drive_functions.reflect_only_poly_diff: + self.pred_with_diff = self.transform_model.pred_with_poly_diff + elif self.use_memory_diff and drive_functions.use_memory_for_training: + self.pred_with_diff = self.transform_model.pred_with_memory_diff + else: + self.pred_with_diff = self.transform_model.pred_with_diff self.F_N_initial_diff = partial( drive_functions.F_with_model_initial_diff, - pred=self.pred, + pred=self.transform_model.pred, i=self.acc_delay_step, j=self.steer_delay_step, acc_time_constant_ctrl=self.acc_time_constant_ctrl, @@ -920,9 +1130,17 @@ def update_model(self): acc_time_constant_ctrl=self.acc_time_constant_ctrl, steer_time_constant_ctrl=self.steer_time_constant_ctrl, ) + self.F_N_only_state = partial( + drive_functions.F_with_model, + pred=self.transform_model.pred_only_state, + i=self.acc_delay_step, + j=self.steer_delay_step, + acc_time_constant_ctrl=self.acc_time_constant_ctrl, + steer_time_constant_ctrl=self.steer_time_constant_ctrl, + ) self.F_N_for_candidates = partial( drive_functions.F_with_model_for_candidates, - Pred=self.Pred, + Pred=self.transform_model.Pred, i=self.acc_delay_step, j=self.steer_delay_step, acc_time_constant_ctrl=self.acc_time_constant_ctrl, diff --git a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/scripts/drive_functions.py b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/scripts/drive_functions.py index fb96e134f7746..81ad4f8b38790 100644 --- a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/scripts/drive_functions.py +++ b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/scripts/drive_functions.py @@ -116,53 +116,40 @@ acc_time_constant = float(nominal_param["nominal_parameter"]["acceleration"]["acc_time_constant"]) steer_time_delay = float(nominal_param["nominal_parameter"]["steering"]["steer_time_delay"]) -steer_delay_step = min( - round(steer_time_delay / ctrl_time_step), steer_ctrl_queue_size_core - mpc_freq -) +steer_delay_step = min(round(steer_time_delay / ctrl_time_step), steer_ctrl_queue_size - mpc_freq) steer_time_constant = float(nominal_param["nominal_parameter"]["steering"]["steer_time_constant"]) -min_steer_rate_transform_for_start = float( - mpc_param["mpc_parameter"]["cost_parameters"]["min_steer_rate_transform_for_start"] -) -power_steer_rate_transform_for_start = int( - mpc_param["mpc_parameter"]["cost_parameters"]["power_steer_rate_transform_for_start"] + +vel_steer_cost_coef_table = np.array( + mpc_param["mpc_parameter"]["cost_parameters"]["vel_steer_cost_coef_table"], dtype=float ) -coef_steer_rate_transform_for_start = float( - mpc_param["mpc_parameter"]["cost_parameters"]["coef_steer_rate_transform_for_start"] +vel_steer_table = np.array( + mpc_param["mpc_parameter"]["cost_parameters"]["vel_steer_table"], dtype=float ) -min_tighten_steer_rate = float( - mpc_param["mpc_parameter"]["to_be_deprecated"]["min_tighten_steer_rate"] -) -power_tighten_steer_rate_by_lateral_error = int( - mpc_param["mpc_parameter"]["to_be_deprecated"]["power_tighten_steer_rate_by_lateral_error"] + +lateral_cost_coef_table = np.array( + mpc_param["mpc_parameter"]["cost_parameters"]["lateral_cost_coef_table"], dtype=float ) -threshold_tighten_steer_rate_by_lateral_error = float( - mpc_param["mpc_parameter"]["to_be_deprecated"]["threshold_tighten_steer_rate_by_lateral_error"] +lateral_error_table = np.array( + mpc_param["mpc_parameter"]["cost_parameters"]["lateral_error_table"], dtype=float ) -power_tighten_steer_rate_by_yaw_error = int( - mpc_param["mpc_parameter"]["to_be_deprecated"]["power_tighten_steer_rate_by_yaw_error"] +yaw_cost_coef_table = np.array( + mpc_param["mpc_parameter"]["cost_parameters"]["yaw_cost_coef_table"], dtype=float ) -threshold_tighten_steer_rate_by_yaw_error = float( - mpc_param["mpc_parameter"]["to_be_deprecated"]["threshold_tighten_steer_rate_by_yaw_error"] +yaw_error_table = np.array( + mpc_param["mpc_parameter"]["cost_parameters"]["yaw_error_table"], dtype=float ) -tighten_horizon = int(mpc_param["mpc_parameter"]["to_be_deprecated"]["tighten_horizon"]) -min_loose_lateral_cost = float( - mpc_param["mpc_parameter"]["cost_parameters"]["min_loose_lateral_cost"] -) -power_loose_lateral_cost = int( - mpc_param["mpc_parameter"]["cost_parameters"]["power_loose_lateral_cost"] + +steer_rate_cost_table = np.array( + mpc_param["mpc_parameter"]["cost_parameters"]["steer_rate_cost_table"], dtype=float ) -threshold_loose_lateral_cost = float( - mpc_param["mpc_parameter"]["cost_parameters"]["threshold_loose_lateral_cost"] +curvature_table = np.array( + mpc_param["mpc_parameter"]["cost_parameters"]["curvature_table"], dtype=float ) -min_loose_yaw_cost = float(mpc_param["mpc_parameter"]["cost_parameters"]["min_loose_yaw_cost"]) -power_loose_yaw_cost = int(mpc_param["mpc_parameter"]["cost_parameters"]["power_loose_yaw_cost"]) -threshold_loose_yaw_cost = float( - mpc_param["mpc_parameter"]["cost_parameters"]["threshold_loose_yaw_cost"] -) +use_max_curvature = str(mpc_param["mpc_parameter"]["cost_parameters"]["use_max_curvature"]) use_sg_for_nominal_inputs = bool( mpc_param["mpc_parameter"]["preprocessing"]["use_sg_for_nominal_inputs"] @@ -173,6 +160,20 @@ sg_window_size_for_nominal_inputs = int( mpc_param["mpc_parameter"]["preprocessing"]["sg_window_size_for_nominal_inputs"] ) + +acc_fb_decay = float(mpc_param["mpc_parameter"]["compensation"]["acc_fb_decay"]) +acc_fb_gain = float(mpc_param["mpc_parameter"]["compensation"]["acc_fb_gain"]) +acc_fb_sec_order_ratio = float(mpc_param["mpc_parameter"]["compensation"]["acc_fb_sec_order_ratio"]) +max_error_acc = float(mpc_param["mpc_parameter"]["compensation"]["max_error_acc"]) + +steer_fb_decay = float(mpc_param["mpc_parameter"]["compensation"]["steer_fb_decay"]) +steer_fb_gain = float(mpc_param["mpc_parameter"]["compensation"]["steer_fb_gain"]) +steer_fb_sec_order_ratio = float( + mpc_param["mpc_parameter"]["compensation"]["steer_fb_sec_order_ratio"] +) +max_error_steer = float(mpc_param["mpc_parameter"]["compensation"]["max_error_steer"]) + + trained_model_param_path = ( package_path["path"] + "/autoware_smart_mpc_trajectory_follower/param/trained_model_param.yaml" ) @@ -181,6 +182,16 @@ use_trained_model_diff = bool( trained_model_param["trained_model_parameter"]["control_application"]["use_trained_model_diff"] ) +minimum_steer_diff = float( + trained_model_param["trained_model_parameter"]["control_application"]["minimum_steer_diff"] +) +use_memory_diff = bool( + trained_model_param["trained_model_parameter"]["memory_for_training"]["use_memory_diff"] +) + +reflect_only_poly_diff = bool( + trained_model_param["trained_model_parameter"]["control_application"]["reflect_only_poly_diff"] +) use_sg_for_trained_model_diff = bool( trained_model_param["trained_model_parameter"]["control_application"][ "use_sg_for_trained_model_diff" @@ -197,6 +208,18 @@ ] ) +use_sg_for_memory_diff = bool( + trained_model_param["trained_model_parameter"]["memory_for_training"]["use_sg_for_memory_diff"] +) +sg_deg_for_trained_model_diff = int( + trained_model_param["trained_model_parameter"]["memory_for_training"]["sg_deg_for_memory_diff"] +) +sg_window_size_for_memory_diff = int( + trained_model_param["trained_model_parameter"]["memory_for_training"][ + "sg_window_size_for_memory_diff" + ] +) + use_sg_for_noise = bool( trained_model_param["trained_model_parameter"]["control_application"]["use_sg_for_noise"] ) @@ -209,6 +232,9 @@ ] ) +use_memory_for_training = bool( + trained_model_param["trained_model_parameter"]["memory_for_training"]["use_memory_for_training"] +) load_dir = os.environ["HOME"] save_dir = os.environ["HOME"] # +"/autoware" @@ -282,6 +308,66 @@ ) +vel_normalize = float(trained_model_param["trained_model_parameter"]["normalize"]["vel_normalize"]) +acc_normalize = float(trained_model_param["trained_model_parameter"]["normalize"]["acc_normalize"]) +steer_normalize = float( + trained_model_param["trained_model_parameter"]["normalize"]["steer_normalize"] +) + +NN_x_weight = float(trained_model_param["trained_model_parameter"]["weight"]["NN_x_weight"]) +NN_y_weight = float(trained_model_param["trained_model_parameter"]["weight"]["NN_y_weight"]) +NN_v_weight = float(trained_model_param["trained_model_parameter"]["weight"]["NN_v_weight"]) +NN_yaw_weight = float(trained_model_param["trained_model_parameter"]["weight"]["NN_yaw_weight"]) +NN_acc_weight = float(trained_model_param["trained_model_parameter"]["weight"]["NN_acc_weight"]) +NN_steer_weight = float(trained_model_param["trained_model_parameter"]["weight"]["NN_steer_weight"]) + +NN_x_weight_diff = float( + trained_model_param["trained_model_parameter"]["weight"]["NN_x_weight_diff"] +) +NN_y_weight_diff = float( + trained_model_param["trained_model_parameter"]["weight"]["NN_y_weight_diff"] +) +NN_v_weight_diff = float( + trained_model_param["trained_model_parameter"]["weight"]["NN_v_weight_diff"] +) +NN_yaw_weight_diff = float( + trained_model_param["trained_model_parameter"]["weight"]["NN_yaw_weight_diff"] +) +NN_acc_weight_diff = float( + trained_model_param["trained_model_parameter"]["weight"]["NN_acc_weight_diff"] +) +NN_steer_weight_diff = float( + trained_model_param["trained_model_parameter"]["weight"]["NN_steer_weight_diff"] +) + +NN_x_weight_two_diff = float( + trained_model_param["trained_model_parameter"]["weight"]["NN_x_weight_two_diff"] +) +NN_y_weight_two_diff = float( + trained_model_param["trained_model_parameter"]["weight"]["NN_y_weight_two_diff"] +) +NN_v_weight_two_diff = float( + trained_model_param["trained_model_parameter"]["weight"]["NN_v_weight_two_diff"] +) +NN_yaw_weight_two_diff = float( + trained_model_param["trained_model_parameter"]["weight"]["NN_yaw_weight_two_diff"] +) +NN_acc_weight_two_diff = float( + trained_model_param["trained_model_parameter"]["weight"]["NN_acc_weight_two_diff"] +) +NN_steer_weight_two_diff = float( + trained_model_param["trained_model_parameter"]["weight"]["NN_steer_weight_two_diff"] +) + +finalize_x_weight = float( + trained_model_param["trained_model_parameter"]["weight"]["finalize_x_weight"] +) +finalize_y_weight = float( + trained_model_param["trained_model_parameter"]["weight"]["finalize_y_weight"] +) +finalize_v_weight = float( + trained_model_param["trained_model_parameter"]["weight"]["finalize_v_weight"] +) # upper limit of input @@ -293,40 +379,52 @@ break control_dir_path += "/" -limit_yaml_path = None -for curDir, dirs, files in os.walk(control_dir_path): - for name in files: - if name == "vehicle_cmd_gate.param.yaml": - if curDir.split("/")[-2] == "vehicle_cmd_gate": - limit_yaml_path = curDir + "/" + name - break - -limit_params = None -if limit_yaml_path is not None: - with open(limit_yaml_path, "r") as yml: - limit_params = yaml.safe_load(yml) -else: - print("Error: limit_yaml_path is None") +read_limit_file = bool(mpc_param["mpc_parameter"]["limit"]["read_limit_file"]) +if read_limit_file: + limit_yaml_path = None + for curDir, dirs, files in os.walk(control_dir_path): + for name in files: + if name == "vehicle_cmd_gate.param.yaml": + if curDir.split("/")[-2] == "autoware_vehicle_cmd_gate": + limit_yaml_path = curDir + "/" + name + break + + limit_params = None + if limit_yaml_path is not None: + with open(limit_yaml_path, "r") as yml: + limit_params = yaml.safe_load(yml) + else: + print("Error: limit_yaml_path is None") -if limit_params is not None: - reference_speed_points = np.array( - limit_params["/**"]["ros__parameters"]["nominal"]["reference_speed_points"] - ) - steer_lim_points = np.array(limit_params["/**"]["ros__parameters"]["nominal"]["steer_lim"]) - steer_rate_lim_points = np.array( - limit_params["/**"]["ros__parameters"]["nominal"]["steer_rate_lim"] - ) - acc_lim_points = np.array(limit_params["/**"]["ros__parameters"]["nominal"]["lon_acc_lim"]) - acc_rate_lim_points = np.array( - limit_params["/**"]["ros__parameters"]["nominal"]["lon_jerk_lim"] - ) - lat_acc_lim_points = np.array(limit_params["/**"]["ros__parameters"]["nominal"]["lat_acc_lim"]) - lat_jerk_lim_points = np.array( - limit_params["/**"]["ros__parameters"]["nominal"]["lat_jerk_lim"] - ) + if limit_params is not None: + reference_speed_points = np.array( + limit_params["/**"]["ros__parameters"]["nominal"]["reference_speed_points"] + ) + steer_lim_points = np.array(limit_params["/**"]["ros__parameters"]["nominal"]["steer_lim"]) + steer_rate_lim_points = np.array( + limit_params["/**"]["ros__parameters"]["nominal"]["steer_rate_lim"] + ) + acc_lim_points = np.array(limit_params["/**"]["ros__parameters"]["nominal"]["lon_acc_lim"]) + acc_rate_lim_points = np.array( + limit_params["/**"]["ros__parameters"]["nominal"]["lon_jerk_lim"] + ) + lat_acc_lim_points = np.array( + limit_params["/**"]["ros__parameters"]["nominal"]["lat_acc_lim"] + ) + lat_jerk_lim_points = np.array( + limit_params["/**"]["ros__parameters"]["nominal"]["lat_jerk_lim"] + ) + else: + print("Error: limit_params is None") + sys.exit(1) else: - print("Error: limit_params is None") - sys.exit(1) + reference_speed_points = np.array([20.0, 30.0]) + steer_lim_points = np.array([10.0, 10.0]) + steer_rate_lim_points = np.array([10.0, 10.0]) + acc_lim_points = np.array([20.0, 20.0]) + acc_rate_lim_points = np.array([20.0, 20.0]) + lat_acc_lim_points = np.array([20.0, 20.0]) + lat_jerk_lim_points = np.array([20.0, 20.0]) @njit(cache=True, fastmath=True) @@ -413,6 +511,55 @@ def calc_limits( ) +@njit(cache=True, fastmath=True) +def calc_steer_rate_cost_coef(curvature: float) -> float: + """Calc steer rate cost coefficient.""" + interval_index = 0 + for i in range(curvature_table.shape[0]): + if curvature > curvature_table[i]: + interval_index += 1 + else: + break + if interval_index == 0: + steer_rate_cost_coef = steer_rate_cost_table[0] + + elif interval_index == steer_rate_cost_table.shape[0]: + steer_rate_cost_coef = steer_rate_cost_table[-1] + + else: + r = (curvature - curvature_table[interval_index - 1]) / ( + curvature_table[interval_index] - curvature_table[interval_index - 1] + ) + steer_rate_cost_coef = (1 - r) * steer_rate_cost_table[ + interval_index - 1 + ] + r * steer_rate_cost_table[interval_index] + + return steer_rate_cost_coef + + +@njit(cache=True, fastmath=True) +def calc_table_value(x: float, table_domain, table_target) -> float: + interval_index = 0 + for i in range(table_domain.shape[0]): + if x > table_domain[i]: + interval_index += 1 + else: + break + if interval_index == 0: + target_val = table_target[0] + + elif interval_index == table_target.shape[0]: + target_val = table_target[-1] + + else: + r = (x - table_domain[interval_index - 1]) / ( + table_domain[interval_index] - table_domain[interval_index - 1] + ) + target_val = (1 - r) * table_target[interval_index - 1] + r * table_target[interval_index] + + return target_val + + @njit(cache=True, fastmath=True) def transform_yaw(yaw_old: float, yaw_current_: float) -> float: """Transform the yaw angle so that the difference in yaw angle is less than or equal to π.""" @@ -431,7 +578,7 @@ def transform_yaw_for_x_current(x_old: np.ndarray, x_current_: np.ndarray) -> np return x_current -def transform_yaw_for_X_des(x_current: np.ndarray, X_des_: np.ndarray) -> np.ndarray: +def transform_yaw_for_X_des(x_current: np.ndarray, X_des_: np.ndarray) -> tuple[np.ndarray, float]: """Transform the yaw angle with respect to the target trajectory. X_des[0] is set to the current state. @@ -506,7 +653,11 @@ def calc_maximum_trajectory_error(traj: np.ndarray, X_des: np.ndarray) -> float: @njit(cache=False, fastmath=True) def transform_Q_R( - X_des: np.ndarray, U_des: np.ndarray, nominal_traj: np.ndarray, nominal_input: np.ndarray + X_des: np.ndarray, + U_des: np.ndarray, + nominal_traj: np.ndarray, + nominal_input: np.ndarray, + steer_rate_coef: float, ) -> tuple: """Calculate the MPC cost weight matrix from the current predicted trajectory and target trajectory. @@ -540,43 +691,17 @@ def transform_Q_R( lateral_error = np.abs((Rot.T @ (X_des[i, :2] - x_current[:2]))[1]) yaw_error = np.abs(X_des[i, 3] - x_current[3]) - cost_tr_steer_rate_by_error = max( - min_tighten_steer_rate, - max( - (lateral_error / threshold_tighten_steer_rate_by_lateral_error) - ** power_tighten_steer_rate_by_lateral_error, - (yaw_error / threshold_tighten_steer_rate_by_yaw_error) - ** power_tighten_steer_rate_by_yaw_error, - ), - ) - cost_tr_steer_rate = 1 / ( - min( - max( - min_steer_rate_transform_for_start, - (coef_steer_rate_transform_for_start * np.abs(v)) - ** power_steer_rate_transform_for_start, - ), - 1.0, - ) + + cost_tr_steer_rate = 1 / calc_table_value( + np.abs(v), vel_steer_table, vel_steer_cost_coef_table ) - if i >= tighten_horizon: - cost_tr_steer_rate = max(cost_tr_steer_rate, 1 / cost_tr_steer_rate_by_error) + if i in timing_Q_c or i == X_des.shape[0] - 1: - lateral_cost_coef = min( - max( - min_loose_lateral_cost, - (lateral_error / threshold_loose_lateral_cost) ** power_loose_lateral_cost, - ), - 1.0, + lateral_cost_coef = calc_table_value( + lateral_error, lateral_error_table, lateral_cost_coef_table ) Q_[1, 1] = lateral_cost_coef * Q_[1, 1] - yaw_cost_coef = min( - max( - min_loose_yaw_cost, - (yaw_error / threshold_loose_yaw_cost) ** power_loose_yaw_cost, - ), - 1.0, - ) + yaw_cost_coef = calc_table_value(yaw_error, yaw_error_table, yaw_cost_coef_table) Q_[3, 3] = yaw_cost_coef * Q_[3, 3] Q_[:2, :2] = Rot @ Q_[:2, :2] @ Rot.T @@ -605,6 +730,7 @@ def transform_Q_R( steer_lim_center[i] = -steer_lim if i != X_des.shape[0] - 1: R_ = R.copy() + R_[1, 1] = steer_rate_coef * R_[1, 1] if acc >= 0: R_[1, 1] = cost_tr_steer_rate * R_[1, 1] R_total[i] = R_ @@ -639,7 +765,12 @@ def transform_Q_R( @njit(cache=False, fastmath=True) def calc_cost( - X_des: np.ndarray, U_des: np.ndarray, Traj: np.ndarray, Inputs: np.ndarray, n: int + X_des: np.ndarray, + U_des: np.ndarray, + Traj: np.ndarray, + Inputs: np.ndarray, + n: int, + steer_rate_coef: float, ) -> np.ndarray: """Calculate the MPC cost of the nth horizon from several candidate predicted trajectories and target trajectories.""" Cost = np.zeros(Traj.shape[0]) @@ -661,43 +792,17 @@ def calc_cost( Qi = Q_.copy() lateral_error = np.abs((Rot.T @ (X_des[:2] - x_current[:2]))[1]) yaw_error = np.abs(X_des[3] - x_current[3]) - cost_tr_steer_rate_by_error = max( - min_tighten_steer_rate, - max( - (lateral_error / threshold_tighten_steer_rate_by_lateral_error) - ** power_tighten_steer_rate_by_lateral_error, - (yaw_error / threshold_tighten_steer_rate_by_yaw_error) - ** power_tighten_steer_rate_by_yaw_error, - ), - ) - cost_tr_steer_rate = 1 / ( - min( - max( - min_steer_rate_transform_for_start, - (coef_steer_rate_transform_for_start * np.abs(v)) - ** power_steer_rate_transform_for_start, - ), - 1.0, - ) + + cost_tr_steer_rate = 1 / calc_table_value( + np.abs(v), vel_steer_table, vel_steer_cost_coef_table ) - if n >= tighten_horizon: - cost_tr_steer_rate = max(cost_tr_steer_rate, 1 / cost_tr_steer_rate_by_error) + if n in timing_Q_c or n == N: - lateral_cost_coef = min( - max( - min_loose_lateral_cost, - (lateral_error / threshold_loose_lateral_cost) ** power_loose_lateral_cost, - ), - 1.0, + lateral_cost_coef = calc_table_value( + lateral_error, lateral_error_table, lateral_cost_coef_table ) Qi[1, 1] = lateral_cost_coef * Qi[1, 1] - yaw_cost_coef = min( - max( - min_loose_yaw_cost, - (yaw_error / threshold_loose_yaw_cost) ** power_loose_yaw_cost, - ), - 1.0, - ) + yaw_cost_coef = calc_table_value(yaw_error, yaw_error_table, yaw_cost_coef_table) Qi[3, 3] = yaw_cost_coef * Qi[3, 3] Qi[:2, :2] = Rot @ Qi[:2, :2] @ Rot.T Cost[i] += 0.5 * np.dot(Qi @ (x_current - X_des), x_current - X_des) @@ -736,6 +841,8 @@ def calc_cost( if n != N: R_ = R.copy() + R_[1, 1] = steer_rate_coef * R_[1, 1] + if acc >= 0: R_[1, 1] = cost_tr_steer_rate * R_[1, 1] @@ -795,22 +902,13 @@ def calc_cost_only_for_states(X_des: np.ndarray, Traj: np.ndarray, n: int) -> np Qi = Q_.copy() if n in timing_Q_c or n == N: lateral_error = np.abs((Rot.T @ (X_des[:2] - x_current[:2]))[1]) - lateral_cost_coef = min( - max( - min_loose_lateral_cost, - (lateral_error / threshold_loose_lateral_cost) ** power_loose_lateral_cost, - ), - 1.0, + lateral_cost_coef = calc_table_value( + lateral_error, lateral_error_table, lateral_cost_coef_table ) Qi[1, 1] = lateral_cost_coef * Qi[1, 1] yaw_error = np.abs(X_des[3] - x_current[3]) - yaw_cost_coef = min( - max( - min_loose_yaw_cost, - (yaw_error / threshold_loose_yaw_cost) ** power_loose_yaw_cost, - ), - 1.0, - ) + yaw_cost_coef = calc_table_value(yaw_error, yaw_error_table, yaw_cost_coef_table) + Qi[3, 3] = yaw_cost_coef * Qi[3, 3] Qi[:2, :2] = Rot @ Qi[:2, :2] @ Rot.T Cost[i] += 0.5 * np.dot(Qi @ (x_current - X_des), x_current - X_des) @@ -1077,12 +1175,14 @@ def F_multiple_for_candidates( def F_with_history( states: np.ndarray, inputs: np.ndarray, + previous_error=None, + k: int = 0, i: int = acc_delay_step, j: int = steer_delay_step, acc_time_constant_ctrl: float = acc_time_constant, steer_time_constant_ctrl: float = steer_time_constant, steer_dead_band_for_ctrl=steer_dead_band_for_ctrl, -) -> np.ndarray: +) -> tuple[np.ndarray, None]: """Integrate up to the MPC time width according to the nominal model. This includes the history of the input to the state. @@ -1097,12 +1197,12 @@ def F_with_history( ] = states[ nx_0 + acc_ctrl_queue_size : nx_0 + acc_ctrl_queue_size + steer_ctrl_queue_size - mpc_freq ].copy() - for k in range(mpc_freq): - states_next[nx_0 + mpc_freq - k - 1] = ( - states_next[nx_0 + mpc_freq - k] + ctrl_time_step * inputs[0] + for index in range(mpc_freq): + states_next[nx_0 + mpc_freq - index - 1] = ( + states_next[nx_0 + mpc_freq - index] + ctrl_time_step * inputs[0] ) - states_next[nx_0 + acc_ctrl_queue_size + mpc_freq - k - 1] = ( - states_next[nx_0 + acc_ctrl_queue_size + mpc_freq - k] + ctrl_time_step * inputs[1] + states_next[nx_0 + acc_ctrl_queue_size + mpc_freq - index - 1] = ( + states_next[nx_0 + acc_ctrl_queue_size + mpc_freq - index] + ctrl_time_step * inputs[1] ) actual_inputs = np.zeros((mpc_freq, inputs.shape[0])) @@ -1117,14 +1217,14 @@ def F_with_history( steer_dead_band_for_ctrl, ) - return states_next + return states_next, None def F_with_history_and_diff( states: np.ndarray, inputs: np.ndarray, previous_error: None = None, - init: None = None, + k: int = 0, i: int = acc_delay_step, j: int = steer_delay_step, acc_time_constant_ctrl: float = acc_time_constant, @@ -1156,18 +1256,18 @@ def F_with_history_and_diff( nx_0 + acc_ctrl_queue_size : nx_0 + acc_ctrl_queue_size + steer_ctrl_queue_size - mpc_freq, ] = np.eye(steer_ctrl_queue_size - mpc_freq) - for k in range(mpc_freq): - states_next[nx_0 + mpc_freq - k - 1] = ( - states_next[nx_0 + mpc_freq - k] + ctrl_time_step * inputs[0] + for index in range(mpc_freq): + states_next[nx_0 + mpc_freq - index - 1] = ( + states_next[nx_0 + mpc_freq - index] + ctrl_time_step * inputs[0] ) - states_next[nx_0 + acc_ctrl_queue_size + mpc_freq - k - 1] = ( - states_next[nx_0 + acc_ctrl_queue_size + mpc_freq - k] + ctrl_time_step * inputs[1] + states_next[nx_0 + acc_ctrl_queue_size + mpc_freq - index - 1] = ( + states_next[nx_0 + acc_ctrl_queue_size + mpc_freq - index] + ctrl_time_step * inputs[1] ) - dF_dx[nx_0 + mpc_freq - k - 1, nx_0] = 1 - dF_dx[nx_0 + acc_ctrl_queue_size + mpc_freq - k - 1, nx_0 + acc_ctrl_queue_size] = 1 + dF_dx[nx_0 + mpc_freq - index - 1, nx_0] = 1 + dF_dx[nx_0 + acc_ctrl_queue_size + mpc_freq - index - 1, nx_0 + acc_ctrl_queue_size] = 1 - dF_du[nx_0 + mpc_freq - k - 1, 0] = (k + 1) * ctrl_time_step - dF_du[nx_0 + acc_ctrl_queue_size + mpc_freq - k - 1, 1] = (k + 1) * ctrl_time_step + dF_du[nx_0 + mpc_freq - index - 1, 0] = (index + 1) * ctrl_time_step + dF_du[nx_0 + acc_ctrl_queue_size + mpc_freq - index - 1, 1] = (index + 1) * ctrl_time_step actual_inputs = np.zeros((mpc_freq, inputs.shape[0])) actual_inputs[:, 0] = states_next[nx_0 + i + np.arange(mpc_freq)[::-1]].copy() @@ -1193,7 +1293,7 @@ def F_with_history_for_candidates( States: np.ndarray, Inputs: np.ndarray, Previous_error=None, - init=None, + k=0, i: int = acc_delay_step, j: int = steer_delay_step, acc_time_constant_ctrl: float = acc_time_constant, @@ -1215,12 +1315,12 @@ def F_with_history_for_candidates( :, nx_0 + acc_ctrl_queue_size : nx_0 + acc_ctrl_queue_size + steer_ctrl_queue_size - mpc_freq, ].copy() - for k in range(mpc_freq): - States_next[:, nx_0 + mpc_freq - k - 1] = ( - States_next[:, nx_0 + mpc_freq - k] + ctrl_time_step * Inputs[:, 0] + for index in range(mpc_freq): + States_next[:, nx_0 + mpc_freq - index - 1] = ( + States_next[:, nx_0 + mpc_freq - index] + ctrl_time_step * Inputs[:, 0] ) - States_next[:, nx_0 + acc_ctrl_queue_size + mpc_freq - k - 1] = ( - States_next[:, nx_0 + acc_ctrl_queue_size + mpc_freq - k] + States_next[:, nx_0 + acc_ctrl_queue_size + mpc_freq - index - 1] = ( + States_next[:, nx_0 + acc_ctrl_queue_size + mpc_freq - index] + ctrl_time_step * Inputs[:, 1] ) actual_Inputs = np.zeros((Inputs.shape[0], mpc_freq, nu_0)) @@ -1235,6 +1335,39 @@ def F_with_history_for_candidates( return States_next, None +def F_with_model( + states: np.ndarray, + inputs: np.ndarray, + previous_error: np.ndarray, + k: int, + pred: Callable, + i: int = acc_delay_step, + j: int = steer_delay_step, + acc_time_constant_ctrl: float = acc_time_constant, + steer_time_constant_ctrl: float = steer_time_constant, +) -> tuple[np.ndarray, np.ndarray]: + """Integrate up to the MPC time width according to the trained model.""" + if k == 0: + error = Error_decay * previous_error + (1 - Error_decay) * pred(states) + else: + error = Error_decay * Error_decay * Error_decay * previous_error + ( + 1 - Error_decay * Error_decay * Error_decay + ) * pred(states) + previous_error_ = error + states_next, _ = F_with_history( + states, + inputs, + i=i, + j=j, + acc_time_constant_ctrl=acc_time_constant_ctrl, + steer_time_constant_ctrl=steer_time_constant_ctrl, + ) + + states_next[:nx_0] += error * mpc_time_step + + return states_next, previous_error_ + + def F_with_model_initial_diff( states: np.ndarray, inputs: np.ndarray, @@ -1326,7 +1459,13 @@ def F_with_model_diff( dF_dx[:2, 3] += d_rot_error * mpc_time_step states_next[:nx_0] += rot_error * mpc_time_step - return states_next, dF_dx, dF_du, pred_with_diff[:, 2:] * mpc_time_step, pred_error_ + C = pred_with_diff[:, 2:] * mpc_time_step + steer_diff = ( + dF_dx[5, nx_0 + acc_ctrl_queue_size :].sum() + C[5, nx_0 + acc_ctrl_queue_size :].sum() + ) + if steer_diff < minimum_steer_diff: + C[5, -1] += minimum_steer_diff - steer_diff + return states_next, dF_dx, dF_du, C, pred_error_ def F_with_model_for_candidates( @@ -1366,6 +1505,137 @@ def F_with_model_for_candidates( return States_next, Previous_error_ +def acc_prediction_error_compensation( + states: np.ndarray, + previous_states: np.ndarray, + inputs: np.ndarray, + previous_error: np.ndarray, + acc_fb_1: float, + acc_fb_2: float, + acc_time_stamp_interval: float, +): + actual_acc_input = np.concatenate( + (np.array([inputs[0]]), previous_states[nx_0 : nx_0 + acc_ctrl_queue_size]) + )[acc_delay_step] + + predicted_acc = previous_states[4] + acc_time_stamp_interval * ( + previous_error[4] + (actual_acc_input - previous_states[4]) / acc_time_constant + ) + prediction_error = predicted_acc - states[4] + new_acc_fb_1 = np.exp(-acc_fb_decay) * acc_fb_1 + acc_fb_decay * prediction_error + new_acc_fb_2 = ( + acc_fb_decay * np.exp(-acc_fb_decay) * acc_fb_1 + np.exp(-acc_fb_decay) * acc_fb_2 + ) + return new_acc_fb_1, new_acc_fb_2 + + +def steer_prediction_error_compensation( + states: np.ndarray, + previous_states: np.ndarray, + inputs: np.ndarray, + previous_error: np.ndarray, + steer_fb_1: float, + steer_fb_2: float, + steer_time_stamp_interval: float, +): + actual_steer_input = np.concatenate( + (np.array([inputs[1]]), previous_states[nx_0 + acc_ctrl_queue_size :]) + )[steer_delay_step] + + delta_diff = actual_steer_input - previous_states[5] + if delta_diff >= steer_dead_band_for_ctrl: + delta_diff = delta_diff - steer_dead_band_for_ctrl + elif delta_diff <= -steer_dead_band_for_ctrl: + delta_diff = delta_diff + steer_dead_band_for_ctrl + else: + delta_diff = 0.0 + + predicted_steer = previous_states[5] + steer_time_stamp_interval * ( + previous_error[5] + delta_diff / steer_time_constant + ) + prediction_error = predicted_steer - states[5] + + new_steer_fb_1 = np.exp(-steer_fb_decay) * steer_fb_1 + steer_fb_decay * (prediction_error) + new_steer_fb_2 = ( + steer_fb_decay * np.exp(-steer_fb_decay) * steer_fb_1 + np.exp(-steer_fb_decay) * steer_fb_2 + ) + return new_steer_fb_1, new_steer_fb_2 + + +def pure_pursuit_control( + pos_xy_obs, + pos_yaw_obs, + longitudinal_vel_obs, + pos_xy_ref, + pos_yaw_ref, + longitudinal_vel_ref, + acc_gain_scaling=1.0, + steer_gain_scaling=1.0, +): + pure_pursuit_acc_kp = acc_gain_scaling * mpc_param["mpc_parameter"]["pure_pursuit"]["acc_kp"] + pure_pursuit_lookahead_time = mpc_param["mpc_parameter"]["pure_pursuit"]["lookahead_time"] + pure_pursuit_min_lookahead = mpc_param["mpc_parameter"]["pure_pursuit"]["min_lookahead"] + pure_pursuit_steer_kp_param = ( + steer_gain_scaling * mpc_param["mpc_parameter"]["pure_pursuit"]["steer_kp_param"] + ) + pure_pursuit_steer_kd_param = ( + steer_gain_scaling * mpc_param["mpc_parameter"]["pure_pursuit"]["steer_kd_param"] + ) + + longitudinal_vel_err = longitudinal_vel_obs - longitudinal_vel_ref + pure_pursuit_acc_cmd = -pure_pursuit_acc_kp * longitudinal_vel_err + + cos_yaw = np.cos(pos_yaw_ref) + sin_yaw = np.sin(pos_yaw_ref) + diff_position = pos_xy_obs - pos_xy_ref + lat_err = -sin_yaw * diff_position[0] + cos_yaw * diff_position[1] + yaw_err = pos_yaw_obs - pos_yaw_ref + while True: + if yaw_err > np.pi: + yaw_err -= 2.0 * np.pi + if yaw_err < (-np.pi): + yaw_err += 2.0 * np.pi + if np.abs(yaw_err) < np.pi: + break + + lookahead = pure_pursuit_min_lookahead + pure_pursuit_lookahead_time * np.abs( + longitudinal_vel_obs + ) + pure_pursuit_steer_kp = pure_pursuit_steer_kp_param * L / (lookahead * lookahead) + pure_pursuit_steer_kd = pure_pursuit_steer_kd_param * L / lookahead + pure_pursuit_steer_cmd = -pure_pursuit_steer_kp * lat_err - pure_pursuit_steer_kd * yaw_err + return np.array([pure_pursuit_acc_cmd, pure_pursuit_steer_cmd]) + + +naive_pure_pursuit_lookahead_coef = float( + mpc_param["mpc_parameter"]["naive_pure_pursuit"]["lookahead_coef"] +) +naive_pure_pursuit_lookahead_intercept = float( + mpc_param["mpc_parameter"]["naive_pure_pursuit"]["lookahead_intercept"] +) + + +def naive_pure_pursuit_control( + pos_xy_obs, + pos_yaw_obs, + longitudinal_vel_obs, + pos_xy_ref_target, + longitudinal_vel_ref_nearest, +): + pure_pursuit_acc_kp = mpc_param["mpc_parameter"]["naive_pure_pursuit"]["acc_kp"] + wheel_base = L + longitudinal_vel_err = longitudinal_vel_obs - longitudinal_vel_ref_nearest + pure_pursuit_acc_cmd = -pure_pursuit_acc_kp * longitudinal_vel_err + + alpha = ( + np.arctan2(pos_xy_ref_target[1] - pos_xy_obs[1], pos_xy_ref_target[0] - pos_xy_obs[0]) + - pos_yaw_obs + ) + angular_velocity_z = 2.0 * longitudinal_vel_ref_nearest * np.sin(alpha) / wheel_base + steer = np.arctan(angular_velocity_z * wheel_base / max(0.1, longitudinal_vel_ref_nearest)) + return np.array([pure_pursuit_acc_cmd, steer]) + + def sg_filter( inputs: np.ndarray, use_sg: bool, @@ -1439,6 +1709,11 @@ def calc_sg_filter_weight(sg_deg, sg_window_size): sg_vector_left_edge_for_trained_model_diff, sg_vector_right_edge_for_trained_model_diff, ) = calc_sg_filter_weight(sg_deg_for_trained_model_diff, sg_window_size_for_trained_model_diff) +( + sg_vector_for_memory_diff, + sg_vector_left_edge_for_memory_diff, + sg_vector_right_edge_for_memory_diff, +) = calc_sg_filter_weight(0, sg_window_size_for_memory_diff) ( sg_vector_for_noise, sg_vector_left_edge_for_noise, @@ -1463,6 +1738,16 @@ def calc_sg_filter_weight(sg_deg, sg_window_size): sg_vector=sg_vector_for_trained_model_diff, ) +sg_filter_for_memory_diff = partial( + sg_filter, + use_sg=use_sg_for_memory_diff, + sg_window_size=sg_window_size_for_memory_diff, # sg_window_size_for_trained_model_diff, + sg_vector_left_edge=sg_vector_left_edge_for_memory_diff, # sg_vector_left_edge_for_trained_model_diff, + sg_vector_right_edge=sg_vector_right_edge_for_memory_diff, # sg_vector_right_edge_for_trained_model_diff, + sg_vector=sg_vector_for_memory_diff, +) + + sg_filter_for_noise = partial( sg_filter, use_sg=use_sg_for_noise, diff --git a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/scripts/drive_iLQR.py b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/scripts/drive_iLQR.py index 2307586f23552..f3e374c5876cc 100644 --- a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/scripts/drive_iLQR.py +++ b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/scripts/drive_iLQR.py @@ -33,11 +33,6 @@ ) acc_index = drive_functions.nx_0 steer_index = drive_functions.nx_0 + drive_functions.acc_ctrl_queue_size -actual_control_index = np.arange( - drive_functions.nx_0 - + drive_functions.acc_ctrl_queue_size - + drive_functions.steer_ctrl_queue_size_core -) actual_state_dim = drive_functions.nx_0 input_dim = drive_functions.nu_0 mpc_freq = drive_functions.mpc_freq @@ -52,7 +47,6 @@ def sparse_right_action_for_state_diff( Mat: np.ndarray, A: np.ndarray, - actual_control_index=actual_control_index, actual_state_dim=actual_state_dim, acc_ctrl_queue_size=acc_ctrl_queue_size, steer_ctrl_queue_size=steer_ctrl_queue_size, @@ -65,7 +59,7 @@ def sparse_right_action_for_state_diff( result = np.zeros( (Mat.shape[0], actual_state_dim + acc_ctrl_queue_size + steer_ctrl_queue_size) ) - result[:, actual_control_index] += Mat[:, :actual_state_dim] @ A + result += Mat[:, :actual_state_dim] @ A result[:, actual_state_dim : actual_state_dim + acc_ctrl_queue_size - mpc_freq] += Mat[ :, actual_state_dim + mpc_freq : actual_state_dim + acc_ctrl_queue_size @@ -97,7 +91,6 @@ def sparse_right_action_for_state_diff( def sparse_left_action_for_state_diff( A: np.ndarray, Mat: np.ndarray, - actual_control_index=actual_control_index, actual_state_dim=actual_state_dim, acc_ctrl_queue_size=acc_ctrl_queue_size, steer_ctrl_queue_size=steer_ctrl_queue_size, @@ -110,7 +103,7 @@ def sparse_left_action_for_state_diff( result = np.zeros( (actual_state_dim + acc_ctrl_queue_size + steer_ctrl_queue_size, Mat.shape[1]) ) - result[actual_control_index] += A.T @ Mat[:actual_state_dim] + result += A.T @ Mat[:actual_state_dim] result[actual_state_dim : actual_state_dim + acc_ctrl_queue_size - mpc_freq] += Mat[ actual_state_dim + mpc_freq : actual_state_dim + acc_ctrl_queue_size @@ -140,7 +133,6 @@ def sparse_left_action_for_state_diff( def vector_sparse_left_action_for_state_diff( A: np.ndarray, vec: np.ndarray, - actual_control_index=actual_control_index, actual_state_dim=actual_state_dim, acc_ctrl_queue_size=acc_ctrl_queue_size, steer_ctrl_queue_size=steer_ctrl_queue_size, @@ -151,7 +143,7 @@ def vector_sparse_left_action_for_state_diff( The remaining A₀ components are known a priori. """ result = np.zeros(actual_state_dim + acc_ctrl_queue_size + steer_ctrl_queue_size) - result[actual_control_index] += A.T @ vec[:actual_state_dim] + result += A.T @ vec[:actual_state_dim] result[actual_state_dim : actual_state_dim + acc_ctrl_queue_size - mpc_freq] += vec[ actual_state_dim + mpc_freq : actual_state_dim + acc_ctrl_queue_size @@ -245,8 +237,6 @@ def compute_iLQR_coef( steer_rate_lim_weights, steer_rate_lim_center, N, - nx, - nu, X_des, U_des, Sigma_x=None, @@ -261,29 +251,35 @@ def compute_iLQR_coef( dSigma_acc=None, Sigma_steer=None, dSigma_steer=None, - index_cost=index_cost, - acc_index=acc_index, - steer_index=steer_index, - actual_control_index=actual_control_index, - actual_state_dim=actual_state_dim, + acc_input_index=acc_index, + steer_input_index=steer_index, + state_dim=actual_state_dim, ): """Perform the main part of iLQR. Performs a Riccati recursion and returns the coefficients needed for the final output. """ + nx = A.shape[2] + nu = B.shape[2] P = np.zeros((N + 1, nx, nx)) w = np.zeros((N + 1, nx)) H_inv_G = np.zeros((N, nu, nx)) H_inv_g = np.zeros((N, nu)) - P[N][:7, :7] = Q_total[-1][:7, :7] - P[N][steer_index, steer_index] = Q_total[-1][7, 7] + P[N][:6, :6] = Q_total[-1][:6, :6] + P[N][acc_input_index, acc_input_index] = Q_total[-1][6, 6] + P[N][steer_input_index, steer_input_index] = Q_total[-1][7, 7] - P[N][acc_index, acc_index] += acc_lim_weights[N] - P[N][steer_index, steer_index] += steer_lim_weights[N] + P[N][acc_input_index, acc_input_index] += acc_lim_weights[N] + P[N][steer_input_index, steer_input_index] += steer_lim_weights[N] - w[N][index_cost] = Q_total[-1] @ (traj[N] - X_des[N]) - w[N, acc_index] += acc_lim_weights[N] * (traj[N, acc_index] - acc_lim_center[N]) - w[N, steer_index] += steer_lim_weights[N] * (traj[N, acc_index + 1] - steer_lim_center[N]) + Q_N_x = Q_total[-1] @ (traj[N] - X_des[N]) + + w[N][:6] = Q_N_x[:6] + w[N][acc_input_index] = Q_N_x[6] + w[N][steer_input_index] = Q_N_x[7] + + w[N, acc_input_index] += acc_lim_weights[N] * (traj[N, acc_index] - acc_lim_center[N]) + w[N, steer_input_index] += steer_lim_weights[N] * (traj[N, acc_index + 1] - steer_lim_center[N]) for i in range(N): j = N - i - 1 @@ -291,15 +287,23 @@ def compute_iLQR_coef( Rj = R_total[j] Aj = A[j] Pj1Aj = sparse_right_action_for_state_diff( - P[j + 1], Aj[:actual_state_dim, actual_control_index] + P[j + 1], Aj[:state_dim], actual_state_dim=state_dim ) - G = sparse_left_action_for_input_diff(Pj1Aj) + G = sparse_left_action_for_input_diff(Pj1Aj, actual_state_dim=state_dim) - H = sparse_left_action_for_input_diff(sparse_right_action_for_input_diff(P[j + 1])) + Rj + H = ( + sparse_left_action_for_input_diff( + sparse_right_action_for_input_diff(P[j + 1], actual_state_dim=state_dim), + actual_state_dim=state_dim, + ) + + Rj + ) H[0, 0] += acc_rate_lim_weights[j] H[1, 1] += steer_rate_lim_weights[j] - g_ = vector_sparse_left_action_for_input_diff(w[j + 1]) + Rj @ (inputs[j] - U_des[j]) + g_ = vector_sparse_left_action_for_input_diff(w[j + 1], actual_state_dim=state_dim) + Rj @ ( + inputs[j] - U_des[j] + ) g_[0] += acc_rate_lim_weights[j] * (inputs[j, 0] - acc_rate_lim_center[j]) g_[1] += steer_rate_lim_weights[j] * (inputs[j, 1] - steer_rate_lim_center[j]) @@ -311,25 +315,32 @@ def compute_iLQR_coef( H_inv_g[j] = H_inv @ g_ P[j] = ( - sparse_left_action_for_state_diff(Aj[:actual_state_dim, actual_control_index], Pj1Aj) + sparse_left_action_for_state_diff(Aj[:state_dim], Pj1Aj, actual_state_dim=state_dim) - G.T @ H_inv_G[j] ) - P[j][:7, :7] += Qj[:7, :7] - P[j][steer_index, steer_index] += Qj[7, 7] + P[j][:6, :6] += Qj[:6, :6] + P[j][acc_input_index, acc_input_index] += Qj[6, 6] + P[j][steer_input_index, steer_input_index] += Qj[7, 7] - P[j][acc_index, acc_index] += acc_lim_weights[j] - P[j][steer_index, steer_index] += steer_lim_weights[j] + P[j][acc_input_index, acc_input_index] += acc_lim_weights[j] + P[j][steer_input_index, steer_input_index] += steer_lim_weights[j] w[j] = ( vector_sparse_left_action_for_state_diff( - Aj[:actual_state_dim, actual_control_index], w[j + 1] + Aj[:state_dim], w[j + 1], actual_state_dim=state_dim ) - H_inv_G[j].T @ g_ ) - w[j, index_cost] += Qj @ (traj[j] - X_des[j]) - w[j, acc_index] += acc_lim_weights[j] * (traj[j, acc_index] - acc_lim_center[j]) - w[j, steer_index] += steer_lim_weights[j] * (traj[j, acc_index + 1] - steer_lim_center[j]) + Q_j_x = Qj @ (traj[j] - X_des[j]) + w[j, :6] += Q_j_x[:6] + w[j, acc_input_index] += Q_j_x[6] + w[j, steer_input_index] += Q_j_x[7] + + w[j, acc_input_index] += acc_lim_weights[j] * (traj[j, acc_index] - acc_lim_center[j]) + w[j, steer_input_index] += steer_lim_weights[j] * ( + traj[j, acc_index + 1] - steer_lim_center[j] + ) P_noise = np.zeros((nx, nx)) w_noise = np.zeros(nx) if Sigma_x is not None: @@ -395,19 +406,19 @@ class drive_iLQR: """Class that runs iLQR to compute optimal input values.""" ls_step: float - """ Line search step width """ + """ Line search step width.""" max_iter_ls: int - """ Number of candidates for line search. """ + """ Number of candidates for line search.""" max_iter_ilqr: int - """ Maximum number of iLQR iterations """ + """ Maximum number of iLQR iterations.""" ilqr_tol: float - """ Tolerance to terminate iLQR iterations """ + """ Tolerance to terminate iLQR iterations.""" use_trained_model_diff: bool - """ Whether to use the derivative of the trained model """ + """ Whether to use the derivative of the trained model.""" def __init__( self, @@ -429,6 +440,10 @@ def __init__( ls[i + 1] = ls_step * ls[i] ls[-1] = 0.0 self.ls = ls + self.add_state_hc = False + self.state_dim = actual_state_dim + self.acc_index = acc_index + self.steer_index = steer_index def calc_forward_trajectories_with_cost( self, @@ -437,6 +452,7 @@ def calc_forward_trajectories_with_cost( X_des: np.ndarray, U_des: np.ndarray, previous_error: np.ndarray, + steer_rate_cost_coef: float, ) -> tuple[np.ndarray, np.ndarray]: """Calculate the predicted trajectory and cost. @@ -456,6 +472,7 @@ def calc_forward_trajectories_with_cost( Traj[:, k, index_cost], Inputs[:, k, :], k, + steer_rate_cost_coef, ) Traj[:, k + 1], Previous_error = self.F_for_candidates( Traj[:, k], Inputs[:, k], Previous_error, k @@ -466,6 +483,7 @@ def calc_forward_trajectories_with_cost( Traj[:, N, index_cost], Inputs[:, N - 1, :], N, + steer_rate_cost_coef, ) return Traj, Cost @@ -485,18 +503,41 @@ def calc_forward_trajectory_with_diff( A = np.zeros((N, nx, nx)) B = np.zeros((N, nx, nu)) C = np.zeros((N, 6, nx)) + if self.add_state_hc: + D = np.zeros((N, 6 + self.h_dim_double, nx + self.h_dim_double)) previous_error_ = previous_error.copy() for k in range(N): if self.use_trained_model_diff: traj[k + 1], A[k], B[k], C[k], previous_error_ = self.F_with_diff( traj[k], inputs[k], previous_error_, k ) + if self.add_state_hc: + D[k][:6, 6 : 6 + self.h_dim_double] = ( + self.get_dy_dhc() * drive_functions.mpc_time_step + ) + dhc_dx = self.get_dhc_dx() + D[k][6:, :6] = dhc_dx[:, :6] + D[k][6:, 6 + self.h_dim_double :] = dhc_dx[:, 6:] + D[k][6:, 6 : 6 + self.h_dim_double] = self.get_dhc_dhc() else: traj[k + 1], A[k], B[k], previous_error_ = self.F_with_initial_diff( traj[k], inputs[k], previous_error_, k ) if self.use_trained_model_diff: A[:, :6] += drive_functions.sg_filter_for_trained_model_diff(C) + if self.add_state_hc: + D = drive_functions.sg_filter_for_memory_diff(D) + A_ = np.zeros((N, nx + self.h_dim_double, nx + self.h_dim_double)) + A_[:, :6, :6] = A[:, :6, :6] + A_[:, 6 + self.h_dim_double :, :6] = A[:, 6:, :6] + A_[:, :6, 6 + self.h_dim_double :] = A[:, :6, 6:] + A_[:, 6 + self.h_dim_double :, 6 + self.h_dim_double :] = A[:, 6:, 6:] + A_[:, : 6 + self.h_dim_double] += D + B_ = np.zeros((N, nx + self.h_dim_double, nu)) + B_[:, :6] = B[:, :6] + B_[:, 6 + self.h_dim_double :] = B[:, 6:] + return traj, A_, B_ + return traj, A, B def compute_optimal_control( @@ -512,14 +553,13 @@ def compute_optimal_control( theta_noise: Callable | None, acc_noise: Callable | None, steer_noise: Callable | None, + steer_rate_cost_coef: float, ) -> tuple[np.ndarray, np.ndarray, np.ndarray, bool]: """Calculate the optimal predictive trajectory and the input sequence at that time. It also determines whether the tolerance has been reached and returns whether the optimization should continue or not. Performs one iteration of iLQR. """ - nx = x_current.shape[0] - nu = inputs.shape[1] N = inputs.shape[0] self.time_1 = time.time() traj, A, B = self.calc_forward_trajectory_with_diff(x_current, inputs, previous_error) @@ -534,7 +574,7 @@ def compute_optimal_control( acc_rate_lim_center, steer_rate_lim_weights, steer_rate_lim_center, - ) = drive_functions.transform_Q_R(X_des, U_des, traj, inputs) + ) = drive_functions.transform_Q_R(X_des, U_des, traj, inputs, steer_rate_cost_coef) self.time_2 = time.time() if x_noise is None: Sigma_x = None @@ -584,44 +624,45 @@ def compute_optimal_control( Sigma_steer = drive_functions.sg_filter_for_noise(Sigma_steer) dSigma_steer = drive_functions.sg_filter_for_noise(dSigma_steer) P, w, H_inv_G, H_inv_g = compute_iLQR_coef( - traj[:, index_cost], - inputs, - A, - B, - Q_total, - R_total, - acc_lim_weights, - acc_lim_center, - steer_lim_weights, - steer_lim_center, - acc_rate_lim_weights, - acc_rate_lim_center, - steer_rate_lim_weights, - steer_rate_lim_center, - N, - nx, - nu, - X_des, - U_des, - Sigma_x, - dSigma_x, - Sigma_y, - dSigma_y, - Sigma_v, - dSigma_v, - Sigma_theta, - dSigma_theta, - Sigma_acc, - dSigma_acc, - Sigma_steer, - dSigma_steer, + traj=traj[:, index_cost], + inputs=inputs, + A=A, + B=B, + Q_total=Q_total, + R_total=R_total, + acc_lim_weights=acc_lim_weights, + acc_lim_center=acc_lim_center, + steer_lim_weights=steer_lim_weights, + steer_lim_center=steer_lim_center, + acc_rate_lim_weights=acc_rate_lim_weights, + acc_rate_lim_center=acc_rate_lim_center, + steer_rate_lim_weights=steer_rate_lim_weights, + steer_rate_lim_center=steer_rate_lim_center, + N=N, + X_des=X_des, + U_des=U_des, + Sigma_x=Sigma_x, + dSigma_x=dSigma_x, + Sigma_y=Sigma_y, + dSigma_y=dSigma_y, + Sigma_v=Sigma_v, + dSigma_v=dSigma_v, + Sigma_theta=Sigma_theta, + dSigma_theta=dSigma_theta, + Sigma_acc=Sigma_acc, + dSigma_acc=dSigma_acc, + Sigma_steer=Sigma_steer, + dSigma_steer=dSigma_steer, + acc_input_index=self.acc_index, + steer_input_index=self.steer_index, + state_dim=self.state_dim, ) self.time_3 = time.time() # start line search Inputs = calc_line_search_candidates(A, B, H_inv_g, H_inv_G, inputs, self.ls) self.time_4 = time.time() Traj, Cost = self.calc_forward_trajectories_with_cost( - x_current, Inputs, X_des, U_des, previous_error + x_current, Inputs, X_des, U_des, previous_error, steer_rate_cost_coef ) best_index = Cost.argmin() self.time_5 = time.time() @@ -642,3 +683,17 @@ def receive_model( self.F_with_initial_diff = F_with_initial_diff self.F_with_diff = F_with_diff self.F_for_candidates = F_for_candidates + + def receive_memory_diff( + self, get_dhc_dx: Callable, get_dhc_dhc: Callable, get_dy_dhc: Callable + ): + """Receive getter hc diff.""" + self.get_dhc_dx = get_dhc_dx + self.get_dhc_dhc = get_dhc_dhc + self.get_dy_dhc = get_dy_dhc + self.h_dim_double = get_dhc_dx().shape[0] + self.add_state_hc = True + print(self.h_dim_double) + self.state_dim += self.h_dim_double + self.acc_index += self.h_dim_double + self.steer_index += self.h_dim_double diff --git a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/scripts/drive_mppi.py b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/scripts/drive_mppi.py index 5e88c79172097..a5cf4c366046f 100644 --- a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/scripts/drive_mppi.py +++ b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/scripts/drive_mppi.py @@ -23,7 +23,7 @@ index_cost = np.concatenate( ( np.arange(drive_functions.nx_0 + 1), - [drive_functions.nx_0 + drive_functions.acc_ctrl_queue_size], + np.array([drive_functions.nx_0 + drive_functions.acc_ctrl_queue_size]), ) ) @@ -105,29 +105,14 @@ def calc_forward_trajectories_with_cost(self, x_current, Inputs, X_des, U_des, P Cost += drive_functions.calc_cost_only_for_states(X_des[N], Traj[:, N, index_cost], N) return Traj, Cost - def proceed_mppi_step(self, x_current, inputs, X_des, U_des, previous_error): + def compute_optimal_control(self, x_current, inputs, X_des, U_des, previous_error): """Proceed with MPPI iteration one time.""" N = inputs.shape[0] - previous_error_ = previous_error.reshape(1, -1).copy() - nominal_traj = np.zeros((N + 1, x_current.shape[0])) - nominal_traj[0] = x_current.copy() - for k in range(N): - if k == 0: - Traj_, previous_error_ = self.F_for_candidates( - nominal_traj[k].reshape(1, -1), inputs[k].reshape(1, -1), previous_error_, True - ) - nominal_traj[k + 1] = Traj_[0] - else: - Traj_, previous_error_ = self.F_for_candidates( - nominal_traj[k].reshape(1, -1), inputs[k].reshape(1, -1), previous_error_, False - ) - nominal_traj[k + 1] = Traj_[0] - Inputs = self.generate_sample_inputs(inputs) Previous_error = np.tile(previous_error, (self.sample_num, 1)) - _, Cost = self.calc_forward_trajectories_with_cost( + Traj, Cost = self.calc_forward_trajectories_with_cost( x_current, Inputs, X_des, U_des, Previous_error ) original_cost = Cost[0] @@ -135,36 +120,20 @@ def proceed_mppi_step(self, x_current, inputs, X_des, U_des, previous_error): Exps = np.exp(-(Cost - best_cost) / self.lam) Exps = Exps / Exps.sum() new_inputs = (Inputs.T @ Exps).T - previous_error_ = previous_error.reshape(1, -1).copy() + previous_error_ = previous_error.copy() new_traj = np.zeros((N + 1, x_current.shape[0])) new_traj[0] = x_current.copy() for k in range(N): - if k == 0: - Traj_, previous_error_ = self.F_for_candidates( - new_traj[k].reshape(1, -1), new_inputs[k].reshape(1, -1), previous_error_, True - ) - new_traj[k + 1] = Traj_[0] - else: - Traj_, previous_error_ = self.F_for_candidates( - new_traj[k].reshape(1, -1), new_inputs[k].reshape(1, -1), previous_error_, False - ) - new_traj[k + 1] = Traj_[0] + new_traj[k + 1], previous_error_ = self.F( + new_traj[k], new_inputs[k], previous_error_, k + ) if np.dot(Exps, Cost) < (1 - self.mppi_tol) * original_cost: proceed = True else: proceed = False - return new_traj, new_inputs, proceed - - def compute_optimal_control(self, x_current, inputs, X_des, U_des, previous_error): - """Calculate the optimal input based on MPPI.""" - for i in range(self.max_iter_mppi): - new_traj, new_inputs, proceed = self.proceed_mppi_step( - x_current, inputs, X_des, U_des, previous_error - ) - if not proceed: - break - return new_inputs, new_inputs[0], new_traj + return new_inputs, new_inputs[0], new_traj, Traj, proceed - def receive_model(self, F_for_candidates: Callable): + def receive_model(self, F_for_candidates: Callable, F): """Receive vehicle model for control.""" self.F_for_candidates = F_for_candidates + self.F = F diff --git a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/scripts/proxima_calc.cpp b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/scripts/proxima_calc.cpp index de65184ec9f8e..6dd39c72d433d 100644 --- a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/scripts/proxima_calc.cpp +++ b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/scripts/proxima_calc.cpp @@ -12,6 +12,8 @@ // See the License for the specific language governing permissions and // limitations under the License. +// cSpell:ignore lstm + #include #include @@ -20,29 +22,19 @@ #include namespace py = pybind11; -double nominal_model_input(Eigen::VectorXd var, double lam, int step) +Eigen::VectorXd tanh(const Eigen::VectorXd & v) { - double nominal_pred = var[0]; - nominal_pred += (var[step] - nominal_pred) * 0.03333 / lam; - nominal_pred += (var[step - 1] - nominal_pred) * 0.03333 / lam; - nominal_pred += (var[step - 2] - nominal_pred) * 0.03333 / lam; - return nominal_pred; + return v.array().tanh(); } -Eigen::RowVectorXd Nominal_model_input(Eigen::MatrixXd Var, double lam, int step) +Eigen::VectorXd d_tanh(const Eigen::VectorXd & v) { - Eigen::RowVectorXd nominal_pred = Var.row(0); - nominal_pred += (Var.row(step) - nominal_pred) * 0.03333 / lam; - nominal_pred += (Var.row(step - 1) - nominal_pred) * 0.03333 / lam; - nominal_pred += (Var.row(step - 2) - nominal_pred) * 0.03333 / lam; - return nominal_pred; + return 1 / (v.array().cosh() * v.array().cosh()); } - -Eigen::VectorXd d_tanh(Eigen::VectorXd v) +Eigen::VectorXd sigmoid(const Eigen::VectorXd & v) { - Eigen::VectorXd result = 1 / (v.array().cosh() * v.array().cosh()); - return result; + return 0.5 * (0.5 * v).array().tanh() + 0.5; } -Eigen::VectorXd relu(Eigen::VectorXd x) +Eigen::VectorXd relu(const Eigen::VectorXd & x) { Eigen::VectorXd x_ = x; for (int i = 0; i < x.size(); i++) { @@ -52,7 +44,7 @@ Eigen::VectorXd relu(Eigen::VectorXd x) } return x_; } -Eigen::VectorXd d_relu(Eigen::VectorXd x) +Eigen::VectorXd d_relu(const Eigen::VectorXd & x) { Eigen::VectorXd result = Eigen::VectorXd::Ones(x.size()); for (int i = 0; i < x.size(); i++) { @@ -62,8 +54,7 @@ Eigen::VectorXd d_relu(Eigen::VectorXd x) } return result; } - -Eigen::MatrixXd d_relu_product(Eigen::MatrixXd m, Eigen::VectorXd x) +Eigen::MatrixXd d_relu_product(const Eigen::MatrixXd & m, const Eigen::VectorXd & x) { Eigen::MatrixXd result = Eigen::MatrixXd::Zero(m.rows(), m.cols()); for (int i = 0; i < m.cols(); i++) { @@ -73,7 +64,7 @@ Eigen::MatrixXd d_relu_product(Eigen::MatrixXd m, Eigen::VectorXd x) } return result; } -Eigen::MatrixXd d_tanh_product(Eigen::MatrixXd m, Eigen::VectorXd x) +Eigen::MatrixXd d_tanh_product(const Eigen::MatrixXd & m, const Eigen::VectorXd & x) { Eigen::MatrixXd result = Eigen::MatrixXd(m.rows(), m.cols()); for (int i = 0; i < m.cols(); i++) { @@ -81,13 +72,34 @@ Eigen::MatrixXd d_tanh_product(Eigen::MatrixXd m, Eigen::VectorXd x) } return result; } -Eigen::MatrixXd test_product(Eigen::VectorXd v, Eigen::MatrixXd m) +Eigen::VectorXd d_tanh_product_vec(const Eigen::VectorXd & v, const Eigen::VectorXd & x) +{ + Eigen::VectorXd result = Eigen::VectorXd(v.size()); + for (int i = 0; i < v.size(); i++) { + result[i] = v[i] / (std::cosh(x[i]) * std::cosh(x[i])); + } + return result; +} +Eigen::MatrixXd d_sigmoid_product(const Eigen::MatrixXd & m, const Eigen::VectorXd & x) { - return v.asDiagonal() * m; + Eigen::MatrixXd result = Eigen::MatrixXd(m.rows(), m.cols()); + for (int i = 0; i < m.cols(); i++) { + result.col(i) = 0.25 * m.col(i) / (std::cosh(0.5 * x[i]) * std::cosh(0.5 * x[i])); + } + return result; } -Eigen::VectorXd get_polynomial_features(Eigen::VectorXd x, int deg, int dim) +Eigen::VectorXd d_sigmoid_product_vec(const Eigen::VectorXd & v, const Eigen::VectorXd & x) { - int n_features = x.size(); + Eigen::VectorXd result = Eigen::VectorXd(v.size()); + for (int i = 0; i < v.size(); i++) { + result[i] = 0.25 * v[i] / (std::cosh(0.5 * x[i]) * std::cosh(0.5 * x[i])); + } + return result; +} + +Eigen::VectorXd get_polynomial_features(const Eigen::VectorXd & x, const int deg, const int dim) +{ + const int n_features = x.size(); Eigen::VectorXd result = Eigen::VectorXd(dim); result.head(n_features) = x; if (deg >= 2) { @@ -98,11 +110,11 @@ Eigen::VectorXd get_polynomial_features(Eigen::VectorXd x, int deg, int dim) int current_idx = n_features; for (int i = 0; i < deg - 1; i++) { std::vector new_index = {}; - int end = index[index.size() - 1]; + const int end = index[index.size() - 1]; for (int feature_idx = 0; feature_idx < n_features; feature_idx++) { - int start = index[feature_idx]; + const int start = index[feature_idx]; new_index.push_back(current_idx); - int next_idx = current_idx + end - start; + const int next_idx = current_idx + end - start; result.segment(current_idx, end - start) = x[feature_idx] * result.segment(start, end - start); current_idx = next_idx; @@ -113,9 +125,10 @@ Eigen::VectorXd get_polynomial_features(Eigen::VectorXd x, int deg, int dim) } return result; } -Eigen::MatrixXd get_polynomial_features_with_diff(Eigen::VectorXd x, int deg, int dim) +Eigen::MatrixXd get_polynomial_features_with_diff( + const Eigen::VectorXd & x, const int deg, const int dim) { - int n_features = x.size(); + const int n_features = x.size(); Eigen::MatrixXd result = Eigen::MatrixXd::Zero(dim, n_features + 1); result.block(0, 0, n_features, 1) = x; result.block(0, 1, n_features, n_features) = Eigen::MatrixXd::Identity(n_features, n_features); @@ -127,11 +140,11 @@ Eigen::MatrixXd get_polynomial_features_with_diff(Eigen::VectorXd x, int deg, in int current_idx = n_features; for (int i = 0; i < deg - 1; i++) { std::vector new_index = {}; - int end = index[index.size() - 1]; + const int end = index[index.size() - 1]; for (int feature_idx = 0; feature_idx < n_features; feature_idx++) { - int start = index[feature_idx]; + const int start = index[feature_idx]; new_index.push_back(current_idx); - int next_idx = current_idx + end - start; + const int next_idx = current_idx + end - start; result.block(current_idx, 0, end - start, n_features + 1) = x[feature_idx] * result.block(start, 0, end - start, n_features + 1); result.block(current_idx, feature_idx + 1, end - start, 1) += @@ -147,124 +160,137 @@ Eigen::MatrixXd get_polynomial_features_with_diff(Eigen::VectorXd x, int deg, in class transform_model_to_eigen { private: - Eigen::MatrixXd weight_0; - Eigen::MatrixXd weight_1; - Eigen::MatrixXd weight_2; - Eigen::MatrixXd weight_3; - Eigen::MatrixXd weight_4; - Eigen::MatrixXd weight_5; - Eigen::MatrixXd weight_6; - Eigen::MatrixXd weight_7; - Eigen::VectorXd bias_0; - Eigen::VectorXd bias_1; - Eigen::VectorXd bias_2; - Eigen::VectorXd bias_3; - Eigen::VectorXd bias_4; - Eigen::VectorXd bias_5; - Eigen::VectorXd bias_6; - Eigen::VectorXd bias_7; - Eigen::MatrixXd A_linear_reg; - Eigen::VectorXd b_linear_reg; - int deg; - double acc_time_constant; - int acc_delay_step; - double steer_time_constant; - int steer_delay_step; - int acc_ctrl_queue_size; - int steer_ctrl_queue_size; - int steer_ctrl_queue_size_core; - double max_acc_error = 20.0; - double max_steer_error = 20.0; + Eigen::MatrixXd weight_acc_layer_1_; + Eigen::MatrixXd weight_steer_layer_1_head_; + Eigen::MatrixXd weight_steer_layer_1_tail_; + Eigen::MatrixXd weight_acc_layer_2_; + Eigen::MatrixXd weight_steer_layer_2_; + Eigen::MatrixXd weight_linear_relu_1_; + Eigen::MatrixXd weight_linear_relu_2_; + Eigen::MatrixXd weight_finalize_; + Eigen::VectorXd bias_acc_layer_1_; + Eigen::VectorXd bias_steer_layer_1_head_; + Eigen::VectorXd bias_steer_layer_1_tail_; + Eigen::VectorXd bias_acc_layer_2_; + Eigen::VectorXd bias_steer_layer_2_; + Eigen::VectorXd bias_linear_relu_1_; + Eigen::VectorXd bias_linear_relu_2_; + Eigen::VectorXd bias_linear_finalize_; + Eigen::MatrixXd A_linear_reg_; + Eigen::VectorXd b_linear_reg_; + int deg_; + int acc_delay_step_; + int steer_delay_step_; + int acc_ctrl_queue_size_; + int steer_ctrl_queue_size_; + int steer_ctrl_queue_size_core_; + double vel_normalize_; + double acc_normalize_; + double steer_normalize_; + static constexpr double max_acc_error_ = 20.0; + static constexpr double max_steer_error_ = 20.0; public: transform_model_to_eigen() {} void set_params( - Eigen::MatrixXd weight_0_, Eigen::MatrixXd weight_1_, Eigen::MatrixXd weight_2_, - Eigen::MatrixXd weight_3_, Eigen::MatrixXd weight_4_, Eigen::MatrixXd weight_5_, - Eigen::MatrixXd weight_6_, Eigen::MatrixXd weight_7_, Eigen::VectorXd bias_0_, - Eigen::VectorXd bias_1_, Eigen::VectorXd bias_2_, Eigen::VectorXd bias_3_, - Eigen::VectorXd bias_4_, Eigen::VectorXd bias_5_, Eigen::VectorXd bias_6_, - Eigen::VectorXd bias_7_, Eigen::MatrixXd A_linear_reg_, Eigen::VectorXd b_linear_reg_, int deg_, - double acc_time_constant_, int acc_delay_step_, double steer_time_constant_, - int steer_delay_step_, int acc_ctrl_queue_size_, int steer_ctrl_queue_size_, - int steer_ctrl_queue_size_core_) + const Eigen::MatrixXd & weight_acc_layer_1, const Eigen::MatrixXd & weight_steer_layer_1_head, + const Eigen::MatrixXd & weight_steer_layer_1_tail, const Eigen::MatrixXd & weight_acc_layer_2, + const Eigen::MatrixXd & weight_steer_layer_2, const Eigen::MatrixXd & weight_linear_relu_1, + const Eigen::MatrixXd & weight_linear_relu_2, const Eigen::MatrixXd & weight_finalize, + const Eigen::VectorXd & bias_acc_layer_1, const Eigen::VectorXd & bias_steer_layer_1_head, + const Eigen::VectorXd & bias_steer_layer_1_tail, const Eigen::VectorXd & bias_acc_layer_2, + const Eigen::VectorXd & bias_steer_layer_2, const Eigen::VectorXd & bias_linear_relu_1, + const Eigen::VectorXd & bias_linear_relu_2, const Eigen::VectorXd & bias_linear_finalize, + const Eigen::MatrixXd & A_linear_reg, const Eigen::VectorXd & b_linear_reg, const int deg, + const int acc_delay_step, const int steer_delay_step, const int acc_ctrl_queue_size, + const int steer_ctrl_queue_size, const int steer_ctrl_queue_size_core, + const double vel_normalize, const double acc_normalize, const double steer_normalize) { - weight_0 = weight_0_; - weight_1 = weight_1_; - weight_2 = weight_2_; - weight_3 = weight_3_; - weight_4 = weight_4_; - weight_5 = weight_5_; - weight_6 = weight_6_; - weight_7 = weight_7_; - bias_0 = bias_0_; - bias_1 = bias_1_; - bias_2 = bias_2_; - bias_3 = bias_3_; - bias_4 = bias_4_; - bias_5 = bias_5_; - bias_6 = bias_6_; - bias_7 = bias_7_; - A_linear_reg = A_linear_reg_; - b_linear_reg = b_linear_reg_; - deg = deg_; - acc_time_constant = acc_time_constant_; - acc_delay_step = acc_delay_step_; - steer_time_constant = steer_time_constant_; - steer_delay_step = steer_delay_step_; - acc_ctrl_queue_size = acc_ctrl_queue_size_; - steer_ctrl_queue_size = steer_ctrl_queue_size_; - steer_ctrl_queue_size_core = steer_ctrl_queue_size_core_; - } - Eigen::VectorXd error_prediction(Eigen::VectorXd x) + weight_acc_layer_1_ = weight_acc_layer_1; + weight_steer_layer_1_head_ = weight_steer_layer_1_head; + weight_steer_layer_1_tail_ = weight_steer_layer_1_tail; + weight_acc_layer_2_ = weight_acc_layer_2; + weight_steer_layer_2_ = weight_steer_layer_2; + weight_linear_relu_1_ = weight_linear_relu_1; + weight_linear_relu_2_ = weight_linear_relu_2; + weight_finalize_ = weight_finalize; + bias_acc_layer_1_ = bias_acc_layer_1; + bias_steer_layer_1_head_ = bias_steer_layer_1_head; + bias_steer_layer_1_tail_ = bias_steer_layer_1_tail; + bias_acc_layer_2_ = bias_acc_layer_2; + bias_steer_layer_2_ = bias_steer_layer_2; + bias_linear_relu_1_ = bias_linear_relu_1; + bias_linear_relu_2_ = bias_linear_relu_2; + bias_linear_finalize_ = bias_linear_finalize; + A_linear_reg_ = A_linear_reg; + b_linear_reg_ = b_linear_reg; + deg_ = deg; + acc_delay_step_ = acc_delay_step; + steer_delay_step_ = steer_delay_step; + acc_ctrl_queue_size_ = acc_ctrl_queue_size; + steer_ctrl_queue_size_ = steer_ctrl_queue_size; + steer_ctrl_queue_size_core_ = steer_ctrl_queue_size_core; + vel_normalize_ = vel_normalize; + acc_normalize_ = acc_normalize; + steer_normalize_ = steer_normalize; + } + Eigen::VectorXd error_prediction(const Eigen::VectorXd & x) const { - Eigen::VectorXd acc_sub(acc_ctrl_queue_size + 1); - acc_sub[0] = x[1]; - acc_sub.tail(acc_ctrl_queue_size) = x.segment(3, acc_ctrl_queue_size); + Eigen::VectorXd acc_sub(acc_ctrl_queue_size_ + 1); + acc_sub[0] = acc_normalize_ * x[1]; + acc_sub.tail(acc_ctrl_queue_size_) = acc_normalize_ * x.segment(3, acc_ctrl_queue_size_); - Eigen::VectorXd steer_sub(steer_ctrl_queue_size_core + 1); - steer_sub[0] = x[2]; - steer_sub.tail(steer_ctrl_queue_size_core) = - x.segment(3 + acc_ctrl_queue_size, steer_ctrl_queue_size_core); - Eigen::VectorXd acc_layer_1 = relu(weight_0 * acc_sub + bias_0); + Eigen::VectorXd steer_sub(steer_ctrl_queue_size_core_ + 1); + steer_sub[0] = steer_normalize_ * x[2]; + steer_sub.tail(steer_ctrl_queue_size_core_) = + steer_normalize_ * x.segment(3 + acc_ctrl_queue_size_, steer_ctrl_queue_size_core_); + const Eigen::VectorXd acc_layer_1 = relu(weight_acc_layer_1_ * acc_sub + bias_acc_layer_1_); - Eigen::VectorXd steer_layer_1(bias_1.size() + bias_2.size()); - steer_layer_1.head(bias_1.size()) = relu(weight_1 * steer_sub + bias_1); + Eigen::VectorXd steer_layer_1( + bias_steer_layer_1_head_.size() + bias_steer_layer_1_tail_.size()); + steer_layer_1.head(bias_steer_layer_1_head_.size()) = + relu(weight_steer_layer_1_head_ * steer_sub + bias_steer_layer_1_head_); - Eigen::VectorXd steer_input_full = x.segment(3 + acc_ctrl_queue_size, steer_ctrl_queue_size); - steer_layer_1.tail(bias_2.size()) = relu(weight_2 * steer_input_full + bias_2); + const Eigen::VectorXd steer_input_full = + steer_normalize_ * x.segment(3 + acc_ctrl_queue_size_, steer_ctrl_queue_size_); + steer_layer_1.tail(bias_steer_layer_1_tail_.size()) = + relu(weight_steer_layer_1_tail_ * steer_input_full + bias_steer_layer_1_tail_); - Eigen::VectorXd acc_layer_2 = (weight_3 * acc_layer_1 + bias_3).array().tanh(); + const Eigen::VectorXd acc_layer_2 = relu(weight_acc_layer_2_ * acc_layer_1 + bias_acc_layer_2_); - Eigen::VectorXd steer_layer_2 = (weight_4 * steer_layer_1 + bias_4).array().tanh(); + const Eigen::VectorXd steer_layer_2 = + relu(weight_steer_layer_2_ * steer_layer_1 + bias_steer_layer_2_); Eigen::VectorXd h1(1 + acc_layer_2.size() + steer_layer_2.size()); - h1[0] = x[0]; + h1[0] = vel_normalize_ * x[0]; h1.segment(1, acc_layer_2.size()) = acc_layer_2; h1.tail(steer_layer_2.size()) = steer_layer_2; - Eigen::VectorXd h2 = relu(weight_5 * h1 + bias_5); - Eigen::VectorXd h3 = relu(weight_6 * h2 + bias_6); + const Eigen::VectorXd h2 = relu(weight_linear_relu_1_ * h1 + bias_linear_relu_1_); + const Eigen::VectorXd h3 = relu(weight_linear_relu_2_ * h2 + bias_linear_relu_2_); Eigen::VectorXd h4(h3.size() + acc_layer_2.size() + steer_layer_2.size()); h4.head(h3.size()) = h3; h4.segment(h3.size(), acc_layer_2.size()) = acc_layer_2; h4.tail(steer_layer_2.size()) = steer_layer_2; - Eigen::VectorXd x_for_polynomial_reg(5); + Eigen::VectorXd x_for_polynomial_reg(9); x_for_polynomial_reg.head(3) = x.head(3); - x_for_polynomial_reg[3] = x[3 + acc_delay_step]; - x_for_polynomial_reg[4] = x[3 + acc_ctrl_queue_size + steer_delay_step]; + const int acc_start = 3 + std::max(acc_delay_step_ - 3, 0); + x_for_polynomial_reg.segment(3, 3) = x.segment(acc_start, 3); + const int steer_start = 3 + acc_ctrl_queue_size_ + std::max(steer_delay_step_ - 3, 0); + x_for_polynomial_reg.segment(6, 3) = x.segment(steer_start, 3); + Eigen::VectorXd y = - weight_7 * h4 + bias_7 + - A_linear_reg * get_polynomial_features(x_for_polynomial_reg, deg, A_linear_reg.cols()) + - b_linear_reg; - y[4] = std::min(std::max(y[4], -max_acc_error), max_acc_error); - y[5] = std::min(std::max(y[5], -max_steer_error), max_steer_error); + weight_finalize_ * h4 + bias_linear_finalize_ + + A_linear_reg_ * get_polynomial_features(x_for_polynomial_reg, deg_, A_linear_reg_.cols()) + + b_linear_reg_; + y[4] = std::min(std::max(y[4], -max_acc_error_), max_acc_error_); + y[5] = std::min(std::max(y[5], -max_steer_error_), max_steer_error_); return y; } - Eigen::VectorXd rot_and_d_rot_error_prediction(Eigen::VectorXd x) + Eigen::VectorXd rot_and_d_rot_error_prediction(const Eigen::VectorXd & x) const { - int x_dim = x.size(); - double theta = x[3]; - double v = x[2]; + const int x_dim = x.size(); + const double theta = x[3]; + const double v = x[2]; double coef = 2.0 * std::abs(v); coef = coef * coef * coef * coef * coef * coef * coef; if (coef > 1.0) { @@ -279,8 +305,8 @@ class transform_model_to_eigen Therefore, it may be safe to always set coef = 1, and this variable may be eliminated once it is confirmed safe to do so. */ - double cos = std::cos(theta); - double sin = std::sin(theta); + const double cos = std::cos(theta); + const double sin = std::sin(theta); Eigen::Matrix2d Rot; Rot << cos, -sin, sin, cos; @@ -291,7 +317,7 @@ class transform_model_to_eigen vars[1] = x[4]; vars[2] = x[5]; vars.tail(x_dim - 6) = x.tail(x_dim - 6); - Eigen::VectorXd pred = error_prediction(vars); + const Eigen::VectorXd pred = error_prediction(vars); Eigen::VectorXd rot_and_d_rot_pred(8); rot_and_d_rot_pred.head(2) = Rot * pred.head(2); rot_and_d_rot_pred.segment(2, 4) = pred.segment(2, 4); @@ -299,117 +325,165 @@ class transform_model_to_eigen return coef * rot_and_d_rot_pred; } - Eigen::MatrixXd error_prediction_with_diff(Eigen::VectorXd x) + Eigen::MatrixXd error_prediction_with_diff(const Eigen::VectorXd & x) const { - Eigen::VectorXd acc_sub(acc_ctrl_queue_size + 1); - acc_sub[0] = x[1]; - acc_sub.tail(acc_ctrl_queue_size) = x.segment(3, acc_ctrl_queue_size); + Eigen::VectorXd acc_sub(acc_ctrl_queue_size_ + 1); + acc_sub[0] = acc_normalize_ * x[1]; + acc_sub.tail(acc_ctrl_queue_size_) = acc_normalize_ * x.segment(3, acc_ctrl_queue_size_); - Eigen::VectorXd steer_sub(steer_ctrl_queue_size_core + 1); - steer_sub[0] = x[2]; - steer_sub.tail(steer_ctrl_queue_size_core) = - x.segment(3 + acc_ctrl_queue_size, steer_ctrl_queue_size_core); - Eigen::VectorXd steer_input_full = x.segment(3 + acc_ctrl_queue_size, steer_ctrl_queue_size); + Eigen::VectorXd steer_sub(steer_ctrl_queue_size_core_ + 1); + steer_sub[0] = steer_normalize_ * x[2]; + steer_sub.tail(steer_ctrl_queue_size_core_) = + steer_normalize_ * x.segment(3 + acc_ctrl_queue_size_, steer_ctrl_queue_size_core_); + const Eigen::VectorXd steer_input_full = + steer_normalize_ * x.segment(3 + acc_ctrl_queue_size_, steer_ctrl_queue_size_); - Eigen::VectorXd u_acc_layer_1 = weight_0 * acc_sub + bias_0; - Eigen::VectorXd acc_layer_1 = relu(u_acc_layer_1); + const Eigen::VectorXd u_acc_layer_1 = weight_acc_layer_1_ * acc_sub + bias_acc_layer_1_; + const Eigen::VectorXd acc_layer_1 = relu(u_acc_layer_1); - Eigen::VectorXd u_steer_layer_1(bias_1.size() + bias_2.size()); - u_steer_layer_1.head(bias_1.size()) = weight_1 * steer_sub + bias_1; - u_steer_layer_1.tail(bias_2.size()) = weight_2 * steer_input_full + bias_2; - Eigen::VectorXd steer_layer_1 = relu(u_steer_layer_1); + Eigen::VectorXd u_steer_layer_1( + bias_steer_layer_1_head_.size() + bias_steer_layer_1_tail_.size()); + u_steer_layer_1.head(bias_steer_layer_1_head_.size()) = + weight_steer_layer_1_head_ * steer_sub + bias_steer_layer_1_head_; + u_steer_layer_1.tail(bias_steer_layer_1_tail_.size()) = + weight_steer_layer_1_tail_ * steer_input_full + bias_steer_layer_1_tail_; + const Eigen::VectorXd steer_layer_1 = relu(u_steer_layer_1); - Eigen::VectorXd u_acc_layer_2 = weight_3 * acc_layer_1 + bias_3; - Eigen::VectorXd acc_layer_2 = u_acc_layer_2.array().tanh(); + const Eigen::VectorXd u_acc_layer_2 = weight_acc_layer_2_ * acc_layer_1 + bias_acc_layer_2_; + const Eigen::VectorXd acc_layer_2 = relu(u_acc_layer_2); - Eigen::VectorXd u_steer_layer_2 = weight_4 * steer_layer_1 + bias_4; - Eigen::VectorXd steer_layer_2 = u_steer_layer_2.array().tanh(); + const Eigen::VectorXd u_steer_layer_2 = + weight_steer_layer_2_ * steer_layer_1 + bias_steer_layer_2_; + const Eigen::VectorXd steer_layer_2 = relu(u_steer_layer_2); Eigen::VectorXd h1(1 + acc_layer_2.size() + steer_layer_2.size()); - h1[0] = x[0]; + h1[0] = vel_normalize_ * x[0]; h1.segment(1, acc_layer_2.size()) = acc_layer_2; h1.tail(steer_layer_2.size()) = steer_layer_2; - Eigen::VectorXd u2 = weight_5 * h1 + bias_5; - Eigen::VectorXd h2 = relu(u2); - Eigen::VectorXd u3 = weight_6 * h2 + bias_6; - Eigen::VectorXd h3 = relu(u3); + const Eigen::VectorXd u2 = weight_linear_relu_1_ * h1 + bias_linear_relu_1_; + const Eigen::VectorXd h2 = relu(u2); + const Eigen::VectorXd u3 = weight_linear_relu_2_ * h2 + bias_linear_relu_2_; + const Eigen::VectorXd h3 = relu(u3); Eigen::VectorXd h4(h3.size() + acc_layer_2.size() + steer_layer_2.size()); h4.head(h3.size()) = h3; h4.segment(h3.size(), acc_layer_2.size()) = acc_layer_2; h4.tail(steer_layer_2.size()) = steer_layer_2; - Eigen::VectorXd x_for_polynomial_reg(5); + + Eigen::VectorXd x_for_polynomial_reg(9); x_for_polynomial_reg.head(3) = x.head(3); - x_for_polynomial_reg[3] = x[3 + acc_delay_step]; - x_for_polynomial_reg[4] = x[3 + acc_ctrl_queue_size + steer_delay_step]; - Eigen::MatrixXd polynomial_features_with_diff = - get_polynomial_features_with_diff(x_for_polynomial_reg, deg, A_linear_reg.cols()); + const int acc_start = 3 + std::max(acc_delay_step_ - 3, 0); + x_for_polynomial_reg.segment(3, 3) = x.segment(acc_start, 3); + const int steer_start = 3 + acc_ctrl_queue_size_ + std::max(steer_delay_step_ - 3, 0); + x_for_polynomial_reg.segment(6, 3) = x.segment(steer_start, 3); + const Eigen::MatrixXd polynomial_features_with_diff = + get_polynomial_features_with_diff(x_for_polynomial_reg, deg_, A_linear_reg_.cols()); Eigen::VectorXd y = - weight_7 * h4 + bias_7 + - A_linear_reg * polynomial_features_with_diff.block(0, 0, A_linear_reg.cols(), 1) + - b_linear_reg; - y[4] = std::min(std::max(y[4], -max_acc_error), max_acc_error); - y[5] = std::min(std::max(y[5], -max_steer_error), max_steer_error); - // Eigen::MatrixXd dy_dh4 = weight_7; - - Eigen::MatrixXd dy_dh3 = weight_7.block(0, 0, y.size(), h3.size()); - Eigen::MatrixXd dy_dh2 = d_relu_product(dy_dh3, u3) * weight_6; - Eigen::MatrixXd dy_dh1 = d_relu_product(dy_dh2, u2) * weight_5; - - Eigen::MatrixXd dy_da2 = dy_dh1.block(0, 1, y.size(), acc_layer_2.size()) + - weight_7.block(0, h3.size(), y.size(), acc_layer_2.size()); - Eigen::MatrixXd dy_ds2 = + weight_finalize_ * h4 + bias_linear_finalize_ + + A_linear_reg_ * polynomial_features_with_diff.block(0, 0, A_linear_reg_.cols(), 1) + + b_linear_reg_; + y[4] = std::min(std::max(y[4], -max_acc_error_), max_acc_error_); + y[5] = std::min(std::max(y[5], -max_steer_error_), max_steer_error_); + + const Eigen::MatrixXd dy_dh3 = weight_finalize_.block(0, 0, y.size(), h3.size()); + const Eigen::MatrixXd dy_dh2 = d_relu_product(dy_dh3, u3) * weight_linear_relu_2_; + const Eigen::MatrixXd dy_dh1 = d_relu_product(dy_dh2, u2) * weight_linear_relu_1_; + + const Eigen::MatrixXd dy_da2 = + dy_dh1.block(0, 1, y.size(), acc_layer_2.size()) + + weight_finalize_.block(0, h3.size(), y.size(), acc_layer_2.size()); + const Eigen::MatrixXd dy_ds2 = dy_dh1.block(0, 1 + acc_layer_2.size(), y.size(), steer_layer_2.size()) + - weight_7.block(0, h3.size() + acc_layer_2.size(), y.size(), steer_layer_2.size()); - Eigen::MatrixXd dy_da1 = d_tanh_product(dy_da2, u_acc_layer_2) * weight_3; - Eigen::MatrixXd dy_ds1 = d_tanh_product(dy_ds2, u_steer_layer_2) * weight_4; + weight_finalize_.block(0, h3.size() + acc_layer_2.size(), y.size(), steer_layer_2.size()); + const Eigen::MatrixXd dy_da1 = d_relu_product(dy_da2, u_acc_layer_2) * weight_acc_layer_2_; + const Eigen::MatrixXd dy_ds1 = d_relu_product(dy_ds2, u_steer_layer_2) * weight_steer_layer_2_; - Eigen::MatrixXd dy_d_acc = d_relu_product(dy_da1, u_acc_layer_1) * weight_0; + const Eigen::MatrixXd dy_d_acc = d_relu_product(dy_da1, u_acc_layer_1) * weight_acc_layer_1_; Eigen::MatrixXd dy_d_steer = Eigen::MatrixXd::Zero(y.size(), steer_input_full.size() + 1); dy_d_steer.block(0, 1, y.size(), steer_input_full.size()) += d_relu_product( - dy_ds1.block(0, bias_1.size(), y.size(), bias_2.size()), - u_steer_layer_1.tail(bias_2.size())) * - weight_2; + dy_ds1.block(0, bias_steer_layer_1_head_.size(), y.size(), bias_steer_layer_1_tail_.size()), + u_steer_layer_1.tail(bias_steer_layer_1_tail_.size())) * + weight_steer_layer_1_tail_; dy_d_steer.block(0, 0, y.size(), steer_sub.size()) += d_relu_product( - dy_ds1.block(0, 0, y.size(), bias_1.size()), u_steer_layer_1.head(bias_1.size())) * - weight_1; + dy_ds1.block(0, 0, y.size(), bias_steer_layer_1_head_.size()), + u_steer_layer_1.head(bias_steer_layer_1_head_.size())) * + weight_steer_layer_1_head_; Eigen::MatrixXd result = Eigen::MatrixXd::Zero(y.size(), x.size() + 1); result.col(0) = y; - result.col(1) = dy_dh1.col(0); - result.col(2) = dy_d_acc.col(0); - result.col(3) = dy_d_steer.col(0); - result.block(0, 4, y.size(), acc_ctrl_queue_size) = - dy_d_acc.block(0, 1, y.size(), acc_ctrl_queue_size); - result.block(0, 4 + acc_ctrl_queue_size, y.size(), steer_ctrl_queue_size) = - dy_d_steer.block(0, 1, y.size(), steer_ctrl_queue_size); - - Eigen::MatrixXd polynomial_reg_diff = - A_linear_reg * - polynomial_features_with_diff.block(0, 1, A_linear_reg.cols(), x_for_polynomial_reg.size()); + result.col(1) = vel_normalize_ * dy_dh1.col(0); + result.col(2) = acc_normalize_ * dy_d_acc.col(0); + result.col(3) = steer_normalize_ * dy_d_steer.col(0); + result.block(0, 4, y.size(), acc_ctrl_queue_size_) = + acc_normalize_ * dy_d_acc.block(0, 1, y.size(), acc_ctrl_queue_size_); + result.block(0, 4 + acc_ctrl_queue_size_, y.size(), steer_ctrl_queue_size_) = + steer_normalize_ * dy_d_steer.block(0, 1, y.size(), steer_ctrl_queue_size_); + + const Eigen::MatrixXd polynomial_reg_diff = + A_linear_reg_ * + polynomial_features_with_diff.block(0, 1, A_linear_reg_.cols(), x_for_polynomial_reg.size()); result.block(0, 1, y.size(), 3) += polynomial_reg_diff.block(0, 0, y.size(), 3); - result.block(0, 4 + acc_delay_step, y.size(), 1) += - polynomial_reg_diff.block(0, 3, y.size(), 1); - result.block(0, 4 + acc_ctrl_queue_size + steer_delay_step, y.size(), 1) += - polynomial_reg_diff.block(0, 4, y.size(), 1); + result.block(0, 1 + acc_start, y.size(), 3) += polynomial_reg_diff.block(0, 3, y.size(), 3); + result.block(0, 1 + steer_start, y.size(), 3) += polynomial_reg_diff.block(0, 6, y.size(), 3); return result; } - Eigen::MatrixXd rot_and_d_rot_error_prediction_with_diff(Eigen::VectorXd x) + Eigen::MatrixXd rot_and_d_rot_error_prediction_with_diff(const Eigen::VectorXd & x) const + { + const int x_dim = x.size(); + const double theta = x[3]; + const double v = x[2]; + double coef = 2.0 * std::abs(v); + coef = coef * coef * coef * coef * coef * coef * coef; + if (coef > 1.0) { + coef = 1.0; + } + const double cos = std::cos(theta); + const double sin = std::sin(theta); + Eigen::Matrix2d Rot; + Rot << cos, -sin, sin, cos; + + Eigen::Matrix2d dRot; + dRot << -sin, -cos, cos, -sin; + Eigen::VectorXd vars(x_dim - 3); + vars[0] = x[2]; + vars[1] = x[4]; + vars[2] = x[5]; + vars.tail(x_dim - 6) = x.tail(x_dim - 6); + + const Eigen::MatrixXd pred_d_pred = error_prediction_with_diff(vars); + const Eigen::VectorXd pred = pred_d_pred.col(0); + const Eigen::MatrixXd d_pred = pred_d_pred.block(0, 1, 6, x_dim - 3); + Eigen::MatrixXd rot_and_d_rot_pred_with_diff = Eigen::MatrixXd::Zero(6, x_dim + 2); + Eigen::MatrixXd rot_pred_with_diff(6, x_dim - 3); + rot_pred_with_diff.block(0, 0, 2, x_dim - 3) = Rot * d_pred.block(0, 0, 2, x_dim - 3); + rot_pred_with_diff.block(2, 0, 4, x_dim - 3) = d_pred.block(2, 0, 4, x_dim - 3); + + rot_and_d_rot_pred_with_diff.block(0, 0, 2, 1) = Rot * pred.head(2); + rot_and_d_rot_pred_with_diff.block(2, 0, 4, 1) = pred.segment(2, 4); + rot_and_d_rot_pred_with_diff.block(0, 1, 2, 1) = dRot * pred.head(2); + rot_and_d_rot_pred_with_diff.col(2 + 2) = rot_pred_with_diff.col(0); + rot_and_d_rot_pred_with_diff.col(2 + 4) = rot_pred_with_diff.col(1); + rot_and_d_rot_pred_with_diff.col(2 + 5) = rot_pred_with_diff.col(2); + rot_and_d_rot_pred_with_diff.block(0, 2 + 6, 6, x_dim - 6) = + rot_pred_with_diff.block(0, 3, 6, x_dim - 6); + return coef * rot_and_d_rot_pred_with_diff; + } + Eigen::MatrixXd rot_and_d_rot_error_prediction_with_poly_diff(const Eigen::VectorXd & x) const { - int x_dim = x.size(); - double theta = x[3]; - double v = x[2]; + const int x_dim = x.size(); + const double theta = x[3]; + const double v = x[2]; double coef = 2.0 * std::abs(v); coef = coef * coef * coef * coef * coef * coef * coef; if (coef > 1.0) { coef = 1.0; } - double cos = std::cos(theta); - double sin = std::sin(theta); + const double cos = std::cos(theta); + const double sin = std::sin(theta); Eigen::Matrix2d Rot; Rot << cos, -sin, sin, cos; @@ -421,9 +495,25 @@ class transform_model_to_eigen vars[2] = x[5]; vars.tail(x_dim - 6) = x.tail(x_dim - 6); - Eigen::MatrixXd pred_d_pred = error_prediction_with_diff(vars); - Eigen::VectorXd pred = pred_d_pred.col(0); - Eigen::MatrixXd d_pred = pred_d_pred.block(0, 1, 6, x_dim - 3); + const Eigen::VectorXd pred = error_prediction(vars); + Eigen::MatrixXd d_pred = Eigen::MatrixXd::Zero(6, x_dim - 3); + + Eigen::VectorXd x_for_polynomial_reg(9); + x_for_polynomial_reg.head(3) = x.head(3); + const int acc_start = 3 + std::max(acc_delay_step_ - 3, 0); + x_for_polynomial_reg.segment(3, 3) = x.segment(acc_start, 3); + const int steer_start = 3 + acc_ctrl_queue_size_ + std::max(steer_delay_step_ - 3, 0); + x_for_polynomial_reg.segment(6, 3) = x.segment(steer_start, 3); + + const Eigen::MatrixXd polynomial_features_with_diff = + get_polynomial_features_with_diff(x_for_polynomial_reg, deg_, A_linear_reg_.cols()); + const Eigen::MatrixXd polynomial_reg_diff = + A_linear_reg_ * + polynomial_features_with_diff.block(0, 1, A_linear_reg_.cols(), x_for_polynomial_reg.size()); + d_pred.block(0, 0, 6, 3) += polynomial_reg_diff.block(0, 0, 6, 3); + d_pred.block(0, 1 + acc_start, 6, 3) += polynomial_reg_diff.block(0, 3, 6, 3); + d_pred.block(0, 1 + steer_start, 6, 3) += polynomial_reg_diff.block(0, 6, 6, 3); + Eigen::MatrixXd rot_and_d_rot_pred_with_diff = Eigen::MatrixXd::Zero(6, x_dim + 2); Eigen::MatrixXd rot_pred_with_diff(6, x_dim - 3); rot_pred_with_diff.block(0, 0, 2, x_dim - 3) = Rot * d_pred.block(0, 0, 2, x_dim - 3); @@ -439,22 +529,47 @@ class transform_model_to_eigen rot_pred_with_diff.block(0, 3, 6, x_dim - 6); return coef * rot_and_d_rot_pred_with_diff; } - Eigen::MatrixXd Rotated_error_prediction(Eigen::MatrixXd X) + Eigen::VectorXd rotated_error_prediction(const Eigen::VectorXd & x) const { - int X_cols = X.cols(); - int x_dim = X.rows(); + const int x_dim = x.size(); + const double theta = x[3]; + const double v = x[2]; + double coef = 2.0 * std::abs(v); + coef = coef * coef * coef * coef * coef * coef * coef; + if (coef > 1.0) { + coef = 1.0; + } + const double cos = std::cos(theta); + const double sin = std::sin(theta); + Eigen::Matrix2d Rot; + Rot << cos, -sin, sin, cos; + Eigen::VectorXd vars(x_dim - 3); + vars[0] = x[2]; + vars[1] = x[4]; + vars[2] = x[5]; + vars.tail(x_dim - 6) = x.tail(x_dim - 6); + const Eigen::VectorXd pred = error_prediction(vars); + Eigen::VectorXd rot_pred(6); + rot_pred.head(2) = coef * Rot * pred.head(2); + rot_pred.tail(4) = coef * pred.tail(4); + return rot_pred; + } + Eigen::MatrixXd Rotated_error_prediction(const Eigen::MatrixXd & X) const + { + const int X_cols = X.cols(); + const int x_dim = X.rows(); Eigen::MatrixXd Pred(6, X_cols); for (int i = 0; i < X_cols; i++) { - Eigen::VectorXd x = X.col(i); - double theta = x[3]; - double v = x[2]; + const Eigen::VectorXd x = X.col(i); + const double theta = x[3]; + const double v = x[2]; double coef = 2.0 * std::abs(v); coef = coef * coef * coef * coef * coef * coef * coef; if (coef > 1.0) { coef = 1.0; } - double cos = std::cos(theta); - double sin = std::sin(theta); + const double cos = std::cos(theta); + const double sin = std::sin(theta); Eigen::Matrix2d Rot; Rot << cos, -sin, sin, cos; Eigen::VectorXd vars(x_dim - 3); @@ -464,14 +579,952 @@ class transform_model_to_eigen vars[1] = x[4]; vars[2] = x[5]; vars.tail(x_dim - 6) = x.tail(x_dim - 6); - Eigen::VectorXd pred = error_prediction(vars); + const Eigen::VectorXd pred = error_prediction(vars); Pred.block(0, i, 2, 1) = coef * Rot * pred.head(2); Pred.block(2, i, 4, 1) = coef * pred.tail(4); } return Pred; } }; +class transform_model_with_memory_to_eigen +{ +private: + Eigen::MatrixXd weight_acc_layer_1_; + Eigen::MatrixXd weight_steer_layer_1_head_; + Eigen::MatrixXd weight_steer_layer_1_tail_; + Eigen::MatrixXd weight_acc_layer_2_; + Eigen::MatrixXd weight_steer_layer_2_; + Eigen::MatrixXd weight_lstm_ih_; + Eigen::MatrixXd weight_lstm_hh_; + Eigen::MatrixXd weight_linear_relu_1_; + Eigen::MatrixXd weight_linear_relu_2_; + Eigen::MatrixXd weight_finalize_; + Eigen::VectorXd bias_acc_layer_1_; + Eigen::VectorXd bias_steer_layer_1_head_; + Eigen::VectorXd bias_steer_layer_1_tail_; + Eigen::VectorXd bias_acc_layer_2_; + Eigen::VectorXd bias_steer_layer_2_; + Eigen::VectorXd bias_lstm_ih_; + Eigen::VectorXd bias_lstm_hh_; + Eigen::VectorXd bias_linear_relu_1_; + Eigen::VectorXd bias_linear_relu_2_; + Eigen::VectorXd bias_linear_finalize_; + Eigen::MatrixXd A_linear_reg_; + Eigen::VectorXd b_linear_reg_; + int deg_; + int acc_delay_step_; + int steer_delay_step_; + int acc_ctrl_queue_size_; + int steer_ctrl_queue_size_; + int steer_ctrl_queue_size_core_; + double vel_normalize_; + double acc_normalize_; + double steer_normalize_; + + static constexpr double max_acc_error_ = 20.0; + static constexpr double max_steer_error_ = 20.0; + Eigen::VectorXd h_, c_; + Eigen::MatrixXd H_, C_; + Eigen::MatrixXd dy_dhc_, dhc_dhc_, dhc_dx_; + Eigen::MatrixXd dy_dhc_pre_, dhc_dx_pre_; + +public: + transform_model_with_memory_to_eigen() {} + void set_params( + const Eigen::MatrixXd & weight_acc_layer_1, const Eigen::MatrixXd & weight_steer_layer_1_head, + const Eigen::MatrixXd & weight_steer_layer_1_tail, const Eigen::MatrixXd & weight_acc_layer_2, + const Eigen::MatrixXd & weight_steer_layer_2, const Eigen::MatrixXd & weight_lstm_ih, + const Eigen::MatrixXd & weight_lstm_hh, const Eigen::MatrixXd & weight_linear_relu_1, + const Eigen::MatrixXd & weight_linear_relu_2, const Eigen::MatrixXd & weight_finalize, + const Eigen::VectorXd & bias_acc_layer_1, const Eigen::VectorXd & bias_steer_layer_1_head, + const Eigen::VectorXd & bias_steer_layer_1_tail, const Eigen::VectorXd & bias_acc_layer_2, + const Eigen::VectorXd & bias_steer_layer_2, const Eigen::VectorXd & bias_lstm_ih, + const Eigen::VectorXd & bias_lstm_hh, const Eigen::VectorXd & bias_linear_relu_1, + const Eigen::VectorXd & bias_linear_relu_2, const Eigen::VectorXd & bias_linear_finalize) + { + weight_acc_layer_1_ = weight_acc_layer_1; + weight_steer_layer_1_head_ = weight_steer_layer_1_head; + weight_steer_layer_1_tail_ = weight_steer_layer_1_tail; + weight_acc_layer_2_ = weight_acc_layer_2; + weight_steer_layer_2_ = weight_steer_layer_2; + weight_lstm_ih_ = weight_lstm_ih; + weight_lstm_hh_ = weight_lstm_hh; + weight_linear_relu_1_ = weight_linear_relu_1; + weight_linear_relu_2_ = weight_linear_relu_2; + weight_finalize_ = weight_finalize; + bias_acc_layer_1_ = bias_acc_layer_1; + bias_steer_layer_1_head_ = bias_steer_layer_1_head; + bias_steer_layer_1_tail_ = bias_steer_layer_1_tail; + bias_acc_layer_2_ = bias_acc_layer_2; + bias_steer_layer_2_ = bias_steer_layer_2; + bias_lstm_ih_ = bias_lstm_ih; + bias_lstm_hh_ = bias_lstm_hh; + bias_linear_relu_1_ = bias_linear_relu_1; + bias_linear_relu_2_ = bias_linear_relu_2; + bias_linear_finalize_ = bias_linear_finalize; + } + void set_params_res( + const Eigen::MatrixXd & A_linear_reg, const Eigen::VectorXd & b_linear_reg, const int deg, + const int acc_delay_step, const int steer_delay_step, const int acc_ctrl_queue_size, + const int steer_ctrl_queue_size, const int steer_ctrl_queue_size_core, + const double vel_normalize, const double acc_normalize, const double steer_normalize) + { + A_linear_reg_ = A_linear_reg; + b_linear_reg_ = b_linear_reg; + deg_ = deg; + acc_delay_step_ = acc_delay_step; + steer_delay_step_ = steer_delay_step; + acc_ctrl_queue_size_ = acc_ctrl_queue_size; + steer_ctrl_queue_size_ = steer_ctrl_queue_size; + steer_ctrl_queue_size_core_ = steer_ctrl_queue_size_core; + vel_normalize_ = vel_normalize; + acc_normalize_ = acc_normalize; + steer_normalize_ = steer_normalize; + const int h_dim = weight_lstm_hh_.cols(); + h_ = Eigen::VectorXd::Zero(h_dim); + c_ = Eigen::VectorXd::Zero(h_dim); + dy_dhc_pre_ = Eigen::MatrixXd::Zero(6, 2 * h_dim); + dhc_dx_pre_ = + Eigen::MatrixXd::Zero(2 * h_dim, 3 + acc_ctrl_queue_size_ + steer_ctrl_queue_size_); + dy_dhc_ = Eigen::MatrixXd::Zero(6, 2 * h_dim); + dhc_dhc_ = Eigen::MatrixXd::Zero(2 * h_dim, 2 * h_dim); + dhc_dx_ = Eigen::MatrixXd::Zero(2 * h_dim, 6 + acc_ctrl_queue_size_ + steer_ctrl_queue_size_); + } + void set_lstm(const Eigen::VectorXd & h, const Eigen::VectorXd & c) + { + h_ = h; + c_ = c; + } + void set_lstm_for_candidate( + const Eigen::VectorXd & h, const Eigen::VectorXd & c, const int sample_size) + { + H_ = Eigen::MatrixXd::Zero(h.size(), sample_size); + C_ = Eigen::MatrixXd::Zero(c.size(), sample_size); + for (int i = 0; i < sample_size; i++) { + H_.col(i) = h; + C_.col(i) = c; + } + } + Eigen::VectorXd get_h() const { return h_; } + Eigen::VectorXd get_c() const { return c_; } + Eigen::MatrixXd get_dy_dhc() const { return dy_dhc_; } + Eigen::MatrixXd get_dhc_dhc() const { return dhc_dhc_; } + Eigen::MatrixXd get_dhc_dx() const { return dhc_dx_; } + Eigen::VectorXd error_prediction(const Eigen::VectorXd & x, const int cell_index) + { + Eigen::VectorXd acc_sub(acc_ctrl_queue_size_ + 1); + acc_sub[0] = acc_normalize_ * x[1]; + acc_sub.tail(acc_ctrl_queue_size_) = acc_normalize_ * x.segment(3, acc_ctrl_queue_size_); + Eigen::VectorXd steer_sub(steer_ctrl_queue_size_core_ + 1); + steer_sub[0] = steer_normalize_ * x[2]; + steer_sub.tail(steer_ctrl_queue_size_core_) = + steer_normalize_ * x.segment(3 + acc_ctrl_queue_size_, steer_ctrl_queue_size_core_); + const Eigen::VectorXd acc_layer_1 = relu(weight_acc_layer_1_ * acc_sub + bias_acc_layer_1_); + Eigen::VectorXd steer_layer_1( + bias_steer_layer_1_head_.size() + bias_steer_layer_1_tail_.size()); + steer_layer_1.head(bias_steer_layer_1_head_.size()) = + relu(weight_steer_layer_1_head_ * steer_sub + bias_steer_layer_1_head_); + + const Eigen::VectorXd steer_input_full = + steer_normalize_ * x.segment(3 + acc_ctrl_queue_size_, steer_ctrl_queue_size_); + steer_layer_1.tail(bias_steer_layer_1_tail_.size()) = + relu(weight_steer_layer_1_tail_ * steer_input_full + bias_steer_layer_1_tail_); + + const Eigen::VectorXd acc_layer_2 = relu(weight_acc_layer_2_ * acc_layer_1 + bias_acc_layer_2_); + + const Eigen::VectorXd steer_layer_2 = + relu(weight_steer_layer_2_ * steer_layer_1 + bias_steer_layer_2_); + Eigen::VectorXd h1(1 + acc_layer_2.size() + steer_layer_2.size()); + h1[0] = vel_normalize_ * x[0]; + h1.segment(1, acc_layer_2.size()) = acc_layer_2; + h1.tail(steer_layer_2.size()) = steer_layer_2; + Eigen::VectorXd h, c; + if (cell_index < 0) { + h = h_; + c = c_; + } else { + h = H_.col(cell_index); + c = C_.col(cell_index); + } + + const Eigen::VectorXd i_new = sigmoid( + weight_lstm_ih_.block(0, 0, h_.size(), h1.size()) * h1 + bias_lstm_ih_.head(h_.size()) + + weight_lstm_hh_.block(0, 0, h_.size(), h_.size()) * h + bias_lstm_hh_.head(h_.size())); + const Eigen::VectorXd f_new = sigmoid( + weight_lstm_ih_.block(h_.size(), 0, h_.size(), h1.size()) * h1 + + bias_lstm_ih_.segment(h_.size(), h_.size()) + + weight_lstm_hh_.block(h_.size(), 0, h_.size(), h_.size()) * h + + bias_lstm_hh_.segment(h_.size(), h_.size())); + const Eigen::VectorXd g_new = tanh( + weight_lstm_ih_.block(2 * h_.size(), 0, h_.size(), h1.size()) * h1 + + bias_lstm_ih_.segment(2 * h_.size(), h_.size()) + + weight_lstm_hh_.block(2 * h_.size(), 0, h_.size(), h_.size()) * h + + bias_lstm_hh_.segment(2 * h_.size(), h_.size())); + const Eigen::VectorXd o_new = sigmoid( + weight_lstm_ih_.block(3 * h_.size(), 0, h_.size(), h1.size()) * h1 + + bias_lstm_ih_.segment(3 * h_.size(), h_.size()) + + weight_lstm_hh_.block(3 * h_.size(), 0, h_.size(), h_.size()) * h + + bias_lstm_hh_.segment(3 * h_.size(), h_.size())); + const Eigen::VectorXd c_new = f_new.array() * c.array() + i_new.array() * g_new.array(); + const Eigen::VectorXd h_new = o_new.array() * tanh(c_new).array(); + + Eigen::VectorXd h2(h_new.size() + bias_linear_relu_1_.size()); + h2.head(h_new.size()) = h_new; + h2.tail(bias_linear_relu_1_.size()) = relu(weight_linear_relu_1_ * h1 + bias_linear_relu_1_); + + const Eigen::VectorXd h3 = relu(weight_linear_relu_2_ * h2 + bias_linear_relu_2_); + Eigen::VectorXd h4(h3.size() + acc_layer_2.size() + steer_layer_2.size()); + h4.head(h3.size()) = h3; + h4.segment(h3.size(), acc_layer_2.size()) = acc_layer_2; + h4.tail(steer_layer_2.size()) = steer_layer_2; + + Eigen::VectorXd x_for_polynomial_reg(9); + x_for_polynomial_reg.head(3) = x.head(3); + const int acc_start = 3 + std::max(acc_delay_step_ - 3, 0); + x_for_polynomial_reg.segment(3, 3) = x.segment(acc_start, 3); + const int steer_start = 3 + acc_ctrl_queue_size_ + std::max(steer_delay_step_ - 3, 0); + x_for_polynomial_reg.segment(6, 3) = x.segment(steer_start, 3); + + Eigen::VectorXd y = + weight_finalize_ * h4 + bias_linear_finalize_ + + A_linear_reg_ * get_polynomial_features(x_for_polynomial_reg, deg_, A_linear_reg_.cols()) + + b_linear_reg_; + + y[4] = std::min(std::max(y[4], -max_acc_error_), max_acc_error_); + y[5] = std::min(std::max(y[5], -max_steer_error_), max_steer_error_); + + if (cell_index < 0) { + h_ = h_new; + c_ = c_new; + } else { + H_.col(cell_index) = h_new; + C_.col(cell_index) = c_new; + } + return y; + } + + Eigen::MatrixXd error_prediction_with_diff(const Eigen::VectorXd & x) + { + Eigen::VectorXd acc_sub(acc_ctrl_queue_size_ + 1); + acc_sub[0] = acc_normalize_ * x[1]; + acc_sub.tail(acc_ctrl_queue_size_) = acc_normalize_ * x.segment(3, acc_ctrl_queue_size_); + Eigen::VectorXd steer_sub(steer_ctrl_queue_size_core_ + 1); + steer_sub[0] = steer_normalize_ * x[2]; + steer_sub.tail(steer_ctrl_queue_size_core_) = + steer_normalize_ * x.segment(3 + acc_ctrl_queue_size_, steer_ctrl_queue_size_core_); + const Eigen::VectorXd steer_input_full = + steer_normalize_ * x.segment(3 + acc_ctrl_queue_size_, steer_ctrl_queue_size_); + + const Eigen::VectorXd u_acc_layer_1 = weight_acc_layer_1_ * acc_sub + bias_acc_layer_1_; + const Eigen::VectorXd acc_layer_1 = relu(u_acc_layer_1); + + Eigen::VectorXd u_steer_layer_1( + bias_steer_layer_1_head_.size() + bias_steer_layer_1_tail_.size()); + u_steer_layer_1.head(bias_steer_layer_1_head_.size()) = + weight_steer_layer_1_head_ * steer_sub + bias_steer_layer_1_head_; + u_steer_layer_1.tail(bias_steer_layer_1_tail_.size()) = + weight_steer_layer_1_tail_ * steer_input_full + bias_steer_layer_1_tail_; + const Eigen::VectorXd steer_layer_1 = relu(u_steer_layer_1); + + const Eigen::VectorXd u_acc_layer_2 = weight_acc_layer_2_ * acc_layer_1 + bias_acc_layer_2_; + const Eigen::VectorXd acc_layer_2 = relu(u_acc_layer_2); + + const Eigen::VectorXd u_steer_layer_2 = + weight_steer_layer_2_ * steer_layer_1 + bias_steer_layer_2_; + const Eigen::VectorXd steer_layer_2 = relu(u_steer_layer_2); + + Eigen::VectorXd h1(1 + acc_layer_2.size() + steer_layer_2.size()); + h1[0] = vel_normalize_ * x[0]; + h1.segment(1, acc_layer_2.size()) = acc_layer_2; + h1.tail(steer_layer_2.size()) = steer_layer_2; + + const Eigen::VectorXd u_i_new = + weight_lstm_ih_.block(0, 0, h_.size(), h1.size()) * h1 + bias_lstm_ih_.head(h_.size()) + + weight_lstm_hh_.block(0, 0, h_.size(), h_.size()) * h_ + bias_lstm_hh_.head(h_.size()); + const Eigen::VectorXd u_f_new = weight_lstm_ih_.block(h_.size(), 0, h_.size(), h1.size()) * h1 + + bias_lstm_ih_.segment(h_.size(), h_.size()) + + weight_lstm_hh_.block(h_.size(), 0, h_.size(), h_.size()) * h_ + + bias_lstm_hh_.segment(h_.size(), h_.size()); + const Eigen::VectorXd u_g_new = + weight_lstm_ih_.block(2 * h_.size(), 0, h_.size(), h1.size()) * h1 + + bias_lstm_ih_.segment(2 * h_.size(), h_.size()) + + weight_lstm_hh_.block(2 * h_.size(), 0, h_.size(), h_.size()) * h_ + + bias_lstm_hh_.segment(2 * h_.size(), h_.size()); + const Eigen::VectorXd u_o_new = + weight_lstm_ih_.block(3 * h_.size(), 0, h_.size(), h1.size()) * h1 + + bias_lstm_ih_.segment(3 * h_.size(), h_.size()) + + weight_lstm_hh_.block(3 * h_.size(), 0, h_.size(), h_.size()) * h_ + + bias_lstm_hh_.segment(3 * h_.size(), h_.size()); + const Eigen::VectorXd i_new = sigmoid(u_i_new); + const Eigen::VectorXd f_new = sigmoid(u_f_new); + const Eigen::VectorXd g_new = tanh(u_g_new); + const Eigen::VectorXd o_new = sigmoid(u_o_new); + + const Eigen::VectorXd c_new = f_new.array() * c_.array() + i_new.array() * g_new.array(); + const Eigen::VectorXd h_new = o_new.array() * tanh(c_new).array(); + + Eigen::VectorXd h2(h_new.size() + bias_linear_relu_1_.size()); + h2.head(h_new.size()) = h_new; + const Eigen::VectorXd u2 = weight_linear_relu_1_ * h1 + bias_linear_relu_1_; + h2.tail(bias_linear_relu_1_.size()) = relu(u2); + + const Eigen::VectorXd u3 = weight_linear_relu_2_ * h2 + bias_linear_relu_2_; + const Eigen::VectorXd h3 = relu(u3); + Eigen::VectorXd h4(h3.size() + acc_layer_2.size() + steer_layer_2.size()); + + h4.head(h3.size()) = h3; + h4.segment(h3.size(), acc_layer_2.size()) = acc_layer_2; + h4.tail(steer_layer_2.size()) = steer_layer_2; + + Eigen::VectorXd x_for_polynomial_reg(9); + x_for_polynomial_reg.head(3) = x.head(3); + const int acc_start = 3 + std::max(acc_delay_step_ - 3, 0); + x_for_polynomial_reg.segment(3, 3) = x.segment(acc_start, 3); + const int steer_start = 3 + acc_ctrl_queue_size_ + std::max(steer_delay_step_ - 3, 0); + x_for_polynomial_reg.segment(6, 3) = x.segment(steer_start, 3); + + const Eigen::MatrixXd polynomial_features_with_diff = + get_polynomial_features_with_diff(x_for_polynomial_reg, deg_, A_linear_reg_.cols()); + + Eigen::VectorXd y = + weight_finalize_ * h4 + bias_linear_finalize_ + + A_linear_reg_ * polynomial_features_with_diff.block(0, 0, A_linear_reg_.cols(), 1) + + b_linear_reg_; + + y[4] = std::min(std::max(y[4], -max_acc_error_), max_acc_error_); + y[5] = std::min(std::max(y[5], -max_steer_error_), max_steer_error_); + + const Eigen::MatrixXd dy_dh3 = weight_finalize_.block(0, 0, y.size(), h3.size()); + const Eigen::MatrixXd dy_dh2 = d_relu_product(dy_dh3, u3) * weight_linear_relu_2_; + const Eigen::MatrixXd dy_dh2_head = dy_dh2.block(0, 0, y.size(), h_new.size()); + const Eigen::MatrixXd dy_dh2_tail = + dy_dh2.block(0, h_new.size(), y.size(), bias_linear_relu_1_.size()); + + const Eigen::MatrixXd dy_do = dy_dh2_head * tanh(c_new).asDiagonal(); + const Eigen::MatrixXd dy_dc_new = d_tanh_product(dy_dh2_head * o_new.asDiagonal(), c_new); + Eigen::MatrixXd dy_dh1 = d_sigmoid_product(dy_do, u_o_new) * + weight_lstm_ih_.block(3 * h_.size(), 0, h_.size(), h1.size()); + + dy_dh1 += d_sigmoid_product(dy_dc_new * c_.asDiagonal(), u_f_new) * + weight_lstm_ih_.block(h_.size(), 0, h_.size(), h1.size()); + dy_dh1 += d_tanh_product(dy_dc_new * i_new.asDiagonal(), u_g_new) * + weight_lstm_ih_.block(2 * h_.size(), 0, h_.size(), h1.size()); + dy_dh1 += d_sigmoid_product(dy_dc_new * g_new.asDiagonal(), u_i_new) * + weight_lstm_ih_.block(0, 0, h_.size(), h1.size()); + + dy_dh1 += d_relu_product(dy_dh2_tail, u2) * weight_linear_relu_1_; + + const Eigen::MatrixXd dy_da2 = + dy_dh1.block(0, 1, y.size(), acc_layer_2.size()) + + weight_finalize_.block(0, h3.size(), y.size(), acc_layer_2.size()); + const Eigen::MatrixXd dy_ds2 = + dy_dh1.block(0, 1 + acc_layer_2.size(), y.size(), steer_layer_2.size()) + + weight_finalize_.block(0, h3.size() + acc_layer_2.size(), y.size(), steer_layer_2.size()); + const Eigen::MatrixXd dy_da1 = d_relu_product(dy_da2, u_acc_layer_2) * weight_acc_layer_2_; + const Eigen::MatrixXd dy_ds1 = d_relu_product(dy_ds2, u_steer_layer_2) * weight_steer_layer_2_; + + const Eigen::MatrixXd dy_d_acc = d_relu_product(dy_da1, u_acc_layer_1) * weight_acc_layer_1_; + Eigen::MatrixXd dy_d_steer = Eigen::MatrixXd::Zero(y.size(), steer_input_full.size() + 1); + dy_d_steer.block(0, 1, y.size(), steer_input_full.size()) += + d_relu_product( + dy_ds1.block(0, bias_steer_layer_1_head_.size(), y.size(), bias_steer_layer_1_tail_.size()), + u_steer_layer_1.tail(bias_steer_layer_1_tail_.size())) * + weight_steer_layer_1_tail_; + dy_d_steer.block(0, 0, y.size(), steer_sub.size()) += + d_relu_product( + dy_ds1.block(0, 0, y.size(), bias_steer_layer_1_head_.size()), + u_steer_layer_1.head(bias_steer_layer_1_head_.size())) * + weight_steer_layer_1_head_; + + Eigen::MatrixXd result = Eigen::MatrixXd::Zero(y.size(), x.size() + 1); + + result.col(0) = y; + + result.col(1) = vel_normalize_ * dy_dh1.col(0); + result.col(2) = acc_normalize_ * dy_d_acc.col(0); + result.col(3) = steer_normalize_ * dy_d_steer.col(0); + result.block(0, 4, y.size(), acc_ctrl_queue_size_) = + acc_normalize_ * dy_d_acc.block(0, 1, y.size(), acc_ctrl_queue_size_); + result.block(0, 4 + acc_ctrl_queue_size_, y.size(), steer_ctrl_queue_size_) = + steer_normalize_ * dy_d_steer.block(0, 1, y.size(), steer_ctrl_queue_size_); + + const Eigen::MatrixXd polynomial_reg_diff = + A_linear_reg_ * + polynomial_features_with_diff.block(0, 1, A_linear_reg_.cols(), x_for_polynomial_reg.size()); + result.block(0, 1, y.size(), 3) += polynomial_reg_diff.block(0, 0, y.size(), 3); + result.block(0, 1 + acc_start, y.size(), 3) += polynomial_reg_diff.block(0, 3, y.size(), 3); + result.block(0, 1 + steer_start, y.size(), 3) += polynomial_reg_diff.block(0, 6, y.size(), 3); + + h_ = h_new; + c_ = c_new; + return result; + } + Eigen::MatrixXd error_prediction_with_memory_diff(const Eigen::VectorXd & x) + { + Eigen::VectorXd acc_sub(acc_ctrl_queue_size_ + 1); + acc_sub[0] = acc_normalize_ * x[1]; + acc_sub.tail(acc_ctrl_queue_size_) = acc_normalize_ * x.segment(3, acc_ctrl_queue_size_); + Eigen::VectorXd steer_sub(steer_ctrl_queue_size_core_ + 1); + steer_sub[0] = steer_normalize_ * x[2]; + steer_sub.tail(steer_ctrl_queue_size_core_) = + steer_normalize_ * x.segment(3 + acc_ctrl_queue_size_, steer_ctrl_queue_size_core_); + const Eigen::VectorXd steer_input_full = + steer_normalize_ * x.segment(3 + acc_ctrl_queue_size_, steer_ctrl_queue_size_); + + const Eigen::VectorXd u_acc_layer_1 = weight_acc_layer_1_ * acc_sub + bias_acc_layer_1_; + const Eigen::VectorXd acc_layer_1 = relu(u_acc_layer_1); + + Eigen::VectorXd u_steer_layer_1( + bias_steer_layer_1_head_.size() + bias_steer_layer_1_tail_.size()); + u_steer_layer_1.head(bias_steer_layer_1_head_.size()) = + weight_steer_layer_1_head_ * steer_sub + bias_steer_layer_1_head_; + u_steer_layer_1.tail(bias_steer_layer_1_tail_.size()) = + weight_steer_layer_1_tail_ * steer_input_full + bias_steer_layer_1_tail_; + const Eigen::VectorXd steer_layer_1 = relu(u_steer_layer_1); + + const Eigen::VectorXd u_acc_layer_2 = weight_acc_layer_2_ * acc_layer_1 + bias_acc_layer_2_; + const Eigen::VectorXd acc_layer_2 = relu(u_acc_layer_2); + + const Eigen::VectorXd u_steer_layer_2 = + weight_steer_layer_2_ * steer_layer_1 + bias_steer_layer_2_; + const Eigen::VectorXd steer_layer_2 = relu(u_steer_layer_2); + + Eigen::VectorXd h1(1 + acc_layer_2.size() + steer_layer_2.size()); + h1[0] = vel_normalize_ * x[0]; + h1.segment(1, acc_layer_2.size()) = acc_layer_2; + h1.tail(steer_layer_2.size()) = steer_layer_2; + + const Eigen::VectorXd u_i_new = + weight_lstm_ih_.block(0, 0, h_.size(), h1.size()) * h1 + bias_lstm_ih_.head(h_.size()) + + weight_lstm_hh_.block(0, 0, h_.size(), h_.size()) * h_ + bias_lstm_hh_.head(h_.size()); + const Eigen::VectorXd u_f_new = weight_lstm_ih_.block(h_.size(), 0, h_.size(), h1.size()) * h1 + + bias_lstm_ih_.segment(h_.size(), h_.size()) + + weight_lstm_hh_.block(h_.size(), 0, h_.size(), h_.size()) * h_ + + bias_lstm_hh_.segment(h_.size(), h_.size()); + const Eigen::VectorXd u_g_new = + weight_lstm_ih_.block(2 * h_.size(), 0, h_.size(), h1.size()) * h1 + + bias_lstm_ih_.segment(2 * h_.size(), h_.size()) + + weight_lstm_hh_.block(2 * h_.size(), 0, h_.size(), h_.size()) * h_ + + bias_lstm_hh_.segment(2 * h_.size(), h_.size()); + const Eigen::VectorXd u_o_new = + weight_lstm_ih_.block(3 * h_.size(), 0, h_.size(), h1.size()) * h1 + + bias_lstm_ih_.segment(3 * h_.size(), h_.size()) + + weight_lstm_hh_.block(3 * h_.size(), 0, h_.size(), h_.size()) * h_ + + bias_lstm_hh_.segment(3 * h_.size(), h_.size()); + const Eigen::VectorXd i_new = sigmoid(u_i_new); + const Eigen::VectorXd f_new = sigmoid(u_f_new); + const Eigen::VectorXd g_new = tanh(u_g_new); + const Eigen::VectorXd o_new = sigmoid(u_o_new); + + const Eigen::VectorXd c_new = f_new.array() * c_.array() + i_new.array() * g_new.array(); + const Eigen::VectorXd h_new = o_new.array() * tanh(c_new).array(); + + Eigen::VectorXd h2(h_new.size() + bias_linear_relu_1_.size()); + h2.head(h_new.size()) = h_new; + const Eigen::VectorXd u2 = weight_linear_relu_1_ * h1 + bias_linear_relu_1_; + h2.tail(bias_linear_relu_1_.size()) = relu(u2); + + const Eigen::VectorXd u3 = weight_linear_relu_2_ * h2 + bias_linear_relu_2_; + const Eigen::VectorXd h3 = relu(u3); + Eigen::VectorXd h4(h3.size() + acc_layer_2.size() + steer_layer_2.size()); + + h4.head(h3.size()) = h3; + h4.segment(h3.size(), acc_layer_2.size()) = acc_layer_2; + h4.tail(steer_layer_2.size()) = steer_layer_2; + + Eigen::VectorXd x_for_polynomial_reg(9); + x_for_polynomial_reg.head(3) = x.head(3); + const int acc_start = 3 + std::max(acc_delay_step_ - 3, 0); + x_for_polynomial_reg.segment(3, 3) = x.segment(acc_start, 3); + const int steer_start = 3 + acc_ctrl_queue_size_ + std::max(steer_delay_step_ - 3, 0); + x_for_polynomial_reg.segment(6, 3) = x.segment(steer_start, 3); + + const Eigen::MatrixXd polynomial_features_with_diff = + get_polynomial_features_with_diff(x_for_polynomial_reg, deg_, A_linear_reg_.cols()); + + Eigen::VectorXd y = + weight_finalize_ * h4 + bias_linear_finalize_ + + A_linear_reg_ * polynomial_features_with_diff.block(0, 0, A_linear_reg_.cols(), 1) + + b_linear_reg_; + + y[4] = std::min(std::max(y[4], -max_acc_error_), max_acc_error_); + y[5] = std::min(std::max(y[5], -max_steer_error_), max_steer_error_); + + const Eigen::MatrixXd dy_dh3 = weight_finalize_.block(0, 0, y.size(), h3.size()); + const Eigen::MatrixXd dy_dh2 = d_relu_product(dy_dh3, u3) * weight_linear_relu_2_; + const Eigen::MatrixXd dy_dh2_head = dy_dh2.block(0, 0, y.size(), h_new.size()); + const Eigen::MatrixXd dy_dh2_tail = + dy_dh2.block(0, h_new.size(), y.size(), bias_linear_relu_1_.size()); + + const Eigen::MatrixXd dy_do = dy_dh2_head * tanh(c_new).asDiagonal(); + const Eigen::MatrixXd dy_dc_new = d_tanh_product(dy_dh2_head * o_new.asDiagonal(), c_new); + + // calc dy_dhc_pre_, dhc_dhc_, dhc_dx_pre_ + const Eigen::VectorXd dc_du_f = d_sigmoid_product_vec(c_, u_f_new); + const Eigen::VectorXd dc_du_g = d_tanh_product_vec(i_new, u_g_new); + const Eigen::VectorXd dc_du_i = d_sigmoid_product_vec(g_new, u_i_new); + const Eigen::VectorXd dh_dc_new = d_tanh_product_vec(o_new, c_new); + const Eigen::VectorXd dh_du_o = d_sigmoid_product_vec(tanh(c_new), u_o_new); + + const Eigen::MatrixXd dc_dc = f_new.asDiagonal(); + const Eigen::MatrixXd dy_dc = dy_dc_new * dc_dc; + + Eigen::MatrixXd dc_dh = + dc_du_f.asDiagonal() * weight_lstm_hh_.block(h_.size(), 0, h_.size(), h_.size()); + dc_dh += dc_du_g.asDiagonal() * weight_lstm_hh_.block(2 * h_.size(), 0, h_.size(), h_.size()); + dc_dh += dc_du_i.asDiagonal() * weight_lstm_hh_.block(0, 0, h_.size(), h_.size()); + const Eigen::VectorXd dh_dc = dh_dc_new.array() * f_new.array(); + + Eigen::MatrixXd dh_dh = + dh_du_o.asDiagonal() * weight_lstm_hh_.block(3 * h_.size(), 0, h_.size(), h_.size()); + + dh_dh += dh_dc_new.asDiagonal() * dc_dh; + + const Eigen::MatrixXd dy_dh = dy_dh2_head * dh_dh; + Eigen::MatrixXd dc_dh1 = + dc_du_f.asDiagonal() * weight_lstm_ih_.block(h_.size(), 0, h_.size(), h1.size()); + dc_dh1 += dc_du_g.asDiagonal() * weight_lstm_ih_.block(2 * h_.size(), 0, h_.size(), h1.size()); + dc_dh1 += dc_du_i.asDiagonal() * weight_lstm_ih_.block(0, 0, h_.size(), h1.size()); + + Eigen::MatrixXd dh_dh1 = + dh_du_o.asDiagonal() * weight_lstm_ih_.block(3 * h_.size(), 0, h_.size(), h1.size()); + dh_dh1 += dh_dc_new.asDiagonal() * dc_dh1; + + const Eigen::MatrixXd dc_da2 = dc_dh1.block(0, 1, h_.size(), acc_layer_2.size()); + const Eigen::MatrixXd dc_ds2 = + dc_dh1.block(0, 1 + acc_layer_2.size(), h_.size(), steer_layer_2.size()); + + const Eigen::MatrixXd dh_da2 = dh_dh1.block(0, 1, h_.size(), acc_layer_2.size()); + const Eigen::MatrixXd dh_ds2 = + dh_dh1.block(0, 1 + acc_layer_2.size(), h_.size(), steer_layer_2.size()); + + const Eigen::MatrixXd dc_da1 = d_relu_product(dc_da2, u_acc_layer_2) * weight_acc_layer_2_; + const Eigen::MatrixXd dc_ds1 = d_relu_product(dc_ds2, u_steer_layer_2) * weight_steer_layer_2_; + + const Eigen::MatrixXd dh_da1 = d_relu_product(dh_da2, u_acc_layer_2) * weight_acc_layer_2_; + const Eigen::MatrixXd dh_ds1 = d_relu_product(dh_ds2, u_steer_layer_2) * weight_steer_layer_2_; + + const Eigen::MatrixXd dc_d_acc = d_relu_product(dc_da1, u_acc_layer_1) * weight_acc_layer_1_; + + Eigen::MatrixXd dc_d_steer = Eigen::MatrixXd::Zero(h_.size(), steer_input_full.size() + 1); + dc_d_steer.block(0, 1, h_.size(), steer_input_full.size()) += + d_relu_product( + dc_ds1.block( + 0, bias_steer_layer_1_head_.size(), h_.size(), bias_steer_layer_1_tail_.size()), + u_steer_layer_1.tail(bias_steer_layer_1_tail_.size())) * + weight_steer_layer_1_tail_; + dc_d_steer.block(0, 0, h_.size(), steer_sub.size()) += + d_relu_product( + dc_ds1.block(0, 0, h_.size(), bias_steer_layer_1_head_.size()), + u_steer_layer_1.head(bias_steer_layer_1_head_.size())) * + weight_steer_layer_1_head_; + + const Eigen::MatrixXd dh_d_acc = d_relu_product(dh_da1, u_acc_layer_1) * weight_acc_layer_1_; + + Eigen::MatrixXd dh_d_steer = Eigen::MatrixXd::Zero(h_.size(), steer_input_full.size() + 1); + dh_d_steer.block(0, 1, h_.size(), steer_input_full.size()) += + d_relu_product( + dh_ds1.block( + 0, bias_steer_layer_1_head_.size(), h_.size(), bias_steer_layer_1_tail_.size()), + u_steer_layer_1.tail(bias_steer_layer_1_tail_.size())) * + weight_steer_layer_1_tail_; + dh_d_steer.block(0, 0, h_.size(), steer_sub.size()) += + d_relu_product( + dh_ds1.block(0, 0, h_.size(), bias_steer_layer_1_head_.size()), + u_steer_layer_1.head(bias_steer_layer_1_head_.size())) * + weight_steer_layer_1_head_; + + Eigen::MatrixXd dc_dx = Eigen::MatrixXd(h_.size(), x.size()); + Eigen::MatrixXd dh_dx = Eigen::MatrixXd(h_.size(), x.size()); + dc_dx.col(0) = vel_normalize_ * dc_dh1.col(0); + dc_dx.col(1) = acc_normalize_ * dc_d_acc.col(0); + dc_dx.col(2) = steer_normalize_ * dc_d_steer.col(0); + dc_dx.block(0, 3, h_.size(), acc_ctrl_queue_size_) = + acc_normalize_ * dc_d_acc.block(0, 1, h_.size(), acc_ctrl_queue_size_); + dc_dx.block(0, 3 + acc_ctrl_queue_size_, h_.size(), steer_ctrl_queue_size_) = + steer_normalize_ * dc_d_steer.block(0, 1, h_.size(), steer_ctrl_queue_size_); + + dh_dx.col(0) = vel_normalize_ * dh_dh1.col(0); + dh_dx.col(1) = acc_normalize_ * dh_d_acc.col(0); + dh_dx.col(2) = steer_normalize_ * dh_d_steer.col(0); + dh_dx.block(0, 3, h_.size(), acc_ctrl_queue_size_) = + acc_normalize_ * dh_d_acc.block(0, 1, h_.size(), acc_ctrl_queue_size_); + dh_dx.block(0, 3 + acc_ctrl_queue_size_, h_.size(), steer_ctrl_queue_size_) = + steer_normalize_ * dh_d_steer.block(0, 1, h_.size(), steer_ctrl_queue_size_); + + dy_dhc_pre_.block(0, 0, y.size(), h_.size()) = dy_dh; + dy_dhc_pre_.block(0, h_.size(), y.size(), h_.size()) = dy_dc; + dhc_dhc_.block(0, 0, h_.size(), h_.size()) = dh_dh; + dhc_dhc_.block(h_.size(), 0, h_.size(), h_.size()) = dc_dh; + dhc_dhc_.block(0, h_.size(), h_.size(), h_.size()) = dh_dc.asDiagonal(); + dhc_dhc_.block(h_.size(), h_.size(), h_.size(), h_.size()) = dc_dc; + dhc_dx_pre_.block(0, 0, h_.size(), x.size()) = dh_dx; + dhc_dx_pre_.block(h_.size(), 0, h_.size(), x.size()) = dc_dx; + + // finished calc dy_dhc_pre_, dhc_dhc_, dhc_dx_pre_ + + Eigen::MatrixXd dy_dh1 = d_sigmoid_product(dy_do, u_o_new) * + weight_lstm_ih_.block(3 * h_.size(), 0, h_.size(), h1.size()); + + dy_dh1 += d_sigmoid_product(dy_dc_new * c_.asDiagonal(), u_f_new) * + weight_lstm_ih_.block(h_.size(), 0, h_.size(), h1.size()); + dy_dh1 += d_tanh_product(dy_dc_new * i_new.asDiagonal(), u_g_new) * + weight_lstm_ih_.block(2 * h_.size(), 0, h_.size(), h1.size()); + dy_dh1 += d_sigmoid_product(dy_dc_new * g_new.asDiagonal(), u_i_new) * + weight_lstm_ih_.block(0, 0, h_.size(), h1.size()); + + dy_dh1 += d_relu_product(dy_dh2_tail, u2) * weight_linear_relu_1_; + const Eigen::MatrixXd dy_da2 = + dy_dh1.block(0, 1, y.size(), acc_layer_2.size()) + + weight_finalize_.block(0, h3.size(), y.size(), acc_layer_2.size()); + const Eigen::MatrixXd dy_ds2 = + dy_dh1.block(0, 1 + acc_layer_2.size(), y.size(), steer_layer_2.size()) + + weight_finalize_.block(0, h3.size() + acc_layer_2.size(), y.size(), steer_layer_2.size()); + const Eigen::MatrixXd dy_da1 = d_relu_product(dy_da2, u_acc_layer_2) * weight_acc_layer_2_; + const Eigen::MatrixXd dy_ds1 = d_relu_product(dy_ds2, u_steer_layer_2) * weight_steer_layer_2_; + + const Eigen::MatrixXd dy_d_acc = d_relu_product(dy_da1, u_acc_layer_1) * weight_acc_layer_1_; + Eigen::MatrixXd dy_d_steer = Eigen::MatrixXd::Zero(y.size(), steer_input_full.size() + 1); + dy_d_steer.block(0, 1, y.size(), steer_input_full.size()) += + d_relu_product( + dy_ds1.block(0, bias_steer_layer_1_head_.size(), y.size(), bias_steer_layer_1_tail_.size()), + u_steer_layer_1.tail(bias_steer_layer_1_tail_.size())) * + weight_steer_layer_1_tail_; + dy_d_steer.block(0, 0, y.size(), steer_sub.size()) += + d_relu_product( + dy_ds1.block(0, 0, y.size(), bias_steer_layer_1_head_.size()), + u_steer_layer_1.head(bias_steer_layer_1_head_.size())) * + weight_steer_layer_1_head_; + + Eigen::MatrixXd result = Eigen::MatrixXd::Zero(y.size(), x.size() + 1); + + result.col(0) = y; + + result.col(1) = vel_normalize_ * dy_dh1.col(0); + result.col(2) = acc_normalize_ * dy_d_acc.col(0); + result.col(3) = steer_normalize_ * dy_d_steer.col(0); + result.block(0, 4, y.size(), acc_ctrl_queue_size_) = + acc_normalize_ * dy_d_acc.block(0, 1, y.size(), acc_ctrl_queue_size_); + result.block(0, 4 + acc_ctrl_queue_size_, y.size(), steer_ctrl_queue_size_) = + steer_normalize_ * dy_d_steer.block(0, 1, y.size(), steer_ctrl_queue_size_); + + const Eigen::MatrixXd polynomial_reg_diff = + A_linear_reg_ * + polynomial_features_with_diff.block(0, 1, A_linear_reg_.cols(), x_for_polynomial_reg.size()); + result.block(0, 1, y.size(), 3) += polynomial_reg_diff.block(0, 0, y.size(), 3); + result.block(0, 1 + acc_start, y.size(), 3) += polynomial_reg_diff.block(0, 3, y.size(), 3); + result.block(0, 1 + steer_start, y.size(), 3) += polynomial_reg_diff.block(0, 6, y.size(), 3); + + h_ = h_new; + c_ = c_new; + return result; + } + + void update_memory(const Eigen::VectorXd & x) + { + Eigen::VectorXd acc_sub(acc_ctrl_queue_size_ + 1); + acc_sub[0] = acc_normalize_ * x[1]; + acc_sub.tail(acc_ctrl_queue_size_) = acc_normalize_ * x.segment(3, acc_ctrl_queue_size_); + Eigen::VectorXd steer_sub(steer_ctrl_queue_size_core_ + 1); + steer_sub[0] = steer_normalize_ * x[2]; + steer_sub.tail(steer_ctrl_queue_size_core_) = + steer_normalize_ * x.segment(3 + acc_ctrl_queue_size_, steer_ctrl_queue_size_core_); + const Eigen::VectorXd acc_layer_1 = relu(weight_acc_layer_1_ * acc_sub + bias_acc_layer_1_); + Eigen::VectorXd steer_layer_1( + bias_steer_layer_1_head_.size() + bias_steer_layer_1_tail_.size()); + steer_layer_1.head(bias_steer_layer_1_head_.size()) = + relu(weight_steer_layer_1_head_ * steer_sub + bias_steer_layer_1_head_); + + const Eigen::VectorXd steer_input_full = + steer_normalize_ * x.segment(3 + acc_ctrl_queue_size_, steer_ctrl_queue_size_); + steer_layer_1.tail(bias_steer_layer_1_tail_.size()) = + relu(weight_steer_layer_1_tail_ * steer_input_full + bias_steer_layer_1_tail_); + + const Eigen::VectorXd acc_layer_2 = relu(weight_acc_layer_2_ * acc_layer_1 + bias_acc_layer_2_); + + const Eigen::VectorXd steer_layer_2 = + relu(weight_steer_layer_2_ * steer_layer_1 + bias_steer_layer_2_); + Eigen::VectorXd h1(1 + acc_layer_2.size() + steer_layer_2.size()); + h1[0] = vel_normalize_ * x[0]; + h1.segment(1, acc_layer_2.size()) = acc_layer_2; + h1.tail(steer_layer_2.size()) = steer_layer_2; + + const Eigen::VectorXd i_new = sigmoid( + weight_lstm_ih_.block(0, 0, h_.size(), h1.size()) * h1 + bias_lstm_ih_.head(h_.size()) + + weight_lstm_hh_.block(0, 0, h_.size(), h_.size()) * h_ + bias_lstm_hh_.head(h_.size())); + const Eigen::VectorXd f_new = sigmoid( + weight_lstm_ih_.block(h_.size(), 0, h_.size(), h1.size()) * h1 + + bias_lstm_ih_.segment(h_.size(), h_.size()) + + weight_lstm_hh_.block(h_.size(), 0, h_.size(), h_.size()) * h_ + + bias_lstm_hh_.segment(h_.size(), h_.size())); + const Eigen::VectorXd g_new = tanh( + weight_lstm_ih_.block(2 * h_.size(), 0, h_.size(), h1.size()) * h1 + + bias_lstm_ih_.segment(2 * h_.size(), h_.size()) + + weight_lstm_hh_.block(2 * h_.size(), 0, h_.size(), h_.size()) * h_ + + bias_lstm_hh_.segment(2 * h_.size(), h_.size())); + const Eigen::VectorXd o_new = sigmoid( + weight_lstm_ih_.block(3 * h_.size(), 0, h_.size(), h1.size()) * h1 + + bias_lstm_ih_.segment(3 * h_.size(), h_.size()) + + weight_lstm_hh_.block(3 * h_.size(), 0, h_.size(), h_.size()) * h_ + + bias_lstm_hh_.segment(3 * h_.size(), h_.size())); + const Eigen::VectorXd c_new = f_new.array() * c_.array() + i_new.array() * g_new.array(); + const Eigen::VectorXd h_new = o_new.array() * tanh(c_new).array(); + h_ = h_new; + c_ = c_new; + } + Eigen::VectorXd rot_and_d_rot_error_prediction(const Eigen::VectorXd & x) + { + const int x_dim = x.size(); + const double theta = x[3]; + const double v = x[2]; + double coef = 2.0 * std::abs(v); + coef = coef * coef * coef * coef * coef * coef * coef; + if (coef > 1.0) { + coef = 1.0; + } + const double cos = std::cos(theta); + const double sin = std::sin(theta); + Eigen::Matrix2d Rot; + Rot << cos, -sin, sin, cos; + + Eigen::Matrix2d dRot; + dRot << -sin, -cos, cos, -sin; + Eigen::VectorXd vars(x_dim - 3); + vars[0] = x[2]; + vars[1] = x[4]; + vars[2] = x[5]; + vars.tail(x_dim - 6) = x.tail(x_dim - 6); + const Eigen::VectorXd pred = error_prediction(vars, -1); + Eigen::VectorXd rot_and_d_rot_pred(8); + rot_and_d_rot_pred.head(2) = Rot * pred.head(2); + rot_and_d_rot_pred.segment(2, 4) = pred.segment(2, 4); + rot_and_d_rot_pred.tail(2) = dRot * pred.head(2); + + return coef * rot_and_d_rot_pred; + } + Eigen::MatrixXd rot_and_d_rot_error_prediction_with_diff(const Eigen::VectorXd & x) + { + const int x_dim = x.size(); + const double theta = x[3]; + const double v = x[2]; + double coef = 2.0 * std::abs(v); + coef = coef * coef * coef * coef * coef * coef * coef; + if (coef > 1.0) { + coef = 1.0; + } + const double cos = std::cos(theta); + const double sin = std::sin(theta); + Eigen::Matrix2d Rot; + Rot << cos, -sin, sin, cos; + + Eigen::Matrix2d dRot; + dRot << -sin, -cos, cos, -sin; + Eigen::VectorXd vars(x_dim - 3); + vars[0] = x[2]; + vars[1] = x[4]; + vars[2] = x[5]; + vars.tail(x_dim - 6) = x.tail(x_dim - 6); + + const Eigen::MatrixXd pred_d_pred = error_prediction_with_diff(vars); + const Eigen::VectorXd pred = pred_d_pred.col(0); + const Eigen::MatrixXd d_pred = pred_d_pred.block(0, 1, 6, x_dim - 3); + Eigen::MatrixXd rot_and_d_rot_pred_with_diff = Eigen::MatrixXd::Zero(6, x_dim + 2); + Eigen::MatrixXd rot_pred_with_diff(6, x_dim - 3); + rot_pred_with_diff.block(0, 0, 2, x_dim - 3) = Rot * d_pred.block(0, 0, 2, x_dim - 3); + rot_pred_with_diff.block(2, 0, 4, x_dim - 3) = d_pred.block(2, 0, 4, x_dim - 3); + + rot_and_d_rot_pred_with_diff.block(0, 0, 2, 1) = Rot * pred.head(2); + rot_and_d_rot_pred_with_diff.block(2, 0, 4, 1) = pred.segment(2, 4); + rot_and_d_rot_pred_with_diff.block(0, 1, 2, 1) = dRot * pred.head(2); + rot_and_d_rot_pred_with_diff.col(2 + 2) = rot_pred_with_diff.col(0); + rot_and_d_rot_pred_with_diff.col(2 + 4) = rot_pred_with_diff.col(1); + rot_and_d_rot_pred_with_diff.col(2 + 5) = rot_pred_with_diff.col(2); + rot_and_d_rot_pred_with_diff.block(0, 2 + 6, 6, x_dim - 6) = + rot_pred_with_diff.block(0, 3, 6, x_dim - 6); + + return coef * rot_and_d_rot_pred_with_diff; + } + Eigen::MatrixXd rot_and_d_rot_error_prediction_with_memory_diff(const Eigen::VectorXd & x) + { + const int x_dim = x.size(); + const double theta = x[3]; + const double v = x[2]; + double coef = 2.0 * std::abs(v); + coef = coef * coef * coef * coef * coef * coef * coef; + if (coef > 1.0) { + coef = 1.0; + } + const double cos = std::cos(theta); + const double sin = std::sin(theta); + Eigen::Matrix2d Rot; + Rot << cos, -sin, sin, cos; + + Eigen::Matrix2d dRot; + dRot << -sin, -cos, cos, -sin; + Eigen::VectorXd vars(x_dim - 3); + vars[0] = x[2]; + vars[1] = x[4]; + vars[2] = x[5]; + vars.tail(x_dim - 6) = x.tail(x_dim - 6); + + const Eigen::MatrixXd pred_d_pred = error_prediction_with_memory_diff(vars); + const Eigen::VectorXd pred = pred_d_pred.col(0); + const Eigen::MatrixXd d_pred = pred_d_pred.block(0, 1, 6, x_dim - 3); + Eigen::MatrixXd rot_and_d_rot_pred_with_diff = Eigen::MatrixXd::Zero(6, x_dim + 2); + Eigen::MatrixXd rot_pred_with_diff(6, x_dim - 3); + rot_pred_with_diff.block(0, 0, 2, x_dim - 3) = Rot * d_pred.block(0, 0, 2, x_dim - 3); + rot_pred_with_diff.block(2, 0, 4, x_dim - 3) = d_pred.block(2, 0, 4, x_dim - 3); + + rot_and_d_rot_pred_with_diff.block(0, 0, 2, 1) = Rot * pred.head(2); + rot_and_d_rot_pred_with_diff.block(2, 0, 4, 1) = pred.segment(2, 4); + rot_and_d_rot_pred_with_diff.block(0, 1, 2, 1) = dRot * pred.head(2); + rot_and_d_rot_pred_with_diff.col(2 + 2) = rot_pred_with_diff.col(0); + rot_and_d_rot_pred_with_diff.col(2 + 4) = rot_pred_with_diff.col(1); + rot_and_d_rot_pred_with_diff.col(2 + 5) = rot_pred_with_diff.col(2); + rot_and_d_rot_pred_with_diff.block(0, 2 + 6, 6, x_dim - 6) = + rot_pred_with_diff.block(0, 3, 6, x_dim - 6); + + dy_dhc_.block(0, 0, 2, 2 * h_.size()) = Rot * dy_dhc_pre_.block(0, 0, 2, 2 * h_.size()); + dy_dhc_.block(2, 0, 4, 2 * h_.size()) = dy_dhc_pre_.block(2, 0, 4, 2 * h_.size()); + + dhc_dx_.col(2) = dhc_dx_pre_.col(0); + dhc_dx_.col(4) = dhc_dx_pre_.col(1); + dhc_dx_.col(5) = dhc_dx_pre_.col(2); + dhc_dx_.block(0, 6, 2 * h_.size(), x_dim - 6) = + dhc_dx_pre_.block(0, 3, 2 * h_.size(), x_dim - 6); + + return coef * rot_and_d_rot_pred_with_diff; + } + Eigen::MatrixXd rot_and_d_rot_error_prediction_with_poly_diff(const Eigen::VectorXd & x) + { + const int x_dim = x.size(); + const double theta = x[3]; + const double v = x[2]; + double coef = 2.0 * std::abs(v); + coef = coef * coef * coef * coef * coef * coef * coef; + if (coef > 1.0) { + coef = 1.0; + } + const double cos = std::cos(theta); + const double sin = std::sin(theta); + Eigen::Matrix2d Rot; + Rot << cos, -sin, sin, cos; + + Eigen::Matrix2d dRot; + dRot << -sin, -cos, cos, -sin; + Eigen::VectorXd vars(x_dim - 3); + vars[0] = x[2]; + vars[1] = x[4]; + vars[2] = x[5]; + vars.tail(x_dim - 6) = x.tail(x_dim - 6); + + const Eigen::VectorXd pred = error_prediction(vars, -1); + Eigen::MatrixXd d_pred = Eigen::MatrixXd::Zero(6, x_dim - 3); + + Eigen::VectorXd x_for_polynomial_reg(9); + x_for_polynomial_reg.head(3) = x.head(3); + const int acc_start = 3 + std::max(acc_delay_step_ - 3, 0); + x_for_polynomial_reg.segment(3, 3) = x.segment(acc_start, 3); + const int steer_start = 3 + acc_ctrl_queue_size_ + std::max(steer_delay_step_ - 3, 0); + x_for_polynomial_reg.segment(6, 3) = x.segment(steer_start, 3); + + const Eigen::MatrixXd polynomial_features_with_diff = + get_polynomial_features_with_diff(x_for_polynomial_reg, deg_, A_linear_reg_.cols()); + const Eigen::MatrixXd polynomial_reg_diff = + A_linear_reg_ * + polynomial_features_with_diff.block(0, 1, A_linear_reg_.cols(), x_for_polynomial_reg.size()); + d_pred.block(0, 0, 6, 3) += polynomial_reg_diff.block(0, 0, 6, 3); + d_pred.block(0, 1 + acc_start, 6, 3) += polynomial_reg_diff.block(0, 3, 6, 3); + d_pred.block(0, 1 + steer_start, 6, 3) += polynomial_reg_diff.block(0, 6, 6, 3); + + Eigen::MatrixXd rot_and_d_rot_pred_with_diff = Eigen::MatrixXd::Zero(6, x_dim + 2); + Eigen::MatrixXd rot_pred_with_diff(6, x_dim - 3); + rot_pred_with_diff.block(0, 0, 2, x_dim - 3) = Rot * d_pred.block(0, 0, 2, x_dim - 3); + rot_pred_with_diff.block(2, 0, 4, x_dim - 3) = d_pred.block(2, 0, 4, x_dim - 3); + + rot_and_d_rot_pred_with_diff.block(0, 0, 2, 1) = Rot * pred.head(2); + rot_and_d_rot_pred_with_diff.block(2, 0, 4, 1) = pred.segment(2, 4); + rot_and_d_rot_pred_with_diff.block(0, 1, 2, 1) = dRot * pred.head(2); + rot_and_d_rot_pred_with_diff.col(2 + 2) = rot_pred_with_diff.col(0); + rot_and_d_rot_pred_with_diff.col(2 + 4) = rot_pred_with_diff.col(1); + rot_and_d_rot_pred_with_diff.col(2 + 5) = rot_pred_with_diff.col(2); + rot_and_d_rot_pred_with_diff.block(0, 2 + 6, 6, x_dim - 6) = + rot_pred_with_diff.block(0, 3, 6, x_dim - 6); + + return coef * rot_and_d_rot_pred_with_diff; + } + Eigen::VectorXd rotated_error_prediction(const Eigen::VectorXd & x) + { + const int x_dim = x.size(); + const double theta = x[3]; + const double v = x[2]; + double coef = 2.0 * std::abs(v); + coef = coef * coef * coef * coef * coef * coef * coef; + if (coef > 1.0) { + coef = 1.0; + } + const double cos = std::cos(theta); + const double sin = std::sin(theta); + Eigen::Matrix2d Rot; + Rot << cos, -sin, sin, cos; + Eigen::VectorXd vars(x_dim - 3); + vars[0] = x[2]; + vars[1] = x[4]; + vars[2] = x[5]; + vars.tail(x_dim - 6) = x.tail(x_dim - 6); + const Eigen::VectorXd pred = error_prediction(vars, -1); + Eigen::VectorXd rot_pred(6); + rot_pred.head(2) = coef * Rot * pred.head(2); + rot_pred.tail(4) = coef * pred.tail(4); + return rot_pred; + } + Eigen::MatrixXd Rotated_error_prediction(const Eigen::MatrixXd & X) + { + const int X_cols = X.cols(); + const int x_dim = X.rows(); + Eigen::MatrixXd Pred(6, X_cols); + for (int i = 0; i < X_cols; i++) { + const Eigen::VectorXd x = X.col(i); + const double theta = x[3]; + const double v = x[2]; + double coef = 2.0 * std::abs(v); + coef = coef * coef * coef * coef * coef * coef * coef; + if (coef > 1.0) { + coef = 1.0; + } + const double cos = std::cos(theta); + const double sin = std::sin(theta); + Eigen::Matrix2d Rot; + Rot << cos, -sin, sin, cos; + Eigen::VectorXd vars(x_dim - 3); + + vars[0] = x[2]; + + vars[1] = x[4]; + vars[2] = x[5]; + vars.tail(x_dim - 6) = x.tail(x_dim - 6); + const Eigen::VectorXd pred = error_prediction(vars, i); + Pred.block(0, i, 2, 1) = coef * Rot * pred.head(2); + Pred.block(2, i, 4, 1) = coef * pred.tail(4); + } + return Pred; + } + void update_memory_by_state_history(const Eigen::MatrixXd & X) + { + const int X_cols = X.cols(); + const int x_dim = X.rows(); + for (int i = 0; i < X_cols; i++) { + const Eigen::VectorXd x = X.col(i); + Eigen::VectorXd vars(x_dim - 3); + vars[0] = x[2]; + vars[1] = x[4]; + vars[2] = x[5]; + vars.tail(x_dim - 6) = x.tail(x_dim - 6); + update_memory(vars); + } + } +}; PYBIND11_MODULE(proxima_calc, m) { py::class_(m, "transform_model_to_eigen") @@ -483,5 +1536,40 @@ PYBIND11_MODULE(proxima_calc, m) .def( "rot_and_d_rot_error_prediction_with_diff", &transform_model_to_eigen::rot_and_d_rot_error_prediction_with_diff) + .def( + "rot_and_d_rot_error_prediction_with_poly_diff", + &transform_model_to_eigen::rot_and_d_rot_error_prediction_with_poly_diff) + .def("rotated_error_prediction", &transform_model_to_eigen::rotated_error_prediction) .def("Rotated_error_prediction", &transform_model_to_eigen::Rotated_error_prediction); + py::class_(m, "transform_model_with_memory_to_eigen") + .def(py::init()) + .def("set_params", &transform_model_with_memory_to_eigen::set_params) + .def("set_params_res", &transform_model_with_memory_to_eigen::set_params_res) + .def("set_lstm", &transform_model_with_memory_to_eigen::set_lstm) + .def("set_lstm_for_candidate", &transform_model_with_memory_to_eigen::set_lstm_for_candidate) + .def("get_h", &transform_model_with_memory_to_eigen::get_h) + .def("get_c", &transform_model_with_memory_to_eigen::get_c) + .def("get_dy_dhc", &transform_model_with_memory_to_eigen::get_dy_dhc) + .def("get_dhc_dx", &transform_model_with_memory_to_eigen::get_dhc_dx) + .def("get_dhc_dhc", &transform_model_with_memory_to_eigen::get_dhc_dhc) + .def("error_prediction", &transform_model_with_memory_to_eigen::error_prediction) + .def( + "rot_and_d_rot_error_prediction", + &transform_model_with_memory_to_eigen::rot_and_d_rot_error_prediction) + .def( + "rot_and_d_rot_error_prediction_with_diff", + &transform_model_with_memory_to_eigen::rot_and_d_rot_error_prediction_with_diff) + .def( + "rot_and_d_rot_error_prediction_with_memory_diff", + &transform_model_with_memory_to_eigen::rot_and_d_rot_error_prediction_with_memory_diff) + .def( + "rot_and_d_rot_error_prediction_with_poly_diff", + &transform_model_with_memory_to_eigen::rot_and_d_rot_error_prediction_with_poly_diff) + .def( + "update_memory_by_state_history", + &transform_model_with_memory_to_eigen::update_memory_by_state_history) + .def( + "rotated_error_prediction", &transform_model_with_memory_to_eigen::rotated_error_prediction) + .def( + "Rotated_error_prediction", &transform_model_with_memory_to_eigen::Rotated_error_prediction); } diff --git a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/scripts/pympc_trajectory_follower.py b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/scripts/pympc_trajectory_follower.py index 78a120fe601f2..f767dad223f0d 100755 --- a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/scripts/pympc_trajectory_follower.py +++ b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/scripts/pympc_trajectory_follower.py @@ -16,6 +16,7 @@ # cspell: ignore interp +from enum import Enum import time from autoware_adapi_v1_msgs.msg import OperationModeState @@ -26,10 +27,15 @@ from autoware_smart_mpc_trajectory_follower.scripts import drive_functions from autoware_vehicle_msgs.msg import SteeringReport from builtin_interfaces.msg import Duration +from diagnostic_msgs.msg import DiagnosticStatus +import diagnostic_updater +from diagnostic_updater import DiagnosticStatusWrapper from geometry_msgs.msg import AccelWithCovarianceStamped +from geometry_msgs.msg import Point from geometry_msgs.msg import PoseStamped from nav_msgs.msg import Odometry import numpy as np +from rcl_interfaces.msg import ParameterDescriptor import rclpy from rclpy.node import Node import scipy @@ -40,6 +46,15 @@ from tier4_debug_msgs.msg import BoolStamped from tier4_debug_msgs.msg import Float32MultiArrayStamped from tier4_debug_msgs.msg import Float32Stamped +from visualization_msgs.msg import Marker +from visualization_msgs.msg import MarkerArray + + +class ControlStatus(Enum): + DRIVE = 0 + STOPPING = 1 + STOPPED = 2 + EMERGENCY = 3 def getYaw(orientation_xyzw): @@ -55,6 +70,13 @@ def __init__(self): # ROS 2 super().__init__("pympc_trajectory_follower") + + self.declare_parameter( + "plot_sampling_paths", + False, + ParameterDescriptor(description="Publish MPPI sampling paths as MarkerArray."), + ) + self.sub_trajectory_ = self.create_subscription( Trajectory, "/planning/scenario_planning/trajectory", @@ -111,14 +133,6 @@ def __init__(self): ) self.sub_goal_pose_ - self.sub_step_response_trigger_ = self.create_subscription( - String, - "/pympc_step_response_trigger", - self.onStepResponseTrigger, - 1, - ) - self.sub_step_response_trigger_ - self.sub_reload_mpc_param_trigger_ = self.create_subscription( String, "/pympc_reload_mpc_param_trigger", @@ -253,6 +267,12 @@ def __init__(self): 1, ) + self.debug_mpc_sampling_paths_marker_array_ = self.create_publisher( + MarkerArray, + "/debug_mpc_sampling_paths_marker_array", + 1, + ) + self._present_trajectory = None self._present_kinematic_state = None self._present_acceleration = None @@ -265,13 +285,14 @@ def __init__(self): self.emergency_stop_mode_flag = False self.stop_mode_reset_request = False - self.step_response_flag = False - self.step_response_trigger = False - self.step_response_time_step = 0 self.last_acc_cmd = 0.0 self.last_steer_cmd = 0.0 self.past_control_trajectory_mode = 1 + self.diagnostic_updater = diagnostic_updater.Updater(self) + self.setup_diagnostic_updater() + self.control_state = ControlStatus.STOPPED + def onTrajectory(self, msg): self._present_trajectory = msg @@ -289,16 +310,11 @@ def onOperationMode(self, msg): def onStopModeResetRequest(self, msg): self.stop_mode_reset_request = True - self.step_response_flag = False self.get_logger().info("receive stop mode reset request") def onGoalPose(self, msg): self._goal_pose = msg - def onStepResponseTrigger(self, msg): - self.step_response_trigger = True - self.get_logger().info("receive step response trigger") - def onReloadMpcParamTrigger(self, msg): self.get_logger().info("receive reload mpc param trigger") @@ -421,12 +437,14 @@ def control(self): orientation_deviation_threshold = 60 * np.pi / 180.0 # The moment the threshold is exceeded, it enters emergency stop mode. + control_state = self.control_state if is_applying_control: if ( position_deviation > position_deviation_threshold or orientation_deviation > orientation_deviation_threshold ): self.emergency_stop_mode_flag = True + control_state = ControlStatus.EMERGENCY # Normal return from emergency stop mode when within the threshold value and a request to cancel the stop mode has been received. if ( @@ -443,42 +461,7 @@ def control(self): self.stop_mode_reset_request = False # [3] Control logic calculation - # [3-1] pure pursuit - # [3-1-1] compute pure pursuit acc cmd - longitudinal_vel_err = ( - present_linear_velocity - trajectory_longitudinal_velocity[nearestIndex] - ) - acc_kp = 0.5 - acc_lim = 2.0 - pure_pursuit_acc_cmd = np.clip(-acc_kp * longitudinal_vel_err, -acc_lim, acc_lim)[0] - - # [3-1-2] compute pure pursuit steer cmd - nearest_trajectory_point_yaw = getYaw(trajectory_orientation[nearestIndex]) - cos_yaw = np.cos(nearest_trajectory_point_yaw) - sin_yaw = np.sin(nearest_trajectory_point_yaw) - diff_position = present_position - trajectory_position[nearestIndex] - lat_err = -sin_yaw * diff_position[0] + cos_yaw * diff_position[1] - yaw_err = getYaw(present_orientation) - nearest_trajectory_point_yaw - while True: - if yaw_err > np.pi: - yaw_err -= 2.0 * np.pi - if yaw_err < (-np.pi): - yaw_err += 2.0 * np.pi - if np.abs(yaw_err) < np.pi: - break - wheel_base = drive_functions.L # 4.0 - lookahead_time = 3.0 - min_lookahead = 3.0 - lookahead = min_lookahead + lookahead_time * np.abs(present_linear_velocity[0]) - steer_kp = 2.0 * wheel_base / (lookahead * lookahead) - steer_kd = 2.0 * wheel_base / lookahead - steer_lim = 0.6 - pure_pursuit_steer_cmd = np.clip( - -steer_kp * lat_err - steer_kd * yaw_err, -steer_lim, steer_lim - )[0] - - # [3-2] MPC control logic calculation - # [3-2-1] State variables in the MPC + # [3-1] State variables in the MPC present_mpc_x = np.array( [ present_position[0], @@ -490,7 +473,7 @@ def control(self): ] ) - # [3-2-2] resampling trajectory by time (except steer angle). + # [3-2] resampling trajectory by time (except steer angle). X_des = np.zeros((drive_functions.N + 1, 8)) dist = np.sqrt(((trajectory_position[1:] - trajectory_position[:-1]) ** 2).sum(axis=1)) timestamp_from_start = [0.0] @@ -532,7 +515,7 @@ def control(self): X_des[:, 4] = acceleration_interpolate(timestamp_mpc) X_des[:, 6] = 1 * X_des[:, 4] - # [3-2-3] resampling trajectory by time (steer angle) + # [3-3] resampling trajectory by time (steer angle) previous_des_steer = 0.0 steer_trajectory2 = np.zeros(len(timestamp_mpc)) downsampling = 5 @@ -621,9 +604,17 @@ def control(self): X_des[:, 5] = steer_trajectory2 X_des[:, 7] = 1 * X_des[:, 5] - # [3-2-4] mpc computation + # [3-4] (optimal) control computation U_des = np.zeros((drive_functions.N, 2)) start_calc_u_opt = time.time() + acc_time_stamp = ( + self._present_acceleration.header.stamp.sec + + 1e-9 * self._present_acceleration.header.stamp.nanosec + ) + steer_time_stamp = ( + self._present_steering_status.stamp.sec + + 1e-9 * self._present_steering_status.stamp.nanosec + ) u_opt = self.controller.update_input_queue_and_get_optimal_control( self.control_cmd_time_stamp_list, self.control_cmd_acc_list, @@ -631,12 +622,14 @@ def control(self): present_mpc_x, X_des, U_des, + acc_time_stamp, + steer_time_stamp, ) end_calc_u_opt = time.time() mpc_acc_cmd = u_opt[0] mpc_steer_cmd = u_opt[1] - # [3-3] Enter goal stop mode (override command value) to maintain stop at goal + # [3-5] Enter goal stop mode (override command value) to maintain stop at goal self.goal_stop_mode_flag = False if self._goal_pose is not None: goal_position = np.array( @@ -652,39 +645,13 @@ def control(self): if distance_from_goal < goal_distance_threshold: self.goal_stop_mode_flag = True - # [3-4] Override when doing step response - if self.step_response_trigger: - self.step_response_time_step = 0 - self.step_response_flag = True - self.step_response_trigger = False - - if self.step_response_flag: - step_response_elapsed_time = self.step_response_time_step * self.timer_period_callback - mpc_acc_cmd = 1.0 * self.last_acc_cmd - if step_response_elapsed_time < 3.0: - mpc_steer_cmd = 0.0 - else: - mpc_steer_cmd = -0.005 - self.get_logger().info( - "step_response_elapsed_time: " - + str(step_response_elapsed_time) - + ", mpc_steer_cmd: " - + str(mpc_steer_cmd) - ) - self.step_response_time_step += 1 - - # [3-5] Determine the control logic to be finally applied and publish it + # [3-6] Determine the control logic to be finally applied and publish it acc_cmd = 0.0 steer_cmd = 0.0 if (not self.emergency_stop_mode_flag) and (not self.goal_stop_mode_flag): # in normal mode acc_cmd = mpc_acc_cmd steer_cmd = mpc_steer_cmd - - overwrite_cmd_by_pure_pursuit_flag = False - if overwrite_cmd_by_pure_pursuit_flag: - steer_cmd = pure_pursuit_steer_cmd - acc_cmd = pure_pursuit_acc_cmd else: # in stop mode acc_cmd_decrease_limit = 5.0 * self.timer_period_callback # lon_jerk_lim @@ -698,12 +665,13 @@ def control(self): steer_cmd = self.last_steer_cmd + steer_cmd_decrease_limit else: steer_cmd = 0.0 + control_state = ControlStatus.STOPPING cmd_msg = Control() cmd_msg.stamp = cmd_msg.lateral.stamp = cmd_msg.longitudinal.stamp = ( self.get_clock().now().to_msg() ) - cmd_msg.longitudinal.speed = trajectory_longitudinal_velocity[nearestIndex] + cmd_msg.longitudinal.velocity = trajectory_longitudinal_velocity[nearestIndex] cmd_msg.longitudinal.acceleration = acc_cmd cmd_msg.lateral.steering_tire_angle = steer_cmd @@ -722,6 +690,15 @@ def control(self): self.control_cmd_steer_list.pop(0) self.control_cmd_acc_list.pop(0) + # [3-7] Update control state + if control_state != ControlStatus.EMERGENCY: + stopped_velocity_threshold = 0.1 + if present_linear_velocity[0] <= stopped_velocity_threshold: + control_state = ControlStatus.STOPPED + elif control_state != ControlStatus.STOPPING: + control_state = ControlStatus.DRIVE + self.control_state = control_state + # [4] Update MPC internal variables if not is_applying_control: self.controller.send_initialize_input_queue() @@ -842,6 +819,56 @@ def control(self): data=(end_ctrl_time - start_ctrl_time), ) ) + if self.controller.mode == "mppi" or self.controller.mode == "mppi_ilqr": + if self.get_parameter("plot_sampling_paths").get_parameter_value().bool_value: + marker_array = MarkerArray() + for i in range(self.controller.mppi_candidates.shape[0]): + marker = Marker() + marker.type = marker.LINE_STRIP + marker.header.stamp = cmd_msg.stamp + marker.header.frame_id = "map" + marker.id = i + marker.action = marker.ADD + + marker.scale.x = 0.05 + marker.scale.y = 0.0 + marker.scale.z = 0.0 + + marker.color.a = 1.0 + marker.color.r = np.random.uniform() + marker.color.g = np.random.uniform() + marker.color.b = np.random.uniform() + + marker.lifetime.nanosec = 500000000 + marker.frame_locked = True + + marker.points = [] + + for j in range(self.controller.mppi_candidates.shape[1]): + tmp_point = Point() + tmp_point.x = self.controller.mppi_candidates[i, j, 0] + tmp_point.y = self.controller.mppi_candidates[i, j, 1] + tmp_point.z = self._present_kinematic_state.pose.pose.position.z + marker.points.append(tmp_point) + marker_array.markers.append(marker) + self.debug_mpc_sampling_paths_marker_array_.publish(marker_array) + + self.diagnostic_updater.force_update() + + def setup_diagnostic_updater(self): + self.diagnostic_updater.setHardwareID("pympc_trajectory_follower") + self.diagnostic_updater.add("control_state", self.check_control_state) + + def check_control_state(self, stat: DiagnosticStatusWrapper): + msg = "emergency occurred" if self.control_state == ControlStatus.EMERGENCY else "OK" + level = ( + DiagnosticStatus.ERROR + if self.control_state == ControlStatus.EMERGENCY + else DiagnosticStatus.OK + ) + stat.summary(level, msg) + stat.add("control_state", str(self.control_state)) + return stat def main(args=None): diff --git a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/training_and_data_check/add_training_data_from_csv.py b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/training_and_data_check/add_training_data_from_csv.py index 5338f3cbaddce..006a51bfe6557 100644 --- a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/training_and_data_check/add_training_data_from_csv.py +++ b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/training_and_data_check/add_training_data_from_csv.py @@ -22,12 +22,34 @@ import json import os from pathlib import Path +import subprocess from autoware_smart_mpc_trajectory_follower.scripts import drive_functions import numpy as np import scipy.interpolate from scipy.ndimage import gaussian_filter from scipy.spatial.transform import Rotation +from sklearn import linear_model +from sklearn.preprocessing import PolynomialFeatures + +ctrl_index_for_polynomial_reg = np.concatenate( + ( + np.arange(3), + 3 + + max(drive_functions.acc_delay_step - drive_functions.mpc_freq, 0) + + np.arange(drive_functions.mpc_freq), + 3 + + drive_functions.acc_ctrl_queue_size + + max(drive_functions.steer_delay_step - drive_functions.mpc_freq, 0) + + np.arange(drive_functions.mpc_freq), + ) +) + + +if drive_functions.use_memory_for_training: + default_add_mode = "divide" +else: + default_add_mode = "as_train" def data_smoothing(data: np.ndarray, sigma: float) -> np.ndarray: @@ -53,19 +75,29 @@ class add_data_from_csv: """Class for loading csv files containing driving data, converting them into teacher data for the NN training, and storing them in lists.""" X_input_list: list[np.ndarray] - """Input side of NN teacher data""" + """Input side of NN teacher data.""" Y_output_list: list[np.ndarray] - """Output side of NN teacher data""" + """Output side of NN teacher data.""" - def __init__(self): + def __init__( + self, alpha_1_for_polynomial_regression=0.1**5, alpha_2_for_polynomial_regression=0.1**5 + ): self.X_input_list = [] self.Y_output_list = [] + self.X_val_list = [] + self.Y_val_list = [] + self.division_indices = [] + self.division_indices_val = [] + self.alpha_1_for_polynomial_regression = alpha_1_for_polynomial_regression + self.alpha_2_for_polynomial_regression = alpha_2_for_polynomial_regression def clear_data(self): """Clear the teacher data for the training.""" self.X_input_list.clear() self.Y_output_list.clear() + self.X_val_list.clear() + self.Y_val_list.clear() def transform_rosbag_to_csv(self, dir_name: str, delete_csv_first: bool = True) -> None: """Convert rosbag file to CSV format.""" @@ -73,19 +105,20 @@ def transform_rosbag_to_csv(self, dir_name: str, delete_csv_first: bool = True) with open(package_path_json, "r") as file: package_path = json.load(file) dir_exe = os.getcwd() - os.system( + subprocess.run( "cp " + package_path["path"] + "/autoware_smart_mpc_trajectory_follower/training_and_data_check/rosbag2.bash " - + dir_name + + dir_name, + shell=True, ) os.chdir(dir_name) if len(glob.glob("*.csv")) > 0 and delete_csv_first: - os.system("rm *.csv") - os.system("bash rosbag2.bash") + subprocess.run("rm *.csv", shell=True) + subprocess.run("bash rosbag2.bash", shell=True) os.chdir(dir_exe) - def add_data_from_csv(self, dir_name: str) -> None: + def add_data_from_csv(self, dir_name: str, add_mode=default_add_mode) -> None: """Adding teacher data for training from a CSV file.""" acc_ctrl_queue_size = drive_functions.acc_ctrl_queue_size steer_ctrl_queue_size = drive_functions.steer_ctrl_queue_size @@ -119,7 +152,7 @@ def add_data_from_csv(self, dir_name: str) -> None: steer_sm = data_smoothing(steer, drive_functions.steer_sigma_for_learning) control_cmd = np.loadtxt( - dir_name + "/control_cmd_orig.csv", delimiter=",", usecols=[0, 1, 4, 9] + dir_name + "/control_cmd_orig.csv", delimiter=",", usecols=[0, 1, 8, 16] ) acc_des = control_cmd[:, 3] acc_des_sm = data_smoothing(acc_des, drive_functions.acc_des_sigma_for_learning) @@ -279,10 +312,30 @@ def get_interpolated_input(s): Y_smooth[:, 5] = data_smoothing( Y_output_list_array[:, 5], drive_functions.steer_out_sigma_for_learning ) - - for i in range(len(X_input_list)): - self.X_input_list.append(X_input_list[i]) - self.Y_output_list.append(Y_smooth[i]) + if add_mode == "divide": + for i in range(len(X_input_list)): + if i < 3 * len(X_input_list) / 4: + self.X_input_list.append(X_input_list[i]) + self.Y_output_list.append(Y_smooth[i]) + else: + self.X_val_list.append(X_input_list[i]) + self.Y_val_list.append(Y_smooth[i]) + + self.division_indices.append(len(self.X_input_list)) + self.division_indices_val.append(len(self.X_val_list)) + + elif add_mode == "as_train": + for i in range(len(X_input_list)): + self.X_input_list.append(X_input_list[i]) + self.Y_output_list.append(Y_smooth[i]) + + self.division_indices.append(len(self.X_input_list)) + if add_mode == "as_val": + for i in range(len(X_input_list)): + self.X_val_list.append(X_input_list[i]) + self.Y_val_list.append(Y_smooth[i]) + + self.division_indices_val.append(len(self.X_val_list)) def save_train_data(self, save_dir=".") -> None: """Save neural net teacher data.""" @@ -291,3 +344,126 @@ def save_train_data(self, save_dir=".") -> None: X_input=np.array(self.X_input_list), Y_output=np.array(self.Y_output_list), ) + + def get_polynomial_regression_result( + self, + X_input_list: list[np.ndarray], + Y_output_list: list[np.ndarray], + use_polynomial_reg: bool, + use_selected_polynomial: bool, + deg: int, + fit_intercept: bool, + use_intercept: bool, + ) -> tuple[np.ndarray, np.ndarray]: + """Get the results of a polynomial regression.""" + X_input = np.array(X_input_list) + Y_output = np.array(Y_output_list) + if use_selected_polynomial: + self.deg = 2 + else: + self.deg = deg + self.A, self.b, Y_output_minus = polynomial_regression( + X_input, + Y_output, + self.alpha_1_for_polynomial_regression, + self.alpha_2_for_polynomial_regression, + use_polynomial_reg, + use_selected_polynomial, + self.deg, + fit_intercept, + use_intercept, + ) + return X_input, Y_output_minus + + +def polynomial_regression( + X, + Y, + alpha_1, + alpha_2, + use_polynomial_reg, + use_selected_polynomial, + deg, + fit_intercept, + use_intercept, + fit_acc_steer=False, +): + polynomial_features = PolynomialFeatures(degree=deg, include_bias=False) + alpha = alpha_1 + alpha_2 + if use_polynomial_reg: + if use_selected_polynomial: + clf_1 = linear_model.ElasticNet( + fit_intercept=fit_intercept, alpha=alpha, l1_ratio=alpha_1 / alpha, max_iter=100000 + ) + clf_2 = linear_model.ElasticNet( + fit_intercept=fit_intercept, alpha=alpha, l1_ratio=alpha_1 / alpha, max_iter=100000 + ) + clf_3 = linear_model.ElasticNet( + fit_intercept=fit_intercept, alpha=alpha, l1_ratio=alpha_1 / alpha, max_iter=100000 + ) + X_poly = polynomial_features.fit_transform(X[:, ctrl_index_for_polynomial_reg]) + A = np.zeros((Y.shape[1], X_poly.shape[1])) + b = np.zeros(Y.shape[1]) + clf_1.fit(X_poly[:, [0, ctrl_index_for_polynomial_reg.shape[0] + 2]], Y[:, [3]]) + # clf_2.fit(X_poly[:, [1, 3,4,5]], Y[:, [4]]) + + acc_input_sum = np.zeros((3, 1)) + acc_input_sum[0, 0] = drive_functions.ctrl_time_step / drive_functions.acc_time_constant + acc_input_sum[1, 0] = acc_input_sum[0, 0] * (1 - acc_input_sum[0, 0]) + acc_input_sum[2, 0] = acc_input_sum[1, 0] * (1 - acc_input_sum[0, 0]) + clf_2.fit(X_poly[:, [3, 4, 5]] @ acc_input_sum, Y[:, [4]]) + + # clf_3.fit(X_poly[:, [2, 6,7,8]], Y[:, [5]]) + + steer_input_sum = np.zeros((3, 1)) + steer_input_sum[0, 0] = ( + drive_functions.ctrl_time_step / drive_functions.steer_time_constant + ) + steer_input_sum[1, 0] = steer_input_sum[0, 0] * (1 - steer_input_sum[0, 0]) + steer_input_sum[2, 0] = steer_input_sum[1, 0] * (1 - steer_input_sum[0, 0]) + clf_3.fit(X_poly[:, [6, 7, 8]] @ steer_input_sum, Y[:, [5]]) + + A[3, [0, ctrl_index_for_polynomial_reg.shape[0] + 2]] = clf_1.coef_ + # A[4, [1, 3,4,5]] = clf_2.coef_ + if fit_acc_steer: + A[4, [3, 4, 5]] = clf_2.coef_ * acc_input_sum.flatten() + + # A[5, [2, 6,7,8]] = clf_3.coef_ + if fit_acc_steer: + A[5, [6, 7, 8]] = clf_3.coef_ * steer_input_sum.flatten() + + if fit_intercept: + b[3] = clf_1.intercept_ + if fit_acc_steer: + b[4] = clf_2.intercept_ + b[5] = clf_3.intercept_ + + if not use_intercept: + b = 0 * b + return A, b, Y - X_poly @ A.T - b + else: + clf = linear_model.ElasticNet( + fit_intercept=fit_intercept, alpha=alpha, l1_ratio=alpha_1 / alpha, max_iter=100000 + ) + X_poly = polynomial_features.fit_transform(X[:, ctrl_index_for_polynomial_reg]) + clf.fit(X_poly, Y) + if fit_intercept: + if use_intercept: + return ( + clf.coef_, + clf.intercept_, + Y - clf.predict(X_poly), + ) + else: + return ( + clf.coef_, + 0 * clf.intercept_, + Y - clf.predict(X_poly) + clf.intercept_, + ) + else: + return clf.coef_, np.zeros(Y.shape[1]), Y - clf.predict(X_poly) + else: + poly_dim = polynomial_features.fit_transform( + X[0, ctrl_index_for_polynomial_reg].reshape(1, -1) + ).shape[1] + return np.zeros((Y.shape[1], poly_dim)), np.zeros(Y.shape[1]), Y diff --git a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/training_and_data_check/data_checker.ipynb b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/training_and_data_check/data_checker.ipynb index 9ddd5fb6fad2a..c93b091b00888 100644 --- a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/training_and_data_check/data_checker.ipynb +++ b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/training_and_data_check/data_checker.ipynb @@ -7,7 +7,7 @@ "metadata": {}, "outputs": [], "source": [ - "# cspell: ignore ndimage usecols kernelspec ipykernel codemirror ipython nbconvert pygments nbformat \n", + "# cspell: ignore ndimage usecols kernelspec ipykernel codemirror ipython nbconvert pygments nbformat #\n", "import numpy as np\n", "from scipy.spatial.transform import Rotation\n", "import scipy.interpolate\n", @@ -31,7 +31,7 @@ " total_time_default=kinematic[-1,0]+1e-9*kinematic[-1,1]-start_time\n", " vehicle_steer = np.loadtxt(dir_name+\"/steering_status.csv\",delimiter=',',usecols=[0,1,2])\n", " steer = vehicle_steer[:,2]\n", - " control_cmd = np.loadtxt(dir_name+\"/control_cmd_orig.csv\",delimiter=',',usecols=[0,1,4,9])\n", + " control_cmd = np.loadtxt(dir_name+\"/control_cmd_orig.csv\",delimiter=',',usecols=[0,1,8,16])\n", " steer_des = control_cmd[:,2]\n", " plt.plot(control_cmd[:,0] + 1e-9*control_cmd[:,1],steer_des,label=\"proxima_input\")\n", " plt.plot(vehicle_steer[:,0] + 1e-9*vehicle_steer[:,1],steer,label=\"observed\")\n", @@ -79,7 +79,7 @@ " total_time_default=kinematic[-1,0]+1e-9*kinematic[-1,1]-start_time\n", " loc_acc = np.loadtxt(dir_name + \"/acceleration.csv\", delimiter=\",\", usecols=[0, 1, 3])\n", " acc = loc_acc[:,2]\n", - " control_cmd = np.loadtxt(dir_name+\"/control_cmd_orig.csv\",delimiter=',',usecols=[0,1,4,9])\n", + " control_cmd = np.loadtxt(dir_name+\"/control_cmd_orig.csv\",delimiter=',',usecols=[0,1,8,16])\n", " acc_des = control_cmd[:,3]\n", " plt.plot(control_cmd[:,0] + 1e-9*control_cmd[:,1],acc_des,label=\"proxima_input\")\n", " plt.plot(loc_acc[:,0] + 1e-9*loc_acc[:,1],acc,label=\"observed\")\n", diff --git a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/training_and_data_check/execute_train_drive_NN_model.ipynb b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/training_and_data_check/execute_train_drive_NN_model.ipynb index c2955883d5699..1f8934256831e 100644 --- a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/training_and_data_check/execute_train_drive_NN_model.ipynb +++ b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/training_and_data_check/execute_train_drive_NN_model.ipynb @@ -7,7 +7,7 @@ "metadata": {}, "outputs": [], "source": [ - "# cspell: ignore kernelspec ipykernel codemirror ipython nbconvert pygments nbformat \n", + "# cspell: ignore kernelspec ipykernel codemirror ipython nbconvert pygments nbformat #\n", "import train_drive_NN_model" ] }, diff --git a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/training_and_data_check/train_drive_GP_model.py b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/training_and_data_check/train_drive_GP_model.py index 914ed7b53c6ec..c16e301f2ebb8 100644 --- a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/training_and_data_check/train_drive_GP_model.py +++ b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/training_and_data_check/train_drive_GP_model.py @@ -15,8 +15,10 @@ # cspell: ignore lengthscale savez import GPy +from autoware_smart_mpc_trajectory_follower.training_and_data_check import ( + add_training_data_from_csv, +) import numpy as np -from smart_mpc_trajectory_follower.training_and_data_check import add_training_data_from_csv class train_drive_GP_model(add_training_data_from_csv.add_data_from_csv): diff --git a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/training_and_data_check/train_drive_NN_model.py b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/training_and_data_check/train_drive_NN_model.py index 449ddda45f460..cf1b54e2868de 100644 --- a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/training_and_data_check/train_drive_NN_model.py +++ b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/training_and_data_check/train_drive_NN_model.py @@ -12,707 +12,16 @@ # See the License for the specific language governing permissions and # limitations under the License. -# cspell: ignore optim savez suptitle - """Class for training neural nets from driving data.""" -from autoware_smart_mpc_trajectory_follower.scripts import drive_NN from autoware_smart_mpc_trajectory_follower.scripts import drive_functions from autoware_smart_mpc_trajectory_follower.training_and_data_check import ( - add_training_data_from_csv, + train_drive_NN_model_with_memory, ) -import matplotlib.pyplot as plt -import numpy as np -from sklearn import linear_model -from sklearn.preprocessing import PolynomialFeatures -import torch -from torch import nn -from torch.utils.data import DataLoader -from torch.utils.data import TensorDataset - -ctrl_index_for_polynomial_reg = np.concatenate( - ( - np.arange(3), - [3 + drive_functions.acc_delay_step], - [3 + drive_functions.acc_ctrl_queue_size + drive_functions.steer_delay_step], - ) +from autoware_smart_mpc_trajectory_follower.training_and_data_check import ( + train_drive_NN_model_without_memory, ) - -def nominal_model_acc(v: float, alpha_0: float, alpha: float) -> np.ndarray: - """Predicted acceleration value according to the nominal model.""" - nominal_pred = alpha_0 - nominal_v = v + nominal_pred * drive_functions.ctrl_time_step - nominal_pred = ( - nominal_pred - + (alpha - nominal_pred) - * drive_functions.ctrl_time_step - / drive_functions.acc_time_constant - ) - nominal_v = v + nominal_pred * drive_functions.ctrl_time_step - nominal_pred = ( - nominal_pred - + (alpha - nominal_pred) - * drive_functions.ctrl_time_step - / drive_functions.acc_time_constant - ) - nominal_v = v + nominal_pred * drive_functions.ctrl_time_step - nominal_pred = ( - nominal_pred - + (alpha - nominal_pred) - * drive_functions.ctrl_time_step - / drive_functions.acc_time_constant - ) - return np.array([nominal_v, nominal_pred]) - - -def nominal_model_steer(delta_0: float, delta: float) -> float: - """Predicted steer angle according to the nominal model.""" - nominal_pred = delta_0 - nominal_pred = ( - nominal_pred - + (delta - nominal_pred) - * drive_functions.ctrl_time_step - / drive_functions.steer_time_constant - ) - nominal_pred = ( - nominal_pred - + (delta - nominal_pred) - * drive_functions.ctrl_time_step - / drive_functions.steer_time_constant - ) - nominal_pred = ( - nominal_pred - + (delta - nominal_pred) - * drive_functions.ctrl_time_step - / drive_functions.steer_time_constant - ) - return nominal_pred - - -class train_drive_NN_model(add_training_data_from_csv.add_data_from_csv): - """Class for training neural nets from driving data.""" - - tanh_gain: float - """Gain of tanh in the NN cost function""" - - lam: float - """Weights of the tanh term in the NN cost function.""" - - tol: float - """Tolerances for terminating training iterations.""" - - alpha_1: float - """L¹ regularization weights in the NN cost function""" - - alpha_2: float - """L² regularization weights in the NN cost function.""" - - alpha_1_for_polynomial_regression: float - """L¹ regularization weights for polynomial regression""" - - alpha_2_for_polynomial_regression: float - """L² regularization weights for polynomial regression""" - - x_loss: list[float] - """List of values for the loss function of the x component in the NN training.""" - - y_loss: list[float] - """List of values for the loss function of the y component in the NN training.""" - - v_loss: list[float] - """List of values for the loss function of the velocity component in the NN training.""" - - theta_loss: list[float] - """List of values for the loss function of the yaw angle component in the NN training.""" - - acc_loss: list[float] - """List of values for the loss function of the acceleration component in the NN training.""" - - steer_loss: list[float] - """List of values for the loss function of the steer angle component in the NN training.""" - - steer_loss_plus_tanh: list[float] - """List of values of the loss function for the steer angle component with tanh term in the NN training.""" - - total_loss: list[float] - """In NN training x_loss, ... , acc_loss, steer_loss_plus_tanh summed list of values""" - - model: drive_NN.DriveNeuralNetwork - """trained neural network model""" - - A: np.ndarray - """Coefficient matrix of polynomial regression model.""" - - b: np.ndarray - """Constant terms in polynomial regression models (bias)""" - - def __init__( - self, - tanh_gain=10, - lam=0.1, - tol=0.00001, - alpha_1=0.1**7, - alpha_2=0.1**7, - alpha_1_for_polynomial_regression=0.1**5, - alpha_2_for_polynomial_regression=0.1**5, - ): - super(train_drive_NN_model, self).__init__() - self.tanh_gain = tanh_gain - self.lam = lam - self.tol = tol - self.alpha_1 = alpha_1 - self.alpha_2 = alpha_2 - self.alpha_1_for_polynomial_regression = alpha_1_for_polynomial_regression - self.alpha_2_for_polynomial_regression = alpha_2_for_polynomial_regression - self.x_loss = [] - self.y_loss = [] - self.v_loss = [] - self.theta_loss = [] - self.acc_loss = [] - self.steer_loss = [] - self.steer_loss_plus_tanh = [] - self.total_loss = [] - - def train_model( - self, - model: drive_NN.DriveNeuralNetwork, - X_input: np.ndarray, - Y_output: np.ndarray, - learning_rates: list[float], - patience: int = 10, - batch_size: int | None = 50, - max_iter: int = 100000, - ) -> None: - """Train the model.""" - if batch_size is None: - batch_size = X_input.shape[0] // 50 - self.x_loss.clear() - self.y_loss.clear() - self.v_loss.clear() - self.theta_loss.clear() - self.acc_loss.clear() - self.steer_loss.clear() - self.steer_loss_plus_tanh.clear() - self.total_loss.clear() - sample_size = X_input.shape[0] - num_train = int(3 * sample_size / 4) - id_all = np.random.choice(sample_size, sample_size, replace=False) - id_train = id_all[:num_train] - id_val = id_all[num_train:] - X_tensor = torch.tensor(X_input.astype(np.float32)).clone() - Y_tensor = torch.tensor(Y_output.astype(np.float32)).clone() - X_train = X_tensor[id_train] - Y_train = Y_tensor[id_train] - X_val = X_tensor[id_val] - Y_val = Y_tensor[id_val] - - print("sample_size: ", X_input.shape[0]) - print("patience:", patience) - loss_fn = torch.nn.L1Loss() - learning_rate = learning_rates[0] - optimizer = torch.optim.Adam(params=model.parameters(), lr=learning_rate) - - drive_data = DataLoader( - TensorDataset(X_train, Y_train), batch_size=batch_size, shuffle=True - ) - initial_loss = drive_NN.loss_fn_plus_tanh( - loss_fn, - torch.tensor(np.zeros(Y_val.shape), dtype=torch.float32), - Y_val, - self.tanh_gain, - self.lam, - ) - print("initial_loss:", initial_loss.detach().item()) - early_stopping = drive_NN.EarlyStopping( - initial_loss=initial_loss, tol=self.tol, patience=patience - ) - k = 0 - print("learning_rate:", learning_rates[0]) - for i in range(max_iter): - model.train() - for X_batch, y_batch in drive_data: - y_pred = model(X_batch) - loss = drive_NN.loss_fn_plus_tanh( - loss_fn, y_pred, y_batch, self.tanh_gain, self.lam - ) - for w in model.parameters(): - loss = ( - loss + self.alpha_1 * torch.norm(w, p=1) + self.alpha_2 * torch.norm(w) ** 2 - ) - optimizer.zero_grad() - loss.backward() - optimizer.step() - - model.eval() - pred = model(X_val) - self.total_loss.append( - drive_NN.loss_fn_plus_tanh(loss_fn, pred, Y_val, self.tanh_gain, self.lam) - .detach() - .item() - ) - self.x_loss.append(loss_fn(pred[:, [0]], Y_val[:, [0]]).detach().item()) - self.y_loss.append(loss_fn(pred[:, [1]], Y_val[:, [1]]).detach().item()) - self.v_loss.append(loss_fn(pred[:, [2]], Y_val[:, [2]]).detach().item()) - self.theta_loss.append(loss_fn(pred[:, [3]], Y_val[:, [3]]).detach().item()) - self.acc_loss.append(loss_fn(pred[:, [4]], Y_val[:, [4]]).detach().item()) - self.steer_loss.append(loss_fn(pred[:, [5]], Y_val[:, [5]]).detach().item()) - self.steer_loss_plus_tanh.append( - loss_fn(pred[:, [5]], Y_val[:, [5]]).detach().item() - + self.lam - * loss_fn( - torch.tanh(self.tanh_gain * (pred[:, -1] - Y_val[:, -1])), - torch.zeros(Y_val.shape[0]), - ) - .detach() - .item() - ) - if i % 10 == 1: - current_loss = drive_NN.loss_fn_plus_tanh( - loss_fn, model(X_val), Y_val, self.tanh_gain, self.lam - ) - print(current_loss.detach().item(), i) - if early_stopping(current_loss): - k += 1 - if k == len(learning_rates): - break - else: - learning_rate = learning_rates[k] - print("update learning_rate to ", learning_rates[k]) - optimizer = torch.optim.Adam(params=model.parameters(), lr=learning_rate) - early_stopping = drive_NN.EarlyStopping( - initial_loss=initial_loss, tol=self.tol, patience=patience - ) - - def get_polynomial_regression_result( - self, - X_input_list: list[np.ndarray], - Y_output_list: list[np.ndarray], - use_polynomial_reg: bool, - use_selected_polynomial: bool, - deg: int, - fit_intercept: bool, - use_intercept: bool, - ) -> tuple[np.ndarray, np.ndarray]: - """Get the results of a polynomial regression.""" - X_input = np.array(X_input_list) - X_input_core = X_input[ - :, - : 3 + drive_functions.acc_ctrl_queue_size + drive_functions.steer_ctrl_queue_size_core, - ].copy() - Y_output = np.array(Y_output_list) - if use_selected_polynomial: - self.deg = 2 - else: - self.deg = deg - self.A, self.b, Y_output_minus = polynomial_regression( - X_input_core, - Y_output, - self.alpha_1_for_polynomial_regression, - self.alpha_2_for_polynomial_regression, - use_polynomial_reg, - use_selected_polynomial, - self.deg, - fit_intercept, - use_intercept, - ) - return X_input, Y_output_minus - - def plot_trained_result( - self, show_flag=False, save_dir=".", plot_range=np.arange(500, 1200) - ) -> None: - """Plot the results of the training.""" - polynomial_features = PolynomialFeatures(degree=self.deg, include_bias=False) - - X_input = np.array(self.X_input_list) - Y_output = np.array(self.Y_output_list) - X_tensor = torch.tensor(X_input.astype(np.float32)).clone() - acc_ctrl_queue_size = drive_functions.acc_ctrl_queue_size - steer_ctrl_queue_size = drive_functions.steer_ctrl_queue_size - ctrl_time_step = drive_functions.ctrl_time_step - Y_pred = ( - self.model(X_tensor).detach().numpy() - + polynomial_features.fit_transform(X_input[:, ctrl_index_for_polynomial_reg]) - @ (self.A).T - + self.b - ) - x = max(acc_ctrl_queue_size, steer_ctrl_queue_size) * ctrl_time_step - y_labels = [ - "x_error [m]", - "y_error [m]", - "vel_error [m/s]", - "yaw_error [rad]", - "acc_error [m/s^2]", - "steer_error [rad]", - ] - - plt.figure(figsize=(24, 15), tight_layout=True) - plt.subplot(231) - ax = [] - for i in range(6): - ax.append(plt.subplot(2, 3, i + 1)) - ax[-1].plot( - drive_functions.ctrl_time_step * plot_range + x, - Y_output[plot_range, i] * drive_functions.mpc_time_step, - label="nominal_error", - ) - ax[-1].plot( - drive_functions.ctrl_time_step * plot_range + x, - Y_pred[plot_range, i] * drive_functions.mpc_time_step, - label="pred_error", - ) - ax[-1].set_xlabel("sec") - ax[-1].set_ylabel(y_labels[i]) - ax[-1].legend() - if show_flag: - plt.show() - else: - plt.savefig(save_dir + "/train_drive_NN_model_fig.png") - plt.close() - - def plot_loss(self, show_flag=False, save_dir=".") -> None: - """Plot the progression of values of the loss function of the training.""" - plt.figure(figsize=(24, 15), tight_layout=True) - - y_loss_labels = [ - "total_loss", - "x_loss", - "y_loss", - "vel_loss", - "yaw_loss", - "acc_loss", - "steer_loss", - "steer_plus_tanh_loss", - ] - plt.subplot(241) - ax_2 = [] - loss_list = [ - np.array(self.total_loss), - np.array(self.x_loss), - np.array(self.y_loss), - np.array(self.v_loss), - np.array(self.theta_loss), - np.array(self.acc_loss), - np.array(self.steer_loss), - np.array(self.steer_loss_plus_tanh), - ] - for i in range(8): - ax_2.append(plt.subplot(2, 4, i + 1)) - ax_2[-1].plot( - np.arange(loss_list[i].shape[0]), - loss_list[i], - label="loss", - ) - ax_2[-1].set_xlabel("iteration") - ax_2[-1].set_ylabel(y_loss_labels[i]) - ax_2[-1].legend() - if show_flag: - plt.show() - else: - plt.savefig(save_dir + "/loss.png") - plt.close() - - def get_trained_model( - self, - learning_rates: list[float] = [1e-3, 1e-4, 1e-5, 1e-6], - randomize=0.01, - acc_drop_out=0.0, - steer_drop_out=0.0, - patience: int = 10, - batch_size: int | None = 50, - max_iter=100000, - use_polynomial_reg=False, - use_selected_polynomial=True, - force_NN_model_to_zero=False, - fit_intercept=True, - use_intercept=None, - deg: int = 2, - ): - """Train on a model for which initial values are randomly given in a suitable range.""" - if use_intercept is None: - if force_NN_model_to_zero: - use_intercept = True - else: - use_intercept = False - X_input, Y_output_minus = self.get_polynomial_regression_result( - self.X_input_list, - self.Y_output_list, - use_polynomial_reg, - use_selected_polynomial, - deg, - fit_intercept, - use_intercept, - ) - self.model = drive_NN.DriveNeuralNetwork( - randomize=randomize, - acc_drop_out=acc_drop_out, - steer_drop_out=steer_drop_out, - acc_queue_size=drive_functions.acc_ctrl_queue_size, - steer_queue_size=drive_functions.steer_ctrl_queue_size, - ) - if force_NN_model_to_zero: - for w in self.model.parameters(): - w.data = nn.Parameter(torch.zeros(w.shape)) - else: - self.train_model( - self.model, X_input, Y_output_minus, learning_rates, patience, batch_size, max_iter - ) - - def update_trained_model( - self, - learning_rates=[1e-4, 1e-5, 1e-6], - patience=5, - batch_size=50, - max_iter=100000, - use_polynomial_reg=False, - use_selected_polynomial=True, - force_NN_model_to_zero=False, - fit_intercept=True, - use_intercept=None, - deg: int = 2, - ): - """Update `self.model` with additional learning.""" - if use_intercept is None: - if force_NN_model_to_zero: - use_intercept = True - else: - use_intercept = False - X_input, Y_output_minus = self.get_polynomial_regression_result( - self.X_input_list, - self.Y_output_list, - use_polynomial_reg, - use_selected_polynomial, - deg, - fit_intercept, - use_intercept, - ) - if force_NN_model_to_zero: - for w in self.model.parameters(): - w.data = nn.Parameter(torch.zeros(w.shape)) - else: - self.train_model( - self.model, X_input, Y_output_minus, learning_rates, patience, batch_size, max_iter - ) - - def update_saved_trained_model( - self, - path="model_for_test_drive.pth", - learning_rates: list[float] = [1e-4, 1e-5, 1e-6], - patience=5, - batch_size: int | None = 50, - max_iter=100000, - use_polynomial_reg=False, - use_selected_polynomial=True, - force_NN_model_to_zero=False, - fit_intercept=True, - use_intercept=None, - deg: int = 2, - ) -> None: - """Load the saved model and update the model with additional training.""" - if use_intercept is None: - if force_NN_model_to_zero: - use_intercept = True - else: - use_intercept = False - X_input, Y_output_minus = self.get_polynomial_regression_result( - self.X_input_list, - self.Y_output_list, - use_polynomial_reg, - use_selected_polynomial, - deg, - fit_intercept, - use_intercept, - ) - self.model = torch.load(path) - if force_NN_model_to_zero: - for w in self.model.parameters(): - w.data = nn.Parameter(torch.zeros(w.shape)) - else: - self.train_model( - self.model, X_input, Y_output_minus, learning_rates, patience, batch_size, max_iter - ) - - def save_model(self, save_dir=".", path="model_for_test_drive.pth") -> None: - """Save trained NN models.""" - torch.save(self.model, save_dir + "/" + path) - - def save_polynomial_reg_info(self, save_dir=".", path="polynomial_reg_info") -> None: - """Save the coefficients and degree of the resulting polynomial regression.""" - np.savez(save_dir + "/" + path, A=self.A, b=self.b, deg=self.deg) - - def save_models( - self, - save_dir=".", - model_name="model_for_test_drive.pth", - polynomial_reg_info_name="polynomial_reg_info", - ) -> None: - """Run save_model and save_polynomial_reg_info.""" - self.save_model(save_dir, model_name) - self.save_polynomial_reg_info(save_dir, polynomial_reg_info_name) - - def predict_error( - self, v: float, alpha_0: float, delta_0: float, alpha: float, delta: float - ) -> np.ndarray: - """Predicts the prediction error when following a nominal model.""" - polynomial_features = PolynomialFeatures(degree=self.deg, include_bias=False) - x_input = np.zeros( - 3 + drive_functions.acc_ctrl_queue_size + drive_functions.steer_ctrl_queue_size - ) - x_input[0] = v - x_input[1] = alpha_0 - x_input[2] = delta_0 - x_input[3 : 3 + drive_functions.acc_ctrl_queue_size] += alpha - x_input[3 + drive_functions.acc_ctrl_queue_size :] += delta - x_input_tensor = torch.tensor(x_input.reshape(1, -1).astype(np.float32)) - return ( - self.model(x_input_tensor)[0].detach().numpy() - + self.A - @ polynomial_features.fit_transform( - x_input[ctrl_index_for_polynomial_reg].reshape(1, -1) - )[0] - + self.b - ) - - def sim( - self, v: float, alpha_0: float, alpha: float, delta_0: float, delta: float, iter_times: int - ) -> tuple[float, float, float]: - """Simulate velocity, acceleration and steer changes according to a trained model.""" - v_pred = v - alpha_pred = alpha_0 - delta_pred = delta_0 - for i in range(iter_times): - pred = ( - np.concatenate( - ( - nominal_model_acc(v_pred, alpha_pred, alpha), - np.array([nominal_model_steer(delta_pred, delta)]), - ) - ) - + self.predict_error(v_pred, alpha_pred, delta_pred, alpha, delta)[[2, 4, 5]] - * drive_functions.mpc_time_step - ) - v_pred = pred[0] - alpha_pred = pred[1] - steer_pred = pred[2] - return v_pred, alpha_pred, steer_pred - - def plot_acc_map(self, iter_times: int, starting_steer=0.0, target_steer=0.0) -> None: - """Run `sim` `iter_times` times. The simulation time is approximately 0.1*iter_times. - - Draw the resulting acceleration map. - """ - acc = np.arange(-1, 1, 0.05) - v_tests = np.arange(0, 12, 0.3) - acc_result = np.zeros((acc.shape[0], v_tests.shape[0])) - v_result = np.zeros((acc.shape[0], v_tests.shape[0])) - steer_result = np.zeros((acc.shape[0], v_tests.shape[0])) - for i in range(acc.shape[0]): - v_tests_tmp = v_tests - 0.1 * iter_times * acc[i] - for j in range(v_tests.shape[0]): - v_result[i, j], acc_result[i, j], steer_result[i, j] = self.sim( - v_tests_tmp[j], acc[i], acc[i], starting_steer, target_steer, iter_times - ) - fig = plt.figure() - ax = fig.add_subplot(111, projection="3d") - ax.plot_surface(np.tile(acc, (v_tests.shape[0], 1)).T, v_result, acc_result) - ax.set_xlabel("acc_input [m/s^2]") - ax.set_ylabel("vel [m/s]") - ax.set_zlabel("acc_sim [m/s^2]") - fig.suptitle(str(iter_times * drive_functions.mpc_time_step) + " sec simulation") - plt.show() - - def plot_steer_map(self, iter_times: int, starting_acc=0.0, target_acc=0.0) -> None: - """Run `sim` `iter_times` times. The simulation time is approximately 0.1*iter_times. - - Draw the resulting steer map. - """ - steer = np.arange(-1, 1, 0.05) - v_tests = np.arange(0, 12, 0.3) - acc_result = np.zeros((steer.shape[0], v_tests.shape[0])) - v_result = np.zeros((steer.shape[0], v_tests.shape[0])) - steer_result = np.zeros((steer.shape[0], v_tests.shape[0])) - for i in range(steer.shape[0]): - for j in range(v_tests.shape[0]): - v_result[i, j], acc_result[i, j], steer_result[i, j] = self.sim( - v_tests[j], starting_acc, target_acc, steer[i], steer[i], iter_times - ) - fig = plt.figure() - ax = fig.add_subplot(111, projection="3d") - ax.plot_surface(np.tile(steer, (v_tests.shape[0], 1)).T, v_result, steer_result) - ax.set_xlabel("steer_input [rad]") - ax.set_ylabel("vel [m/s]") - ax.set_zlabel("steer_sim [rad]") - fig.suptitle(str(iter_times * drive_functions.mpc_time_step) + " sec simulation") - plt.show() - - -def polynomial_regression( - X, - Y, - alpha_1, - alpha_2, - use_polynomial_reg, - use_selected_polynomial, - deg, - fit_intercept, - use_intercept, -): - polynomial_features = PolynomialFeatures(degree=deg, include_bias=False) - alpha = alpha_1 + alpha_2 - if use_polynomial_reg: - if use_selected_polynomial: - clf_1 = linear_model.ElasticNet( - fit_intercept=fit_intercept, alpha=alpha, l1_ratio=alpha_1 / alpha, max_iter=100000 - ) - clf_2 = linear_model.ElasticNet( - fit_intercept=fit_intercept, alpha=alpha, l1_ratio=alpha_1 / alpha, max_iter=100000 - ) - clf_3 = linear_model.ElasticNet( - fit_intercept=fit_intercept, alpha=alpha, l1_ratio=alpha_1 / alpha, max_iter=100000 - ) - X_poly = polynomial_features.fit_transform(X[:, ctrl_index_for_polynomial_reg]) - Y_error = Y.copy() - A = np.zeros((Y.shape[1], X_poly.shape[1])) - b = np.zeros(Y.shape[1]) - clf_1.fit(X_poly[:, [0, 7]], Y[:, [3]]) - clf_2.fit(X_poly[:, [1, 3]], Y[:, [4]]) - clf_3.fit(X_poly[:, [2, 4]], Y[:, [5]]) - A[3, [0, 7]] = clf_1.coef_ - A[4, [1, 3]] = clf_2.coef_ - A[5, [2, 4]] = clf_3.coef_ - if fit_intercept: - b[3] = clf_1.intercept_ - b[4] = clf_2.intercept_ - b[5] = clf_3.intercept_ - Y_error[:, 3] -= clf_1.predict(X_poly[:, [0, 7]]) - Y_error[:, 4] -= clf_2.predict(X_poly[:, [1, 3]]) - Y_error[:, 5] -= clf_3.predict(X_poly[:, [2, 4]]) - if not use_intercept: - Y_error += b - b = 0 * b - return A, b, Y_error - else: - clf = linear_model.ElasticNet( - fit_intercept=fit_intercept, alpha=alpha, l1_ratio=alpha_1 / alpha, max_iter=100000 - ) - X_poly = polynomial_features.fit_transform(X[:, ctrl_index_for_polynomial_reg]) - clf.fit(X_poly, Y) - if fit_intercept: - if use_intercept: - return ( - clf.coef_, - clf.intercept_, - Y - clf.predict(X_poly), - ) - else: - return ( - clf.coef_, - 0 * clf.intercept_, - Y - clf.predict(X_poly) + clf.intercept_, - ) - else: - return clf.coef_, np.zeros(Y.shape[1]), Y - clf.predict(X_poly) - else: - poly_dim = polynomial_features.fit_transform( - X[0, ctrl_index_for_polynomial_reg].reshape(1, -1) - ).shape[1] - return np.zeros((Y.shape[1], poly_dim)), np.zeros(Y.shape[1]), Y +if drive_functions.use_memory_for_training: + train_drive_NN_model = train_drive_NN_model_with_memory.train_drive_NN_model_with_memory +else: + train_drive_NN_model = train_drive_NN_model_without_memory.train_drive_NN_model_without_memory diff --git a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/training_and_data_check/train_drive_NN_model_with_memory.py b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/training_and_data_check/train_drive_NN_model_with_memory.py new file mode 100644 index 0000000000000..84e0bbe7a7a83 --- /dev/null +++ b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/training_and_data_check/train_drive_NN_model_with_memory.py @@ -0,0 +1,664 @@ +# Copyright 2024 Proxima Technology Inc, TIER IV +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# cspell: ignore optim savez suptitle + +"""Class for training neural nets with memory from driving data.""" +from autoware_smart_mpc_trajectory_follower.scripts import drive_NN +from autoware_smart_mpc_trajectory_follower.scripts import drive_functions +from autoware_smart_mpc_trajectory_follower.training_and_data_check import ( + add_training_data_from_csv, +) +import matplotlib.pyplot as plt # type: ignore +import numpy as np +from sklearn.preprocessing import PolynomialFeatures # type: ignore +import torch +from torch import nn +from torch.utils.data import DataLoader +from torch.utils.data import TensorDataset + +ctrl_index_for_polynomial_reg = np.concatenate( + ( + np.arange(3), + 3 + + max(drive_functions.acc_delay_step - drive_functions.mpc_freq, 0) + + np.arange(drive_functions.mpc_freq), + 3 + + drive_functions.acc_ctrl_queue_size + + max(drive_functions.steer_delay_step - drive_functions.mpc_freq, 0) + + np.arange(drive_functions.mpc_freq), + ) +) + + +def transform_to_sequence_data(X, seq_size, indices): + X_seq = [] + start_num = 0 + for i in range(len(indices)): + j = 0 + while start_num + j + drive_functions.mpc_freq * seq_size <= indices[i]: + X_seq.append(X[start_num + j + drive_functions.mpc_freq * np.arange(seq_size)]) + j += 1 + start_num = indices[i] + + return np.array(X_seq) + + +class train_drive_NN_model_with_memory(add_training_data_from_csv.add_data_from_csv): + """Class for training neural nets from driving data.""" + + tanh_gain: float + """Gain of tanh in the NN cost function.""" + + lam: float + """Weights of the tanh term in the NN cost function.""" + + tol: float + """Tolerances for terminating training iterations.""" + + alpha_1: float + """L¹ regularization weights in the NN cost function.""" + + alpha_2: float + """L² regularization weights in the NN cost function.""" + + alpha_1_for_polynomial_regression: float + """L¹ regularization weights for polynomial regression.""" + + alpha_2_for_polynomial_regression: float + """L² regularization weights for polynomial regression.""" + + x_loss: list[float] + """List of values for the loss function of the x component in the NN training.""" + + y_loss: list[float] + """List of values for the loss function of the y component in the NN training.""" + + v_loss: list[float] + """List of values for the loss function of the velocity component in the NN training.""" + + theta_loss: list[float] + """List of values for the loss function of the yaw angle component in the NN training.""" + + acc_loss: list[float] + """List of values for the loss function of the acceleration component in the NN training.""" + + steer_loss: list[float] + """List of values for the loss function of the steer angle component in the NN training.""" + + steer_loss_plus_tanh: list[float] + """List of values of the loss function for the steer angle component with tanh term in the NN training.""" + + total_loss: list[float] + """In NN training x_loss, ... , acc_loss, steer_loss_plus_tanh summed list of values.""" + + model: drive_NN.DriveNeuralNetworkWithMemory + """trained neural network model.""" + + A: np.ndarray + """Coefficient matrix of polynomial regression model.""" + + b: np.ndarray + """Constant terms in polynomial regression models (bias).""" + + def __init__( + self, + tanh_gain=10, + lam=0.1, + tol=0.00001, + alpha_1=0.1**7, + alpha_2=0.1**7, + alpha_1_for_polynomial_regression=0.1**5, + alpha_2_for_polynomial_regression=0.1**5, + seq_size=2 * drive_functions.N + 1, + first_order_weight=1.0, + second_order_weight=1.0, + ): + super(train_drive_NN_model_with_memory, self).__init__() + self.tanh_gain = tanh_gain + self.lam = lam + self.tol = tol + self.alpha_1 = alpha_1 + self.alpha_2 = alpha_2 + self.alpha_1_for_polynomial_regression = alpha_1_for_polynomial_regression + self.alpha_2_for_polynomial_regression = alpha_2_for_polynomial_regression + self.x_loss = [] + self.y_loss = [] + self.v_loss = [] + self.theta_loss = [] + self.acc_loss = [] + self.steer_loss = [] + self.steer_loss_plus_tanh = [] + self.total_loss = [] + self.seq_size = seq_size + self.first_order_weight = first_order_weight + self.second_order_weight = second_order_weight + + def train_model( + self, + model: drive_NN.DriveNeuralNetworkWithMemory, + X_input: np.ndarray, + Y_output: np.ndarray, + learning_rates: list[float], + patience: int = 10, + batch_size: int | None = 5, + max_iter: int = 100000, + X_val_np=None, + Y_val_np=None, + ) -> None: + """Train the model.""" + if batch_size is None: + batch_size = X_input.shape[0] // 50 + self.x_loss.clear() + self.y_loss.clear() + self.v_loss.clear() + self.theta_loss.clear() + self.acc_loss.clear() + self.steer_loss.clear() + self.steer_loss_plus_tanh.clear() + self.total_loss.clear() + + if X_val_np is None: + X_tensor = torch.tensor( + transform_to_sequence_data(X_input, self.seq_size, self.division_indices).astype( + np.float32 + ) + ).clone() + Y_tensor = torch.tensor( + transform_to_sequence_data(Y_output, self.seq_size, self.division_indices).astype( + np.float32 + ) + ).clone() + sample_size = X_tensor.shape[0] + num_train = int(3 * sample_size / 4) + id_all = np.random.choice(sample_size, sample_size, replace=False) + id_train = id_all[:num_train] + id_val = id_all[num_train:] + X_train = X_tensor[id_train] + Y_train = Y_tensor[id_train] + X_val = X_tensor[id_val] + Y_val = Y_tensor[id_val] + print("sample_size: ", X_input.shape[0]) + else: + X_train = torch.tensor( + transform_to_sequence_data(X_input, self.seq_size, self.division_indices).astype( + np.float32 + ) + ).clone() + Y_train = torch.tensor( + transform_to_sequence_data(Y_output, self.seq_size, self.division_indices).astype( + np.float32 + ) + ).clone() + X_val = torch.tensor( + transform_to_sequence_data( + X_val_np, self.seq_size, self.division_indices_val + ).astype(np.float32) + ).clone() + Y_val = torch.tensor( + transform_to_sequence_data( + Y_val_np, self.seq_size, self.division_indices_val + ).astype(np.float32) + ).clone() + print("sample_size", X_input.shape[0] + X_val_np.shape[0]) + print("patience:", patience) + loss_fn = torch.nn.L1Loss() + learning_rate = learning_rates[0] + optimizer = torch.optim.Adam(params=model.parameters(), lr=learning_rate) + + drive_data = DataLoader( + TensorDataset(X_train, Y_train), batch_size=batch_size, shuffle=True + ) + initial_loss = drive_NN.loss_fn_plus_tanh_with_memory( + loss_fn, + torch.tensor(np.zeros(Y_val.shape)[:, self.seq_size // 2 :], dtype=torch.float32), + Y_val[:, self.seq_size // 2 :], + self.tanh_gain, + self.lam, + self.first_order_weight, + self.second_order_weight, + ) + print("initial_loss:", initial_loss.detach().item()) + early_stopping = drive_NN.EarlyStopping( + initial_loss=initial_loss, tol=self.tol, patience=patience + ) + k = 0 + print("learning_rate:", learning_rates[0]) + for i in range(max_iter): + model.train() + for X_batch, y_batch in drive_data: + y_pred = model(X_batch) + loss = drive_NN.loss_fn_plus_tanh_with_memory( + loss_fn, + y_pred[:, self.seq_size // 2 :], + y_batch[:, self.seq_size // 2 :], + self.tanh_gain, + self.lam, + self.first_order_weight, + self.second_order_weight, + ) + for w in model.named_parameters(): + loss = ( + loss + + self.alpha_1 * torch.norm(w[1], p=1) + + self.alpha_2 * torch.norm(w[1]) ** 2 + ) + if w[0] in ["finalize.weight", "finalize.bias"]: + loss = loss + (drive_functions.finalize_x_weight - 1) * ( + self.alpha_1 * torch.norm(w[1][0], p=1) + + self.alpha_2 * torch.norm(w[1][0]) ** 2 + ) + loss = loss + (drive_functions.finalize_y_weight - 1) * ( + self.alpha_1 * torch.norm(w[1][1], p=1) + + self.alpha_2 * torch.norm(w[1][1]) ** 2 + ) + loss = loss + (drive_functions.finalize_v_weight - 1) * ( + self.alpha_1 * torch.norm(w[1][2], p=1) + + self.alpha_2 * torch.norm(w[1][2]) ** 2 + ) + optimizer.zero_grad() + loss.backward() + optimizer.step() + + model.eval() + pred = model(X_val) + self.total_loss.append( + drive_NN.loss_fn_plus_tanh_with_memory( + loss_fn, + pred[:, self.seq_size // 2 :], + Y_val[:, self.seq_size // 2 :], + self.tanh_gain, + self.lam, + self.first_order_weight, + self.second_order_weight, + ) + .detach() + .item() + ) + self.x_loss.append( + loss_fn(pred[:, self.seq_size // 2 :, [0]], Y_val[:, self.seq_size // 2 :, [0]]) + .detach() + .item() + ) + self.y_loss.append( + loss_fn(pred[:, self.seq_size // 2 :, [1]], Y_val[:, self.seq_size // 2 :, [1]]) + .detach() + .item() + ) + self.v_loss.append( + loss_fn(pred[:, self.seq_size // 2 :, [2]], Y_val[:, self.seq_size // 2 :, [2]]) + .detach() + .item() + ) + self.theta_loss.append( + loss_fn(pred[:, self.seq_size // 2 :, [3]], Y_val[:, self.seq_size // 2 :, [3]]) + .detach() + .item() + ) + self.acc_loss.append( + loss_fn(pred[:, self.seq_size // 2 :, [4]], Y_val[:, self.seq_size // 2 :, [4]]) + .detach() + .item() + ) + self.steer_loss.append( + loss_fn(pred[:, self.seq_size // 2 :, [5]], Y_val[:, self.seq_size // 2 :, [5]]) + .detach() + .item() + ) + self.steer_loss_plus_tanh.append( + loss_fn(pred[:, self.seq_size // 2 :, [5]], Y_val[:, self.seq_size // 2 :, [5]]) + .detach() + .item() + + self.lam + * loss_fn( + torch.tanh( + self.tanh_gain + * (pred[:, self.seq_size // 2 :, -1] - Y_val[:, self.seq_size // 2 :, -1]) + ), + torch.zeros((Y_val.shape[0], Y_val.shape[1] - self.seq_size // 2)), + ) + .detach() + .item() + ) + + current_loss = drive_NN.loss_fn_plus_tanh_with_memory( + loss_fn, + model(X_val)[:, self.seq_size // 2 :], + Y_val[:, self.seq_size // 2 :], + self.tanh_gain, + self.lam, + self.first_order_weight, + self.second_order_weight, + ) + if i % 10 == 1: + print(current_loss.detach().item(), i) + if early_stopping(current_loss): + k += 1 + if k == len(learning_rates): + break + else: + learning_rate = learning_rates[k] + print("update learning_rate to ", learning_rates[k]) + optimizer = torch.optim.Adam(params=model.parameters(), lr=learning_rate) + early_stopping = drive_NN.EarlyStopping( + initial_loss=initial_loss, tol=self.tol, patience=patience + ) + + def plot_loss(self, show_flag=False, save_dir=".") -> None: + """Plot the progression of values of the loss function of the training.""" + plt.figure(figsize=(24, 15), tight_layout=True) + + y_loss_labels = [ + "total_loss", + "x_loss", + "y_loss", + "vel_loss", + "yaw_loss", + "acc_loss", + "steer_loss", + "steer_plus_tanh_loss", + ] + plt.subplot(2, 4, 1) + ax_2 = [] + loss_list = [ + np.array(self.total_loss), + np.array(self.x_loss), + np.array(self.y_loss), + np.array(self.v_loss), + np.array(self.theta_loss), + np.array(self.acc_loss), + np.array(self.steer_loss), + np.array(self.steer_loss_plus_tanh), + ] + for i in range(8): + ax_2.append(plt.subplot(2, 4, i + 1)) + ax_2[-1].plot( + np.arange(loss_list[i].shape[0]), + loss_list[i], + label="loss", + ) + ax_2[-1].set_xlabel("iteration") + ax_2[-1].set_ylabel(y_loss_labels[i]) + ax_2[-1].legend() + if show_flag: + plt.show() + else: + plt.savefig(save_dir + "/loss.png") + plt.close() + + def plot_trained_result( + self, show_flag=False, save_dir=".", plot_range=np.arange(500, 1200) + ) -> None: + """Plot the results of the training.""" + polynomial_features = PolynomialFeatures(degree=self.deg, include_bias=False) + + X_input = np.array(self.X_input_list) + Y_output = np.array(self.Y_output_list) + X_tensor = torch.tensor(X_input.astype(np.float32)).clone() + acc_ctrl_queue_size = drive_functions.acc_ctrl_queue_size + steer_ctrl_queue_size = drive_functions.steer_ctrl_queue_size + ctrl_time_step = drive_functions.ctrl_time_step + Y_pred = np.zeros(Y_output.shape) + start_index = 0 + for i in range(len(self.division_indices)): + Y_pred[start_index : self.division_indices[i]] = ( + self.model(X_tensor[start_index : self.division_indices[i]].unsqueeze(dim=0))[0] + .detach() + .numpy() + + polynomial_features.fit_transform( + X_input[start_index : self.division_indices[i], ctrl_index_for_polynomial_reg] + ) + @ (self.A).T + + self.b + ) + start_index = self.division_indices[i] + x = max(acc_ctrl_queue_size, steer_ctrl_queue_size) * ctrl_time_step + y_labels = [ + "x_error [m]", + "y_error [m]", + "vel_error [m/s]", + "yaw_error [rad]", + "acc_error [m/s^2]", + "steer_error [rad]", + ] + + plt.figure(figsize=(24, 15), tight_layout=True) + plt.subplot(2, 3, 1) + ax = [] + for i in range(6): + ax.append(plt.subplot(2, 3, i + 1)) + ax[-1].plot( + drive_functions.ctrl_time_step * plot_range + x, + Y_output[plot_range, i] * drive_functions.mpc_time_step, + label="nominal_error", + ) + ax[-1].plot( + drive_functions.ctrl_time_step * plot_range + x, + Y_pred[plot_range, i] * drive_functions.mpc_time_step, + label="pred_error", + ) + ax[-1].set_xlabel("sec") + ax[-1].set_ylabel(y_labels[i]) + ax[-1].legend() + if show_flag: + plt.show() + else: + plt.savefig(save_dir + "/train_drive_NN_model_fig.png") + plt.close() + + def get_trained_model( + self, + hidden_layer_sizes=(16, 16), + hidden_layer_lstm=8, + learning_rates: list[float] = [1e-3, 1e-4, 1e-5, 1e-6], + randomize=0.01, + acc_drop_out=0.0, + steer_drop_out=0.0, + patience: int = 10, + batch_size: int | None = 5, + max_iter=100000, + use_polynomial_reg=False, + use_selected_polynomial=True, + force_NN_model_to_zero=False, + fit_intercept=True, + use_intercept=None, + deg: int = 2, + ): + """Train on a model for which initial values are randomly given in a suitable range.""" + if use_intercept is None: + if force_NN_model_to_zero: + use_intercept = True + else: + use_intercept = False + X_input, Y_output_minus = self.get_polynomial_regression_result( + self.X_input_list, + self.Y_output_list, + use_polynomial_reg, + use_selected_polynomial, + deg, + fit_intercept, + use_intercept, + ) + + if len(self.X_val_list) > 0: + X_val = np.array(self.X_val_list) + Y_val = np.array(self.Y_val_list) + polynomial_features = PolynomialFeatures(degree=self.deg, include_bias=False) + Y_val = ( + Y_val + - polynomial_features.fit_transform(X_val[:, ctrl_index_for_polynomial_reg]) + @ (self.A).T + - self.b + ) + else: + X_val = None + Y_val = None + self.model = drive_NN.DriveNeuralNetworkWithMemory( + hidden_layer_sizes=hidden_layer_sizes, + hidden_layer_lstm=hidden_layer_lstm, + randomize=randomize, + acc_drop_out=acc_drop_out, + steer_drop_out=steer_drop_out, + acc_queue_size=drive_functions.acc_ctrl_queue_size, + steer_queue_size=drive_functions.steer_ctrl_queue_size, + ) + if force_NN_model_to_zero: + for w in self.model.parameters(): + w.data = nn.Parameter(torch.zeros(w.shape)) + else: + self.train_model( + self.model, + X_input, + Y_output_minus, + learning_rates, + patience, + batch_size, + max_iter, + X_val, + Y_val, + ) + + def update_trained_model( + self, + learning_rates=[1e-4, 1e-5, 1e-6], + patience=10, + batch_size=5, + max_iter=100000, + use_polynomial_reg=False, + use_selected_polynomial=True, + force_NN_model_to_zero=False, + fit_intercept=True, + use_intercept=None, + deg: int = 2, + ): + """Update `self.model` with additional learning.""" + if use_intercept is None: + if force_NN_model_to_zero: + use_intercept = True + else: + use_intercept = False + X_input, Y_output_minus = self.get_polynomial_regression_result( + self.X_input_list, + self.Y_output_list, + use_polynomial_reg, + use_selected_polynomial, + deg, + fit_intercept, + use_intercept, + ) + if len(self.X_val_list) > 0: + X_val = np.array(self.X_val_list) + Y_val = np.array(self.Y_val_list) + polynomial_features = PolynomialFeatures(degree=self.deg, include_bias=False) + Y_val = ( + Y_val + - polynomial_features.fit_transform(X_val[:, ctrl_index_for_polynomial_reg]) + @ (self.A).T + - self.b + ) + else: + X_val = None + Y_val = None + if force_NN_model_to_zero: + for w in self.model.parameters(): + w.data = nn.Parameter(torch.zeros(w.shape)) + else: + self.train_model( + self.model, + X_input, + Y_output_minus, + learning_rates, + patience, + batch_size, + max_iter, + X_val, + Y_val, + ) + + def update_saved_trained_model( + self, + path="model_for_test_drive.pth", + learning_rates: list[float] = [1e-4, 1e-5, 1e-6], + patience=10, + batch_size: int | None = 5, + max_iter=100000, + use_polynomial_reg=False, + use_selected_polynomial=True, + force_NN_model_to_zero=False, + fit_intercept=True, + use_intercept=None, + deg: int = 2, + ) -> None: + """Load the saved model and update the model with additional training.""" + if use_intercept is None: + if force_NN_model_to_zero: + use_intercept = True + else: + use_intercept = False + X_input, Y_output_minus = self.get_polynomial_regression_result( + self.X_input_list, + self.Y_output_list, + use_polynomial_reg, + use_selected_polynomial, + deg, + fit_intercept, + use_intercept, + ) + if len(self.X_val_list) > 0: + X_val = np.array(self.X_val_list) + Y_val = np.array(self.Y_val_list) + polynomial_features = PolynomialFeatures(degree=self.deg, include_bias=False) + Y_val = ( + Y_val + - polynomial_features.fit_transform(X_val[:, ctrl_index_for_polynomial_reg]) + @ (self.A).T + - self.b + ) + else: + X_val = None + Y_val = None + self.model = torch.load(path) + if force_NN_model_to_zero: + for w in self.model.parameters(): + w.data = nn.Parameter(torch.zeros(w.shape)) + else: + self.train_model( + self.model, + X_input, + Y_output_minus, + learning_rates, + patience, + batch_size, + max_iter, + X_val, + Y_val, + ) + + def save_model(self, save_dir=".", path="model_for_test_drive.pth") -> None: + """Save trained NN models.""" + torch.save(self.model, save_dir + "/" + path) + + def save_polynomial_reg_info(self, save_dir=".", path="polynomial_reg_info") -> None: + """Save the coefficients and degree of the resulting polynomial regression.""" + np.savez(save_dir + "/" + path, A=self.A, b=self.b, deg=self.deg) + + def save_models( + self, + save_dir=".", + model_name="model_for_test_drive.pth", + polynomial_reg_info_name="polynomial_reg_info", + ) -> None: + """Run save_model and save_polynomial_reg_info.""" + self.save_model(save_dir, model_name) + self.save_polynomial_reg_info(save_dir, polynomial_reg_info_name) diff --git a/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/training_and_data_check/train_drive_NN_model_without_memory.py b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/training_and_data_check/train_drive_NN_model_without_memory.py new file mode 100644 index 0000000000000..0117bb1707682 --- /dev/null +++ b/control/autoware_smart_mpc_trajectory_follower/autoware_smart_mpc_trajectory_follower/training_and_data_check/train_drive_NN_model_without_memory.py @@ -0,0 +1,704 @@ +# Copyright 2024 Proxima Technology Inc, TIER IV +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# cspell: ignore optim savez suptitle + +"""Class for training neural nets from driving data.""" +from autoware_smart_mpc_trajectory_follower.scripts import drive_NN +from autoware_smart_mpc_trajectory_follower.scripts import drive_functions +from autoware_smart_mpc_trajectory_follower.training_and_data_check import ( + add_training_data_from_csv, +) +import matplotlib.pyplot as plt # type: ignore +import numpy as np +from sklearn.preprocessing import PolynomialFeatures # type: ignore +import torch +from torch import nn +from torch.utils.data import DataLoader +from torch.utils.data import TensorDataset + +ctrl_index_for_polynomial_reg = np.concatenate( + ( + np.arange(3), + 3 + + max(drive_functions.acc_delay_step - drive_functions.mpc_freq, 0) + + np.arange(drive_functions.mpc_freq), + 3 + + drive_functions.acc_ctrl_queue_size + + max(drive_functions.steer_delay_step - drive_functions.mpc_freq, 0) + + np.arange(drive_functions.mpc_freq), + ) +) + + +def nominal_model_acc(v: float, alpha_0: float, alpha: float) -> np.ndarray: + """Predicted acceleration value according to the nominal model.""" + nominal_pred = alpha_0 + nominal_v = v + nominal_pred * drive_functions.ctrl_time_step + nominal_pred = ( + nominal_pred + + (alpha - nominal_pred) + * drive_functions.ctrl_time_step + / drive_functions.acc_time_constant + ) + nominal_v = v + nominal_pred * drive_functions.ctrl_time_step + nominal_pred = ( + nominal_pred + + (alpha - nominal_pred) + * drive_functions.ctrl_time_step + / drive_functions.acc_time_constant + ) + nominal_v = v + nominal_pred * drive_functions.ctrl_time_step + nominal_pred = ( + nominal_pred + + (alpha - nominal_pred) + * drive_functions.ctrl_time_step + / drive_functions.acc_time_constant + ) + return np.array([nominal_v, nominal_pred]) + + +def nominal_model_steer(delta_0: float, delta: float) -> float: + """Predicted steer angle according to the nominal model.""" + nominal_pred = delta_0 + nominal_pred = ( + nominal_pred + + (delta - nominal_pred) + * drive_functions.ctrl_time_step + / drive_functions.steer_time_constant + ) + nominal_pred = ( + nominal_pred + + (delta - nominal_pred) + * drive_functions.ctrl_time_step + / drive_functions.steer_time_constant + ) + nominal_pred = ( + nominal_pred + + (delta - nominal_pred) + * drive_functions.ctrl_time_step + / drive_functions.steer_time_constant + ) + return nominal_pred + + +class train_drive_NN_model_without_memory(add_training_data_from_csv.add_data_from_csv): + """Class for training neural nets from driving data.""" + + tanh_gain: float + """Gain of tanh in the NN cost function""" + + lam: float + """Weights of the tanh term in the NN cost function.""" + + tol: float + """Tolerances for terminating training iterations.""" + + alpha_1: float + """L¹ regularization weights in the NN cost function""" + + alpha_2: float + """L² regularization weights in the NN cost function.""" + + alpha_1_for_polynomial_regression: float + """L¹ regularization weights for polynomial regression""" + + alpha_2_for_polynomial_regression: float + """L² regularization weights for polynomial regression""" + + x_loss: list[float] + """List of values for the loss function of the x component in the NN training.""" + + y_loss: list[float] + """List of values for the loss function of the y component in the NN training.""" + + v_loss: list[float] + """List of values for the loss function of the velocity component in the NN training.""" + + theta_loss: list[float] + """List of values for the loss function of the yaw angle component in the NN training.""" + + acc_loss: list[float] + """List of values for the loss function of the acceleration component in the NN training.""" + + steer_loss: list[float] + """List of values for the loss function of the steer angle component in the NN training.""" + + steer_loss_plus_tanh: list[float] + """List of values of the loss function for the steer angle component with tanh term in the NN training.""" + + total_loss: list[float] + """In NN training x_loss, ... , acc_loss, steer_loss_plus_tanh summed list of values""" + + model: drive_NN.DriveNeuralNetwork + """trained neural network model""" + + A: np.ndarray + """Coefficient matrix of polynomial regression model.""" + + b: np.ndarray + """Constant terms in polynomial regression models (bias)""" + + def __init__( + self, + tanh_gain=10, + lam=0.1, + tol=0.00001, + alpha_1=0.1**7, + alpha_2=0.1**7, + alpha_1_for_polynomial_regression=0.1**5, + alpha_2_for_polynomial_regression=0.1**5, + ): + super(train_drive_NN_model_without_memory, self).__init__( + alpha_1_for_polynomial_regression, alpha_2_for_polynomial_regression + ) + self.tanh_gain = tanh_gain + self.lam = lam + self.tol = tol + self.alpha_1 = alpha_1 + self.alpha_2 = alpha_2 + self.alpha_1_for_polynomial_regression = alpha_1_for_polynomial_regression + self.alpha_2_for_polynomial_regression = alpha_2_for_polynomial_regression + self.x_loss = [] + self.y_loss = [] + self.v_loss = [] + self.theta_loss = [] + self.acc_loss = [] + self.steer_loss = [] + self.steer_loss_plus_tanh = [] + self.total_loss = [] + + def train_model( + self, + model: drive_NN.DriveNeuralNetwork, + X_input: np.ndarray, + Y_output: np.ndarray, + learning_rates: list[float], + patience: int = 10, + batch_size: int | None = 50, + max_iter: int = 100000, + X_val_np=None, + Y_val_np=None, + ) -> None: + """Train the model.""" + if batch_size is None: + batch_size = X_input.shape[0] // 50 + self.x_loss.clear() + self.y_loss.clear() + self.v_loss.clear() + self.theta_loss.clear() + self.acc_loss.clear() + self.steer_loss.clear() + self.steer_loss_plus_tanh.clear() + self.total_loss.clear() + if X_val_np is None: + sample_size = X_input.shape[0] + num_train = int(3 * sample_size / 4) + id_all = np.random.choice(sample_size, sample_size, replace=False) + id_train = id_all[:num_train] + id_val = id_all[num_train:] + X_tensor = torch.tensor(X_input.astype(np.float32)).clone() + Y_tensor = torch.tensor(Y_output.astype(np.float32)).clone() + X_train = X_tensor[id_train] + Y_train = Y_tensor[id_train] + X_val = X_tensor[id_val] + Y_val = Y_tensor[id_val] + print("sample_size: ", X_input.shape[0]) + else: + X_train = torch.tensor(X_input.astype(np.float32)).clone() + Y_train = torch.tensor(Y_output.astype(np.float32)).clone() + X_val = torch.tensor(X_val_np.astype(np.float32)).clone() + Y_val = torch.tensor(Y_val_np.astype(np.float32)).clone() + print("sample_size", X_input.shape[0] + X_val_np.shape[0]) + print("patience:", patience) + loss_fn = torch.nn.L1Loss() + learning_rate = learning_rates[0] + optimizer = torch.optim.Adam(params=model.parameters(), lr=learning_rate) + + drive_data = DataLoader( + TensorDataset(X_train, Y_train), batch_size=batch_size, shuffle=True + ) + initial_loss = drive_NN.loss_fn_plus_tanh( + loss_fn, + torch.tensor(np.zeros(Y_val.shape), dtype=torch.float32), + Y_val, + self.tanh_gain, + self.lam, + ) + print("initial_loss:", initial_loss.detach().item()) + early_stopping = drive_NN.EarlyStopping( + initial_loss=initial_loss, tol=self.tol, patience=patience + ) + k = 0 + print("learning_rate:", learning_rates[0]) + for i in range(max_iter): + model.train() + for X_batch, y_batch in drive_data: + y_pred = model(X_batch) + loss = drive_NN.loss_fn_plus_tanh( + loss_fn, y_pred, y_batch, self.tanh_gain, self.lam + ) + for w in model.parameters(): + loss = ( + loss + self.alpha_1 * torch.norm(w, p=1) + self.alpha_2 * torch.norm(w) ** 2 + ) + if w[0] in ["finalize.weight", "finalize.bias"]: + loss = loss + (drive_functions.finalize_x_weight - 1) * ( + self.alpha_1 * torch.norm(w[1][0], p=1) + + self.alpha_2 * torch.norm(w[1][0]) ** 2 + ) + loss = loss + (drive_functions.finalize_y_weight - 1) * ( + self.alpha_1 * torch.norm(w[1][1], p=1) + + self.alpha_2 * torch.norm(w[1][1]) ** 2 + ) + loss = loss + (drive_functions.finalize_v_weight - 1) * ( + self.alpha_1 * torch.norm(w[1][2], p=1) + + self.alpha_2 * torch.norm(w[1][2]) ** 2 + ) + optimizer.zero_grad() + loss.backward() + optimizer.step() + + model.eval() + pred = model(X_val) + self.total_loss.append( + drive_NN.loss_fn_plus_tanh(loss_fn, pred, Y_val, self.tanh_gain, self.lam) + .detach() + .item() + ) + self.x_loss.append(loss_fn(pred[:, [0]], Y_val[:, [0]]).detach().item()) + self.y_loss.append(loss_fn(pred[:, [1]], Y_val[:, [1]]).detach().item()) + self.v_loss.append(loss_fn(pred[:, [2]], Y_val[:, [2]]).detach().item()) + self.theta_loss.append(loss_fn(pred[:, [3]], Y_val[:, [3]]).detach().item()) + self.acc_loss.append(loss_fn(pred[:, [4]], Y_val[:, [4]]).detach().item()) + self.steer_loss.append(loss_fn(pred[:, [5]], Y_val[:, [5]]).detach().item()) + self.steer_loss_plus_tanh.append( + loss_fn(pred[:, [5]], Y_val[:, [5]]).detach().item() + + self.lam + * loss_fn( + torch.tanh(self.tanh_gain * (pred[:, -1] - Y_val[:, -1])), + torch.zeros(Y_val.shape[0]), + ) + .detach() + .item() + ) + current_loss = drive_NN.loss_fn_plus_tanh( + loss_fn, model(X_val), Y_val, self.tanh_gain, self.lam + ) + if i % 10 == 1: + print(current_loss.detach().item(), i) + if early_stopping(current_loss): + k += 1 + if k == len(learning_rates): + break + else: + learning_rate = learning_rates[k] + print("update learning_rate to ", learning_rates[k]) + optimizer = torch.optim.Adam(params=model.parameters(), lr=learning_rate) + early_stopping = drive_NN.EarlyStopping( + initial_loss=initial_loss, tol=self.tol, patience=patience + ) + + def plot_trained_result( + self, show_flag=False, save_dir=".", plot_range=np.arange(500, 1200) + ) -> None: + """Plot the results of the training.""" + polynomial_features = PolynomialFeatures(degree=self.deg, include_bias=False) + + X_input = np.array(self.X_input_list) + Y_output = np.array(self.Y_output_list) + X_tensor = torch.tensor(X_input.astype(np.float32)).clone() + acc_ctrl_queue_size = drive_functions.acc_ctrl_queue_size + steer_ctrl_queue_size = drive_functions.steer_ctrl_queue_size + ctrl_time_step = drive_functions.ctrl_time_step + Y_pred = ( + self.model(X_tensor).detach().numpy() + + polynomial_features.fit_transform(X_input[:, ctrl_index_for_polynomial_reg]) + @ (self.A).T + + self.b + ) + x = max(acc_ctrl_queue_size, steer_ctrl_queue_size) * ctrl_time_step + y_labels = [ + "x_error [m]", + "y_error [m]", + "vel_error [m/s]", + "yaw_error [rad]", + "acc_error [m/s^2]", + "steer_error [rad]", + ] + + plt.figure(figsize=(24, 15), tight_layout=True) + plt.subplot(2, 3, 1) + ax = [] + for i in range(6): + ax.append(plt.subplot(2, 3, i + 1)) + ax[-1].plot( + drive_functions.ctrl_time_step * plot_range + x, + Y_output[plot_range, i] * drive_functions.mpc_time_step, + label="nominal_error", + ) + ax[-1].plot( + drive_functions.ctrl_time_step * plot_range + x, + Y_pred[plot_range, i] * drive_functions.mpc_time_step, + label="pred_error", + ) + ax[-1].set_xlabel("sec") + ax[-1].set_ylabel(y_labels[i]) + ax[-1].legend() + if show_flag: + plt.show() + else: + plt.savefig(save_dir + "/train_drive_NN_model_fig.png") + plt.close() + + def plot_loss(self, show_flag=False, save_dir=".") -> None: + """Plot the progression of values of the loss function of the training.""" + plt.figure(figsize=(24, 15), tight_layout=True) + + y_loss_labels = [ + "total_loss", + "x_loss", + "y_loss", + "vel_loss", + "yaw_loss", + "acc_loss", + "steer_loss", + "steer_plus_tanh_loss", + ] + plt.subplot(2, 4, 1) + ax_2 = [] + loss_list = [ + np.array(self.total_loss), + np.array(self.x_loss), + np.array(self.y_loss), + np.array(self.v_loss), + np.array(self.theta_loss), + np.array(self.acc_loss), + np.array(self.steer_loss), + np.array(self.steer_loss_plus_tanh), + ] + for i in range(8): + ax_2.append(plt.subplot(2, 4, i + 1)) + ax_2[-1].plot( + np.arange(loss_list[i].shape[0]), + loss_list[i], + label="loss", + ) + ax_2[-1].set_xlabel("iteration") + ax_2[-1].set_ylabel(y_loss_labels[i]) + ax_2[-1].legend() + if show_flag: + plt.show() + else: + plt.savefig(save_dir + "/loss.png") + plt.close() + + def get_trained_model( + self, + hidden_layer_sizes=(32, 16), + hidden_layer_lstm=None, + learning_rates: list[float] = [1e-3, 1e-4, 1e-5, 1e-6], + randomize=0.01, + acc_drop_out=0.0, + steer_drop_out=0.0, + patience: int = 10, + batch_size: int | None = 50, + max_iter=100000, + use_polynomial_reg=False, + use_selected_polynomial=True, + force_NN_model_to_zero=False, + fit_intercept=True, + use_intercept=None, + deg: int = 2, + ): + """Train on a model for which initial values are randomly given in a suitable range.""" + if use_intercept is None: + if force_NN_model_to_zero: + use_intercept = True + else: + use_intercept = False + X_input, Y_output_minus = self.get_polynomial_regression_result( + self.X_input_list, + self.Y_output_list, + use_polynomial_reg, + use_selected_polynomial, + deg, + fit_intercept, + use_intercept, + ) + if len(self.X_val_list) > 0: + X_val = np.array(self.X_val_list) + Y_val = np.array(self.Y_val_list) + polynomial_features = PolynomialFeatures(degree=self.deg, include_bias=False) + Y_val = ( + Y_val + - polynomial_features.fit_transform(X_val[:, ctrl_index_for_polynomial_reg]) + @ (self.A).T + - self.b + ) + else: + X_val = None + Y_val = None + self.model = drive_NN.DriveNeuralNetwork( + hidden_layer_sizes=hidden_layer_sizes, + randomize=randomize, + acc_drop_out=acc_drop_out, + steer_drop_out=steer_drop_out, + acc_queue_size=drive_functions.acc_ctrl_queue_size, + steer_queue_size=drive_functions.steer_ctrl_queue_size, + ) + if force_NN_model_to_zero: + for w in self.model.parameters(): + w.data = nn.Parameter(torch.zeros(w.shape)) + else: + self.train_model( + self.model, + X_input, + Y_output_minus, + learning_rates, + patience, + batch_size, + max_iter, + X_val, + Y_val, + ) + + def update_trained_model( + self, + learning_rates=[1e-4, 1e-5, 1e-6], + patience=10, + batch_size=50, + max_iter=100000, + use_polynomial_reg=False, + use_selected_polynomial=True, + force_NN_model_to_zero=False, + fit_intercept=True, + use_intercept=None, + deg: int = 2, + ): + """Update `self.model` with additional learning.""" + if use_intercept is None: + if force_NN_model_to_zero: + use_intercept = True + else: + use_intercept = False + X_input, Y_output_minus = self.get_polynomial_regression_result( + self.X_input_list, + self.Y_output_list, + use_polynomial_reg, + use_selected_polynomial, + deg, + fit_intercept, + use_intercept, + ) + if len(self.X_val_list) > 0: + X_val = np.array(self.X_val_list) + Y_val = np.array(self.Y_val_list) + polynomial_features = PolynomialFeatures(degree=self.deg, include_bias=False) + Y_val = ( + Y_val + - polynomial_features.fit_transform(X_val[:, ctrl_index_for_polynomial_reg]) + @ (self.A).T + - self.b + ) + else: + X_val = None + Y_val = None + if force_NN_model_to_zero: + for w in self.model.parameters(): + w.data = nn.Parameter(torch.zeros(w.shape)) + else: + self.train_model( + self.model, + X_input, + Y_output_minus, + learning_rates, + patience, + batch_size, + max_iter, + X_val, + Y_val, + ) + + def update_saved_trained_model( + self, + path="model_for_test_drive.pth", + learning_rates: list[float] = [1e-4, 1e-5, 1e-6], + patience=10, + batch_size: int | None = 50, + max_iter=100000, + use_polynomial_reg=False, + use_selected_polynomial=True, + force_NN_model_to_zero=False, + fit_intercept=True, + use_intercept=None, + deg: int = 2, + ) -> None: + """Load the saved model and update the model with additional training.""" + if use_intercept is None: + if force_NN_model_to_zero: + use_intercept = True + else: + use_intercept = False + X_input, Y_output_minus = self.get_polynomial_regression_result( + self.X_input_list, + self.Y_output_list, + use_polynomial_reg, + use_selected_polynomial, + deg, + fit_intercept, + use_intercept, + ) + if len(self.X_val_list) > 0: + X_val = np.array(self.X_val_list) + Y_val = np.array(self.Y_val_list) + polynomial_features = PolynomialFeatures(degree=self.deg, include_bias=False) + Y_val = ( + Y_val + - polynomial_features.fit_transform(X_val[:, ctrl_index_for_polynomial_reg]) + @ (self.A).T + - self.b + ) + else: + X_val = None + Y_val = None + self.model = torch.load(path) + if force_NN_model_to_zero: + for w in self.model.parameters(): + w.data = nn.Parameter(torch.zeros(w.shape)) + else: + self.train_model( + self.model, + X_input, + Y_output_minus, + learning_rates, + patience, + batch_size, + max_iter, + X_val, + Y_val, + ) + + def save_model(self, save_dir=".", path="model_for_test_drive.pth") -> None: + """Save trained NN models.""" + torch.save(self.model, save_dir + "/" + path) + + def save_polynomial_reg_info(self, save_dir=".", path="polynomial_reg_info") -> None: + """Save the coefficients and degree of the resulting polynomial regression.""" + np.savez(save_dir + "/" + path, A=self.A, b=self.b, deg=self.deg) + + def save_models( + self, + save_dir=".", + model_name="model_for_test_drive.pth", + polynomial_reg_info_name="polynomial_reg_info", + ) -> None: + """Run save_model and save_polynomial_reg_info.""" + self.save_model(save_dir, model_name) + self.save_polynomial_reg_info(save_dir, polynomial_reg_info_name) + + def predict_error( + self, v: float, alpha_0: float, delta_0: float, alpha: float, delta: float + ) -> np.ndarray: + """Predicts the prediction error when following a nominal model.""" + polynomial_features = PolynomialFeatures(degree=self.deg, include_bias=False) + x_input = np.zeros( + 3 + drive_functions.acc_ctrl_queue_size + drive_functions.steer_ctrl_queue_size + ) + x_input[0] = v + x_input[1] = alpha_0 + x_input[2] = delta_0 + x_input[3 : 3 + drive_functions.acc_ctrl_queue_size] += alpha + x_input[3 + drive_functions.acc_ctrl_queue_size :] += delta + x_input_tensor = torch.tensor(x_input.reshape(1, -1).astype(np.float32)) + return ( + self.model(x_input_tensor)[0].detach().numpy() + + self.A + @ polynomial_features.fit_transform( + x_input[ctrl_index_for_polynomial_reg].reshape(1, -1) + )[0] + + self.b + ) + + def sim( + self, v: float, alpha_0: float, alpha: float, delta_0: float, delta: float, iter_times: int + ) -> tuple[float, float, float]: + """Simulate velocity, acceleration and steer changes according to a trained model.""" + v_pred = v + alpha_pred = alpha_0 + delta_pred = delta_0 + for i in range(iter_times): + pred = ( + np.concatenate( + ( + nominal_model_acc(v_pred, alpha_pred, alpha), + np.array([nominal_model_steer(delta_pred, delta)]), + ) + ) + + self.predict_error(v_pred, alpha_pred, delta_pred, alpha, delta)[[2, 4, 5]] + * drive_functions.mpc_time_step + ) + v_pred = pred[0] + alpha_pred = pred[1] + steer_pred = pred[2] + return v_pred, alpha_pred, steer_pred + + def plot_acc_map(self, iter_times: int, starting_steer=0.0, target_steer=0.0) -> None: + """Run `sim` `iter_times` times. The simulation time is approximately 0.1*iter_times. + + Draw the resulting acceleration map. + """ + acc = np.arange(-1, 1, 0.05) + v_tests = np.arange(0, 12, 0.3) + acc_result = np.zeros((acc.shape[0], v_tests.shape[0])) + v_result = np.zeros((acc.shape[0], v_tests.shape[0])) + steer_result = np.zeros((acc.shape[0], v_tests.shape[0])) + for i in range(acc.shape[0]): + v_tests_tmp = v_tests - 0.1 * iter_times * acc[i] + for j in range(v_tests.shape[0]): + v_result[i, j], acc_result[i, j], steer_result[i, j] = self.sim( + v_tests_tmp[j], acc[i], acc[i], starting_steer, target_steer, iter_times + ) + fig = plt.figure() + ax = fig.add_subplot(111, projection="3d") + ax.plot_surface(np.tile(acc, (v_tests.shape[0], 1)).T, v_result, acc_result) + ax.set_xlabel("acc_input [m/s^2]") + ax.set_ylabel("vel [m/s]") + ax.set_zlabel("acc_sim [m/s^2]") + fig.suptitle(str(iter_times * drive_functions.mpc_time_step) + " sec simulation") + plt.show() + + def plot_steer_map(self, iter_times: int, starting_acc=0.0, target_acc=0.0) -> None: + """Run `sim` `iter_times` times. The simulation time is approximately 0.1*iter_times. + + Draw the resulting steer map. + """ + steer = np.arange(-1, 1, 0.05) + v_tests = np.arange(0, 12, 0.3) + acc_result = np.zeros((steer.shape[0], v_tests.shape[0])) + v_result = np.zeros((steer.shape[0], v_tests.shape[0])) + steer_result = np.zeros((steer.shape[0], v_tests.shape[0])) + for i in range(steer.shape[0]): + for j in range(v_tests.shape[0]): + v_result[i, j], acc_result[i, j], steer_result[i, j] = self.sim( + v_tests[j], starting_acc, target_acc, steer[i], steer[i], iter_times + ) + fig = plt.figure() + ax = fig.add_subplot(111, projection="3d") + ax.plot_surface(np.tile(steer, (v_tests.shape[0], 1)).T, v_result, steer_result) + ax.set_xlabel("steer_input [rad]") + ax.set_ylabel("vel [m/s]") + ax.set_zlabel("steer_sim [rad]") + fig.suptitle(str(iter_times * drive_functions.mpc_time_step) + " sec simulation") + plt.show() diff --git a/control/autoware_smart_mpc_trajectory_follower/images/lateral_error_nominal_model.png 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b/control/autoware_smart_mpc_trajectory_follower/package.xml @@ -2,7 +2,7 @@ autoware_smart_mpc_trajectory_follower - 1.0.0 + 2.0.0 Nodes to follow a trajectory by generating control commands using smart mpc diff --git a/control/autoware_smart_mpc_trajectory_follower/sample_models/wheel_base_changed/model_for_test_drive.pth b/control/autoware_smart_mpc_trajectory_follower/sample_models/wheel_base_changed/model_for_test_drive.pth new file mode 100644 index 0000000000000..1aec4fe8c71ff Binary files /dev/null and b/control/autoware_smart_mpc_trajectory_follower/sample_models/wheel_base_changed/model_for_test_drive.pth differ diff --git a/control/autoware_smart_mpc_trajectory_follower/sample_models/wheel_base_changed/polynomial_reg_info.npz b/control/autoware_smart_mpc_trajectory_follower/sample_models/wheel_base_changed/polynomial_reg_info.npz new file mode 100644 index 0000000000000..be3e01443b805 Binary files /dev/null and b/control/autoware_smart_mpc_trajectory_follower/sample_models/wheel_base_changed/polynomial_reg_info.npz differ diff --git a/control/autoware_smart_mpc_trajectory_follower/setup.py b/control/autoware_smart_mpc_trajectory_follower/setup.py index fee1d04e826c0..22844b0f2d869 100644 --- a/control/autoware_smart_mpc_trajectory_follower/setup.py +++ b/control/autoware_smart_mpc_trajectory_follower/setup.py @@ -1,25 +1,30 @@ # cspell: ignore numba import glob import json -import os from pathlib import Path +import subprocess from setuptools import find_packages from setuptools import setup -os.system("pip3 install numba==0.58.1 --force-reinstall") -os.system("pip3 install pybind11") -os.system("pip3 install GPy") -os.system("pip3 install torch") -package_path = {} -package_path["path"] = str(Path(__file__).parent) -with open("autoware_smart_mpc_trajectory_follower/package_path.json", "w") as f: - json.dump(package_path, f) -build_cpp_command = "g++ -Ofast -Wall -shared -std=c++11 -fPIC $(python3 -m pybind11 --includes) " -build_cpp_command += "autoware_smart_mpc_trajectory_follower/scripts/proxima_calc.cpp " -build_cpp_command += "-o autoware_smart_mpc_trajectory_follower/scripts/proxima_calc$(python3-config --extension-suffix) " -build_cpp_command += "-lrt -I/usr/include/eigen3" -os.system(build_cpp_command) +SKIP_PRE_INSTALL_FLAG = False + +if not SKIP_PRE_INSTALL_FLAG: + subprocess.run("pip3 install numba==0.58.1 --force-reinstall", shell=True) + subprocess.run("pip3 install pybind11", shell=True) + subprocess.run("pip3 install GPy", shell=True) + subprocess.run("pip3 install torch", shell=True) + package_path = {} + package_path["path"] = str(Path(__file__).parent) + with open("autoware_smart_mpc_trajectory_follower/package_path.json", "w") as f: + json.dump(package_path, f) + build_cpp_command = ( + "g++ -Ofast -Wall -shared -std=c++11 -fPIC $(python3 -m pybind11 --includes) " + ) + build_cpp_command += "autoware_smart_mpc_trajectory_follower/scripts/proxima_calc.cpp " + build_cpp_command += "-o autoware_smart_mpc_trajectory_follower/scripts/proxima_calc$(python3-config --extension-suffix) " + build_cpp_command += "-lrt -I/usr/include/eigen3" + subprocess.run(build_cpp_command, shell=True) so_path = ( "scripts/" @@ -28,8 +33,8 @@ ] ) setup( - name="smart_mpc_trajectory_follower", - version="1.0.0", + name="autoware_smart_mpc_trajectory_follower", + version="2.0.0", packages=find_packages(), package_data={ "autoware_smart_mpc_trajectory_follower": ["package_path.json", so_path], diff --git a/control/autoware_trajectory_follower_node/CMakeLists.txt b/control/autoware_trajectory_follower_node/CMakeLists.txt index 945d3224d76e5..26488bd474149 100644 --- a/control/autoware_trajectory_follower_node/CMakeLists.txt +++ b/control/autoware_trajectory_follower_node/CMakeLists.txt @@ -37,23 +37,25 @@ if(BUILD_TESTING) target_link_libraries( ${TRAJECTORY_FOLLOWER_NODES_TEST} ${CONTROLLER_NODE}) - find_package(autoware_testing REQUIRED) - # smoke test for MPC controller - add_smoke_test(${PROJECT_NAME} ${CONTROLLER_NODE}_exe - PARAM_FILENAMES "lateral/mpc.param.yaml longitudinal/pid.param.yaml -trajectory_follower_node.param.yaml" - TEST_PARAM_FILENAMES "test_controller_mpc.param.yaml test_controller_pid.param.yaml -test_vehicle_info.param.yaml test_nearest_search.param.yaml" - TARGET_INFIX "mpc" - ) - # smoke test for pure pursuit controller - add_smoke_test(${PROJECT_NAME} ${CONTROLLER_NODE}_exe - PARAM_FILENAMES "lateral/pure_pursuit.param.yaml longitudinal/pid.param.yaml -trajectory_follower_node.param.yaml" - TEST_PARAM_FILENAMES "test_controller_pure_pursuit.param.yaml test_controller_pid.param.yaml -test_vehicle_info.param.yaml test_nearest_search.param.yaml" - TARGET_INFIX "pure_pursuit" - ) + # Temporary disabled, tracked by: + # https://github.com/autowarefoundation/autoware.universe/issues/7733 +# find_package(autoware_testing REQUIRED) +# # smoke test for MPC controller +# add_smoke_test(${PROJECT_NAME} ${CONTROLLER_NODE}_exe +# PARAM_FILENAMES "lateral/mpc.param.yaml longitudinal/pid.param.yaml +#trajectory_follower_node.param.yaml" +# TEST_PARAM_FILENAMES "test_controller_mpc.param.yaml test_controller_pid.param.yaml +#test_vehicle_info.param.yaml test_nearest_search.param.yaml" +# TARGET_INFIX "mpc" +# ) +# # smoke test for pure pursuit controller +# add_smoke_test(${PROJECT_NAME} ${CONTROLLER_NODE}_exe +# PARAM_FILENAMES "lateral/pure_pursuit.param.yaml longitudinal/pid.param.yaml +#trajectory_follower_node.param.yaml" +# TEST_PARAM_FILENAMES "test_controller_pure_pursuit.param.yaml test_controller_pid.param.yaml +#test_vehicle_info.param.yaml test_nearest_search.param.yaml" +# TARGET_INFIX "pure_pursuit" +# ) endif() diff --git a/control/autoware_trajectory_follower_node/include/autoware/trajectory_follower_node/controller_node.hpp b/control/autoware_trajectory_follower_node/include/autoware/trajectory_follower_node/controller_node.hpp index 916f921a40e7b..a5f8665328f34 100644 --- a/control/autoware_trajectory_follower_node/include/autoware/trajectory_follower_node/controller_node.hpp +++ b/control/autoware_trajectory_follower_node/include/autoware/trajectory_follower_node/controller_node.hpp @@ -30,6 +30,7 @@ #include #include #include +#include #include "autoware_control_msgs/msg/control.hpp" #include "autoware_control_msgs/msg/longitudinal.hpp" @@ -73,6 +74,10 @@ class TRAJECTORY_FOLLOWER_PUBLIC Controller : public rclcpp::Node double timeout_thr_sec_; boost::optional longitudinal_output_{boost::none}; + std::shared_ptr diag_updater_ = + std::make_shared( + this); // Diagnostic updater for publishing diagnostic data. + std::shared_ptr longitudinal_controller_; std::shared_ptr lateral_controller_; diff --git a/control/autoware_trajectory_follower_node/src/controller_node.cpp b/control/autoware_trajectory_follower_node/src/controller_node.cpp index 60f7481f862a9..d5167f5096786 100644 --- a/control/autoware_trajectory_follower_node/src/controller_node.cpp +++ b/control/autoware_trajectory_follower_node/src/controller_node.cpp @@ -39,7 +39,8 @@ Controller::Controller(const rclcpp::NodeOptions & node_options) : Node("control getLateralControllerMode(declare_parameter("lateral_controller_mode")); switch (lateral_controller_mode) { case LateralControllerMode::MPC: { - lateral_controller_ = std::make_shared(*this); + lateral_controller_ = + std::make_shared(*this, diag_updater_); break; } case LateralControllerMode::PURE_PURSUIT: { @@ -56,7 +57,8 @@ Controller::Controller(const rclcpp::NodeOptions & node_options) : Node("control switch (longitudinal_controller_mode) { case LongitudinalControllerMode::PID: { longitudinal_controller_ = - std::make_shared(*this); + std::make_shared( + *this, diag_updater_); break; } default: diff --git a/control/autoware_vehicle_cmd_gate/test/src/test_filter_in_vehicle_cmd_gate_node.cpp b/control/autoware_vehicle_cmd_gate/test/src/test_filter_in_vehicle_cmd_gate_node.cpp index 46130fc8f9941..66dde51780ffa 100644 --- a/control/autoware_vehicle_cmd_gate/test/src/test_filter_in_vehicle_cmd_gate_node.cpp +++ b/control/autoware_vehicle_cmd_gate/test/src/test_filter_in_vehicle_cmd_gate_node.cpp @@ -294,7 +294,12 @@ class PubSubNode : public rclcpp::Node const auto max_lat_acc_lim = *std::max_element(lat_acc_lim.begin(), lat_acc_lim.end()); const auto max_lat_jerk_lim = *std::max_element(lat_jerk_lim.begin(), lat_jerk_lim.end()); + // This test is designed to verify that the filter is applied correctly. However, if topic + // communication is delayed, the allowable range of change in the command values between the + // sender and receiver of the topic will vary, making the results dependent on processing time. + // We define the allowable error margin to account for this. constexpr auto threshold_scale = 1.1; + // Output command must be smaller than maximum limit. // TODO(Horibe): check for each velocity range. if (std::abs(lon_vel) > 0.01) { @@ -433,7 +438,7 @@ TEST_P(TestFixture, CheckFilterForSinCmd) // << cmd_generator_.p_.steering.freq << " * dt + " << cmd_generator_.p_.steering.bias // << ")" << std::endl; - for (size_t i = 0; i < 100; ++i) { + for (size_t i = 0; i < 30; ++i) { auto start_time = std::chrono::steady_clock::now(); const bool reset_clock = (i == 0); @@ -449,7 +454,13 @@ TEST_P(TestFixture, CheckFilterForSinCmd) std::chrono::milliseconds elapsed = std::chrono::duration_cast(end_time - start_time); - std::chrono::milliseconds sleep_duration = std::chrono::milliseconds{10} - elapsed; + // The value determines the period of the topic. The filter logic of vehicle_cmd_gate depends on + // real-time, and if the time from publishing to subscribing becomes too long, this test will + // fail (the test specification itself should be improved). To prevent processing bottlenecks, + // please set this value appropriately. It is set to 30ms because it occasionally fails at 10ms. + constexpr int running_ms = 30; + + std::chrono::milliseconds sleep_duration = std::chrono::milliseconds{running_ms} - elapsed; if (sleep_duration.count() > 0) { std::this_thread::sleep_for(sleep_duration); } diff --git a/evaluator/autoware_control_evaluator/CMakeLists.txt b/evaluator/autoware_control_evaluator/CMakeLists.txt index 189745349a592..2e6f9851cafce 100644 --- a/evaluator/autoware_control_evaluator/CMakeLists.txt +++ b/evaluator/autoware_control_evaluator/CMakeLists.txt @@ -12,7 +12,7 @@ ament_auto_add_library(control_evaluator_node SHARED ) rclcpp_components_register_node(control_evaluator_node - PLUGIN "control_diagnostics::controlEvaluatorNode" + PLUGIN "control_diagnostics::ControlEvaluatorNode" EXECUTABLE control_evaluator ) diff --git a/evaluator/autoware_control_evaluator/include/autoware/control_evaluator/control_evaluator_node.hpp b/evaluator/autoware_control_evaluator/include/autoware/control_evaluator/control_evaluator_node.hpp index 279bada80e1b9..da60c820c45b1 100644 --- a/evaluator/autoware_control_evaluator/include/autoware/control_evaluator/control_evaluator_node.hpp +++ b/evaluator/autoware_control_evaluator/include/autoware/control_evaluator/control_evaluator_node.hpp @@ -17,15 +17,17 @@ #include "autoware/control_evaluator/metrics/deviation_metrics.hpp" +#include #include #include +#include "geometry_msgs/msg/accel_with_covariance_stamped.hpp" +#include #include #include -#include #include -#include +#include #include #include #include @@ -39,14 +41,17 @@ using diagnostic_msgs::msg::DiagnosticStatus; using geometry_msgs::msg::Point; using geometry_msgs::msg::Pose; using nav_msgs::msg::Odometry; +using LaneletMapBin = autoware_map_msgs::msg::LaneletMapBin; +using autoware_planning_msgs::msg::LaneletRoute; +using geometry_msgs::msg::AccelWithCovarianceStamped; /** * @brief Node for control evaluation */ -class controlEvaluatorNode : public rclcpp::Node +class ControlEvaluatorNode : public rclcpp::Node { public: - explicit controlEvaluatorNode(const rclcpp::NodeOptions & node_options); + explicit ControlEvaluatorNode(const rclcpp::NodeOptions & node_options); void removeOldDiagnostics(const rclcpp::Time & stamp); void removeDiagnosticsByName(const std::string & name); void addDiagnostic(const DiagnosticStatus & diag, const rclcpp::Time & stamp); @@ -62,6 +67,9 @@ class controlEvaluatorNode : public rclcpp::Node const DiagnosticArray & diag, const std::string & function_name); DiagnosticStatus generateAEBDiagnosticStatus(const DiagnosticStatus & diag); + DiagnosticStatus generateLaneletDiagnosticStatus(const Pose & ego_pose) const; + DiagnosticStatus generateKinematicStateDiagnosticStatus( + const Odometry & odom, const AccelWithCovarianceStamped & accel_stamped); void onDiagnostics(const DiagnosticArray::ConstSharedPtr diag_msg); void onTimer(); @@ -74,11 +82,20 @@ class controlEvaluatorNode : public rclcpp::Node autoware::universe_utils::InterProcessPollingSubscriber odometry_sub_{ this, "~/input/odometry"}; + autoware::universe_utils::InterProcessPollingSubscriber accel_sub_{ + this, "~/input/acceleration"}; autoware::universe_utils::InterProcessPollingSubscriber traj_sub_{ this, "~/input/trajectory"}; + autoware::universe_utils::InterProcessPollingSubscriber route_subscriber_{ + this, "~/input/route", rclcpp::QoS{1}.transient_local()}; + autoware::universe_utils::InterProcessPollingSubscriber vector_map_subscriber_{ + this, "~/input/vector_map", rclcpp::QoS{1}.transient_local()}; rclcpp::Publisher::SharedPtr metrics_pub_; + // update Route Handler + void getRouteData(); + // Calculator // Metrics std::deque stamps_; @@ -87,7 +104,9 @@ class controlEvaluatorNode : public rclcpp::Node std::deque> diag_queue_; const std::vector target_functions_ = {"autonomous_emergency_braking"}; + autoware::route_handler::RouteHandler route_handler_; rclcpp::TimerBase::SharedPtr timer_; + std::optional prev_acc_stamped_{std::nullopt}; }; } // namespace control_diagnostics diff --git a/evaluator/autoware_control_evaluator/launch/control_evaluator.launch.xml b/evaluator/autoware_control_evaluator/launch/control_evaluator.launch.xml index e119f1787d21b..84e11208770b8 100644 --- a/evaluator/autoware_control_evaluator/launch/control_evaluator.launch.xml +++ b/evaluator/autoware_control_evaluator/launch/control_evaluator.launch.xml @@ -1,15 +1,21 @@ + + + + + + diff --git a/evaluator/autoware_control_evaluator/package.xml b/evaluator/autoware_control_evaluator/package.xml index 49631c566eaf5..21f48b1c64485 100644 --- a/evaluator/autoware_control_evaluator/package.xml +++ b/evaluator/autoware_control_evaluator/package.xml @@ -16,6 +16,7 @@ autoware_motion_utils autoware_planning_msgs + autoware_route_handler autoware_universe_utils diagnostic_msgs pluginlib diff --git a/evaluator/autoware_control_evaluator/src/control_evaluator_node.cpp b/evaluator/autoware_control_evaluator/src/control_evaluator_node.cpp index ce749e6e8d5eb..5862522d582f7 100644 --- a/evaluator/autoware_control_evaluator/src/control_evaluator_node.cpp +++ b/evaluator/autoware_control_evaluator/src/control_evaluator_node.cpp @@ -14,22 +14,23 @@ #include "autoware/control_evaluator/control_evaluator_node.hpp" -#include -#include -#include -#include +#include +#include + +#include +#include #include #include #include namespace control_diagnostics { -controlEvaluatorNode::controlEvaluatorNode(const rclcpp::NodeOptions & node_options) +ControlEvaluatorNode::ControlEvaluatorNode(const rclcpp::NodeOptions & node_options) : Node("control_evaluator", node_options) { using std::placeholders::_1; control_diag_sub_ = create_subscription( - "~/input/diagnostics", 1, std::bind(&controlEvaluatorNode::onDiagnostics, this, _1)); + "~/input/diagnostics", 1, std::bind(&ControlEvaluatorNode::onDiagnostics, this, _1)); // Publisher metrics_pub_ = create_publisher("~/metrics", 1); @@ -37,10 +38,33 @@ controlEvaluatorNode::controlEvaluatorNode(const rclcpp::NodeOptions & node_opti // Timer callback to publish evaluator diagnostics using namespace std::literals::chrono_literals; timer_ = - rclcpp::create_timer(this, get_clock(), 100ms, std::bind(&controlEvaluatorNode::onTimer, this)); + rclcpp::create_timer(this, get_clock(), 100ms, std::bind(&ControlEvaluatorNode::onTimer, this)); +} + +void ControlEvaluatorNode::getRouteData() +{ + // route + { + const auto msg = route_subscriber_.takeNewData(); + if (msg) { + if (msg->segments.empty()) { + RCLCPP_ERROR(get_logger(), "input route is empty. ignored"); + } else { + route_handler_.setRoute(*msg); + } + } + } + + // map + { + const auto msg = vector_map_subscriber_.takeNewData(); + if (msg) { + route_handler_.setMap(*msg); + } + } } -void controlEvaluatorNode::removeOldDiagnostics(const rclcpp::Time & stamp) +void ControlEvaluatorNode::removeOldDiagnostics(const rclcpp::Time & stamp) { constexpr double KEEP_TIME = 1.0; diag_queue_.erase( @@ -52,7 +76,7 @@ void controlEvaluatorNode::removeOldDiagnostics(const rclcpp::Time & stamp) diag_queue_.end()); } -void controlEvaluatorNode::removeDiagnosticsByName(const std::string & name) +void ControlEvaluatorNode::removeDiagnosticsByName(const std::string & name) { diag_queue_.erase( std::remove_if( @@ -63,13 +87,13 @@ void controlEvaluatorNode::removeDiagnosticsByName(const std::string & name) diag_queue_.end()); } -void controlEvaluatorNode::addDiagnostic( +void ControlEvaluatorNode::addDiagnostic( const diagnostic_msgs::msg::DiagnosticStatus & diag, const rclcpp::Time & stamp) { diag_queue_.push_back(std::make_pair(diag, stamp)); } -void controlEvaluatorNode::updateDiagnosticQueue( +void ControlEvaluatorNode::updateDiagnosticQueue( const DiagnosticArray & input_diagnostics, const std::string & function, const rclcpp::Time & stamp) { @@ -86,7 +110,7 @@ void controlEvaluatorNode::updateDiagnosticQueue( removeOldDiagnostics(stamp); } -void controlEvaluatorNode::onDiagnostics(const DiagnosticArray::ConstSharedPtr diag_msg) +void ControlEvaluatorNode::onDiagnostics(const DiagnosticArray::ConstSharedPtr diag_msg) { // add target diagnostics to the queue and remove old ones for (const auto & function : target_functions_) { @@ -94,7 +118,7 @@ void controlEvaluatorNode::onDiagnostics(const DiagnosticArray::ConstSharedPtr d } } -DiagnosticStatus controlEvaluatorNode::generateAEBDiagnosticStatus(const DiagnosticStatus & diag) +DiagnosticStatus ControlEvaluatorNode::generateAEBDiagnosticStatus(const DiagnosticStatus & diag) { DiagnosticStatus status; status.level = status.OK; @@ -102,13 +126,79 @@ DiagnosticStatus controlEvaluatorNode::generateAEBDiagnosticStatus(const Diagnos diagnostic_msgs::msg::KeyValue key_value; key_value.key = "decision"; const bool is_emergency_brake = (diag.level == DiagnosticStatus::ERROR); - key_value.value = (is_emergency_brake) ? "stop" : "none"; + key_value.value = (is_emergency_brake) ? "deceleration" : "none"; + status.values.push_back(key_value); + return status; +} + +DiagnosticStatus ControlEvaluatorNode::generateLaneletDiagnosticStatus(const Pose & ego_pose) const +{ + const auto current_lanelets = [&]() { + lanelet::ConstLanelet closest_route_lanelet; + route_handler_.getClosestLaneletWithinRoute(ego_pose, &closest_route_lanelet); + const auto shoulder_lanelets = route_handler_.getShoulderLaneletsAtPose(ego_pose); + lanelet::ConstLanelets closest_lanelets{closest_route_lanelet}; + closest_lanelets.insert( + closest_lanelets.end(), shoulder_lanelets.begin(), shoulder_lanelets.end()); + return closest_lanelets; + }(); + const auto arc_coordinates = lanelet::utils::getArcCoordinates(current_lanelets, ego_pose); + lanelet::ConstLanelet current_lane; + lanelet::utils::query::getClosestLanelet(current_lanelets, ego_pose, ¤t_lane); + + DiagnosticStatus status; + status.name = "ego_lane_info"; + status.level = status.OK; + diagnostic_msgs::msg::KeyValue key_value; + key_value.key = "lane_id"; + key_value.value = std::to_string(current_lane.id()); status.values.push_back(key_value); + key_value.key = "s"; + key_value.value = std::to_string(arc_coordinates.length); + status.values.push_back(key_value); + key_value.key = "t"; + key_value.value = std::to_string(arc_coordinates.distance); + status.values.push_back(key_value); + return status; +} + +DiagnosticStatus ControlEvaluatorNode::generateKinematicStateDiagnosticStatus( + const Odometry & odom, const AccelWithCovarianceStamped & accel_stamped) +{ + DiagnosticStatus status; + status.name = "kinematic_state"; + status.level = status.OK; + diagnostic_msgs::msg::KeyValue key_value; + key_value.key = "vel"; + key_value.value = std::to_string(odom.twist.twist.linear.x); + status.values.push_back(key_value); + key_value.key = "acc"; + const auto & acc = accel_stamped.accel.accel.linear.x; + key_value.value = std::to_string(acc); + status.values.push_back(key_value); + key_value.key = "jerk"; + const auto jerk = [&]() { + if (!prev_acc_stamped_.has_value()) { + prev_acc_stamped_ = accel_stamped; + return 0.0; + } + const auto t = static_cast(accel_stamped.header.stamp.sec) + + static_cast(accel_stamped.header.stamp.nanosec) * 1e-9; + const auto prev_t = static_cast(prev_acc_stamped_.value().header.stamp.sec) + + static_cast(prev_acc_stamped_.value().header.stamp.nanosec) * 1e-9; + const auto dt = t - prev_t; + if (dt < std::numeric_limits::epsilon()) return 0.0; + const auto prev_acc = prev_acc_stamped_.value().accel.accel.linear.x; + prev_acc_stamped_ = accel_stamped; + return (acc - prev_acc) / dt; + }(); + key_value.value = std::to_string(jerk); + status.values.push_back(key_value); return status; } -DiagnosticStatus controlEvaluatorNode::generateLateralDeviationDiagnosticStatus( +DiagnosticStatus ControlEvaluatorNode::generateLateralDeviationDiagnosticStatus( const Trajectory & traj, const Point & ego_point) { const double lateral_deviation = metrics::calcLateralDeviation(traj, ego_point); @@ -124,7 +214,7 @@ DiagnosticStatus controlEvaluatorNode::generateLateralDeviationDiagnosticStatus( return status; } -DiagnosticStatus controlEvaluatorNode::generateYawDeviationDiagnosticStatus( +DiagnosticStatus ControlEvaluatorNode::generateYawDeviationDiagnosticStatus( const Trajectory & traj, const Pose & ego_pose) { const double yaw_deviation = metrics::calcYawDeviation(traj, ego_pose); @@ -140,11 +230,12 @@ DiagnosticStatus controlEvaluatorNode::generateYawDeviationDiagnosticStatus( return status; } -void controlEvaluatorNode::onTimer() +void ControlEvaluatorNode::onTimer() { DiagnosticArray metrics_msg; const auto traj = traj_sub_.takeData(); const auto odom = odometry_sub_.takeData(); + const auto acc = accel_sub_.takeData(); // generate decision diagnostics from input diagnostics for (const auto & function : target_functions_) { @@ -171,10 +262,20 @@ void controlEvaluatorNode::onTimer() metrics_msg.status.push_back(generateYawDeviationDiagnosticStatus(*traj, ego_pose)); } + getRouteData(); + if (odom && route_handler_.isHandlerReady()) { + const Pose ego_pose = odom->pose.pose; + metrics_msg.status.push_back(generateLaneletDiagnosticStatus(ego_pose)); + } + + if (odom && acc) { + metrics_msg.status.push_back(generateKinematicStateDiagnosticStatus(*odom, *acc)); + } + metrics_msg.header.stamp = now(); metrics_pub_->publish(metrics_msg); } } // namespace control_diagnostics #include "rclcpp_components/register_node_macro.hpp" -RCLCPP_COMPONENTS_REGISTER_NODE(control_diagnostics::controlEvaluatorNode) +RCLCPP_COMPONENTS_REGISTER_NODE(control_diagnostics::ControlEvaluatorNode) diff --git a/evaluator/autoware_planning_evaluator/include/autoware/planning_evaluator/planning_evaluator_node.hpp b/evaluator/autoware_planning_evaluator/include/autoware/planning_evaluator/planning_evaluator_node.hpp index 0121425c15d1d..74523398c3a77 100644 --- a/evaluator/autoware_planning_evaluator/include/autoware/planning_evaluator/planning_evaluator_node.hpp +++ b/evaluator/autoware_planning_evaluator/include/autoware/planning_evaluator/planning_evaluator_node.hpp @@ -21,12 +21,17 @@ #include "tf2_ros/buffer.h" #include "tf2_ros/transform_listener.h" +#include +#include + #include "autoware_perception_msgs/msg/predicted_objects.hpp" #include "autoware_planning_msgs/msg/pose_with_uuid_stamped.hpp" #include "autoware_planning_msgs/msg/trajectory.hpp" #include "autoware_planning_msgs/msg/trajectory_point.hpp" #include "diagnostic_msgs/msg/diagnostic_array.hpp" +#include "geometry_msgs/msg/accel_with_covariance_stamped.hpp" #include "nav_msgs/msg/odometry.hpp" +#include #include #include @@ -43,6 +48,9 @@ using autoware_planning_msgs::msg::TrajectoryPoint; using diagnostic_msgs::msg::DiagnosticArray; using diagnostic_msgs::msg::DiagnosticStatus; using nav_msgs::msg::Odometry; +using LaneletMapBin = autoware_map_msgs::msg::LaneletMapBin; +using autoware_planning_msgs::msg::LaneletRoute; +using geometry_msgs::msg::AccelWithCovarianceStamped; /** * @brief Node for planning evaluation */ @@ -88,9 +96,22 @@ class PlanningEvaluatorNode : public rclcpp::Node DiagnosticStatus generateDiagnosticStatus( const Metric & metric, const Stat & metric_stat) const; + /** + * @brief publish current ego lane info + */ + DiagnosticStatus generateLaneletDiagnosticStatus(); + + /** + * @brief publish current ego kinematic state + */ + DiagnosticStatus generateKinematicStateDiagnosticStatus( + const AccelWithCovarianceStamped & accel_stamped); + private: static bool isFinite(const TrajectoryPoint & p); void publishModifiedGoalDeviationMetrics(); + // update Route Handler + void getRouteData(); // ROS rclcpp::Subscription::SharedPtr traj_sub_; @@ -98,10 +119,17 @@ class PlanningEvaluatorNode : public rclcpp::Node rclcpp::Subscription::SharedPtr objects_sub_; rclcpp::Subscription::SharedPtr modified_goal_sub_; rclcpp::Subscription::SharedPtr odom_sub_; + autoware::universe_utils::InterProcessPollingSubscriber route_subscriber_{ + this, "~/input/route", rclcpp::QoS{1}.transient_local()}; + autoware::universe_utils::InterProcessPollingSubscriber vector_map_subscriber_{ + this, "~/input/vector_map", rclcpp::QoS{1}.transient_local()}; + autoware::universe_utils::InterProcessPollingSubscriber accel_sub_{ + this, "~/input/acceleration"}; rclcpp::Publisher::SharedPtr metrics_pub_; std::shared_ptr transform_listener_{nullptr}; std::unique_ptr tf_buffer_; + autoware::route_handler::RouteHandler route_handler_; // Parameters std::string output_file_str_; @@ -116,6 +144,7 @@ class PlanningEvaluatorNode : public rclcpp::Node Odometry::ConstSharedPtr ego_state_ptr_; PoseWithUuidStamped::ConstSharedPtr modified_goal_ptr_; + std::optional prev_acc_stamped_{std::nullopt}; }; } // namespace planning_diagnostics diff --git a/evaluator/autoware_planning_evaluator/launch/planning_evaluator.launch.xml b/evaluator/autoware_planning_evaluator/launch/planning_evaluator.launch.xml index 2e358f6272379..70301ebe6275f 100644 --- a/evaluator/autoware_planning_evaluator/launch/planning_evaluator.launch.xml +++ b/evaluator/autoware_planning_evaluator/launch/planning_evaluator.launch.xml @@ -1,19 +1,25 @@ + + + + + + diff --git a/evaluator/autoware_planning_evaluator/package.xml b/evaluator/autoware_planning_evaluator/package.xml index 5bd500f200eac..3c3d4bd8bb595 100644 --- a/evaluator/autoware_planning_evaluator/package.xml +++ b/evaluator/autoware_planning_evaluator/package.xml @@ -16,6 +16,7 @@ autoware_motion_utils autoware_perception_msgs autoware_planning_msgs + autoware_route_handler autoware_universe_utils diagnostic_msgs eigen diff --git a/evaluator/autoware_planning_evaluator/src/planning_evaluator_node.cpp b/evaluator/autoware_planning_evaluator/src/planning_evaluator_node.cpp index 103e14c73f26d..5e959aed123af 100644 --- a/evaluator/autoware_planning_evaluator/src/planning_evaluator_node.cpp +++ b/evaluator/autoware_planning_evaluator/src/planning_evaluator_node.cpp @@ -14,6 +14,11 @@ #include "autoware/planning_evaluator/planning_evaluator_node.hpp" +#include +#include + +#include + #include "boost/lexical_cast.hpp" #include @@ -105,6 +110,97 @@ PlanningEvaluatorNode::~PlanningEvaluatorNode() } } +void PlanningEvaluatorNode::getRouteData() +{ + // route + { + const auto msg = route_subscriber_.takeNewData(); + if (msg) { + if (msg->segments.empty()) { + RCLCPP_ERROR(get_logger(), "input route is empty. ignored"); + } else { + route_handler_.setRoute(*msg); + } + } + } + + // map + { + const auto msg = vector_map_subscriber_.takeNewData(); + if (msg) { + route_handler_.setMap(*msg); + } + } +} + +DiagnosticStatus PlanningEvaluatorNode::generateLaneletDiagnosticStatus() +{ + const auto & ego_pose = ego_state_ptr_->pose.pose; + const auto current_lanelets = [&]() { + lanelet::ConstLanelet closest_route_lanelet; + route_handler_.getClosestLaneletWithinRoute(ego_pose, &closest_route_lanelet); + const auto shoulder_lanelets = route_handler_.getShoulderLaneletsAtPose(ego_pose); + lanelet::ConstLanelets closest_lanelets{closest_route_lanelet}; + closest_lanelets.insert( + closest_lanelets.end(), shoulder_lanelets.begin(), shoulder_lanelets.end()); + return closest_lanelets; + }(); + const auto arc_coordinates = lanelet::utils::getArcCoordinates(current_lanelets, ego_pose); + lanelet::ConstLanelet current_lane; + lanelet::utils::query::getClosestLanelet(current_lanelets, ego_pose, ¤t_lane); + + DiagnosticStatus status; + status.name = "ego_lane_info"; + status.level = status.OK; + diagnostic_msgs::msg::KeyValue key_value; + key_value.key = "lane_id"; + key_value.value = std::to_string(current_lane.id()); + status.values.push_back(key_value); + key_value.key = "s"; + key_value.value = std::to_string(arc_coordinates.length); + status.values.push_back(key_value); + key_value.key = "t"; + key_value.value = std::to_string(arc_coordinates.distance); + status.values.push_back(key_value); + return status; +} + +DiagnosticStatus PlanningEvaluatorNode::generateKinematicStateDiagnosticStatus( + const AccelWithCovarianceStamped & accel_stamped) +{ + DiagnosticStatus status; + status.name = "kinematic_state"; + status.level = status.OK; + diagnostic_msgs::msg::KeyValue key_value; + key_value.key = "vel"; + key_value.value = std::to_string(ego_state_ptr_->twist.twist.linear.x); + status.values.push_back(key_value); + key_value.key = "acc"; + const auto & acc = accel_stamped.accel.accel.linear.x; + key_value.value = std::to_string(acc); + status.values.push_back(key_value); + key_value.key = "jerk"; + const auto jerk = [&]() { + if (!prev_acc_stamped_.has_value()) { + prev_acc_stamped_ = accel_stamped; + return 0.0; + } + const auto t = static_cast(accel_stamped.header.stamp.sec) + + static_cast(accel_stamped.header.stamp.nanosec) * 1e-9; + const auto prev_t = static_cast(prev_acc_stamped_.value().header.stamp.sec) + + static_cast(prev_acc_stamped_.value().header.stamp.nanosec) * 1e-9; + const auto dt = t - prev_t; + if (dt < std::numeric_limits::epsilon()) return 0.0; + + const auto prev_acc = prev_acc_stamped_.value().accel.accel.linear.x; + prev_acc_stamped_ = accel_stamped; + return (acc - prev_acc) / dt; + }(); + key_value.value = std::to_string(jerk); + status.values.push_back(key_value); + return status; +} + DiagnosticStatus PlanningEvaluatorNode::generateDiagnosticStatus( const Metric & metric, const Stat & metric_stat) const { @@ -172,6 +268,25 @@ void PlanningEvaluatorNode::onOdometry(const Odometry::ConstSharedPtr odometry_m { ego_state_ptr_ = odometry_msg; metrics_calculator_.setEgoPose(odometry_msg->pose.pose); + { + DiagnosticArray metrics_msg; + metrics_msg.header.stamp = now(); + + getRouteData(); + if (route_handler_.isHandlerReady() && ego_state_ptr_) { + metrics_msg.status.push_back(generateLaneletDiagnosticStatus()); + } + + const auto acc = accel_sub_.takeData(); + + if (acc && ego_state_ptr_) { + metrics_msg.status.push_back(generateKinematicStateDiagnosticStatus(*acc)); + } + + if (!metrics_msg.status.empty()) { + metrics_pub_->publish(metrics_msg); + } + } if (modified_goal_ptr_) { publishModifiedGoalDeviationMetrics(); diff --git a/evaluator/perception_online_evaluator/package.xml b/evaluator/perception_online_evaluator/package.xml index 082a9bc2d1366..23b1210063705 100644 --- a/evaluator/perception_online_evaluator/package.xml +++ b/evaluator/perception_online_evaluator/package.xml @@ -17,6 +17,7 @@ ament_cmake_auto autoware_cmake + autoware_lanelet2_extension autoware_motion_utils autoware_perception_msgs autoware_universe_utils @@ -25,7 +26,6 @@ eigen geometry_msgs glog - lanelet2_extension nav_msgs object_recognition_utils pluginlib diff --git a/launch/tier4_control_launch/launch/control.launch.py b/launch/tier4_control_launch/launch/control.launch.py deleted file mode 100644 index a61f29014dcce..0000000000000 --- a/launch/tier4_control_launch/launch/control.launch.py +++ /dev/null @@ -1,513 +0,0 @@ -# Copyright 2020 Tier IV, Inc. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import launch -from launch.actions import DeclareLaunchArgument -from launch.actions import GroupAction -from launch.actions import IncludeLaunchDescription -from launch.actions import OpaqueFunction -from launch.actions import SetLaunchConfiguration -from launch.conditions import IfCondition -from launch.conditions import UnlessCondition -from launch.launch_description_sources import PythonLaunchDescriptionSource -from launch.substitutions import LaunchConfiguration -from launch_ros.actions import ComposableNodeContainer -from launch_ros.actions import LoadComposableNodes -from launch_ros.actions import Node -from launch_ros.actions import PushRosNamespace -from launch_ros.descriptions import ComposableNode -from launch_ros.substitutions import FindPackageShare -import yaml - - -def launch_setup(context, *args, **kwargs): - with open(LaunchConfiguration("vehicle_param_file").perform(context), "r") as f: - vehicle_info_param = yaml.safe_load(f)["/**"]["ros__parameters"] - - with open(LaunchConfiguration("nearest_search_param_path").perform(context), "r") as f: - nearest_search_param = yaml.safe_load(f)["/**"]["ros__parameters"] - - with open( - LaunchConfiguration("trajectory_follower_node_param_path").perform(context), "r" - ) as f: - trajectory_follower_node_param = yaml.safe_load(f)["/**"]["ros__parameters"] - with open(LaunchConfiguration("lat_controller_param_path").perform(context), "r") as f: - lat_controller_param = yaml.safe_load(f)["/**"]["ros__parameters"] - with open(LaunchConfiguration("lon_controller_param_path").perform(context), "r") as f: - lon_controller_param = yaml.safe_load(f)["/**"]["ros__parameters"] - with open(LaunchConfiguration("vehicle_cmd_gate_param_path").perform(context), "r") as f: - vehicle_cmd_gate_param = yaml.safe_load(f)["/**"]["ros__parameters"] - with open(LaunchConfiguration("lane_departure_checker_param_path").perform(context), "r") as f: - lane_departure_checker_param = yaml.safe_load(f)["/**"]["ros__parameters"] - with open(LaunchConfiguration("control_validator_param_path").perform(context), "r") as f: - control_validator_param = yaml.safe_load(f)["/**"]["ros__parameters"] - with open( - LaunchConfiguration("operation_mode_transition_manager_param_path").perform(context), - "r", - ) as f: - operation_mode_transition_manager_param = yaml.safe_load(f)["/**"]["ros__parameters"] - with open(LaunchConfiguration("shift_decider_param_path").perform(context), "r") as f: - shift_decider_param = yaml.safe_load(f)["/**"]["ros__parameters"] - with open( - LaunchConfiguration("obstacle_collision_checker_param_path").perform(context), "r" - ) as f: - obstacle_collision_checker_param = yaml.safe_load(f)["/**"]["ros__parameters"] - with open(LaunchConfiguration("aeb_param_path").perform(context), "r") as f: - aeb_param = yaml.safe_load(f)["/**"]["ros__parameters"] - with open(LaunchConfiguration("predicted_path_checker_param_path").perform(context), "r") as f: - predicted_path_checker_param = yaml.safe_load(f)["/**"]["ros__parameters"] - trajectory_follower_mode = LaunchConfiguration("trajectory_follower_mode").perform(context) - - controller_component = ComposableNode( - package="autoware_trajectory_follower_node", - plugin="autoware::motion::control::trajectory_follower_node::Controller", - name="controller_node_exe", - namespace="trajectory_follower", - remappings=[ - ("~/input/reference_trajectory", "/planning/scenario_planning/trajectory"), - ("~/input/current_odometry", "/localization/kinematic_state"), - ("~/input/current_steering", "/vehicle/status/steering_status"), - ("~/input/current_accel", "/localization/acceleration"), - ("~/input/current_operation_mode", "/system/operation_mode/state"), - ("~/output/predicted_trajectory", "lateral/predicted_trajectory"), - ("~/output/lateral_diagnostic", "lateral/diagnostic"), - ("~/output/slope_angle", "longitudinal/slope_angle"), - ("~/output/longitudinal_diagnostic", "longitudinal/diagnostic"), - ("~/output/stop_reason", "longitudinal/stop_reason"), - ("~/output/control_cmd", "control_cmd"), - ], - parameters=[ - { - "lateral_controller_mode": LaunchConfiguration("lateral_controller_mode"), - "longitudinal_controller_mode": LaunchConfiguration("longitudinal_controller_mode"), - }, - nearest_search_param, - trajectory_follower_node_param, - lon_controller_param, - lat_controller_param, - vehicle_info_param, - ], - extra_arguments=[{"use_intra_process_comms": LaunchConfiguration("use_intra_process")}], - ) - - # lane departure checker - lane_departure_component = ComposableNode( - package="autoware_lane_departure_checker", - plugin="autoware::lane_departure_checker::LaneDepartureCheckerNode", - name="lane_departure_checker_node", - namespace="trajectory_follower", - remappings=[ - ("~/input/odometry", "/localization/kinematic_state"), - ("~/input/lanelet_map_bin", "/map/vector_map"), - ("~/input/route", "/planning/mission_planning/route"), - ("~/input/reference_trajectory", "/planning/scenario_planning/trajectory"), - ( - "~/input/predicted_trajectory", - "/control/trajectory_follower/lateral/predicted_trajectory", - ), - ], - parameters=[nearest_search_param, lane_departure_checker_param, vehicle_info_param], - extra_arguments=[{"use_intra_process_comms": LaunchConfiguration("use_intra_process")}], - ) - - # shift decider - shift_decider_component = ComposableNode( - package="autoware_shift_decider", - plugin="autoware::shift_decider::ShiftDecider", - name="autoware_shift_decider", - remappings=[ - ("input/control_cmd", "/control/trajectory_follower/control_cmd"), - ("input/state", "/autoware/state"), - ("input/current_gear", "/vehicle/status/gear_status"), - ("output/gear_cmd", "/control/shift_decider/gear_cmd"), - ], - parameters=[ - shift_decider_param, - ], - extra_arguments=[{"use_intra_process_comms": LaunchConfiguration("use_intra_process")}], - ) - - # autonomous emergency braking - autonomous_emergency_braking = ComposableNode( - package="autoware_autonomous_emergency_braking", - plugin="autoware::motion::control::autonomous_emergency_braking::AEB", - name="autonomous_emergency_braking", - remappings=[ - ("~/input/pointcloud", "/perception/obstacle_segmentation/pointcloud"), - ("~/input/velocity", "/vehicle/status/velocity_status"), - ("~/input/imu", "/sensing/imu/imu_data"), - ("~/input/odometry", "/localization/kinematic_state"), - ( - "~/input/predicted_trajectory", - "/control/trajectory_follower/lateral/predicted_trajectory", - ), - ], - parameters=[ - aeb_param, - ], - extra_arguments=[{"use_intra_process_comms": LaunchConfiguration("use_intra_process")}], - ) - - autonomous_emergency_braking_loader = LoadComposableNodes( - condition=IfCondition(LaunchConfiguration("enable_autonomous_emergency_braking")), - composable_node_descriptions=[autonomous_emergency_braking], - target_container="/control/control_container", - ) - - # autonomous emergency braking - predicted_path_checker = ComposableNode( - package="predicted_path_checker", - plugin="autoware::motion::control::predicted_path_checker::PredictedPathCheckerNode", - name="predicted_path_checker", - remappings=[ - ("~/input/objects", "/perception/object_recognition/objects"), - ("~/input/reference_trajectory", "/planning/scenario_planning/trajectory"), - ("~/input/current_accel", "/localization/acceleration"), - ("~/input/odometry", "/localization/kinematic_state"), - ( - "~/input/predicted_trajectory", - "/control/trajectory_follower/lateral/predicted_trajectory", - ), - ], - parameters=[ - vehicle_info_param, - predicted_path_checker_param, - ], - extra_arguments=[{"use_intra_process_comms": LaunchConfiguration("use_intra_process")}], - ) - - predicted_path_checker_loader = LoadComposableNodes( - condition=IfCondition(LaunchConfiguration("enable_predicted_path_checker")), - composable_node_descriptions=[predicted_path_checker], - target_container="/control/control_container", - ) - - # vehicle cmd gate - vehicle_cmd_gate_component = ComposableNode( - package="autoware_vehicle_cmd_gate", - plugin="autoware::vehicle_cmd_gate::VehicleCmdGate", - name="vehicle_cmd_gate", - remappings=[ - ("input/steering", "/vehicle/status/steering_status"), - ("input/operation_mode", "/system/operation_mode/state"), - ("input/auto/control_cmd", "/control/trajectory_follower/control_cmd"), - ("input/auto/turn_indicators_cmd", "/planning/turn_indicators_cmd"), - ("input/auto/hazard_lights_cmd", "/planning/hazard_lights_cmd"), - ("input/auto/gear_cmd", "/control/shift_decider/gear_cmd"), - ("input/external/control_cmd", "/external/selected/control_cmd"), - ("input/external/turn_indicators_cmd", "/external/selected/turn_indicators_cmd"), - ("input/external/hazard_lights_cmd", "/external/selected/hazard_lights_cmd"), - ("input/external/gear_cmd", "/external/selected/gear_cmd"), - ("input/external_emergency_stop_heartbeat", "/external/selected/heartbeat"), - ("input/gate_mode", "/control/gate_mode_cmd"), - ("input/emergency/control_cmd", "/system/emergency/control_cmd"), - ("input/emergency/hazard_lights_cmd", "/system/emergency/hazard_lights_cmd"), - ("input/emergency/gear_cmd", "/system/emergency/gear_cmd"), - ("input/mrm_state", "/system/fail_safe/mrm_state"), - ("input/kinematics", "/localization/kinematic_state"), - ("input/acceleration", "/localization/acceleration"), - ("output/vehicle_cmd_emergency", "/control/command/emergency_cmd"), - ("output/control_cmd", "/control/command/control_cmd"), - ("output/gear_cmd", "/control/command/gear_cmd"), - ("output/turn_indicators_cmd", "/control/command/turn_indicators_cmd"), - ("output/hazard_lights_cmd", "/control/command/hazard_lights_cmd"), - ("output/gate_mode", "/control/current_gate_mode"), - ("output/engage", "/api/autoware/get/engage"), - ("output/external_emergency", "/api/autoware/get/emergency"), - ("output/operation_mode", "/control/vehicle_cmd_gate/operation_mode"), - ("~/service/engage", "/api/autoware/set/engage"), - ("~/service/external_emergency", "/api/autoware/set/emergency"), - # TODO(Takagi, Isamu): deprecated - ("input/engage", "/autoware/engage"), - ("~/service/external_emergency_stop", "~/external_emergency_stop"), - ("~/service/clear_external_emergency_stop", "~/clear_external_emergency_stop"), - ], - parameters=[ - vehicle_cmd_gate_param, - vehicle_info_param, - { - "check_external_emergency_heartbeat": LaunchConfiguration( - "check_external_emergency_heartbeat" - ), - }, - ], - extra_arguments=[{"use_intra_process_comms": LaunchConfiguration("use_intra_process")}], - ) - - # operation mode transition manager - autoware_operation_mode_transition_manager_component = ComposableNode( - package="autoware_operation_mode_transition_manager", - plugin="autoware::operation_mode_transition_manager::OperationModeTransitionManager", - name="autoware_operation_mode_transition_manager", - remappings=[ - # input - ("kinematics", "/localization/kinematic_state"), - ("steering", "/vehicle/status/steering_status"), - ("trajectory", "/planning/scenario_planning/trajectory"), - ("control_cmd", "/control/command/control_cmd"), - ("trajectory_follower_control_cmd", "/control/trajectory_follower/control_cmd"), - ("control_mode_report", "/vehicle/status/control_mode"), - ("gate_operation_mode", "/control/vehicle_cmd_gate/operation_mode"), - # output - ("is_autonomous_available", "/control/is_autonomous_available"), - ("control_mode_request", "/control/control_mode_request"), - ], - parameters=[ - nearest_search_param, - operation_mode_transition_manager_param, - vehicle_info_param, - ], - ) - - # external cmd selector - external_cmd_selector_loader = IncludeLaunchDescription( - PythonLaunchDescriptionSource( - [ - FindPackageShare("autoware_external_cmd_selector"), - "/launch/external_cmd_selector.launch.py", - ] - ), - launch_arguments=[ - ("use_intra_process", LaunchConfiguration("use_intra_process")), - ("target_container", "/control/control_container"), - ( - "external_cmd_selector_param_path", - LaunchConfiguration("external_cmd_selector_param_path"), - ), - ], - ) - - # external cmd converter - external_cmd_converter_loader = IncludeLaunchDescription( - PythonLaunchDescriptionSource( - [ - FindPackageShare("autoware_external_cmd_converter"), - "/launch/external_cmd_converter.launch.py", - ] - ), - launch_arguments=[ - ("use_intra_process", LaunchConfiguration("use_intra_process")), - ("target_container", "/control/control_container"), - ], - ) - - # obstacle collision checker - obstacle_collision_checker_component = ComposableNode( - package="obstacle_collision_checker", - plugin="obstacle_collision_checker::ObstacleCollisionCheckerNode", - name="obstacle_collision_checker", - remappings=[ - ("input/lanelet_map_bin", "/map/vector_map"), - ("input/obstacle_pointcloud", "/perception/obstacle_segmentation/pointcloud"), - ("input/reference_trajectory", "/planning/scenario_planning/trajectory"), - ( - "input/predicted_trajectory", - "/control/trajectory_follower/lateral/predicted_trajectory", - ), - ("input/odometry", "/localization/kinematic_state"), - ], - parameters=[ - obstacle_collision_checker_param, - vehicle_info_param, - ], - extra_arguments=[{"use_intra_process_comms": LaunchConfiguration("use_intra_process")}], - ) - - obstacle_collision_checker_loader = LoadComposableNodes( - condition=IfCondition(LaunchConfiguration("enable_obstacle_collision_checker")), - composable_node_descriptions=[obstacle_collision_checker_component], - target_container="/control/control_container", - ) - - glog_component = ComposableNode( - package="glog_component", - plugin="GlogComponent", - name="glog_component", - ) - - # set container to run all required components in the same process - if trajectory_follower_mode == "trajectory_follower_node": - container = ComposableNodeContainer( - name="control_container", - namespace="", - package="rclcpp_components", - executable=LaunchConfiguration("container_executable"), - composable_node_descriptions=[ - controller_component, - lane_departure_component, - shift_decider_component, - vehicle_cmd_gate_component, - autoware_operation_mode_transition_manager_component, - glog_component, - ], - ) - - elif trajectory_follower_mode == "smart_mpc_trajectory_follower": - container = ComposableNodeContainer( - name="control_container", - namespace="", - package="rclcpp_components", - executable=LaunchConfiguration("container_executable"), - composable_node_descriptions=[ - lane_departure_component, - shift_decider_component, - vehicle_cmd_gate_component, - autoware_operation_mode_transition_manager_component, - glog_component, - ], - ) - else: - raise Exception( - f"The argument trajectory_follower_mode must be either trajectory_follower_node or smart_mpc_trajectory_follower, but {trajectory_follower_mode} was given." - ) - - # control evaluator - control_evaluator_component = ComposableNode( - package="autoware_control_evaluator", - plugin="control_diagnostics::controlEvaluatorNode", - name="control_evaluator", - remappings=[ - ("~/input/diagnostics", "/diagnostics"), - ("~/input/odometry", "/localization/kinematic_state"), - ("~/input/trajectory", "/planning/scenario_planning/trajectory"), - ("~/metrics", "/diagnostic/control_evaluator/metrics"), - ], - ) - - control_evaluator_loader = LoadComposableNodes( - composable_node_descriptions=[control_evaluator_component], - target_container="/control/control_container", - ) - - # control validator checker - control_validator_component = ComposableNode( - package="autoware_control_validator", - plugin="autoware::control_validator::ControlValidator", - name="control_validator", - remappings=[ - ("~/input/kinematics", "/localization/kinematic_state"), - ("~/input/reference_trajectory", "/planning/scenario_planning/trajectory"), - ( - "~/input/predicted_trajectory", - "/control/trajectory_follower/lateral/predicted_trajectory", - ), - ("~/output/validation_status", "~/validation_status"), - ], - parameters=[control_validator_param], - ) - - group = GroupAction( - [ - PushRosNamespace("control"), - container, - external_cmd_selector_loader, - external_cmd_converter_loader, - obstacle_collision_checker_loader, - autonomous_emergency_braking_loader, - predicted_path_checker_loader, - control_evaluator_loader, - ] - ) - - control_validator_group = GroupAction( - [ - PushRosNamespace("control"), - ComposableNodeContainer( - name="control_validator_container", - namespace="", - package="rclcpp_components", - executable=LaunchConfiguration("container_executable"), - composable_node_descriptions=[ - control_validator_component, - ComposableNode( - package="glog_component", - plugin="GlogComponent", - name="glog_validator_component", - ), - ], - ), - ] - ) - - smart_mpc_trajectory_follower = Node( - package="autoware_smart_mpc_trajectory_follower", - executable="pympc_trajectory_follower.py", - name="pympc_trajectory_follower", - ) - if trajectory_follower_mode == "trajectory_follower_node": - return [group, control_validator_group] - elif trajectory_follower_mode == "smart_mpc_trajectory_follower": - return [group, control_validator_group, smart_mpc_trajectory_follower] - - -def generate_launch_description(): - launch_arguments = [] - - def add_launch_arg(name: str, default_value=None, description=None): - launch_arguments.append( - DeclareLaunchArgument(name, default_value=default_value, description=description) - ) - - # option - add_launch_arg("vehicle_param_file") - add_launch_arg("vehicle_id") - add_launch_arg("enable_obstacle_collision_checker") - add_launch_arg("lateral_controller_mode") - add_launch_arg("longitudinal_controller_mode") - # common param path - add_launch_arg("nearest_search_param_path") - # package param path - add_launch_arg("trajectory_follower_node_param_path") - add_launch_arg("lat_controller_param_path") - add_launch_arg("lon_controller_param_path") - add_launch_arg("vehicle_cmd_gate_param_path") - add_launch_arg("lane_departure_checker_param_path") - add_launch_arg("control_validator_param_path") - add_launch_arg("operation_mode_transition_manager_param_path") - add_launch_arg("shift_decider_param_path") - add_launch_arg("obstacle_collision_checker_param_path") - add_launch_arg("external_cmd_selector_param_path") - add_launch_arg("aeb_param_path") - add_launch_arg("predicted_path_checker_param_path") - add_launch_arg("enable_predicted_path_checker") - add_launch_arg("enable_autonomous_emergency_braking") - add_launch_arg("check_external_emergency_heartbeat") - - # component - add_launch_arg("use_intra_process", "false", "use ROS 2 component container communication") - add_launch_arg("use_multithread", "true", "use multithread") - add_launch_arg( - "trajectory_follower_mode", - "trajectory_follower_node", - "Options for which trajectory_follower to use. Options: `trajectory_follower_node`, `smart_mpc_trajectory_follower`", - ) - set_container_executable = SetLaunchConfiguration( - "container_executable", - "component_container", - condition=UnlessCondition(LaunchConfiguration("use_multithread")), - ) - - set_container_mt_executable = SetLaunchConfiguration( - "container_executable", - "component_container_mt", - condition=IfCondition(LaunchConfiguration("use_multithread")), - ) - return launch.LaunchDescription( - launch_arguments - + [ - set_container_executable, - set_container_mt_executable, - ] - + [OpaqueFunction(function=launch_setup)] - ) diff --git a/launch/tier4_control_launch/launch/control.launch.xml b/launch/tier4_control_launch/launch/control.launch.xml new file mode 100644 index 0000000000000..f23d9f74986e6 --- /dev/null +++ b/launch/tier4_control_launch/launch/control.launch.xml @@ -0,0 +1,251 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/launch/tier4_localization_launch/launch/pose_twist_fusion_filter/pose_twist_fusion_filter.launch.xml b/launch/tier4_localization_launch/launch/pose_twist_fusion_filter/pose_twist_fusion_filter.launch.xml index bdea584bd58ae..b39346de8f8da 100644 --- a/launch/tier4_localization_launch/launch/pose_twist_fusion_filter/pose_twist_fusion_filter.launch.xml +++ b/launch/tier4_localization_launch/launch/pose_twist_fusion_filter/pose_twist_fusion_filter.launch.xml @@ -17,7 +17,7 @@ - + diff --git a/launch/tier4_perception_launch/launch/object_recognition/detection/detection.launch.xml b/launch/tier4_perception_launch/launch/object_recognition/detection/detection.launch.xml index 754d07c6d99bf..373e343ad64fa 100644 --- a/launch/tier4_perception_launch/launch/object_recognition/detection/detection.launch.xml +++ b/launch/tier4_perception_launch/launch/object_recognition/detection/detection.launch.xml @@ -1,271 +1,386 @@ - - + + + + + + + + + + + + + - + + + - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - - - + + + - - + + + + - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - - + + + + + - - + + + + + - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - + + - - - - - - - - - + + + + + + + + + + + + + - - - - - - - - - - - - - - - + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + - - - - - - - - - - - - - - + + + diff --git a/launch/tier4_perception_launch/launch/object_recognition/detection/detector/camera_lidar_detector.launch.xml b/launch/tier4_perception_launch/launch/object_recognition/detection/detector/camera_lidar_detector.launch.xml index fe0ba0e614e60..4c288aa632182 100644 --- a/launch/tier4_perception_launch/launch/object_recognition/detection/detector/camera_lidar_detector.launch.xml +++ b/launch/tier4_perception_launch/launch/object_recognition/detection/detector/camera_lidar_detector.launch.xml @@ -1,106 +1,97 @@ - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + - + + - - - - - - - - - - - - - - + @@ -111,7 +102,7 @@ - + @@ -122,32 +113,32 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -178,32 +169,32 @@ - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + - - - - - - - - @@ -229,7 +220,7 @@ - + diff --git a/launch/tier4_perception_launch/launch/object_recognition/detection/detector/lidar_dnn_detector.launch.xml b/launch/tier4_perception_launch/launch/object_recognition/detection/detector/lidar_dnn_detector.launch.xml index 274b39be681b8..98e8ec7c8ed4b 100644 --- a/launch/tier4_perception_launch/launch/object_recognition/detection/detector/lidar_dnn_detector.launch.xml +++ b/launch/tier4_perception_launch/launch/object_recognition/detection/detector/lidar_dnn_detector.launch.xml @@ -1,17 +1,19 @@ - - - + + + + + @@ -20,14 +22,14 @@ - + - + @@ -40,7 +42,7 @@ - + @@ -48,7 +50,7 @@ - + @@ -76,7 +78,7 @@ - + diff --git a/launch/tier4_perception_launch/launch/object_recognition/detection/detector/lidar_rule_detector.launch.xml b/launch/tier4_perception_launch/launch/object_recognition/detection/detector/lidar_rule_detector.launch.xml index e78823429195f..38ee946201f7e 100644 --- a/launch/tier4_perception_launch/launch/object_recognition/detection/detector/lidar_rule_detector.launch.xml +++ b/launch/tier4_perception_launch/launch/object_recognition/detection/detector/lidar_rule_detector.launch.xml @@ -1,32 +1,22 @@ - - - - - - - - - - - - - - - + + + + + - + - + @@ -41,7 +31,7 @@ - + diff --git a/launch/tier4_perception_launch/launch/object_recognition/detection/detector/tracker_based_detector.launch.xml b/launch/tier4_perception_launch/launch/object_recognition/detection/detector/tracker_based_detector.launch.xml index 0efe7444fcba5..b85d4b02847b3 100644 --- a/launch/tier4_perception_launch/launch/object_recognition/detection/detector/tracker_based_detector.launch.xml +++ b/launch/tier4_perception_launch/launch/object_recognition/detection/detector/tracker_based_detector.launch.xml @@ -1,10 +1,12 @@ + + diff --git a/launch/tier4_perception_launch/launch/object_recognition/detection/filter/object_filter.launch.xml b/launch/tier4_perception_launch/launch/object_recognition/detection/filter/object_filter.launch.xml index 7047aec77c660..d7bbcb01472eb 100644 --- a/launch/tier4_perception_launch/launch/object_recognition/detection/filter/object_filter.launch.xml +++ b/launch/tier4_perception_launch/launch/object_recognition/detection/filter/object_filter.launch.xml @@ -1,11 +1,11 @@ - + - - + + diff --git a/launch/tier4_perception_launch/launch/object_recognition/detection/filter/object_validator.launch.xml b/launch/tier4_perception_launch/launch/object_recognition/detection/filter/object_validator.launch.xml index b3704f130232a..9a09ad2871e85 100644 --- a/launch/tier4_perception_launch/launch/object_recognition/detection/filter/object_validator.launch.xml +++ b/launch/tier4_perception_launch/launch/object_recognition/detection/filter/object_validator.launch.xml @@ -1,12 +1,12 @@ - + - - - + + + diff --git a/launch/tier4_perception_launch/launch/object_recognition/detection/filter/radar_filter.launch.xml b/launch/tier4_perception_launch/launch/object_recognition/detection/filter/radar_filter.launch.xml index d837505649cf1..fe7ab70e9331e 100644 --- a/launch/tier4_perception_launch/launch/object_recognition/detection/filter/radar_filter.launch.xml +++ b/launch/tier4_perception_launch/launch/object_recognition/detection/filter/radar_filter.launch.xml @@ -4,12 +4,12 @@ - + - - + + @@ -36,7 +36,7 @@ - + diff --git a/launch/tier4_perception_launch/launch/object_recognition/detection/merger/camera_lidar_merger.launch.xml b/launch/tier4_perception_launch/launch/object_recognition/detection/merger/camera_lidar_merger.launch.xml index 5bcfe49643dce..d4a9e40f29d97 100644 --- a/launch/tier4_perception_launch/launch/object_recognition/detection/merger/camera_lidar_merger.launch.xml +++ b/launch/tier4_perception_launch/launch/object_recognition/detection/merger/camera_lidar_merger.launch.xml @@ -8,20 +8,45 @@ - + + - - - + + - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -51,31 +76,31 @@ - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + @@ -83,7 +108,7 @@ - + diff --git a/launch/tier4_perception_launch/launch/object_recognition/detection/merger/camera_lidar_radar_merger.launch.xml b/launch/tier4_perception_launch/launch/object_recognition/detection/merger/camera_lidar_radar_merger.launch.xml index 07ab78cd37f72..6ea745a8bae2c 100644 --- a/launch/tier4_perception_launch/launch/object_recognition/detection/merger/camera_lidar_radar_merger.launch.xml +++ b/launch/tier4_perception_launch/launch/object_recognition/detection/merger/camera_lidar_radar_merger.launch.xml @@ -11,27 +11,52 @@ - + + - - - + + - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + @@ -74,31 +99,31 @@ - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + @@ -106,7 +131,7 @@ - + diff --git a/launch/tier4_perception_launch/launch/object_recognition/detection/merger/lidar_merger.launch.xml b/launch/tier4_perception_launch/launch/object_recognition/detection/merger/lidar_merger.launch.xml index f0ce19db6c342..c82de41a28b50 100644 --- a/launch/tier4_perception_launch/launch/object_recognition/detection/merger/lidar_merger.launch.xml +++ b/launch/tier4_perception_launch/launch/object_recognition/detection/merger/lidar_merger.launch.xml @@ -8,21 +8,21 @@ - + + - - - + + - - + + - + @@ -46,7 +46,7 @@ - + diff --git a/launch/tier4_perception_launch/launch/object_recognition/tracking/tracking.launch.xml b/launch/tier4_perception_launch/launch/object_recognition/tracking/tracking.launch.xml index b8172daa616fa..1975ad2d15eb9 100644 --- a/launch/tier4_perception_launch/launch/object_recognition/tracking/tracking.launch.xml +++ b/launch/tier4_perception_launch/launch/object_recognition/tracking/tracking.launch.xml @@ -1,5 +1,8 @@ + + + @@ -11,52 +14,87 @@ + - - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/launch/tier4_perception_launch/launch/obstacle_segmentation/ground_segmentation/ground_segmentation.launch.py b/launch/tier4_perception_launch/launch/obstacle_segmentation/ground_segmentation/ground_segmentation.launch.py index 25fb1e6ac6c4d..684ecee757aad 100644 --- a/launch/tier4_perception_launch/launch/obstacle_segmentation/ground_segmentation/ground_segmentation.launch.py +++ b/launch/tier4_perception_launch/launch/obstacle_segmentation/ground_segmentation/ground_segmentation.launch.py @@ -82,6 +82,12 @@ def create_additional_pipeline(self, lidar_name): "margin_min_z" ] ) + # Get the plugin name from the full plugin path + ground_segmentation_plugin_name = self.ground_segmentation_param[ + f"{lidar_name}_ground_filter" + ]["plugin"] + ground_segmentation_plugin_name = ground_segmentation_plugin_name.split("::")[-1] + components = [] components.append( ComposableNode( @@ -110,7 +116,7 @@ def create_additional_pipeline(self, lidar_name): components.append( ComposableNode( package="ground_segmentation", - plugin=self.ground_segmentation_param[f"{lidar_name}_ground_filter"]["plugin"], + plugin="autoware::ground_segmentation::" + ground_segmentation_plugin_name, name=f"{lidar_name}_ground_filter", remappings=[ ("input", f"{lidar_name}/range_cropped/pointcloud"), @@ -203,7 +209,7 @@ def create_ransac_pipeline(self): components.append( ComposableNode( package="ground_segmentation", - plugin="ground_segmentation::RANSACGroundFilterComponent", + plugin="autoware::ground_segmentation::" + "RANSACGroundFilterComponent", name="ransac_ground_filter", namespace="plane_fitting", remappings=[ @@ -228,6 +234,12 @@ def create_common_pipeline(self, input_topic, output_topic): self.vehicle_info["min_height_offset"] + self.ground_segmentation_param["common_crop_box_filter"]["parameters"]["margin_min_z"] ) + # Get the plugin name from the full plugin path + ground_segmentation_plugin_name = self.ground_segmentation_param["common_ground_filter"][ + "plugin" + ] + ground_segmentation_plugin_name = ground_segmentation_plugin_name.split("::")[-1] + components = [] components.append( ComposableNode( @@ -256,7 +268,7 @@ def create_common_pipeline(self, input_topic, output_topic): components.append( ComposableNode( package="ground_segmentation", - plugin=self.ground_segmentation_param["common_ground_filter"]["plugin"], + plugin="autoware::ground_segmentation::" + ground_segmentation_plugin_name, name="common_ground_filter", remappings=[ ("input", "range_cropped/pointcloud"), diff --git a/launch/tier4_perception_launch/launch/perception.launch.xml b/launch/tier4_perception_launch/launch/perception.launch.xml index dd80ab5b2cde7..5d8d7563e7a7c 100644 --- a/launch/tier4_perception_launch/launch/perception.launch.xml +++ b/launch/tier4_perception_launch/launch/perception.launch.xml @@ -77,6 +77,7 @@ + - - + + @@ -115,12 +116,16 @@ - + + + + + @@ -186,25 +191,36 @@ - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -218,11 +234,14 @@ + + + - + @@ -240,7 +259,9 @@ + + diff --git a/launch/tier4_planning_launch/launch/scenario_planning/lane_driving/motion_planning/motion_planning.launch.xml b/launch/tier4_planning_launch/launch/scenario_planning/lane_driving/motion_planning/motion_planning.launch.xml index ae43c2d81efb6..d0c34ea01b2c6 100644 --- a/launch/tier4_planning_launch/launch/scenario_planning/lane_driving/motion_planning/motion_planning.launch.xml +++ b/launch/tier4_planning_launch/launch/scenario_planning/lane_driving/motion_planning/motion_planning.launch.xml @@ -160,6 +160,7 @@ + diff --git a/launch/tier4_planning_launch/package.xml b/launch/tier4_planning_launch/package.xml index 1c65c975bc5bf..2b80c2c6ea32e 100644 --- a/launch/tier4_planning_launch/package.xml +++ b/launch/tier4_planning_launch/package.xml @@ -65,6 +65,7 @@ autoware_freespace_planner autoware_mission_planner autoware_obstacle_cruise_planner + autoware_obstacle_stop_planner autoware_path_optimizer autoware_planning_evaluator autoware_planning_topic_converter @@ -74,7 +75,6 @@ autoware_surround_obstacle_checker autoware_velocity_smoother glog_component - obstacle_stop_planner ament_lint_auto autoware_lint_common diff --git a/launch/tier4_simulator_launch/launch/simulator.launch.xml b/launch/tier4_simulator_launch/launch/simulator.launch.xml index 97b07410d108a..7dc33e0e82ea9 100644 --- a/launch/tier4_simulator_launch/launch/simulator.launch.xml +++ b/launch/tier4_simulator_launch/launch/simulator.launch.xml @@ -78,6 +78,7 @@ + diff --git a/launch/tier4_system_launch/launch/system.launch.xml b/launch/tier4_system_launch/launch/system.launch.xml index 6253dce864ddf..55c4403b65923 100644 --- a/launch/tier4_system_launch/launch/system.launch.xml +++ b/launch/tier4_system_launch/launch/system.launch.xml @@ -26,7 +26,7 @@ - + diff --git a/localization/autoware_landmark_based_localizer/README.md b/localization/autoware_landmark_based_localizer/README.md index 49588a19ac620..ad4f1f5748f59 100644 --- a/localization/autoware_landmark_based_localizer/README.md +++ b/localization/autoware_landmark_based_localizer/README.md @@ -43,7 +43,7 @@ So, if the 4 vertices are considered as forming a tetrahedron and its volume exc ## Map specifications -See +See ## About `consider_orientation` diff --git a/localization/autoware_landmark_based_localizer/autoware_ar_tag_based_localizer/README.md b/localization/autoware_landmark_based_localizer/autoware_ar_tag_based_localizer/README.md index 37596ee9820b9..14501c689dbe6 100644 --- a/localization/autoware_landmark_based_localizer/autoware_ar_tag_based_localizer/README.md +++ b/localization/autoware_landmark_based_localizer/autoware_ar_tag_based_localizer/README.md @@ -46,7 +46,7 @@ ros2 launch autoware_launch ... \ ### Rosbag -#### [Sample rosbag and map (AWSIM data)](https://drive.google.com/file/d/1uMVwQQFcfs8JOqfoA1FqfH_fLPwQ71jK/view) +#### [Sample rosbag and map (AWSIM data)](https://drive.google.com/file/d/1ZPsfDvOXFrMxtx7fb1W5sOXdAK1e71hY/view) This data is simulated data created by [AWSIM](https://tier4.github.io/AWSIM/). Essentially, AR tag-based self-localization is not intended for such public road driving, but for driving in a smaller area, so the max driving speed is set at 15 km/h. @@ -55,7 +55,7 @@ It is a known problem that the timing of when each AR tag begins to be detected ![sample_result_in_awsim](./doc_image/sample_result_in_awsim.png) -#### [Sample rosbag and map (Real world data)](https://drive.google.com/file/d/1wiCQjyjRnYbb0dg8G6mRecdSGh8tv3zR/view) +#### [Sample rosbag and map (Real world data)](https://drive.google.com/file/d/1VQCQ_qiEZpCMI3-z6SNs__zJ-4HJFQjx/view) Please remap the topic names and play it. diff --git a/localization/autoware_landmark_based_localizer/autoware_ar_tag_based_localizer/package.xml b/localization/autoware_landmark_based_localizer/autoware_ar_tag_based_localizer/package.xml index bf40719d7a5e6..1b2469d108dfb 100644 --- a/localization/autoware_landmark_based_localizer/autoware_ar_tag_based_localizer/package.xml +++ b/localization/autoware_landmark_based_localizer/autoware_ar_tag_based_localizer/package.xml @@ -19,9 +19,9 @@ ament_index_cpp aruco autoware_landmark_manager + autoware_lanelet2_extension cv_bridge diagnostic_msgs - lanelet2_extension localization_util rclcpp rclcpp_components diff --git a/localization/autoware_landmark_based_localizer/autoware_ar_tag_based_localizer/src/ar_tag_based_localizer.cpp b/localization/autoware_landmark_based_localizer/autoware_ar_tag_based_localizer/src/ar_tag_based_localizer.cpp index c39969fb3cc5f..cef3debf22a4f 100644 --- a/localization/autoware_landmark_based_localizer/autoware_ar_tag_based_localizer/src/ar_tag_based_localizer.cpp +++ b/localization/autoware_landmark_based_localizer/autoware_ar_tag_based_localizer/src/ar_tag_based_localizer.cpp @@ -51,7 +51,11 @@ #include #include -#include +#if __has_include() +#include // for ROS 2 Jazzy or newer +#else +#include // for ROS 2 Humble or older +#endif #include #include diff --git a/localization/autoware_landmark_based_localizer/autoware_landmark_manager/include/autoware/landmark_manager/landmark_manager.hpp b/localization/autoware_landmark_based_localizer/autoware_landmark_manager/include/autoware/landmark_manager/landmark_manager.hpp index 3fc6505e72c31..99fe623290ee5 100644 --- a/localization/autoware_landmark_based_localizer/autoware_landmark_manager/include/autoware/landmark_manager/landmark_manager.hpp +++ b/localization/autoware_landmark_based_localizer/autoware_landmark_manager/include/autoware/landmark_manager/landmark_manager.hpp @@ -15,7 +15,7 @@ #ifndef AUTOWARE__LANDMARK_MANAGER__LANDMARK_MANAGER_HPP_ #define AUTOWARE__LANDMARK_MANAGER__LANDMARK_MANAGER_HPP_ -#include "lanelet2_extension/localization/landmark.hpp" +#include "autoware_lanelet2_extension/localization/landmark.hpp" #include diff --git a/localization/autoware_landmark_based_localizer/autoware_landmark_manager/package.xml b/localization/autoware_landmark_based_localizer/autoware_landmark_manager/package.xml index 1471b5182def8..4880cf88bec5d 100644 --- a/localization/autoware_landmark_based_localizer/autoware_landmark_manager/package.xml +++ b/localization/autoware_landmark_based_localizer/autoware_landmark_manager/package.xml @@ -18,9 +18,9 @@ eigen + autoware_lanelet2_extension autoware_map_msgs geometry_msgs - lanelet2_extension localization_util rclcpp tf2_eigen diff --git a/localization/autoware_landmark_based_localizer/autoware_landmark_manager/src/landmark_manager.cpp b/localization/autoware_landmark_based_localizer/autoware_landmark_manager/src/landmark_manager.cpp index 2fa0fdbdb315d..488f250e325d2 100644 --- a/localization/autoware_landmark_based_localizer/autoware_landmark_manager/src/landmark_manager.cpp +++ b/localization/autoware_landmark_based_localizer/autoware_landmark_manager/src/landmark_manager.cpp @@ -15,7 +15,7 @@ #include "autoware/landmark_manager/landmark_manager.hpp" #include "autoware/universe_utils/geometry/geometry.hpp" -#include "lanelet2_extension/utility/message_conversion.hpp" +#include "autoware_lanelet2_extension/utility/message_conversion.hpp" #include "localization_util/util_func.hpp" #include diff --git a/localization/stop_filter/CMakeLists.txt b/localization/autoware_stop_filter/CMakeLists.txt similarity index 78% rename from localization/stop_filter/CMakeLists.txt rename to localization/autoware_stop_filter/CMakeLists.txt index 4776a1f4967b2..96123e89c5050 100644 --- a/localization/stop_filter/CMakeLists.txt +++ b/localization/autoware_stop_filter/CMakeLists.txt @@ -1,15 +1,16 @@ cmake_minimum_required(VERSION 3.14) -project(stop_filter) +project(autoware_stop_filter) find_package(autoware_cmake REQUIRED) autoware_package() ament_auto_add_library(${PROJECT_NAME} SHARED src/stop_filter.cpp + src/stop_filter.hpp ) rclcpp_components_register_node(${PROJECT_NAME} - PLUGIN "StopFilter" + PLUGIN "autoware::stop_filter::StopFilter" EXECUTABLE ${PROJECT_NAME}_node EXECUTOR SingleThreadedExecutor ) diff --git a/localization/stop_filter/README.md b/localization/autoware_stop_filter/README.md similarity index 100% rename from localization/stop_filter/README.md rename to localization/autoware_stop_filter/README.md diff --git a/localization/stop_filter/config/stop_filter.param.yaml b/localization/autoware_stop_filter/config/stop_filter.param.yaml similarity index 100% rename from localization/stop_filter/config/stop_filter.param.yaml rename to localization/autoware_stop_filter/config/stop_filter.param.yaml diff --git a/localization/stop_filter/launch/stop_filter.launch.xml b/localization/autoware_stop_filter/launch/stop_filter.launch.xml similarity index 69% rename from localization/stop_filter/launch/stop_filter.launch.xml rename to localization/autoware_stop_filter/launch/stop_filter.launch.xml index c53b37efc9954..ff7b6cafe6e8f 100644 --- a/localization/stop_filter/launch/stop_filter.launch.xml +++ b/localization/autoware_stop_filter/launch/stop_filter.launch.xml @@ -1,9 +1,9 @@ - + - + diff --git a/localization/stop_filter/package.xml b/localization/autoware_stop_filter/package.xml similarity index 97% rename from localization/stop_filter/package.xml rename to localization/autoware_stop_filter/package.xml index 2e6d5c4f8bbe1..dfdbd5f182983 100644 --- a/localization/stop_filter/package.xml +++ b/localization/autoware_stop_filter/package.xml @@ -1,7 +1,7 @@ - stop_filter + autoware_stop_filter 0.0.0 The stop filter package Koji Minoda diff --git a/localization/stop_filter/schema/stop_filter.schema.json b/localization/autoware_stop_filter/schema/stop_filter.schema.json similarity index 100% rename from localization/stop_filter/schema/stop_filter.schema.json rename to localization/autoware_stop_filter/schema/stop_filter.schema.json diff --git a/localization/stop_filter/src/stop_filter.cpp b/localization/autoware_stop_filter/src/stop_filter.cpp similarity index 92% rename from localization/stop_filter/src/stop_filter.cpp rename to localization/autoware_stop_filter/src/stop_filter.cpp index c1c4de2fb6b6e..f5e29419105d4 100644 --- a/localization/stop_filter/src/stop_filter.cpp +++ b/localization/autoware_stop_filter/src/stop_filter.cpp @@ -12,7 +12,7 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "stop_filter/stop_filter.hpp" +#include "stop_filter.hpp" #include @@ -24,6 +24,8 @@ using std::placeholders::_1; +namespace autoware::stop_filter +{ StopFilter::StopFilter(const rclcpp::NodeOptions & node_options) : rclcpp::Node("stop_filter", node_options) { @@ -57,6 +59,7 @@ void StopFilter::callback_odometry(const nav_msgs::msg::Odometry::SharedPtr msg) pub_stop_flag_->publish(stop_flag_msg); pub_odom_->publish(odom_msg); } +} // namespace autoware::stop_filter #include -RCLCPP_COMPONENTS_REGISTER_NODE(StopFilter) +RCLCPP_COMPONENTS_REGISTER_NODE(autoware::stop_filter::StopFilter) diff --git a/localization/stop_filter/include/stop_filter/stop_filter.hpp b/localization/autoware_stop_filter/src/stop_filter.hpp similarity index 91% rename from localization/stop_filter/include/stop_filter/stop_filter.hpp rename to localization/autoware_stop_filter/src/stop_filter.hpp index e8428788820b2..ce6e67f0b7265 100644 --- a/localization/stop_filter/include/stop_filter/stop_filter.hpp +++ b/localization/autoware_stop_filter/src/stop_filter.hpp @@ -12,8 +12,8 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef STOP_FILTER__STOP_FILTER_HPP_ -#define STOP_FILTER__STOP_FILTER_HPP_ +#ifndef STOP_FILTER_HPP_ +#define STOP_FILTER_HPP_ #include @@ -34,6 +34,8 @@ #include #include +namespace autoware::stop_filter +{ class StopFilter : public rclcpp::Node { public: @@ -54,4 +56,5 @@ class StopFilter : public rclcpp::Node */ void callback_odometry(const nav_msgs::msg::Odometry::SharedPtr msg); }; -#endif // STOP_FILTER__STOP_FILTER_HPP_ +} // namespace autoware::stop_filter +#endif // STOP_FILTER_HPP_ diff --git a/localization/ekf_localizer/README.md b/localization/ekf_localizer/README.md index b91282015732e..3b417b12bc6e9 100644 --- a/localization/ekf_localizer/README.md +++ b/localization/ekf_localizer/README.md @@ -186,10 +186,12 @@ Note that, although the dimension gets larger since the analytical expansion can - The number of consecutive no measurement update via the Pose/Twist topic exceeds the `pose_no_update_count_threshold_warn`/`twist_no_update_count_threshold_warn`. - The timestamp of the Pose/Twist topic is beyond the delay compensation range. - The Pose/Twist topic is beyond the range of Mahalanobis distance for covariance estimation. +- The covariance ellipse is bigger than threshold `warn_ellipse_size` for long axis or `warn_ellipse_size_lateral_direction` for lateral_direction. ### The conditions that result in an ERROR state - The number of consecutive no measurement update via the Pose/Twist topic exceeds the `pose_no_update_count_threshold_error`/`twist_no_update_count_threshold_error`. +- The covariance ellipse is bigger than threshold `error_ellipse_size` for long axis or `error_ellipse_size_lateral_direction` for lateral_direction. ## Known issues diff --git a/localization/ekf_localizer/config/ekf_localizer.param.yaml b/localization/ekf_localizer/config/ekf_localizer.param.yaml index 19bfd2d498b68..4a7696ec9e65e 100644 --- a/localization/ekf_localizer/config/ekf_localizer.param.yaml +++ b/localization/ekf_localizer/config/ekf_localizer.param.yaml @@ -39,6 +39,11 @@ pose_no_update_count_threshold_error: 100 twist_no_update_count_threshold_warn: 50 twist_no_update_count_threshold_error: 100 + ellipse_scale: 3.0 + error_ellipse_size: 1.5 + warn_ellipse_size: 1.2 + error_ellipse_size_lateral_direction: 0.3 + warn_ellipse_size_lateral_direction: 0.25 misc: # for velocity measurement limitation (Set 0.0 if you want to ignore) diff --git a/localization/ekf_localizer/include/ekf_localizer/diagnostics.hpp b/localization/ekf_localizer/include/ekf_localizer/diagnostics.hpp index 4c92b2947642b..8815a1758de01 100644 --- a/localization/ekf_localizer/include/ekf_localizer/diagnostics.hpp +++ b/localization/ekf_localizer/include/ekf_localizer/diagnostics.hpp @@ -33,6 +33,9 @@ diagnostic_msgs::msg::DiagnosticStatus check_measurement_delay_gate( diagnostic_msgs::msg::DiagnosticStatus check_measurement_mahalanobis_gate( const std::string & measurement_type, const bool is_passed_mahalanobis_gate, const double mahalanobis_distance, const double mahalanobis_distance_threshold); +diagnostic_msgs::msg::DiagnosticStatus check_covariance_ellipse( + const std::string & name, const double curr_size, const double warn_threshold, + const double error_threshold); diagnostic_msgs::msg::DiagnosticStatus merge_diagnostic_status( const std::vector & stat_array); diff --git a/localization/ekf_localizer/include/ekf_localizer/ekf_localizer.hpp b/localization/ekf_localizer/include/ekf_localizer/ekf_localizer.hpp index 65003f5fe9864..ada9024faceee 100644 --- a/localization/ekf_localizer/include/ekf_localizer/ekf_localizer.hpp +++ b/localization/ekf_localizer/include/ekf_localizer/ekf_localizer.hpp @@ -216,7 +216,8 @@ class EKFLocalizer : public rclcpp::Node /** * @brief publish diagnostics message */ - void publish_diagnostics(const rclcpp::Time & current_time); + void publish_diagnostics( + const geometry_msgs::msg::PoseStamped & current_ekf_pose, const rclcpp::Time & current_time); /** * @brief update simple 1d filter diff --git a/localization/ekf_localizer/include/ekf_localizer/hyper_parameters.hpp b/localization/ekf_localizer/include/ekf_localizer/hyper_parameters.hpp index 8d76102e5d64d..7c3c03c83b778 100644 --- a/localization/ekf_localizer/include/ekf_localizer/hyper_parameters.hpp +++ b/localization/ekf_localizer/include/ekf_localizer/hyper_parameters.hpp @@ -57,6 +57,13 @@ class HyperParameters node->declare_parameter("diagnostics.twist_no_update_count_threshold_warn")), twist_no_update_count_threshold_error( node->declare_parameter("diagnostics.twist_no_update_count_threshold_error")), + ellipse_scale(node->declare_parameter("diagnostics.ellipse_scale")), + error_ellipse_size(node->declare_parameter("diagnostics.error_ellipse_size")), + warn_ellipse_size(node->declare_parameter("diagnostics.warn_ellipse_size")), + error_ellipse_size_lateral_direction( + node->declare_parameter("diagnostics.error_ellipse_size_lateral_direction")), + warn_ellipse_size_lateral_direction( + node->declare_parameter("diagnostics.warn_ellipse_size_lateral_direction")), threshold_observable_velocity_mps( node->declare_parameter("misc.threshold_observable_velocity_mps")) { @@ -86,6 +93,12 @@ class HyperParameters const size_t pose_no_update_count_threshold_error; const size_t twist_no_update_count_threshold_warn; const size_t twist_no_update_count_threshold_error; + double ellipse_scale; + double error_ellipse_size; + double warn_ellipse_size; + double error_ellipse_size_lateral_direction; + double warn_ellipse_size_lateral_direction; + const double threshold_observable_velocity_mps; }; diff --git a/localization/ekf_localizer/media/ekf_diagnostics.png b/localization/ekf_localizer/media/ekf_diagnostics.png index 16de42ac2d85a..234b2f1e19b6d 100644 Binary files a/localization/ekf_localizer/media/ekf_diagnostics.png and b/localization/ekf_localizer/media/ekf_diagnostics.png differ diff --git a/localization/ekf_localizer/package.xml b/localization/ekf_localizer/package.xml index 07efca6339b98..9c89867025632 100644 --- a/localization/ekf_localizer/package.xml +++ b/localization/ekf_localizer/package.xml @@ -28,6 +28,7 @@ fmt geometry_msgs kalman_filter + localization_util nav_msgs rclcpp rclcpp_components diff --git a/localization/ekf_localizer/schema/sub/diagnostics.sub_schema.json b/localization/ekf_localizer/schema/sub/diagnostics.sub_schema.json index 2e2dca411971e..eda9e7d06f6f4 100644 --- a/localization/ekf_localizer/schema/sub/diagnostics.sub_schema.json +++ b/localization/ekf_localizer/schema/sub/diagnostics.sub_schema.json @@ -24,6 +24,31 @@ "type": "integer", "description": "The threshold at which an ERROR state is triggered due to the Twist Topic update not happening continuously for a certain number of times", "default": 100 + }, + "ellipse_scale": { + "type": "number", + "description": "The scale factor to apply the error ellipse size", + "default": 3.0 + }, + "error_ellipse_size": { + "type": "number", + "description": "The long axis size of the error ellipse to trigger a ERROR state", + "default": 1.5 + }, + "warn_ellipse_size": { + "type": "number", + "description": "The long axis size of the error ellipse to trigger a WARN state", + "default": 1.2 + }, + "error_ellipse_size_lateral_direction": { + "type": "number", + "description": "The lateral direction size of the error ellipse to trigger a ERROR state", + "default": 0.3 + }, + "warn_ellipse_size_lateral_direction": { + "type": "number", + "description": "The lateral direction size of the error ellipse to trigger a WARN state", + "default": 0.25 } }, "required": [ diff --git a/localization/ekf_localizer/src/diagnostics.cpp b/localization/ekf_localizer/src/diagnostics.cpp index ecbeaf8b2445a..902efeafacdb3 100644 --- a/localization/ekf_localizer/src/diagnostics.cpp +++ b/localization/ekf_localizer/src/diagnostics.cpp @@ -139,6 +139,39 @@ diagnostic_msgs::msg::DiagnosticStatus check_measurement_mahalanobis_gate( return stat; } +diagnostic_msgs::msg::DiagnosticStatus check_covariance_ellipse( + const std::string & name, const double curr_size, const double warn_threshold, + const double error_threshold) +{ + diagnostic_msgs::msg::DiagnosticStatus stat; + + diagnostic_msgs::msg::KeyValue key_value; + key_value.key = name + "_size"; + key_value.value = std::to_string(curr_size); + stat.values.push_back(key_value); + + key_value.key = name + "_warn_threshold"; + key_value.value = std::to_string(warn_threshold); + stat.values.push_back(key_value); + + key_value.key = name + "_error_threshold"; + key_value.value = std::to_string(error_threshold); + stat.values.push_back(key_value); + + stat.level = diagnostic_msgs::msg::DiagnosticStatus::OK; + stat.message = "OK"; + if (curr_size >= warn_threshold) { + stat.level = diagnostic_msgs::msg::DiagnosticStatus::WARN; + stat.message = "[WARN]" + name + " is large"; + } + if (curr_size >= error_threshold) { + stat.level = diagnostic_msgs::msg::DiagnosticStatus::ERROR; + stat.message = "[ERROR]" + name + " is large"; + } + + return stat; +} + // The highest level within the stat_array will be reflected in the merged_stat. // When all stat_array entries are 'OK,' the message of merged_stat will be "OK" diagnostic_msgs::msg::DiagnosticStatus merge_diagnostic_status( diff --git a/localization/ekf_localizer/src/ekf_localizer.cpp b/localization/ekf_localizer/src/ekf_localizer.cpp index 11fa2b6713a52..e541939026d95 100644 --- a/localization/ekf_localizer/src/ekf_localizer.cpp +++ b/localization/ekf_localizer/src/ekf_localizer.cpp @@ -17,6 +17,7 @@ #include "ekf_localizer/diagnostics.hpp" #include "ekf_localizer/string.hpp" #include "ekf_localizer/warning_message.hpp" +#include "localization_util/covariance_ellipse.hpp" #include #include @@ -148,7 +149,7 @@ void EKFLocalizer::timer_callback() if (!is_activated_) { warning_->warn_throttle( "The node is not activated. Provide initial pose to pose_initializer", 2000); - publish_diagnostics(current_time); + publish_diagnostics(geometry_msgs::msg::PoseStamped{}, current_time); return; } @@ -241,7 +242,7 @@ void EKFLocalizer::timer_callback() /* publish ekf result */ publish_estimate_result(current_ekf_pose, current_biased_ekf_pose, current_ekf_twist); - publish_diagnostics(current_time); + publish_diagnostics(current_ekf_pose, current_time); } /* @@ -390,7 +391,8 @@ void EKFLocalizer::publish_estimate_result( pub_odom_->publish(odometry); } -void EKFLocalizer::publish_diagnostics(const rclcpp::Time & current_time) +void EKFLocalizer::publish_diagnostics( + const geometry_msgs::msg::PoseStamped & current_ekf_pose, const rclcpp::Time & current_time) { std::vector diag_status_array; @@ -418,6 +420,18 @@ void EKFLocalizer::publish_diagnostics(const rclcpp::Time & current_time) diag_status_array.push_back(check_measurement_mahalanobis_gate( "twist", twist_diag_info_.is_passed_mahalanobis_gate, twist_diag_info_.mahalanobis_distance, params_.twist_gate_dist)); + + geometry_msgs::msg::PoseWithCovariance pose_cov; + pose_cov.pose = current_ekf_pose.pose; + pose_cov.covariance = ekf_module_->get_current_pose_covariance(); + const autoware::localization_util::Ellipse ellipse = + autoware::localization_util::calculate_xy_ellipse(pose_cov, params_.ellipse_scale); + diag_status_array.push_back(check_covariance_ellipse( + "cov_ellipse_long_axis", ellipse.long_radius, params_.warn_ellipse_size, + params_.error_ellipse_size)); + diag_status_array.push_back(check_covariance_ellipse( + "cov_ellipse_lateral_direction", ellipse.size_lateral_direction, + params_.warn_ellipse_size_lateral_direction, params_.error_ellipse_size_lateral_direction)); } diagnostic_msgs::msg::DiagnosticStatus diag_merged_status; diff --git a/localization/localization_error_monitor/include/localization_error_monitor/localization_error_monitor.hpp b/localization/localization_error_monitor/include/localization_error_monitor/localization_error_monitor.hpp index a178a305f6d12..0f293e4d31cac 100644 --- a/localization/localization_error_monitor/include/localization_error_monitor/localization_error_monitor.hpp +++ b/localization/localization_error_monitor/include/localization_error_monitor/localization_error_monitor.hpp @@ -15,6 +15,8 @@ #ifndef LOCALIZATION_ERROR_MONITOR__LOCALIZATION_ERROR_MONITOR_HPP_ #define LOCALIZATION_ERROR_MONITOR__LOCALIZATION_ERROR_MONITOR_HPP_ +#include "localization_util/covariance_ellipse.hpp" + #include #include #include @@ -25,15 +27,6 @@ #include -struct Ellipse -{ - double long_radius; - double short_radius; - double yaw; - Eigen::Matrix2d P; - double size_lateral_direction; -}; - class LocalizationErrorMonitor : public rclcpp::Node { private: @@ -50,12 +43,9 @@ class LocalizationErrorMonitor : public rclcpp::Node double warn_ellipse_size_; double error_ellipse_size_lateral_direction_; double warn_ellipse_size_lateral_direction_; - Ellipse ellipse_; + autoware::localization_util::Ellipse ellipse_; void on_odom(nav_msgs::msg::Odometry::ConstSharedPtr input_msg); - visualization_msgs::msg::Marker create_ellipse_marker( - const Ellipse & ellipse, nav_msgs::msg::Odometry::ConstSharedPtr odom); - static double measure_size_ellipse_along_body_frame(const Eigen::Matrix2d & Pinv, double theta); public: explicit LocalizationErrorMonitor(const rclcpp::NodeOptions & options); diff --git a/localization/localization_error_monitor/package.xml b/localization/localization_error_monitor/package.xml index 681da7e57c6c5..b8a466f95dfab 100644 --- a/localization/localization_error_monitor/package.xml +++ b/localization/localization_error_monitor/package.xml @@ -23,6 +23,7 @@ autoware_universe_utils diagnostic_msgs diagnostic_updater + localization_util nav_msgs rclcpp_components tf2 diff --git a/localization/localization_error_monitor/src/localization_error_monitor.cpp b/localization/localization_error_monitor/src/localization_error_monitor.cpp index 204afa7bdec2f..44223b0fd1670 100644 --- a/localization/localization_error_monitor/src/localization_error_monitor.cpp +++ b/localization/localization_error_monitor/src/localization_error_monitor.cpp @@ -58,61 +58,12 @@ LocalizationErrorMonitor::LocalizationErrorMonitor(const rclcpp::NodeOptions & o logger_configure_ = std::make_unique(this); } -visualization_msgs::msg::Marker LocalizationErrorMonitor::create_ellipse_marker( - const Ellipse & ellipse, nav_msgs::msg::Odometry::ConstSharedPtr odom) -{ - tf2::Quaternion quat; - quat.setEuler(0, 0, ellipse.yaw); - - const double ellipse_long_radius = std::min(ellipse.long_radius, 30.0); - const double ellipse_short_radius = std::min(ellipse.short_radius, 30.0); - visualization_msgs::msg::Marker marker; - marker.header = odom->header; - marker.header.stamp = this->now(); - marker.ns = "error_ellipse"; - marker.id = 0; - marker.type = visualization_msgs::msg::Marker::SPHERE; - marker.action = visualization_msgs::msg::Marker::ADD; - marker.pose = odom->pose.pose; - marker.pose.orientation = tf2::toMsg(quat); - marker.scale.x = ellipse_long_radius * 2; - marker.scale.y = ellipse_short_radius * 2; - marker.scale.z = 0.01; - marker.color.a = 0.1; - marker.color.r = 0.0; - marker.color.g = 0.0; - marker.color.b = 1.0; - return marker; -} - void LocalizationErrorMonitor::on_odom(nav_msgs::msg::Odometry::ConstSharedPtr input_msg) { - // create xy covariance (2x2 matrix) - // input geometry_msgs::PoseWithCovariance contain 6x6 matrix - Eigen::Matrix2d xy_covariance; - const auto cov = input_msg->pose.covariance; - xy_covariance(0, 0) = cov[0 * 6 + 0]; - xy_covariance(0, 1) = cov[0 * 6 + 1]; - xy_covariance(1, 0) = cov[1 * 6 + 0]; - xy_covariance(1, 1) = cov[1 * 6 + 1]; - - Eigen::SelfAdjointEigenSolver eigensolver(xy_covariance); - - // eigen values and vectors are sorted in ascending order - ellipse_.long_radius = scale_ * std::sqrt(eigensolver.eigenvalues()(1)); - ellipse_.short_radius = scale_ * std::sqrt(eigensolver.eigenvalues()(0)); - - // principal component vector - const Eigen::Vector2d pc_vector = eigensolver.eigenvectors().col(1); - ellipse_.yaw = std::atan2(pc_vector.y(), pc_vector.x()); - - // ellipse size along lateral direction (body-frame) - ellipse_.P = xy_covariance; - const double yaw_vehicle = tf2::getYaw(input_msg->pose.pose.orientation); - ellipse_.size_lateral_direction = - scale_ * measure_size_ellipse_along_body_frame(ellipse_.P.inverse(), yaw_vehicle); - - const auto ellipse_marker = create_ellipse_marker(ellipse_, input_msg); + ellipse_ = autoware::localization_util::calculate_xy_ellipse(input_msg->pose, scale_); + + const auto ellipse_marker = autoware::localization_util::create_ellipse_marker( + ellipse_, input_msg->header, input_msg->pose); ellipse_marker_pub_->publish(ellipse_marker); // diagnostics @@ -134,16 +85,5 @@ void LocalizationErrorMonitor::on_odom(nav_msgs::msg::Odometry::ConstSharedPtr i diag_pub_->publish(diag_msg); } -double LocalizationErrorMonitor::measure_size_ellipse_along_body_frame( - const Eigen::Matrix2d & Pinv, const double theta) -{ - Eigen::MatrixXd e(2, 1); - e(0, 0) = std::cos(theta); - e(1, 0) = std::sin(theta); - - double d = std::sqrt((e.transpose() * Pinv * e)(0, 0) / Pinv.determinant()); - return d; -} - #include RCLCPP_COMPONENTS_REGISTER_NODE(LocalizationErrorMonitor) diff --git a/localization/localization_util/CMakeLists.txt b/localization/localization_util/CMakeLists.txt index 9a9086314f42e..ebf2b6dc10878 100644 --- a/localization/localization_util/CMakeLists.txt +++ b/localization/localization_util/CMakeLists.txt @@ -8,6 +8,7 @@ ament_auto_add_library(localization_util SHARED src/util_func.cpp src/smart_pose_buffer.cpp src/tree_structured_parzen_estimator.cpp + src/covariance_ellipse.cpp ) if(BUILD_TESTING) diff --git a/localization/localization_util/include/localization_util/covariance_ellipse.hpp b/localization/localization_util/include/localization_util/covariance_ellipse.hpp new file mode 100644 index 0000000000000..1f187d411dc63 --- /dev/null +++ b/localization/localization_util/include/localization_util/covariance_ellipse.hpp @@ -0,0 +1,44 @@ +// Copyright 2024 Autoware Foundation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef LOCALIZATION_UTIL__COVARIANCE_ELLIPSE_HPP_ +#define LOCALIZATION_UTIL__COVARIANCE_ELLIPSE_HPP_ + +#include + +#include +#include + +namespace autoware::localization_util +{ + +struct Ellipse +{ + double long_radius; + double short_radius; + double yaw; + Eigen::Matrix2d P; + double size_lateral_direction; +}; + +Ellipse calculate_xy_ellipse( + const geometry_msgs::msg::PoseWithCovariance & pose_with_covariance, const double scale); + +visualization_msgs::msg::Marker create_ellipse_marker( + const Ellipse & ellipse, const std_msgs::msg::Header & header, + const geometry_msgs::msg::PoseWithCovariance & pose_with_covariance); + +} // namespace autoware::localization_util + +#endif // LOCALIZATION_UTIL__COVARIANCE_ELLIPSE_HPP_ diff --git a/localization/localization_util/src/covariance_ellipse.cpp b/localization/localization_util/src/covariance_ellipse.cpp new file mode 100644 index 0000000000000..885ce2dcee19c --- /dev/null +++ b/localization/localization_util/src/covariance_ellipse.cpp @@ -0,0 +1,90 @@ +// Copyright 2024 Autoware Foundation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "localization_util/covariance_ellipse.hpp" + +#include +#ifdef ROS_DISTRO_GALACTIC +#include +#else +#include +#endif + +namespace autoware::localization_util +{ + +Ellipse calculate_xy_ellipse( + const geometry_msgs::msg::PoseWithCovariance & pose_with_covariance, const double scale) +{ + // input geometry_msgs::PoseWithCovariance contain 6x6 matrix + Eigen::Matrix2d xy_covariance; + const auto cov = pose_with_covariance.covariance; + xy_covariance(0, 0) = cov[0 * 6 + 0]; + xy_covariance(0, 1) = cov[0 * 6 + 1]; + xy_covariance(1, 0) = cov[1 * 6 + 0]; + xy_covariance(1, 1) = cov[1 * 6 + 1]; + + Eigen::SelfAdjointEigenSolver eigensolver(xy_covariance); + + Ellipse ellipse; + + // eigen values and vectors are sorted in ascending order + ellipse.long_radius = scale * std::sqrt(eigensolver.eigenvalues()(1)); + ellipse.short_radius = scale * std::sqrt(eigensolver.eigenvalues()(0)); + + // principal component vector + const Eigen::Vector2d pc_vector = eigensolver.eigenvectors().col(1); + ellipse.yaw = std::atan2(pc_vector.y(), pc_vector.x()); + + // ellipse size along lateral direction (body-frame) + ellipse.P = xy_covariance; + const double yaw_vehicle = tf2::getYaw(pose_with_covariance.pose.orientation); + const Eigen::Matrix2d & p_inv = ellipse.P.inverse(); + Eigen::MatrixXd e(2, 1); + e(0, 0) = std::cos(yaw_vehicle); + e(1, 0) = std::sin(yaw_vehicle); + const double d = std::sqrt((e.transpose() * p_inv * e)(0, 0) / p_inv.determinant()); + ellipse.size_lateral_direction = scale * d; + + return ellipse; +} + +visualization_msgs::msg::Marker create_ellipse_marker( + const Ellipse & ellipse, const std_msgs::msg::Header & header, + const geometry_msgs::msg::PoseWithCovariance & pose_with_covariance) +{ + tf2::Quaternion quat; + quat.setEuler(0, 0, ellipse.yaw); + + const double ellipse_long_radius = std::min(ellipse.long_radius, 30.0); + const double ellipse_short_radius = std::min(ellipse.short_radius, 30.0); + visualization_msgs::msg::Marker marker; + marker.header = header; + marker.ns = "error_ellipse"; + marker.id = 0; + marker.type = visualization_msgs::msg::Marker::SPHERE; + marker.action = visualization_msgs::msg::Marker::ADD; + marker.pose = pose_with_covariance.pose; + marker.pose.orientation = tf2::toMsg(quat); + marker.scale.x = ellipse_long_radius * 2; + marker.scale.y = ellipse_short_radius * 2; + marker.scale.z = 0.01; + marker.color.a = 0.1; + marker.color.r = 0.0; + marker.color.g = 0.0; + marker.color.b = 1.0; + return marker; +} + +} // namespace autoware::localization_util diff --git a/localization/ndt_scan_matcher/src/ndt_scan_matcher_core.cpp b/localization/ndt_scan_matcher/src/ndt_scan_matcher_core.cpp index 3bf6e355000bf..ca486a320d9dc 100644 --- a/localization/ndt_scan_matcher/src/ndt_scan_matcher_core.cpp +++ b/localization/ndt_scan_matcher/src/ndt_scan_matcher_core.cpp @@ -1068,8 +1068,8 @@ geometry_msgs::msg::PoseWithCovarianceStamped NDTScanMatcher::align_pose( const pclomp::NdtResult ndt_result = ndt_ptr_->getResult(); Particle particle( - initial_pose, matrix4f_to_pose(ndt_result.pose), ndt_result.transform_probability, - ndt_result.iteration_num); + initial_pose, matrix4f_to_pose(ndt_result.pose), + ndt_result.nearest_voxel_transformation_likelihood, ndt_result.iteration_num); particle_array.push_back(particle); push_debug_markers(marker_array, get_clock()->now(), param_.frame.map_frame, particle, i); if ( @@ -1101,6 +1101,13 @@ geometry_msgs::msg::PoseWithCovarianceStamped NDTScanMatcher::align_pose( std::begin(particle_array), std::end(particle_array), [](const Particle & lhs, const Particle & rhs) { return lhs.score < rhs.score; }); + if ( + best_particle_ptr->score < + param_.score_estimation.converged_param_nearest_voxel_transformation_likelihood) + RCLCPP_WARN_STREAM( + this->get_logger(), + "Initial Pose Estimation is Unstable. Score is " << best_particle_ptr->score); + geometry_msgs::msg::PoseWithCovarianceStamped result_pose_with_cov_msg; result_pose_with_cov_msg.header.stamp = initial_pose_with_cov.header.stamp; result_pose_with_cov_msg.header.frame_id = param_.frame.map_frame; diff --git a/localization/pose_estimator_arbiter/README.md b/localization/pose_estimator_arbiter/README.md index a28da699ad926..7b9397dfdba47 100644 --- a/localization/pose_estimator_arbiter/README.md +++ b/localization/pose_estimator_arbiter/README.md @@ -45,7 +45,7 @@ The following video demonstrates the switching of four different pose estimators Users can reproduce the demonstration using the following data and launch command: -[sample data (rosbag & map)](https://drive.google.com/file/d/1MxLo1Sw6PdvfkyOYf_9A5dZ9uli1vPvS/view) +[sample data (rosbag & map)](https://drive.google.com/file/d/1ZNlkyCtwe04iKFREdeZ5xuMU_jWpwM3W/view) The rosbag is simulated data created by [AWSIM](https://tier4.github.io/AWSIM/). The map is an edited version of the [original map data](https://github.com/tier4/AWSIM/releases/download/v1.1.0/nishishinjuku_autoware_map.zip) published on the AWSIM documentation page to make it suitable for multiple pose_estimators. diff --git a/localization/pose_estimator_arbiter/example_rule/src/pose_estimator_arbiter/rule_helper/pose_estimator_area.cpp b/localization/pose_estimator_arbiter/example_rule/src/pose_estimator_arbiter/rule_helper/pose_estimator_area.cpp index df7f9c6c1035f..1bf359b6ab54d 100644 --- a/localization/pose_estimator_arbiter/example_rule/src/pose_estimator_arbiter/rule_helper/pose_estimator_area.cpp +++ b/localization/pose_estimator_arbiter/example_rule/src/pose_estimator_arbiter/rule_helper/pose_estimator_area.cpp @@ -14,8 +14,8 @@ #include "pose_estimator_arbiter/rule_helper/pose_estimator_area.hpp" -#include -#include +#include +#include #include #include diff --git a/localization/pose_estimator_arbiter/example_rule/test/test_rule_helper.cpp b/localization/pose_estimator_arbiter/example_rule/test/test_rule_helper.cpp index 0001ce674a2e8..3258a1be34fda 100644 --- a/localization/pose_estimator_arbiter/example_rule/test/test_rule_helper.cpp +++ b/localization/pose_estimator_arbiter/example_rule/test/test_rule_helper.cpp @@ -16,7 +16,7 @@ #include "pose_estimator_arbiter/rule_helper/pcd_occupancy.hpp" #include "pose_estimator_arbiter/rule_helper/pose_estimator_area.hpp" -#include +#include #include diff --git a/localization/pose_estimator_arbiter/example_rule/test/test_vector_map_based_rule.cpp b/localization/pose_estimator_arbiter/example_rule/test/test_vector_map_based_rule.cpp index a4fd91365f433..ec8c905bf8311 100644 --- a/localization/pose_estimator_arbiter/example_rule/test/test_vector_map_based_rule.cpp +++ b/localization/pose_estimator_arbiter/example_rule/test/test_vector_map_based_rule.cpp @@ -14,7 +14,7 @@ #include "pose_estimator_arbiter/switch_rule/vector_map_based_rule.hpp" -#include +#include #include diff --git a/localization/pose_estimator_arbiter/package.xml b/localization/pose_estimator_arbiter/package.xml index 4880e048ea392..1c1c926c75b7a 100644 --- a/localization/pose_estimator_arbiter/package.xml +++ b/localization/pose_estimator_arbiter/package.xml @@ -18,11 +18,11 @@ autoware_cmake autoware_adapi_v1_msgs + autoware_lanelet2_extension autoware_map_msgs autoware_universe_utils diagnostic_msgs geometry_msgs - lanelet2_extension magic_enum pcl_conversions pcl_ros diff --git a/localization/yabloc/yabloc_common/package.xml b/localization/yabloc/yabloc_common/package.xml index 18038d80fbfca..ee0ef087579f7 100644 --- a/localization/yabloc/yabloc_common/package.xml +++ b/localization/yabloc/yabloc_common/package.xml @@ -16,12 +16,12 @@ autoware_cmake rosidl_default_generators + autoware_lanelet2_extension autoware_map_msgs autoware_universe_utils cv_bridge geometry_msgs lanelet2_core - lanelet2_extension pcl_conversions rclcpp rclcpp_components diff --git a/localization/yabloc/yabloc_common/src/cv_decompress.cpp b/localization/yabloc/yabloc_common/src/cv_decompress.cpp index bf26908c9f0b0..3401f458f388a 100644 --- a/localization/yabloc/yabloc_common/src/cv_decompress.cpp +++ b/localization/yabloc/yabloc_common/src/cv_decompress.cpp @@ -17,7 +17,11 @@ #include #include -#include +#if __has_include() +#include // for ROS 2 Jazzy or newer +#else +#include // for ROS 2 Humble or older +#endif #include diff --git a/localization/yabloc/yabloc_common/src/ground_server/ground_server_core.cpp b/localization/yabloc/yabloc_common/src/ground_server/ground_server_core.cpp index 1366ecaebccf1..52752bff65770 100644 --- a/localization/yabloc/yabloc_common/src/ground_server/ground_server_core.cpp +++ b/localization/yabloc/yabloc_common/src/ground_server/ground_server_core.cpp @@ -17,7 +17,7 @@ #include "yabloc_common/ground_server/util.hpp" #include -#include +#include #include #include diff --git a/localization/yabloc/yabloc_common/src/ll2_decomposer/ll2_decomposer_core.cpp b/localization/yabloc/yabloc_common/src/ll2_decomposer/ll2_decomposer_core.cpp index 3ea23ded92efa..c95431d128000 100644 --- a/localization/yabloc/yabloc_common/src/ll2_decomposer/ll2_decomposer_core.cpp +++ b/localization/yabloc/yabloc_common/src/ll2_decomposer/ll2_decomposer_core.cpp @@ -15,7 +15,7 @@ #include "yabloc_common/ll2_decomposer/ll2_decomposer.hpp" #include -#include +#include #include #include diff --git a/localization/yabloc/yabloc_common/src/pub_sub.cpp b/localization/yabloc/yabloc_common/src/pub_sub.cpp index 102a9012033fe..79d48b70ac172 100644 --- a/localization/yabloc/yabloc_common/src/pub_sub.cpp +++ b/localization/yabloc/yabloc_common/src/pub_sub.cpp @@ -14,7 +14,11 @@ #include "yabloc_common/pub_sub.hpp" -#include +#if __has_include() +#include // for ROS 2 Jazzy or newer +#else +#include // for ROS 2 Humble or older +#endif #include namespace yabloc::common diff --git a/localization/yabloc/yabloc_image_processing/src/line_segments_overlay/line_segments_overlay_core.cpp b/localization/yabloc/yabloc_image_processing/src/line_segments_overlay/line_segments_overlay_core.cpp index 70e1bf2d6e6e2..c7d7aa67fd3ab 100644 --- a/localization/yabloc/yabloc_image_processing/src/line_segments_overlay/line_segments_overlay_core.cpp +++ b/localization/yabloc/yabloc_image_processing/src/line_segments_overlay/line_segments_overlay_core.cpp @@ -18,7 +18,11 @@ #include #include -#include +#if __has_include() +#include // for ROS 2 Jazzy or newer +#else +#include // for ROS 2 Humble or older +#endif #include namespace yabloc::line_segments_overlay diff --git a/localization/yabloc/yabloc_image_processing/src/undistort/undistort_node.cpp b/localization/yabloc/yabloc_image_processing/src/undistort/undistort_node.cpp index 5590387e5ba26..46f5165c5f4e2 100644 --- a/localization/yabloc/yabloc_image_processing/src/undistort/undistort_node.cpp +++ b/localization/yabloc/yabloc_image_processing/src/undistort/undistort_node.cpp @@ -24,7 +24,11 @@ #include #include -#include +#if __has_include() +#include // for ROS 2 Jazzy or newer +#else +#include // for ROS 2 Humble or older +#endif #include diff --git a/localization/yabloc/yabloc_monitor/CMakeLists.txt b/localization/yabloc/yabloc_monitor/CMakeLists.txt index aa1515661b2f6..7267cda53714f 100644 --- a/localization/yabloc/yabloc_monitor/CMakeLists.txt +++ b/localization/yabloc/yabloc_monitor/CMakeLists.txt @@ -4,12 +4,16 @@ project(yabloc_monitor) find_package(autoware_cmake REQUIRED) autoware_package() -ament_auto_add_executable(yabloc_monitor - src/yabloc_monitor_node.cpp +ament_auto_add_library(${PROJECT_NAME} src/yabloc_monitor_core.cpp src/availability_module.cpp ) -ament_target_dependencies(yabloc_monitor) + +rclcpp_components_register_node(${PROJECT_NAME} + PLUGIN "YabLocMonitor" + EXECUTABLE ${PROJECT_NAME}_node + EXECUTOR SingleThreadedExecutor +) ament_auto_package( INSTALL_TO_SHARE diff --git a/localization/yabloc/yabloc_monitor/launch/yabloc_monitor.launch.xml b/localization/yabloc/yabloc_monitor/launch/yabloc_monitor.launch.xml index cf9f73977d35d..3073208e2e1a2 100644 --- a/localization/yabloc/yabloc_monitor/launch/yabloc_monitor.launch.xml +++ b/localization/yabloc/yabloc_monitor/launch/yabloc_monitor.launch.xml @@ -1,7 +1,7 @@ - + diff --git a/localization/yabloc/yabloc_monitor/package.xml b/localization/yabloc/yabloc_monitor/package.xml index 22a5d0eded6b6..a42a734dbab31 100644 --- a/localization/yabloc/yabloc_monitor/package.xml +++ b/localization/yabloc/yabloc_monitor/package.xml @@ -20,6 +20,7 @@ diagnostic_updater geometry_msgs rclcpp + rclcpp_components ament_cmake_gtest ament_lint_auto diff --git a/localization/yabloc/yabloc_monitor/src/yabloc_monitor_core.cpp b/localization/yabloc/yabloc_monitor/src/yabloc_monitor_core.cpp index 876b86fd2bc9e..31e6ec9c51e7a 100644 --- a/localization/yabloc/yabloc_monitor/src/yabloc_monitor_core.cpp +++ b/localization/yabloc/yabloc_monitor/src/yabloc_monitor_core.cpp @@ -18,7 +18,8 @@ #include -YabLocMonitor::YabLocMonitor() : Node("yabloc_monitor"), updater_(this) +YabLocMonitor::YabLocMonitor(const rclcpp::NodeOptions & options) +: Node("yabloc_monitor", options), updater_(this) { updater_.setHardwareID(get_name()); updater_.add("yabloc_status", this, &YabLocMonitor::update_diagnostics); @@ -46,3 +47,6 @@ void YabLocMonitor::update_diagnostics(diagnostic_updater::DiagnosticStatusWrapp stat.summary(diagnostic_msgs::msg::DiagnosticStatus::ERROR, "NG"); } } + +#include +RCLCPP_COMPONENTS_REGISTER_NODE(YabLocMonitor) diff --git a/localization/yabloc/yabloc_monitor/src/yabloc_monitor_core.hpp b/localization/yabloc/yabloc_monitor/src/yabloc_monitor_core.hpp index 8b8b937da205b..5dd46d63de6f2 100644 --- a/localization/yabloc/yabloc_monitor/src/yabloc_monitor_core.hpp +++ b/localization/yabloc/yabloc_monitor/src/yabloc_monitor_core.hpp @@ -25,7 +25,7 @@ class YabLocMonitor : public rclcpp::Node { public: - YabLocMonitor(); + explicit YabLocMonitor(const rclcpp::NodeOptions & options = rclcpp::NodeOptions()); private: void update_diagnostics(diagnostic_updater::DiagnosticStatusWrapper & stat); diff --git a/localization/yabloc/yabloc_monitor/src/yabloc_monitor_node.cpp b/localization/yabloc/yabloc_monitor/src/yabloc_monitor_node.cpp deleted file mode 100644 index 9b58325c852ea..0000000000000 --- a/localization/yabloc/yabloc_monitor/src/yabloc_monitor_node.cpp +++ /dev/null @@ -1,28 +0,0 @@ -// Copyright 2023 TIER IV -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "yabloc_monitor_core.hpp" - -#include - -int main(int argc, char ** argv) -{ - rclcpp::init(argc, argv); - rclcpp::NodeOptions node_options; - auto node = std::make_shared(); - - rclcpp::spin(node); - - return 0; -} diff --git a/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/camera_corrector/camera_particle_corrector.hpp b/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/camera_corrector/camera_particle_corrector.hpp index 582abb03901a1..4ca8c01b3be64 100644 --- a/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/camera_corrector/camera_particle_corrector.hpp +++ b/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/camera_corrector/camera_particle_corrector.hpp @@ -33,7 +33,7 @@ namespace yabloc::modularized_particle_filter { -cv::Point2f cv2pt(const Eigen::Vector3f v); +cv::Point2f cv2pt(const Eigen::Vector3f & v); float abs_cos(const Eigen::Vector3f & t, float deg); class CameraParticleCorrector : public modularized_particle_filter::AbstractCorrector diff --git a/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/camera_corrector/logit.hpp b/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/camera_corrector/logit.hpp index caf93abd0028f..0b2954dd46ba5 100644 --- a/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/camera_corrector/logit.hpp +++ b/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/camera_corrector/logit.hpp @@ -18,15 +18,6 @@ namespace yabloc { float logit_to_prob(float logit, float gain = 1.0f); - -/** - * Convert probability to logit - * This function is much faster than logit_to_prob() because it refers to pre-computed table - * - * @param[in] prob - * @return logit - */ -float prob_to_logit(float prob); } // namespace yabloc #endif // YABLOC_PARTICLE_FILTER__CAMERA_CORRECTOR__LOGIT_HPP_ diff --git a/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/common/mean.hpp b/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/common/mean.hpp index e6af4f0d76d49..4e2c6cd8a842c 100644 --- a/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/common/mean.hpp +++ b/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/common/mean.hpp @@ -20,18 +20,13 @@ #include -namespace yabloc -{ -namespace modularized_particle_filter +namespace yabloc::modularized_particle_filter { geometry_msgs::msg::Pose get_mean_pose( const yabloc_particle_filter::msg::ParticleArray & particle_array); Eigen::Matrix3f std_of_distribution( const yabloc_particle_filter::msg::ParticleArray & particle_array); - -float std_of_weight(const yabloc_particle_filter::msg::ParticleArray & particle_array); -} // namespace modularized_particle_filter -} // namespace yabloc +} // namespace yabloc::modularized_particle_filter #endif // YABLOC_PARTICLE_FILTER__COMMON__MEAN_HPP_ diff --git a/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/common/prediction_util.hpp b/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/common/prediction_util.hpp index 6059f16826c98..140f1553b88ee 100644 --- a/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/common/prediction_util.hpp +++ b/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/common/prediction_util.hpp @@ -22,14 +22,12 @@ #include #include -namespace yabloc +namespace yabloc::modularized_particle_filter::util { -namespace modularized_particle_filter::util -{ -std::random_device seed_gen; -std::default_random_engine engine(seed_gen()); +inline std::random_device seed_gen; +inline std::default_random_engine engine(seed_gen()); -Eigen::Vector2d nrand_2d(const Eigen::Matrix2d cov) +inline Eigen::Vector2d nrand_2d(const Eigen::Matrix2d & cov) { Eigen::JacobiSVD svd; svd.compute(cov, Eigen::ComputeFullU | Eigen::ComputeFullV); @@ -50,7 +48,7 @@ T nrand(T std) return dist(engine); } -double normalize_radian(const double rad, const double min_rad = -M_PI) +inline double normalize_radian(const double rad, const double min_rad = -M_PI) { const auto max_rad = min_rad + 2 * M_PI; @@ -63,6 +61,5 @@ double normalize_radian(const double rad, const double min_rad = -M_PI) return value - std::copysign(2 * M_PI, value); } -} // namespace modularized_particle_filter::util -} // namespace yabloc +} // namespace yabloc::modularized_particle_filter::util #endif // YABLOC_PARTICLE_FILTER__COMMON__PREDICTION_UTIL_HPP_ diff --git a/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/common/visualize.hpp b/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/common/visualize.hpp index f1ecf35e81761..3b35d1a9a0da6 100644 --- a/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/common/visualize.hpp +++ b/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/common/visualize.hpp @@ -21,9 +21,7 @@ #include #include -namespace yabloc -{ -namespace modularized_particle_filter +namespace yabloc::modularized_particle_filter { class ParticleVisualizer { @@ -37,9 +35,7 @@ class ParticleVisualizer private: rclcpp::Publisher::SharedPtr pub_marker_array_; - std_msgs::msg::ColorRGBA compute_color(float value); }; -} // namespace modularized_particle_filter -} // namespace yabloc +} // namespace yabloc::modularized_particle_filter #endif // YABLOC_PARTICLE_FILTER__COMMON__VISUALIZE_HPP_ diff --git a/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/correction/abstract_corrector.hpp b/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/correction/abstract_corrector.hpp index 4deaeec0810ae..33802bd666440 100644 --- a/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/correction/abstract_corrector.hpp +++ b/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/correction/abstract_corrector.hpp @@ -26,9 +26,7 @@ #include #include -namespace yabloc -{ -namespace modularized_particle_filter +namespace yabloc::modularized_particle_filter { class AbstractCorrector : public rclcpp::Node { @@ -55,7 +53,6 @@ class AbstractCorrector : public rclcpp::Node private: void on_particle_array(const ParticleArray & particle_array); }; -} // namespace modularized_particle_filter -} // namespace yabloc +} // namespace yabloc::modularized_particle_filter #endif // YABLOC_PARTICLE_FILTER__CORRECTION__ABSTRACT_CORRECTOR_HPP_ diff --git a/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/correction/correction_util.hpp b/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/correction/correction_util.hpp deleted file mode 100644 index 2c2f10bf7ad2e..0000000000000 --- a/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/correction/correction_util.hpp +++ /dev/null @@ -1,35 +0,0 @@ -// Copyright 2023 TIER IV, Inc. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef YABLOC_PARTICLE_FILTER__CORRECTION__CORRECTION_UTIL_HPP_ -#define YABLOC_PARTICLE_FILTER__CORRECTION__CORRECTION_UTIL_HPP_ - -#include "rclcpp/rclcpp.hpp" -#include "yabloc_particle_filter/msg/particle_array.hpp" - -#include - -#include - -namespace yabloc -{ -namespace modularized_particle_filter -{ -std::optional find_synced_particles( - boost::circular_buffer circular_buffer, - rclcpp::Time time); -} -} // namespace yabloc - -#endif // YABLOC_PARTICLE_FILTER__CORRECTION__CORRECTION_UTIL_HPP_ diff --git a/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/gnss_corrector/weight_manager.hpp b/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/gnss_corrector/weight_manager.hpp index 791f65eb9b835..f22662a188355 100644 --- a/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/gnss_corrector/weight_manager.hpp +++ b/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/gnss_corrector/weight_manager.hpp @@ -35,41 +35,48 @@ struct WeightManager } }; - Parameter for_fixed_; - Parameter for_not_fixed_; + Parameter for_fixed_{}; + Parameter for_not_fixed_{}; explicit WeightManager(rclcpp::Node * node) { - for_fixed_.flat_radius_ = node->declare_parameter("for_fixed/flat_radius"); - for_fixed_.max_radius_ = node->declare_parameter("for_fixed/max_radius"); - for_fixed_.min_weight_ = node->declare_parameter("for_fixed/min_weight"); - for_fixed_.max_weight_ = node->declare_parameter("for_fixed/max_weight"); + for_fixed_.flat_radius_ = + static_cast(node->declare_parameter("for_fixed/flat_radius")); + for_fixed_.max_radius_ = + static_cast(node->declare_parameter("for_fixed/max_radius")); + for_fixed_.min_weight_ = + static_cast(node->declare_parameter("for_fixed/min_weight")); + for_fixed_.max_weight_ = + static_cast(node->declare_parameter("for_fixed/max_weight")); for_fixed_.compute_coeff(); - for_not_fixed_.flat_radius_ = node->declare_parameter("for_not_fixed/flat_radius"); - for_not_fixed_.max_radius_ = node->declare_parameter("for_not_fixed/max_radius"); - for_not_fixed_.min_weight_ = node->declare_parameter("for_not_fixed/min_weight"); - for_not_fixed_.max_weight_ = node->declare_parameter("for_not_fixed/max_weight"); + for_not_fixed_.flat_radius_ = + static_cast(node->declare_parameter("for_not_fixed/flat_radius")); + for_not_fixed_.max_radius_ = + static_cast(node->declare_parameter("for_not_fixed/max_radius")); + for_not_fixed_.min_weight_ = + static_cast(node->declare_parameter("for_not_fixed/min_weight")); + for_not_fixed_.max_weight_ = + static_cast(node->declare_parameter("for_not_fixed/max_weight")); for_not_fixed_.compute_coeff(); } - float normal_pdf(float distance, const Parameter & param) const + [[nodiscard]] static float normal_pdf(float distance, const Parameter & param) { // NOTE: This is not exact normal distribution because of no scale factor depending on sigma float d = std::clamp(std::abs(distance) - param.flat_radius_, 0.f, param.max_radius_); return param.max_weight_ * std::exp(-param.coeff_ * d * d); } - float normal_pdf(float distance, bool is_rtk_fixed) const + [[nodiscard]] float normal_pdf(float distance, bool is_rtk_fixed) const { if (is_rtk_fixed) { return normal_pdf(distance, for_fixed_); - } else { - return normal_pdf(distance, for_not_fixed_); } + return normal_pdf(distance, for_not_fixed_); } - float inverse_normal_pdf(float prob, const Parameter & param) const + [[nodiscard]] static float inverse_normal_pdf(float prob, const Parameter & param) { prob = (param.max_weight_ - param.min_weight_) * prob + param.min_weight_; @@ -78,13 +85,12 @@ struct WeightManager return param.flat_radius_ + std::sqrt(-std::log(prob / param.max_weight_) / param.coeff_); } - float inverse_normal_pdf(float prob, bool is_rtk_fixed) const + [[nodiscard]] float inverse_normal_pdf(float prob, bool is_rtk_fixed) const { if (is_rtk_fixed) { return inverse_normal_pdf(prob, for_fixed_); - } else { - return inverse_normal_pdf(prob, for_not_fixed_); } + return inverse_normal_pdf(prob, for_not_fixed_); } }; } // namespace yabloc::modularized_particle_filter diff --git a/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/ll2_cost_map/direct_cost_map.hpp b/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/ll2_cost_map/direct_cost_map.hpp index 5f56b8f079cd1..8169719a21f3b 100644 --- a/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/ll2_cost_map/direct_cost_map.hpp +++ b/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/ll2_cost_map/direct_cost_map.hpp @@ -20,9 +20,6 @@ namespace yabloc { cv::Mat direct_cost_map(const cv::Mat & cost_map, const cv::Mat & intensity); - -cv::Mat visualize_direction_map(const cv::Mat & cost_map); - } // namespace yabloc #endif // YABLOC_PARTICLE_FILTER__LL2_COST_MAP__DIRECT_COST_MAP_HPP_ diff --git a/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/ll2_cost_map/hierarchical_cost_map.hpp b/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/ll2_cost_map/hierarchical_cost_map.hpp index 32d579c4ab595..d6cc4553e29bc 100644 --- a/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/ll2_cost_map/hierarchical_cost_map.hpp +++ b/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/ll2_cost_map/hierarchical_cost_map.hpp @@ -37,34 +37,37 @@ namespace yabloc { struct Area { - Area() {} + Area() = default; explicit Area(const Eigen::Vector2f & v) { - if (unit_length_ < 0) { + if (unit_length < 0) { throw_error(); } - x = static_cast(std::floor(v.x() / unit_length_)); - y = static_cast(std::floor(v.y() / unit_length_)); + x = static_cast(std::floor(v.x() / unit_length)); + y = static_cast(std::floor(v.y() / unit_length)); } - Eigen::Vector2f real_scale() const { return {x * unit_length_, y * unit_length_}; } + [[nodiscard]] Eigen::Vector2f real_scale() const + { + return {static_cast(x) * unit_length, static_cast(y) * unit_length}; + } - std::array real_scale_boundary() const + [[nodiscard]] std::array real_scale_boundary() const { std::array boundary; boundary.at(0) = real_scale(); - boundary.at(1) = real_scale() + Eigen::Vector2f(unit_length_, unit_length_); + boundary.at(1) = real_scale() + Eigen::Vector2f(unit_length, unit_length); return boundary; }; - void throw_error() const + static void throw_error() { std::cerr << "Area::unit_length_ is not initialized" << std::endl; throw std::runtime_error("invalid Area::unit_length"); } - int x, y; - static float unit_length_; - static float image_size_; + int x{}, y{}; + static float unit_length; + static float image_size; friend bool operator==(const Area & one, const Area & other) { @@ -129,7 +132,7 @@ class HierarchicalCostMap rclcpp::Logger logger_; std::optional height_{std::nullopt}; - common::GammaConverter gamma_converter{4.0f}; + common::GammaConverter gamma_converter_{4.0f}; std::unordered_map map_accessed_; @@ -138,7 +141,7 @@ class HierarchicalCostMap std::vector bounding_boxes_; std::unordered_map cost_maps_; - cv::Point to_cv_point(const Area & are, const Eigen::Vector2f) const; + cv::Point to_cv_point(const Area & area, const Eigen::Vector2f & p) const; void build_map(const Area & area); cv::Mat create_available_area_image(const Area & area) const; diff --git a/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/prediction/predictor.hpp b/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/prediction/predictor.hpp index e4fb58f22e17f..b619ed524affd 100644 --- a/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/prediction/predictor.hpp +++ b/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/prediction/predictor.hpp @@ -106,7 +106,7 @@ class Predictor : public rclcpp::Node void initialize_particles(const PoseCovStamped & initialpose); // void update_with_dynamic_noise( - ParticleArray & particle_array, const TwistCovStamped & twist, double dt); + ParticleArray & particle_array, const TwistCovStamped & twist, double dt) const; // void publish_mean_pose(const geometry_msgs::msg::Pose & mean_pose, const rclcpp::Time & stamp); void publish_range_marker(const Eigen::Vector3f & pos, const Eigen::Vector3f & tangent); diff --git a/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/prediction/resampler.hpp b/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/prediction/resampler.hpp index f2a517216020a..7b3aa8553f90e 100644 --- a/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/prediction/resampler.hpp +++ b/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/prediction/resampler.hpp @@ -22,10 +22,10 @@ namespace yabloc::modularized_particle_filter { -class resampling_skip_exception : public std::runtime_error +class ResamplingSkipException : public std::runtime_error { public: - explicit resampling_skip_exception(const char * message) : runtime_error(message) {} + explicit ResamplingSkipException(const char * message) : runtime_error(message) {} }; class RetroactiveResampler @@ -56,9 +56,10 @@ class RetroactiveResampler int latest_resampling_generation_; // Random generator from 0 to 1 - double random_from_01_uniformly() const; + [[nodiscard]] static double random_from_01_uniformly(); // Check the sanity of the particles obtained from the particle corrector. - bool check_weighted_particles_validity(const ParticleArray & weighted_particles) const; + [[nodiscard]] bool check_weighted_particles_validity( + const ParticleArray & weighted_particles) const; }; } // namespace yabloc::modularized_particle_filter diff --git a/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/prediction/resampling_history.hpp b/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/prediction/resampling_history.hpp index 5b24ee96d6df5..6c1e9737c3f0d 100644 --- a/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/prediction/resampling_history.hpp +++ b/localization/yabloc/yabloc_particle_filter/include/yabloc_particle_filter/prediction/resampling_history.hpp @@ -35,7 +35,7 @@ class ResamplingHistory } } - bool check_history_validity() const + [[nodiscard]] bool check_history_validity() const { for (auto & generation : resampling_history_) { bool result = std::any_of(generation.begin(), generation.end(), [this](int x) { diff --git a/localization/yabloc/yabloc_particle_filter/src/camera_corrector/camera_particle_corrector_core.cpp b/localization/yabloc/yabloc_particle_filter/src/camera_corrector/camera_particle_corrector_core.cpp index c894cd999fb14..d6a38e7b97b96 100644 --- a/localization/yabloc/yabloc_particle_filter/src/camera_corrector/camera_particle_corrector_core.cpp +++ b/localization/yabloc/yabloc_particle_filter/src/camera_corrector/camera_particle_corrector_core.cpp @@ -25,13 +25,15 @@ #include +#include + namespace yabloc::modularized_particle_filter { CameraParticleCorrector::CameraParticleCorrector(const rclcpp::NodeOptions & options) : AbstractCorrector("camera_particle_corrector", options), - min_prob_(declare_parameter("min_prob")), - far_weight_gain_(declare_parameter("far_weight_gain")), + min_prob_(static_cast(declare_parameter("min_prob"))), + far_weight_gain_(static_cast(declare_parameter("far_weight_gain"))), cost_map_(this) { using std::placeholders::_1; @@ -98,7 +100,8 @@ CameraParticleCorrector::split_line_segments(const PointCloud2 & msg) { LineSegments all_line_segments_cloud; pcl::fromROSMsg(msg, all_line_segments_cloud); - LineSegments reliable_cloud, iffy_cloud; + LineSegments reliable_cloud; + LineSegments iffy_cloud; { for (const auto & p : all_line_segments_cloud) { if (p.label == 0) @@ -111,7 +114,7 @@ CameraParticleCorrector::split_line_segments(const PointCloud2 & msg) auto [good_cloud, bad_cloud] = filt(iffy_cloud); { cv::Mat debug_image = cv::Mat::zeros(800, 800, CV_8UC3); - auto draw = [&debug_image](LineSegments & cloud, cv::Scalar color) -> void { + auto draw = [&debug_image](const LineSegments & cloud, const cv::Scalar & color) -> void { for (const auto & line : cloud) { const Eigen::Vector3f p1 = line.getVector3fMap(); const Eigen::Vector3f p2 = line.getNormalVector3fMap(); @@ -162,7 +165,7 @@ void CameraParticleCorrector::on_line_segments(const PointCloud2 & line_segments } } - cost_map_.set_height(mean_pose.position.z); + cost_map_.set_height(static_cast(mean_pose.position.z)); if (publish_weighted_particles) { for (auto & particle : weighted_particles.particles) { @@ -187,8 +190,7 @@ void CameraParticleCorrector::on_line_segments(const PointCloud2 & line_segments // DEBUG: just visualization { - Pose mean_pose = get_mean_pose(weighted_particles); - Sophus::SE3f transform = common::pose_to_se3(mean_pose); + Sophus::SE3f transform = common::pose_to_se3(get_mean_pose(weighted_particles)); pcl::PointCloud cloud = evaluate_cloud( common::transform_line_segments(line_segments_cloud, transform), transform.translation()); @@ -199,10 +201,9 @@ void CameraParticleCorrector::on_line_segments(const PointCloud2 & line_segments pcl::PointCloud rgb_cloud; pcl::PointCloud rgb_iffy_cloud; - float max_score = 0; - for (const auto p : cloud) { - max_score = std::max(max_score, std::abs(p.intensity)); - } + float max_score = std::accumulate( + cloud.begin(), cloud.end(), 0.0f, + [](float max_score, const auto & p) { return std::max(max_score, std::abs(p.intensity)); }); for (const auto p : cloud) { pcl::PointXYZRGB rgb; rgb.getVector3fMap() = p.getVector3fMap(); @@ -256,7 +257,7 @@ void CameraParticleCorrector::on_ll2(const PointCloud2 & ll2_msg) float abs_cos(const Eigen::Vector3f & t, float deg) { - const float radian = deg * M_PI / 180.0; + const auto radian = static_cast(deg * M_PI / 180.0); Eigen::Vector2f x(t.x(), t.y()); Eigen::Vector2f y(autoware::universe_utils::cos(radian), autoware::universe_utils::sin(radian)); x.normalize(); @@ -276,7 +277,7 @@ float CameraParticleCorrector::compute_logit( // NOTE: Close points are prioritized float squared_norm = (p - self_position).topRows(2).squaredNorm(); - float gain = exp(-far_weight_gain_ * squared_norm); // 0 < gain < 1 + float gain = std::exp(-far_weight_gain_ * squared_norm); // 0 < gain < 1 const CostMapValue v3 = cost_map_.at(p.topRows(2)); @@ -285,9 +286,10 @@ float CameraParticleCorrector::compute_logit( continue; } if (pn.label == 0) { // posteriori - logit += 0.2f * gain * (abs_cos(tangent, v3.angle) * v3.intensity - 0.5f); + logit += + 0.2f * gain * (abs_cos(tangent, static_cast(v3.angle)) * v3.intensity - 0.5f); } else { // apriori - logit += gain * (abs_cos(tangent, v3.angle) * v3.intensity - 0.5f); + logit += gain * (abs_cos(tangent, static_cast(v3.angle)) * v3.intensity - 0.5f); } } } @@ -307,11 +309,14 @@ pcl::PointCloud CameraParticleCorrector::evaluate_cloud( // NOTE: Close points are prioritized float squared_norm = (p - self_position).topRows(2).squaredNorm(); - float gain = std::exp(-far_weight_gain_ * squared_norm); CostMapValue v3 = cost_map_.at(p.topRows(2)); float logit = 0; - if (!v3.unmapped) logit = gain * (abs_cos(tangent, v3.angle) * v3.intensity - 0.5f); + if (!v3.unmapped) { + float gain = std::exp(-far_weight_gain_ * squared_norm); + + logit = gain * (abs_cos(tangent, static_cast(v3.angle)) * v3.intensity - 0.5f); + } pcl::PointXYZI xyzi(logit_to_prob(logit, 10.f)); xyzi.getVector3fMap() = p; diff --git a/localization/yabloc/yabloc_particle_filter/src/camera_corrector/filter_line_segments.cpp b/localization/yabloc/yabloc_particle_filter/src/camera_corrector/filter_line_segments.cpp index 3f782d85aec43..105f72920a6de 100644 --- a/localization/yabloc/yabloc_particle_filter/src/camera_corrector/filter_line_segments.cpp +++ b/localization/yabloc/yabloc_particle_filter/src/camera_corrector/filter_line_segments.cpp @@ -22,33 +22,35 @@ namespace yabloc::modularized_particle_filter { -cv::Point2f cv2pt(const Eigen::Vector3f v) +cv::Point2f cv2pt(const Eigen::Vector3f & v) { - const float METRIC_PER_PIXEL = 0.05; - const float IMAGE_RADIUS = 400; - return {-v.y() / METRIC_PER_PIXEL + IMAGE_RADIUS, -v.x() / METRIC_PER_PIXEL + 2 * IMAGE_RADIUS}; + const float metric_per_pixel = 0.05; + const float image_radius = 400; + return {-v.y() / metric_per_pixel + image_radius, -v.x() / metric_per_pixel + 2 * image_radius}; } float normalized_atan2(const Eigen::Vector3f & t, float deg) { - float diff = std::atan2(t.y(), t.x()) - deg * M_PI / 180; - diff = std::fmod(diff, M_PI); + auto diff = static_cast(std::atan2(t.y(), t.x()) - deg * M_PI / 180); + diff = static_cast(std::fmod(diff, M_PI)); if (diff < 0) diff = -diff; if (diff < M_PI_2) { - return 1 - diff / M_PI_2; - } else if (diff < M_PI) { - return diff / M_PI_2 - 1; - } else { - throw std::runtime_error("invalid cos"); + return static_cast(1.0 - diff / M_PI_2); } + + if (diff < M_PI) { + return static_cast(diff / M_PI_2 - 1.0); + } + throw std::runtime_error("invalid cos"); } std::pair CameraParticleCorrector::filt(const LineSegments & iffy_lines) { - LineSegments good, bad; + LineSegments good; + LineSegments bad; if (!latest_pose_.has_value()) { throw std::runtime_error("latest_pose_ is nullopt"); } @@ -67,7 +69,7 @@ CameraParticleCorrector::filt(const LineSegments & iffy_lines) for (float distance = 0; distance < length; distance += 0.1f) { Eigen::Vector3f px = pose * (p2 + tangent * distance); CostMapValue v3 = cost_map_.at(px.topRows(2)); - float cos2 = normalized_atan2(pose.so3() * tangent, v3.angle); + float cos2 = normalized_atan2(pose.so3() * tangent, static_cast(v3.angle)); score += (cos2 * v3.intensity); count++; @@ -77,7 +79,7 @@ CameraParticleCorrector::filt(const LineSegments & iffy_lines) // rgb_cloud.push_back(rgb); } - if (score / count > 0.5f) { + if (score / static_cast(count) > 0.5f) { good.push_back(line); } else { bad.push_back(line); diff --git a/localization/yabloc/yabloc_particle_filter/src/camera_corrector/logit.cpp b/localization/yabloc/yabloc_particle_filter/src/camera_corrector/logit.cpp index d8bb5690b6fc4..65b7039e25051 100644 --- a/localization/yabloc/yabloc_particle_filter/src/camera_corrector/logit.cpp +++ b/localization/yabloc/yabloc_particle_filter/src/camera_corrector/logit.cpp @@ -27,7 +27,7 @@ struct ProbToLogitTable ProbToLogitTable() { for (int i = 0; i < 100; ++i) { - float p = i / 100.0f; + float p = static_cast(i) / 100.0f; table_.at(i) = std::log(p / std::max(1 - p, 1e-6f)); } } @@ -37,7 +37,7 @@ struct ProbToLogitTable return table_.at(index); } - std::array table_; + std::array table_{}; } prob_to_logit_table; } // namespace @@ -47,9 +47,4 @@ float logit_to_prob(float logit, float gain) return 1.f / (1 + std::exp(-gain * logit)); } -float prob_to_logit(float prob) -{ - return prob_to_logit_table(prob); -} - } // namespace yabloc diff --git a/localization/yabloc/yabloc_particle_filter/src/common/mean.cpp b/localization/yabloc/yabloc_particle_filter/src/common/mean.cpp index 0710c8c4dca64..1a0ef05508c76 100644 --- a/localization/yabloc/yabloc_particle_filter/src/common/mean.cpp +++ b/localization/yabloc/yabloc_particle_filter/src/common/mean.cpp @@ -70,7 +70,9 @@ geometry_msgs::msg::Pose get_mean_pose( mean_pose.position.y += particle.pose.position.y * normalized_weight; mean_pose.position.z += particle.pose.position.z * normalized_weight; - double yaw{0.0}, pitch{0.0}, roll{0.0}; + double yaw{0.0}; + double pitch{0.0}; + double roll{0.0}; tf2::getEulerYPR(particle.pose.orientation, yaw, pitch, roll); rolls.push_back(roll); @@ -93,43 +95,25 @@ Eigen::Matrix3f std_of_distribution(const yabloc_particle_filter::msg::ParticleA { using Particle = yabloc_particle_filter::msg::Particle; auto ori = get_mean_pose(array).orientation; - Eigen::Quaternionf orientation(ori.w, ori.x, ori.y, ori.z); - float invN = 1.f / array.particles.size(); + Eigen::Quaternionf orientation( + static_cast(ori.w), static_cast(ori.x), static_cast(ori.y), + static_cast(ori.z)); + float inv_n = 1.f / static_cast(array.particles.size()); Eigen::Vector3f mean = Eigen::Vector3f::Zero(); for (const Particle & p : array.particles) { Eigen::Affine3f affine = common::pose_to_affine(p.pose); mean += affine.translation(); } - mean *= invN; + mean *= inv_n; Eigen::Matrix3f sigma = Eigen::Matrix3f::Zero(); for (const Particle & p : array.particles) { Eigen::Affine3f affine = common::pose_to_affine(p.pose); Eigen::Vector3f d = affine.translation() - mean; d = orientation.conjugate() * d; - sigma += (d * d.transpose()) * invN; + sigma += (d * d.transpose()) * inv_n; } return sigma; } - -float std_of_weight(const yabloc_particle_filter::msg::ParticleArray & particle_array) -{ - using Particle = yabloc_particle_filter::msg::Particle; - - const float invN = 1.f / particle_array.particles.size(); - float mean = 0; - for (const Particle & p : particle_array.particles) { - mean += p.weight; - } - mean *= invN; - - float sigma = 0.0; - for (const Particle & p : particle_array.particles) { - sigma += (p.weight - mean) * (p.weight - mean); - } - sigma *= invN; - - return std::sqrt(sigma); -} } // namespace yabloc::modularized_particle_filter diff --git a/localization/yabloc/yabloc_particle_filter/src/common/particle_visualize_node.cpp b/localization/yabloc/yabloc_particle_filter/src/common/particle_visualize_node.cpp index b63e87b9462f1..48f2041595a77 100644 --- a/localization/yabloc/yabloc_particle_filter/src/common/particle_visualize_node.cpp +++ b/localization/yabloc/yabloc_particle_filter/src/common/particle_visualize_node.cpp @@ -39,36 +39,12 @@ class ParticleVisualize : public rclcpp::Node "/particle_array", 10, std::bind(&ParticleVisualize::on_particles, this, _1)); // Publisher - pub_marker_array = this->create_publisher("/marker_array", 10); + pub_marker_array_ = this->create_publisher("/marker_array", 10); } private: rclcpp::Subscription::SharedPtr sub_particles_; - rclcpp::Publisher::SharedPtr pub_marker_array; - - std_msgs::msg::ColorRGBA compute_color(float value) const - { - float r = 1.0f, g = 1.0f, b = 1.0f; - // clang-format off - value = std::clamp(value, 0.0f, 1.0f); - if (value < 0.25f) { - r = 0; g = 4 * (value); - } else if (value < 0.5f) { - r = 0; b = 1 + 4 * (0.25f - value); - } else if (value < 0.75f) { - r = 4 * (value - 0.5f); b = 0; - } else { - g = 1 + 4 * (0.75f - value); b = 0; - } - // clang-format on - - std_msgs::msg::ColorRGBA rgba; - rgba.r = r; - rgba.g = g; - rgba.b = b; - rgba.a = 1.0f; - return rgba; - } + rclcpp::Publisher::SharedPtr pub_marker_array_; void on_particles(const ParticleArray & msg) { @@ -102,7 +78,7 @@ class ParticleVisualize : public rclcpp::Node marker.pose.position.z = p.pose.position.z; marker_array.markers.push_back(marker); } - pub_marker_array->publish(marker_array); + pub_marker_array_->publish(marker_array); } }; } // namespace yabloc::modularized_particle_filter diff --git a/localization/yabloc/yabloc_particle_filter/src/common/visualize.cpp b/localization/yabloc/yabloc_particle_filter/src/common/visualize.cpp index 888baf0bea0ac..17b22757b4bc5 100644 --- a/localization/yabloc/yabloc_particle_filter/src/common/visualize.cpp +++ b/localization/yabloc/yabloc_particle_filter/src/common/visualize.cpp @@ -33,7 +33,7 @@ void ParticleVisualizer::publish(const ParticleArray & msg) float min = minmax_weight.first->weight; float max = minmax_weight.second->weight; max = std::max(max, min + 1e-7f); - auto boundWeight = [min, max](float raw) -> float { return (raw - min) / (max - min); }; + auto bound_weight = [min, max](float raw) -> float { return (raw - min) / (max - min); }; int id = 0; for (const Particle & p : msg.particles) { @@ -46,7 +46,7 @@ void ParticleVisualizer::publish(const ParticleArray & msg) marker.scale.y = 0.1; marker.scale.z = 0.1; marker.color = - static_cast(common::color_scale::rainbow(boundWeight(p.weight))); + static_cast(common::color_scale::rainbow(bound_weight(p.weight))); marker.pose.orientation = p.pose.orientation; marker.pose.position.x = p.pose.position.x; marker.pose.position.y = p.pose.position.y; diff --git a/localization/yabloc/yabloc_particle_filter/src/correction/abstract_corrector.cpp b/localization/yabloc/yabloc_particle_filter/src/correction/abstract_corrector.cpp index 5221256c9e392..1c8e2f5976a1d 100644 --- a/localization/yabloc/yabloc_particle_filter/src/correction/abstract_corrector.cpp +++ b/localization/yabloc/yabloc_particle_filter/src/correction/abstract_corrector.cpp @@ -19,7 +19,7 @@ namespace yabloc::modularized_particle_filter AbstractCorrector::AbstractCorrector( const std::string & node_name, const rclcpp::NodeOptions & options) : Node(node_name, options), - acceptable_max_delay_(declare_parameter("acceptable_max_delay")), + acceptable_max_delay_(static_cast(declare_parameter("acceptable_max_delay"))), visualize_(declare_parameter("visualize")), logger_(rclcpp::get_logger("abstract_corrector")) { diff --git a/localization/yabloc/yabloc_particle_filter/src/correction/correction_util.cpp b/localization/yabloc/yabloc_particle_filter/src/correction/correction_util.cpp deleted file mode 100644 index 67ca4f5add947..0000000000000 --- a/localization/yabloc/yabloc_particle_filter/src/correction/correction_util.cpp +++ /dev/null @@ -1,38 +0,0 @@ -// Copyright 2023 TIER IV, Inc. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "yabloc_particle_filter/correction/correction_util.hpp" - -namespace yabloc::modularized_particle_filter -{ -std::optional find_synced_particles( - boost::circular_buffer circular_buffer, - rclcpp::Time time) -{ - for (int i{1}; i < static_cast(circular_buffer.size()); i++) { - if (rclcpp::Time(circular_buffer[i].header.stamp) < time) { - return circular_buffer[i]; - } - } - if (0 < static_cast(circular_buffer.size())) { - RCLCPP_WARN_STREAM( - rclcpp::get_logger("modularized_particle_filter.correction_util"), - "the sensor data is too old: " - << rclcpp::Time(circular_buffer[static_cast(circular_buffer.size()) - 1].header.stamp) - .seconds() - - time.seconds()); - } - return std::nullopt; -} -} // namespace yabloc::modularized_particle_filter diff --git a/localization/yabloc/yabloc_particle_filter/src/gnss_corrector/gnss_corrector_core.cpp b/localization/yabloc/yabloc_particle_filter/src/gnss_corrector/gnss_corrector_core.cpp index f1dd22426a07d..1ccb24bfbea5c 100644 --- a/localization/yabloc/yabloc_particle_filter/src/gnss_corrector/gnss_corrector_core.cpp +++ b/localization/yabloc/yabloc_particle_filter/src/gnss_corrector/gnss_corrector_core.cpp @@ -21,7 +21,8 @@ namespace yabloc::modularized_particle_filter { GnssParticleCorrector::GnssParticleCorrector(const rclcpp::NodeOptions & options) : AbstractCorrector("gnss_particle_corrector", options), - mahalanobis_distance_threshold_(declare_parameter("mahalanobis_distance_threshold")), + mahalanobis_distance_threshold_( + static_cast(declare_parameter("mahalanobis_distance_threshold"))), weight_manager_(this) { using std::placeholders::_1; @@ -61,7 +62,8 @@ void GnssParticleCorrector::on_pose(const PoseCovStamped::ConstSharedPtr pose_ms const rclcpp::Time stamp = pose_msg->header.stamp; const auto & position = pose_msg->pose.pose.position; Eigen::Vector3f gnss_position; - gnss_position << position.x, position.y, position.z; + gnss_position << static_cast(position.x), static_cast(position.y), + static_cast(position.z); constexpr bool is_rtk_fixed = false; process(gnss_position, stamp, is_rtk_fixed); @@ -96,7 +98,6 @@ void GnssParticleCorrector::process( // Compute travel distance from last update position // If the distance is too short, skip weighting { - Eigen::Vector3f mean_position = common::pose_to_affine(mean_pose).translation(); if ((mean_position - last_mean_position_).squaredNorm() > 1) { this->set_weighted_particle_array(weighted_particles); last_mean_position_ = mean_position; @@ -113,12 +114,12 @@ void GnssParticleCorrector::publish_marker(const Eigen::Vector3f & position, boo using namespace std::literals::chrono_literals; using Point = geometry_msgs::msg::Point; - auto drawCircle = [](std::vector & points, float radius) -> void { - const int N = 10; - for (int theta = 0; theta < 2 * N + 1; theta++) { + auto draw_circle = [](std::vector & points, float radius) -> void { + const int n = 10; + for (int theta = 0; theta < 2 * n + 1; theta++) { geometry_msgs::msg::Point pt; - pt.x = radius * std::cos(theta * M_PI / N); - pt.y = radius * std::sin(theta * M_PI / N); + pt.x = radius * std::cos(theta * M_PI / n); + pt.y = radius * std::sin(theta * M_PI / n); points.push_back(pt); } }; @@ -135,11 +136,11 @@ void GnssParticleCorrector::publish_marker(const Eigen::Vector3f & position, boo marker.pose.position.y = position.y(); marker.pose.position.z = latest_height_.data; - float prob = i / 4.0f; + float prob = static_cast(i) / 4.0f; marker.color = static_cast(common::color_scale::rainbow(prob)); marker.color.a = 0.5f; marker.scale.x = 0.1; - drawCircle(marker.points, weight_manager_.inverse_normal_pdf(prob, is_rtk_fixed)); + draw_circle(marker.points, weight_manager_.inverse_normal_pdf(prob, is_rtk_fixed)); array_msg.markers.push_back(marker); } marker_pub_->publish(array_msg); @@ -151,7 +152,7 @@ GnssParticleCorrector::ParticleArray GnssParticleCorrector::weight_particles( ParticleArray weighted_particles{predicted_particles}; for (auto & particle : weighted_particles.particles) { - float distance = static_cast( + auto distance = static_cast( std::hypot(particle.pose.position.x - pose.x(), particle.pose.position.y - pose.y())); particle.weight = weight_manager_.normal_pdf(distance, is_rtk_fixed); } diff --git a/localization/yabloc/yabloc_particle_filter/src/ll2_cost_map/direct_cost_map.cpp b/localization/yabloc/yabloc_particle_filter/src/ll2_cost_map/direct_cost_map.cpp index 7da87e8050ebe..c6359a8c8f5fc 100644 --- a/localization/yabloc/yabloc_particle_filter/src/ll2_cost_map/direct_cost_map.cpp +++ b/localization/yabloc/yabloc_particle_filter/src/ll2_cost_map/direct_cost_map.cpp @@ -18,14 +18,14 @@ namespace yabloc { cv::Mat direct_cost_map(const cv::Mat & cost_map, const cv::Mat & intensity) { - constexpr int MAX_INT = std::numeric_limits::max(); + constexpr int max_int = std::numeric_limits::max(); std::vector> distances; distances.resize(cost_map.rows); for (int i = 0; i < cost_map.rows; i++) { distances.at(i).resize(cost_map.cols); - std::fill(distances.at(i).begin(), distances.at(i).end(), MAX_INT); - const uchar * intensity_ptr = intensity.ptr(i); + std::fill(distances.at(i).begin(), distances.at(i).end(), max_int); + const auto * intensity_ptr = intensity.ptr(i); for (int j = 0; j < cost_map.cols; j++) { if (intensity_ptr[j] == 0) distances.at(i).at(j) = 0; } @@ -36,7 +36,7 @@ cv::Mat direct_cost_map(const cv::Mat & cost_map, const cv::Mat & intensity) // Forward for (int r = 1; r < cost_map.rows; r++) { const uchar * upper_ptr = dst.ptr(r - 1); - uchar * current_ptr = dst.ptr(r); + auto * current_ptr = dst.ptr(r); for (int c = 1; c < cost_map.cols; c++) { int up = distances.at(r - 1).at(c); @@ -56,7 +56,7 @@ cv::Mat direct_cost_map(const cv::Mat & cost_map, const cv::Mat & intensity) // Backward for (int r = cost_map.rows - 2; r >= 0; r--) { const uchar * downer_ptr = dst.ptr(r + 1); - uchar * current_ptr = dst.ptr(r); + auto * current_ptr = dst.ptr(r); for (int c = cost_map.cols - 2; c >= 0; c--) { int down = distances.at(r + 1).at(c); @@ -76,24 +76,6 @@ cv::Mat direct_cost_map(const cv::Mat & cost_map, const cv::Mat & intensity) return dst; } -cv::Mat visualize_direction_map(const cv::Mat & cost_map) -{ - cv::Mat s = 255 * cv::Mat::ones(cost_map.size(), CV_8UC1); - cv::Mat v = 255 * cv::Mat::ones(cost_map.size(), CV_8UC1); - cv::Mat hsv, rgb; - cv::merge(std::vector{cost_map, s, v}, hsv); - cv::cvtColor(hsv, rgb, cv::COLOR_HSV2BGR); - - for (int r = 0; r < cost_map.rows; r++) { - const uchar * src_ptr = cost_map.ptr(r); - cv::Vec3b * dst_ptr = rgb.ptr(r); - for (int c = 0; c < cost_map.cols; c++) { - if (src_ptr[c] == 0) dst_ptr[c] = cv::Vec3b(0, 0, 0); - } - } - return rgb; -} - } // namespace yabloc // #include diff --git a/localization/yabloc/yabloc_particle_filter/src/ll2_cost_map/hierarchical_cost_map.cpp b/localization/yabloc/yabloc_particle_filter/src/ll2_cost_map/hierarchical_cost_map.cpp index 6255236f2802e..4f03e3d26ecb6 100644 --- a/localization/yabloc/yabloc_particle_filter/src/ll2_cost_map/hierarchical_cost_map.cpp +++ b/localization/yabloc/yabloc_particle_filter/src/ll2_cost_map/hierarchical_cost_map.cpp @@ -24,20 +24,20 @@ namespace yabloc { -float Area::unit_length_ = -1; +float Area::unit_length = -1; HierarchicalCostMap::HierarchicalCostMap(rclcpp::Node * node) -: max_range_(node->declare_parameter("max_range")), - image_size_(node->declare_parameter("image_size")), +: max_range_(static_cast(node->declare_parameter("max_range"))), + image_size_(static_cast(node->declare_parameter("image_size"))), max_map_count_(10), logger_(node->get_logger()) { - Area::unit_length_ = max_range_; - float gamma = node->declare_parameter("gamma"); - gamma_converter.reset(gamma); + Area::unit_length = max_range_; + float gamma = static_cast(node->declare_parameter("gamma")); + gamma_converter_.reset(gamma); } -cv::Point2i HierarchicalCostMap::to_cv_point(const Area & area, const Eigen::Vector2f p) const +cv::Point2i HierarchicalCostMap::to_cv_point(const Area & area, const Eigen::Vector2f & p) const { Eigen::Vector2f relative = p - area.real_scale(); float px = relative.x() / max_range_ * image_size_; @@ -59,7 +59,7 @@ CostMapValue HierarchicalCostMap::at(const Eigen::Vector2f & position) cv::Point2i tmp = to_cv_point(key, position); cv::Vec3b b3 = cost_maps_.at(key).ptr(tmp.y)[tmp.x]; - return {b3[0] / 255.f, b3[1], b3[2] == 1}; + return {static_cast(b3[0]) / 255.f, b3[1], b3[2] == 1}; } void HierarchicalCostMap::set_height(float height) @@ -103,10 +103,13 @@ void HierarchicalCostMap::build_map(const Area & area) { if (!cloud_.has_value()) return; - cv::Mat image = 255 * cv::Mat::ones(cv::Size(image_size_, image_size_), CV_8UC1); - cv::Mat orientation = cv::Mat::zeros(cv::Size(image_size_, image_size_), CV_8UC1); + cv::Mat image = + 255 * + cv::Mat::ones(cv::Size(static_cast(image_size_), static_cast(image_size_)), CV_8UC1); + cv::Mat orientation = + cv::Mat::zeros(cv::Size(static_cast(image_size_), static_cast(image_size_)), CV_8UC1); - auto cvPoint = [this, area](const Eigen::Vector3f & p) -> cv::Point { + auto cv_point = [this, area](const Eigen::Vector3f & p) -> cv::Point { return this->to_cv_point(area, p.topRows(2)); }; @@ -117,12 +120,12 @@ void HierarchicalCostMap::build_map(const Area & area) if (std::abs(pn.normal_z - *height_) > 4) continue; } - cv::Point2i from = cvPoint(pn.getVector3fMap()); - cv::Point2i to = cvPoint(pn.getNormalVector3fMap()); + cv::Point2i from = cv_point(pn.getVector3fMap()); + cv::Point2i to = cv_point(pn.getNormalVector3fMap()); - float radian = std::atan2(from.y - to.y, from.x - to.x); + auto radian = static_cast(std::atan2(from.y - to.y, from.x - to.x)); if (radian < 0) radian += M_PI; - float degree = radian * 180 / M_PI; + auto degree = static_cast(radian * 180 / M_PI); cv::line(image, from, to, cv::Scalar::all(0), 1); cv::line(orientation, from, to, cv::Scalar::all(degree), 1); @@ -142,7 +145,7 @@ void HierarchicalCostMap::build_map(const Area & area) cv::Mat directed_cost_map; cv::merge( - std::vector{gamma_converter(distance), whole_orientation, available_area}, + std::vector{gamma_converter_(distance), whole_orientation, available_area}, directed_cost_map); cost_maps_[area] = directed_cost_map; @@ -173,9 +176,10 @@ HierarchicalCostMap::MarkerArray HierarchicalCostMap::show_map_range() const marker.scale.x = 0.1; Eigen::Vector2f xy = area.real_scale(); marker.points.push_back(point_msg(xy.x(), xy.y())); - marker.points.push_back(point_msg(xy.x() + area.unit_length_, xy.y())); - marker.points.push_back(point_msg(xy.x() + area.unit_length_, xy.y() + area.unit_length_)); - marker.points.push_back(point_msg(xy.x(), xy.y() + area.unit_length_)); + marker.points.push_back(point_msg(xy.x() + yabloc::Area::unit_length, xy.y())); + marker.points.push_back( + point_msg(xy.x() + yabloc::Area::unit_length, xy.y() + yabloc::Area::unit_length)); + marker.points.push_back(point_msg(xy.x(), xy.y() + yabloc::Area::unit_length)); marker.points.push_back(point_msg(xy.x(), xy.y())); array_msg.markers.push_back(marker); } @@ -188,27 +192,33 @@ cv::Mat HierarchicalCostMap::get_map_image(const Pose & pose) // return cv::Mat::zeros(cv::Size(image_size_, image_size_), CV_8UC3); Eigen::Vector2f center; - center << pose.position.x, pose.position.y; + center << static_cast(pose.position.x), static_cast(pose.position.y); - float w = pose.orientation.w; - float z = pose.orientation.z; - Eigen::Matrix2f R = Eigen::Rotation2Df(2.f * std::atan2(z, w) - M_PI_2).toRotationMatrix(); + auto w = static_cast(pose.orientation.w); + auto z = static_cast(pose.orientation.z); + Eigen::Matrix2f r = + Eigen::Rotation2Df(static_cast(2.f * std::atan2(z, w) - M_PI_2)).toRotationMatrix(); - auto toVector2f = [this, center, R](float h, float w) -> Eigen::Vector2f { + auto to_vector2f = [this, center, r](float h, float w) -> Eigen::Vector2f { Eigen::Vector2f offset; offset.x() = (w / this->image_size_ - 0.5f) * this->max_range_ * 1.5f; offset.y() = -(h / this->image_size_ - 0.5f) * this->max_range_ * 1.5f; - return center + R * offset; + return center + r * offset; }; - cv::Mat image = cv::Mat::zeros(cv::Size(image_size_, image_size_), CV_8UC3); - for (int w = 0; w < image_size_; w++) { - for (int h = 0; h < image_size_; h++) { - CostMapValue v3 = this->at(toVector2f(h, w)); + cv::Mat image = + cv::Mat::zeros(cv::Size(static_cast(image_size_), static_cast(image_size_)), CV_8UC3); + for (int w_index = 0; static_cast(w_index) < image_size_; w_index++) { + for (int h_index = 0; static_cast(h_index) < image_size_; h_index++) { + CostMapValue v3 = + this->at(to_vector2f(static_cast(h_index), static_cast(w_index))); if (v3.unmapped) - image.at(h, w) = cv::Vec3b(v3.angle, 255 * v3.intensity, 50); + image.at(h_index, w_index) = + cv::Vec3b(v3.angle, static_cast(255 * v3.intensity), 50); else - image.at(h, w) = cv::Vec3b(v3.angle, 255 * v3.intensity, 255 * v3.intensity); + image.at(h_index, w_index) = cv::Vec3b( + v3.angle, static_cast(255 * v3.intensity), + static_cast(255 * v3.intensity)); } } @@ -235,7 +245,8 @@ void HierarchicalCostMap::erase_obsolete() cv::Mat HierarchicalCostMap::create_available_area_image(const Area & area) const { - cv::Mat available_area = cv::Mat::zeros(cv::Size(image_size_, image_size_), CV_8UC1); + cv::Mat available_area = + cv::Mat::zeros(cv::Size(static_cast(image_size_), static_cast(image_size_)), CV_8UC1); if (bounding_boxes_.empty()) return available_area; // Define current area diff --git a/localization/yabloc/yabloc_particle_filter/src/prediction/predictor.cpp b/localization/yabloc/yabloc_particle_filter/src/prediction/predictor.cpp index 8046d43dc34a3..701a2b8763fa3 100644 --- a/localization/yabloc/yabloc_particle_filter/src/prediction/predictor.cpp +++ b/localization/yabloc/yabloc_particle_filter/src/prediction/predictor.cpp @@ -33,10 +33,13 @@ namespace yabloc::modularized_particle_filter Predictor::Predictor(const rclcpp::NodeOptions & options) : Node("predictor", options), - number_of_particles_(declare_parameter("num_of_particles")), - resampling_interval_seconds_(declare_parameter("resampling_interval_seconds")), - static_linear_covariance_(declare_parameter("static_linear_covariance")), - static_angular_covariance_(declare_parameter("static_angular_covariance")), + number_of_particles_(static_cast(declare_parameter("num_of_particles"))), + resampling_interval_seconds_( + static_cast(declare_parameter("resampling_interval_seconds"))), + static_linear_covariance_( + static_cast(declare_parameter("static_linear_covariance"))), + static_angular_covariance_( + static_cast(declare_parameter("static_angular_covariance"))), cov_xx_yy_{this->template declare_parameter>("cov_xx_yy")} { tf2_broadcaster_ = std::make_unique(*this); @@ -93,11 +96,11 @@ void Predictor::on_trigger_service( RCLCPP_INFO_STREAM(get_logger(), "yabloc particle filter is deactivated"); } - const bool before_activated_ = yabloc_activated_; + const bool before_activated = yabloc_activated_; yabloc_activated_ = request->data; response->success = true; - if (yabloc_activated_ && (!before_activated_)) { + if (yabloc_activated_ && (!before_activated)) { RCLCPP_INFO_STREAM(get_logger(), "restart particle filter"); if (latest_ekf_pose_ptr_) { on_initial_pose(latest_ekf_pose_ptr_); @@ -113,10 +116,11 @@ void Predictor::on_initial_pose(const PoseCovStamped::ConstSharedPtr initialpose // Publish initial pose marker auto position = initialpose->pose.pose.position; Eigen::Vector3f pos_vec3f; - pos_vec3f << position.x, position.y, position.z; + pos_vec3f << static_cast(position.x), static_cast(position.y), + static_cast(position.z); auto orientation = initialpose->pose.pose.orientation; - float theta = 2 * std::atan2(orientation.z, orientation.w); + auto theta = static_cast(2 * std::atan2(orientation.z, orientation.w)); Eigen::Vector3f tangent; tangent << std::cos(theta), std::sin(theta), 0; @@ -152,7 +156,7 @@ void Predictor::initialize_particles(const PoseCovStamped & initialpose) pose.position.x += noise.x(); pose.position.y += noise.y(); - float noised_yaw = util::normalize_radian(yaw + util::nrand(yaw_std)); + auto noised_yaw = static_cast(util::normalize_radian(yaw + util::nrand(yaw_std))); pose.orientation.w = std::cos(noised_yaw / 2.0); pose.orientation.x = 0.0; pose.orientation.y = 0.0; @@ -184,14 +188,14 @@ void Predictor::on_twist_cov(const TwistCovStamped::ConstSharedPtr twist_cov) } void Predictor::update_with_dynamic_noise( - ParticleArray & particle_array, const TwistCovStamped & twist, double dt) + ParticleArray & particle_array, const TwistCovStamped & twist, double dt) const { // linear & angular velocity - const float linear_x = twist.twist.twist.linear.x; - const float angular_z = twist.twist.twist.angular.z; + const auto linear_x = static_cast(twist.twist.twist.linear.x); + const auto angular_z = static_cast(twist.twist.twist.angular.z); // standard deviation of linear & angular velocity - const float std_linear_x = std::sqrt(twist.twist.covariance[6 * 0 + 0]); - const float std_angular_z = std::sqrt(twist.twist.covariance[6 * 5 + 5]); + const auto std_linear_x = static_cast(std::sqrt(twist.twist.covariance[6 * 0 + 0])); + const auto std_angular_z = static_cast(std::sqrt(twist.twist.covariance[6 * 5 + 5])); // 1[rad/s] = 60[deg/s] // 1[m/s] = 3.6[km/h] const float truncated_angular_std = @@ -272,7 +276,7 @@ void Predictor::on_weighted_particles(const ParticleArray::ConstSharedPtr weight try { particle_array = resampler_ptr_->add_weight_retroactively(particle_array, *weighted_particles_ptr); - } catch (const resampling_skip_exception & e) { + } catch (const ResamplingSkipException & e) { // Do nothing (just skipping the resample()) RCLCPP_INFO_STREAM(this->get_logger(), "skipped resampling"); } @@ -284,16 +288,16 @@ void Predictor::on_weighted_particles(const ParticleArray::ConstSharedPtr weight // Exit if previous resampling time is not valid. if (!previous_resampling_time_opt_.has_value()) { previous_resampling_time_opt_ = current_time; - throw resampling_skip_exception("previous resampling time is not valid"); + throw ResamplingSkipException("previous resampling time is not valid"); } if (current_time - previous_resampling_time_opt_.value() <= resampling_interval_seconds_) { - throw resampling_skip_exception("it is not time to resample"); + throw ResamplingSkipException("it is not time to resample"); } particle_array = resampler_ptr_->resample(particle_array); previous_resampling_time_opt_ = current_time; - } catch (const resampling_skip_exception & e) { + } catch (const ResamplingSkipException & e) { void(); // Do nothing (just skipping the resample()) } @@ -397,7 +401,7 @@ Predictor::PoseCovStamped Predictor::rectify_initial_pose( msg.pose.pose.orientation.z = std::sin(theta / 2); Eigen::Matrix2f cov; - cov << cov_xx_yy_.at(0), 0, 0, cov_xx_yy_.at(1); + cov << static_cast(cov_xx_yy_.at(0)), 0, 0, static_cast(cov_xx_yy_.at(1)); Eigen::Rotation2D r(theta); cov = r * cov * r.inverse(); diff --git a/localization/yabloc/yabloc_particle_filter/src/prediction/resampler.cpp b/localization/yabloc/yabloc_particle_filter/src/prediction/resampler.cpp index cc4cbc3730e0c..c8c3971ba85b2 100644 --- a/localization/yabloc/yabloc_particle_filter/src/prediction/resampler.cpp +++ b/localization/yabloc/yabloc_particle_filter/src/prediction/resampler.cpp @@ -65,7 +65,7 @@ RetroactiveResampler::ParticleArray RetroactiveResampler::add_weight_retroactive { if (!check_weighted_particles_validity(weighted_particles)) { RCLCPP_ERROR_STREAM(logger_, "weighted_particles has invalid data"); - throw resampling_skip_exception("weighted_particles has invalid data"); + throw ResamplingSkipException("weighted_particles has invalid data"); } // Initialize corresponding index lookup table @@ -143,7 +143,7 @@ RetroactiveResampler::ParticleArray RetroactiveResampler::resample( // Copy particle to resampled variable resampled_particles.particles[m] = predicted_particles.particles[predicted_particle_index]; // Reset weight uniformly - resampled_particles.particles[m].weight = num_of_particles_inv; + resampled_particles.particles[m].weight = static_cast(num_of_particles_inv); // Make history resampling_history_[latest_resampling_generation_][m] = predicted_particle_index; } @@ -157,7 +157,7 @@ RetroactiveResampler::ParticleArray RetroactiveResampler::resample( return resampled_particles; } -double RetroactiveResampler::random_from_01_uniformly() const +double RetroactiveResampler::random_from_01_uniformly() { static std::default_random_engine engine(0); std::uniform_real_distribution dist(0.0, 1.0); diff --git a/localization/yabloc/yabloc_particle_filter/test/src/test_resampler.cpp b/localization/yabloc/yabloc_particle_filter/test/src/test_resampler.cpp index 124b0500e010e..915237b2b125c 100644 --- a/localization/yabloc/yabloc_particle_filter/test/src/test_resampler.cpp +++ b/localization/yabloc/yabloc_particle_filter/test/src/test_resampler.cpp @@ -22,20 +22,20 @@ namespace mpf = yabloc::modularized_particle_filter; using Particle = yabloc_particle_filter::msg::Particle; using ParticleArray = yabloc_particle_filter::msg::ParticleArray; -constexpr int PARTICLE_COUNT = 10; -constexpr int HISTORY_SIZE = 10; +constexpr int particle_count = 10; +constexpr int history_size = 10; TEST(ResamplerTestSuite, outOfHistory) { - mpf::RetroactiveResampler resampler(PARTICLE_COUNT, HISTORY_SIZE); + mpf::RetroactiveResampler resampler(particle_count, history_size); ParticleArray predicted; ParticleArray weighted; predicted.header.stamp = rclcpp::Time(0); predicted.id = 0; weighted.id = 0; - predicted.particles.resize(PARTICLE_COUNT); - weighted.particles.resize(PARTICLE_COUNT); + predicted.particles.resize(particle_count); + weighted.particles.resize(particle_count); for (auto & p : predicted.particles) p.weight = 1; for (auto & p : weighted.particles) p.weight = 1; @@ -64,7 +64,7 @@ TEST(ResamplerTestSuite, outOfHistory) } // Iterate resampling to fill all history - for (int t = 0; t < HISTORY_SIZE; ++t) { + for (int t = 0; t < history_size; ++t) { auto resampled = resampler.resample(predicted); EXPECT_EQ(resampled.id, t + 1); predicted = resampled; @@ -85,26 +85,26 @@ TEST(ResamplerTestSuite, outOfHistory) TEST(ResamplerTestSuite, simpleResampling) { - mpf::RetroactiveResampler resampler(PARTICLE_COUNT, HISTORY_SIZE); + mpf::RetroactiveResampler resampler(particle_count, history_size); ParticleArray predicted; predicted.header.stamp = rclcpp::Time(0); - predicted.particles.resize(PARTICLE_COUNT); + predicted.particles.resize(particle_count); predicted.id = 0; - for (int i = 0; i < PARTICLE_COUNT; ++i) predicted.particles.at(i).weight = 1; + for (int i = 0; i < particle_count; ++i) predicted.particles.at(i).weight = 1; ParticleArray weighted; - weighted.particles.resize(PARTICLE_COUNT); + weighted.particles.resize(particle_count); // Update by uniform distribution { // Weight weighted.id = 0; - for (int i = 0; i < PARTICLE_COUNT; ++i) weighted.particles.at(i).weight = 0.5; + for (int i = 0; i < particle_count; ++i) weighted.particles.at(i).weight = 0.5; ParticleArray array1 = resampler.add_weight_retroactively(predicted, weighted); // All weights must be equal - for (const auto & p : array1.particles) EXPECT_NEAR(p.weight, 1.0 / PARTICLE_COUNT, 1e-3); + for (const auto & p : array1.particles) EXPECT_NEAR(p.weight, 1.0 / particle_count, 1e-3); // Resample predicted = array1; @@ -117,10 +117,10 @@ TEST(ResamplerTestSuite, simpleResampling) { // Weight weighted.id = 0; - for (int i = 0; i < PARTICLE_COUNT; ++i) { + for (int i = 0; i < particle_count; ++i) { auto & p = predicted.particles.at(i); auto & q = weighted.particles.at(i); - if (i < PARTICLE_COUNT / 2) { + if (i < particle_count / 2) { p.pose.position.x = 1; q.weight = 2.0; } else { @@ -131,12 +131,12 @@ TEST(ResamplerTestSuite, simpleResampling) ParticleArray array1 = resampler.add_weight_retroactively(predicted, weighted); // All weight must match with following expectation - for (int i = 0; i < PARTICLE_COUNT; ++i) { + for (int i = 0; i < particle_count; ++i) { const auto & p = array1.particles.at(i); - if (i < PARTICLE_COUNT / 2) { - EXPECT_NEAR(p.weight, 2.0 / 1.5 / PARTICLE_COUNT, 1e-3f); + if (i < particle_count / 2) { + EXPECT_NEAR(p.weight, 2.0 / 1.5 / particle_count, 1e-3f); } else { - EXPECT_NEAR(p.weight, 1.0 / 1.5 / PARTICLE_COUNT, 1e-3f); + EXPECT_NEAR(p.weight, 1.0 / 1.5 / particle_count, 1e-3f); } } @@ -146,32 +146,33 @@ TEST(ResamplerTestSuite, simpleResampling) predicted = resampled; EXPECT_EQ(predicted.id, 2); - int centroid = 0; - for (const auto & p : predicted.particles) centroid += p.pose.position.x; + int centroid = std::accumulate( + predicted.particles.begin(), predicted.particles.end(), 0, + [](int sum, const auto & p) { return sum + static_cast(p.pose.position.x); }); EXPECT_GT(centroid, 0); } } TEST(ResamplerTestSuite, resamplingWithRetrogression) { - mpf::RetroactiveResampler resampler(PARTICLE_COUNT, HISTORY_SIZE); + mpf::RetroactiveResampler resampler(particle_count, history_size); ParticleArray predicted; predicted.header.stamp = rclcpp::Time(0); - predicted.particles.resize(PARTICLE_COUNT); + predicted.particles.resize(particle_count); predicted.id = 0; - for (int i = 0; i < PARTICLE_COUNT; ++i) { + for (int i = 0; i < particle_count; ++i) { auto & p = predicted.particles.at(i); p.weight = 1.0; - if (i < PARTICLE_COUNT / 2) + if (i < particle_count / 2) p.pose.position.x = 1; else p.pose.position.x = -1; } // Fill all history with biased weighted particles - for (int p = 0; p < HISTORY_SIZE; ++p) { + for (int p = 0; p < history_size; ++p) { auto resampled = resampler.resample(predicted); predicted = resampled; EXPECT_EQ(predicted.id, p + 1); @@ -179,18 +180,17 @@ TEST(ResamplerTestSuite, resamplingWithRetrogression) // Update by ancient measurement { - double before_centroid = 0; - for (const auto & p : predicted.particles) { - before_centroid += p.pose.position.x * p.weight; - } + double before_centroid = std::accumulate( + predicted.particles.begin(), predicted.particles.end(), 0.0, + [](double sum, const auto & p) { return sum + p.pose.position.x * p.weight; }); // Weight ParticleArray weighted; - weighted.particles.resize(PARTICLE_COUNT); + weighted.particles.resize(particle_count); weighted.id = 1; // ancient generation id - for (int i = 0; i < PARTICLE_COUNT; ++i) { + for (int i = 0; i < particle_count; ++i) { auto & q = weighted.particles.at(i); - if (i < PARTICLE_COUNT / 2) { + if (i < particle_count / 2) { q.weight = 2.0; } else { q.weight = 1.0; @@ -199,10 +199,10 @@ TEST(ResamplerTestSuite, resamplingWithRetrogression) predicted = resampler.add_weight_retroactively(predicted, weighted); - double after_centroid = 0; - for (const auto & p : predicted.particles) { - after_centroid += p.pose.position.x * p.weight; - } + double after_centroid = std::accumulate( + predicted.particles.begin(), predicted.particles.end(), 0.0, + [](double sum, const auto & p) { return sum + p.pose.position.x * p.weight; }); + EXPECT_TRUE(after_centroid > before_centroid); } } diff --git a/localization/yabloc/yabloc_pose_initializer/CMakeLists.txt b/localization/yabloc/yabloc_pose_initializer/CMakeLists.txt index 91b272b413c4c..2e6d31ae2f12b 100644 --- a/localization/yabloc/yabloc_pose_initializer/CMakeLists.txt +++ b/localization/yabloc/yabloc_pose_initializer/CMakeLists.txt @@ -20,18 +20,22 @@ find_package(OpenCV REQUIRED) # =================================================== # Executable # Camera -set(TARGET camera_pose_initializer_node) -ament_auto_add_executable(${TARGET} +ament_auto_add_library(${PROJECT_NAME} src/camera/lane_image.cpp src/camera/marker_module.cpp src/camera/projector_module.cpp src/camera/semantic_segmentation.cpp - src/camera/camera_pose_initializer_core.cpp - src/camera/camera_pose_initializer_node.cpp) -target_include_directories(${TARGET} PUBLIC include) -target_include_directories(${TARGET} SYSTEM PRIVATE ${EIGEN3_INCLUDE_DIRS} ${PCL_INCLUDE_DIRS}) -target_link_libraries(${TARGET} ${PCL_LIBRARIES} Sophus::Sophus) -ament_target_dependencies(${TARGET} OpenCV) + src/camera/camera_pose_initializer_core.cpp) +target_include_directories(${PROJECT_NAME} PUBLIC include) +target_include_directories(${PROJECT_NAME} SYSTEM PRIVATE ${EIGEN3_INCLUDE_DIRS} ${PCL_INCLUDE_DIRS}) +target_link_libraries(${PROJECT_NAME} ${PCL_LIBRARIES} Sophus::Sophus) +ament_target_dependencies(${PROJECT_NAME} OpenCV) + +rclcpp_components_register_node(${PROJECT_NAME} + PLUGIN "yabloc::CameraPoseInitializer" + EXECUTABLE ${PROJECT_NAME}_node + EXECUTOR MultiThreadedExecutor +) # =================================================== ament_auto_package(INSTALL_TO_SHARE config launch) diff --git a/localization/yabloc/yabloc_pose_initializer/include/yabloc_pose_initializer/camera/camera_pose_initializer.hpp b/localization/yabloc/yabloc_pose_initializer/include/yabloc_pose_initializer/camera/camera_pose_initializer.hpp index 979d1b370699e..9a382f71e6b33 100644 --- a/localization/yabloc/yabloc_pose_initializer/include/yabloc_pose_initializer/camera/camera_pose_initializer.hpp +++ b/localization/yabloc/yabloc_pose_initializer/include/yabloc_pose_initializer/camera/camera_pose_initializer.hpp @@ -43,7 +43,7 @@ class CameraPoseInitializer : public rclcpp::Node using LaneletMapBin = autoware_map_msgs::msg::LaneletMapBin; using RequestPoseAlignment = tier4_localization_msgs::srv::PoseWithCovarianceStamped; - CameraPoseInitializer(); + explicit CameraPoseInitializer(const rclcpp::NodeOptions & options = rclcpp::NodeOptions()); private: const int angle_resolution_; @@ -65,9 +65,9 @@ class CameraPoseInitializer : public rclcpp::Node void on_map(const LaneletMapBin & msg); void on_service( - const RequestPoseAlignment::Request::SharedPtr, - RequestPoseAlignment::Response::SharedPtr request); - PoseCovStamped create_rectified_initial_pose( + const RequestPoseAlignment::Request::SharedPtr request, + RequestPoseAlignment::Response::SharedPtr response); + static PoseCovStamped create_rectified_initial_pose( const Eigen::Vector3f & pos, double yaw_angle_rad, const PoseCovStamped & src_msg); std::optional estimate_pose( diff --git a/localization/yabloc/yabloc_pose_initializer/include/yabloc_pose_initializer/camera/semantic_segmentation.hpp b/localization/yabloc/yabloc_pose_initializer/include/yabloc_pose_initializer/camera/semantic_segmentation.hpp index 061d0548c6f18..4558a503b8ac4 100644 --- a/localization/yabloc/yabloc_pose_initializer/include/yabloc_pose_initializer/camera/semantic_segmentation.hpp +++ b/localization/yabloc/yabloc_pose_initializer/include/yabloc_pose_initializer/camera/semantic_segmentation.hpp @@ -31,13 +31,13 @@ class SemanticSegmentation static void print_error_message(const rclcpp::Logger & logger); private: - cv::Mat make_blob(const cv::Mat & image); + static cv::Mat make_blob(const cv::Mat & image); - cv::Mat convert_blob_to_image(const cv::Mat & blob); + static cv::Mat convert_blob_to_image(const cv::Mat & blob); - cv::Mat normalize(const cv::Mat & mask, double score_threshold = 0.5); + static cv::Mat normalize(const cv::Mat & mask, double score_threshold = 0.5); - cv::Mat draw_overlay(const cv::Mat & image, const cv::Mat & segmentation); + static cv::Mat draw_overlay(const cv::Mat & image, const cv::Mat & segmentation); struct Impl; std::shared_ptr impl_{nullptr}; diff --git a/localization/yabloc/yabloc_pose_initializer/launch/yabloc_pose_initializer.launch.xml b/localization/yabloc/yabloc_pose_initializer/launch/yabloc_pose_initializer.launch.xml index 83c2c8fe09287..964cc61420b9c 100644 --- a/localization/yabloc/yabloc_pose_initializer/launch/yabloc_pose_initializer.launch.xml +++ b/localization/yabloc/yabloc_pose_initializer/launch/yabloc_pose_initializer.launch.xml @@ -2,7 +2,7 @@ - + diff --git a/localization/yabloc/yabloc_pose_initializer/package.xml b/localization/yabloc/yabloc_pose_initializer/package.xml index 7ed16c9a8b82d..359557ac94090 100644 --- a/localization/yabloc/yabloc_pose_initializer/package.xml +++ b/localization/yabloc/yabloc_pose_initializer/package.xml @@ -16,12 +16,13 @@ autoware_cmake rosidl_default_generators + autoware_lanelet2_extension autoware_map_msgs cv_bridge geometry_msgs - lanelet2_extension libopencv-dev rclcpp + rclcpp_components sensor_msgs tier4_localization_msgs visualization_msgs diff --git a/localization/yabloc/yabloc_pose_initializer/src/camera/camera_pose_initializer_core.cpp b/localization/yabloc/yabloc_pose_initializer/src/camera/camera_pose_initializer_core.cpp index 083e7dd5bcd43..6aff744ef4975 100644 --- a/localization/yabloc/yabloc_pose_initializer/src/camera/camera_pose_initializer_core.cpp +++ b/localization/yabloc/yabloc_pose_initializer/src/camera/camera_pose_initializer_core.cpp @@ -14,20 +14,25 @@ #include "yabloc_pose_initializer/camera/camera_pose_initializer.hpp" -#include -#include -#include +#include +#include +#include #include #include -#include +#if __has_include() +#include // for ROS 2 Jazzy or newer +#else +#include // for ROS 2 Humble or older +#endif #include namespace yabloc { -CameraPoseInitializer::CameraPoseInitializer() -: Node("camera_pose_initializer"), angle_resolution_(declare_parameter("angle_resolution")) +CameraPoseInitializer::CameraPoseInitializer(const rclcpp::NodeOptions & options) +: Node("camera_pose_initializer", options), + angle_resolution_(static_cast(declare_parameter("angle_resolution"))) { using std::placeholders::_1; using std::placeholders::_2; @@ -57,7 +62,7 @@ CameraPoseInitializer::CameraPoseInitializer() } } -cv::Mat bitwise_and_3ch(const cv::Mat src1, const cv::Mat src2) +cv::Mat bitwise_and_3ch(const cv::Mat & src1, const cv::Mat & src2) { std::vector src1_array; std::vector src2_array; @@ -74,7 +79,7 @@ cv::Mat bitwise_and_3ch(const cv::Mat src1, const cv::Mat src2) return merged; } -int count_non_zero(cv::Mat image_3ch) +int count_non_zero(const cv::Mat & image_3ch) { std::vector images; cv::split(image_3ch, images); @@ -148,13 +153,13 @@ std::optional CameraPoseInitializer::estimate_pose( // consider lanelet direction float gain = 1; if (lane_angle_rad) { - gain = 2 + std::cos((lane_angle_rad.value() - angle_rad) / 2.0); + gain = static_cast(2 + std::cos((lane_angle_rad.value() - angle_rad) / 2.0)); } // If count_non_zero() returns 0 everywhere, the orientation is chosen by the only gain - const float score = gain * (1 + count_non_zero(dst)); + const float score = gain * static_cast(1 + count_non_zero(dst)); scores.push_back(score); - angles_rad.push_back(angle_rad); + angles_rad.push_back(static_cast(angle_rad)); } marker_module_->publish_marker(scores, angles_rad, position); @@ -171,7 +176,7 @@ void CameraPoseInitializer::on_map(const LaneletMapBin & msg) lane_image_ = std::make_unique(lanelet_map); const_lanelets_.clear(); - for (auto l : lanelet_map->laneletLayer) { + for (const auto & l : lanelet_map->laneletLayer) { const_lanelets_.push_back(l); } } @@ -186,7 +191,9 @@ void CameraPoseInitializer::on_service( const auto query_pos = request->pose_with_covariance.pose.pose.position; const auto orientation = request->pose_with_covariance.pose.pose.orientation; const double yaw_std_rad = std::sqrt(query_pos_with_cov.pose.covariance.at(35)); - const Eigen::Vector3f pos_vec3f(query_pos.x, query_pos.y, query_pos.z); + const Eigen::Vector3f pos_vec3f( + static_cast(query_pos.x), static_cast(query_pos.y), + static_cast(query_pos.z)); RCLCPP_INFO_STREAM(get_logger(), "Given initial position " << pos_vec3f.transpose()); // Estimate orientation @@ -216,3 +223,6 @@ CameraPoseInitializer::PoseCovStamped CameraPoseInitializer::create_rectified_in } } // namespace yabloc + +#include +RCLCPP_COMPONENTS_REGISTER_NODE(yabloc::CameraPoseInitializer) diff --git a/localization/yabloc/yabloc_pose_initializer/src/camera/lane_image.cpp b/localization/yabloc/yabloc_pose_initializer/src/camera/lane_image.cpp index 52ab8c8205815..d6949c43dbfc5 100644 --- a/localization/yabloc/yabloc_pose_initializer/src/camera/lane_image.cpp +++ b/localization/yabloc/yabloc_pose_initializer/src/camera/lane_image.cpp @@ -27,22 +27,22 @@ namespace yabloc { namespace bg = boost::geometry; -typedef bg::model::d2::point_xy point_t; -typedef bg::model::box box_t; -typedef bg::model::polygon polygon_t; +using point_t = bg::model::d2::point_xy; +using box_t = bg::model::box; +using polygon_t = bg::model::polygon; -LaneImage::LaneImage(lanelet::LaneletMapPtr map) : map_(map) +LaneImage::LaneImage(lanelet::LaneletMapPtr map) : map_(std::move(map)) { } cv::Point2i to_cv_point(const Eigen::Vector3f & v) { - const float image_size_ = 800; - const float max_range_ = 30; + const float image_size = 800; + const float max_range = 30; cv::Point pt; - pt.x = -v.y() / max_range_ * image_size_ * 0.5f + image_size_ / 2.f; - pt.y = -v.x() / max_range_ * image_size_ * 0.5f + image_size_ / 2.f; + pt.x = static_cast(-v.y() / max_range * image_size * 0.5f + image_size / 2.f); + pt.y = static_cast(-v.x() / max_range * image_size * 0.5f + image_size / 2.f); return pt; } @@ -59,7 +59,8 @@ void draw_lane(cv::Mat & image, const polygon_t & polygon) { std::vector contour; for (auto p : polygon.outer()) { - cv::Point2i pt = to_cv_point(Eigen::Vector3f(p.x(), p.y(), 0)); + cv::Point2i pt = + to_cv_point(Eigen::Vector3f(static_cast(p.x()), static_cast(p.y()), 0)); contour.push_back(pt); } @@ -71,8 +72,9 @@ void draw_lane(cv::Mat & image, const polygon_t & polygon) void draw_line(cv::Mat & image, const lanelet::LineString2d & line, geometry_msgs::msg::Point xyz) { std::vector contour; - for (auto p : line) { - cv::Point2i pt = to_cv_point(Eigen::Vector3f(p.x() - xyz.x, p.y() - xyz.y, 0)); + for (const auto & p : line) { + cv::Point2i pt = to_cv_point( + Eigen::Vector3f(static_cast(p.x() - xyz.x), static_cast(p.y() - xyz.y), 0)); contour.push_back(pt); } cv::polylines(image, contour, false, cv::Scalar(0, 0, 255), 2); @@ -83,7 +85,9 @@ cv::Mat LaneImage::get_image(const Pose & pose) const auto xyz = pose.position; box_t box(point_t(-20, -20), point_t(20, 20)); - cv::Mat image = cv::Mat::zeros(cv::Size(800, 800), CV_8UC3); + cv::Mat image = cv::Mat::zeros(cv::Size(800, 800), CV_8UC3); // NOLINT + // suppress hicpp-signed-bitwise + // from OpenCV std::vector joint_lanes; for (auto lanelet : map_->laneletLayer) { @@ -91,7 +95,7 @@ cv::Mat LaneImage::get_image(const Pose & pose) for (auto right : lanelet.rightBound2d()) { polygon.outer().push_back(point_t(right.x() - xyz.x, right.y() - xyz.y)); } - for (auto left : boost::adaptors::reverse(lanelet.leftBound2d())) { + for (const auto & left : boost::adaptors::reverse(lanelet.leftBound2d())) { polygon.outer().push_back(point_t(left.x() - xyz.x, left.y() - xyz.y)); } diff --git a/localization/yabloc/yabloc_pose_initializer/src/camera/marker_module.cpp b/localization/yabloc/yabloc_pose_initializer/src/camera/marker_module.cpp index 8a5fc65e2fac3..0d4dd8bb373e5 100644 --- a/localization/yabloc/yabloc_pose_initializer/src/camera/marker_module.cpp +++ b/localization/yabloc/yabloc_pose_initializer/src/camera/marker_module.cpp @@ -28,22 +28,22 @@ void MarkerModule::publish_marker( const std::vector & scores, const std::vector & angles, const Eigen::Vector3f & position) { - const int N = scores.size(); + const int n = static_cast(scores.size()); auto minmax = std::minmax_element(scores.begin(), scores.end()); auto normalize = [minmax](int score) -> float { - return static_cast(score - *minmax.first) / + return static_cast(static_cast(score) - *minmax.first) / std::max(1e-4f, static_cast(*minmax.second - *minmax.first)); }; MarkerArray array; - for (int i = 0; i < N; i++) { + for (int i = 0; i < n; i++) { Marker marker; marker.header.frame_id = "map"; marker.type = Marker::ARROW; marker.id = i; marker.ns = "arrow"; - marker.color = - static_cast(common::color_scale::rainbow(normalize(scores.at(i)))); + marker.color = static_cast( + common::color_scale::rainbow(normalize(static_cast(scores.at(i))))); marker.color.a = 0.5; marker.pose.position.x = position.x(); diff --git a/localization/yabloc/yabloc_pose_initializer/src/camera/projector_module.cpp b/localization/yabloc/yabloc_pose_initializer/src/camera/projector_module.cpp index baa199d5548a1..449812eff6e09 100644 --- a/localization/yabloc/yabloc_pose_initializer/src/camera/projector_module.cpp +++ b/localization/yabloc/yabloc_pose_initializer/src/camera/projector_module.cpp @@ -27,12 +27,12 @@ ProjectorModule::ProjectorModule(rclcpp::Node * node) cv::Point2i to_cv_point(const Eigen::Vector3f & v) { - const float image_size_ = 800; - const float max_range_ = 30; + const float image_size = 800; + const float max_range = 30; cv::Point pt; - pt.x = -v.y() / max_range_ * image_size_ * 0.5f + image_size_ / 2.f; - pt.y = -v.x() / max_range_ * image_size_ * 0.5f + image_size_ / 2.f; + pt.x = static_cast(-v.y() / max_range * image_size * 0.5f + image_size / 2.f); + pt.y = static_cast(-v.x() / max_range * image_size * 0.5f + image_size / 2.f); return pt; } @@ -44,7 +44,10 @@ cv::Mat ProjectorModule::project_image(const cv::Mat & mask_image) std::vector colors = { cv::Scalar(255, 0, 0), cv::Scalar(0, 255, 0), cv::Scalar(0, 0, 255)}; - cv::Mat projected_image = cv::Mat::zeros(cv::Size(800, 800), CV_8UC3); + cv::Mat projected_image = + cv::Mat::zeros(cv::Size(800, 800), CV_8UC3); // NOLINT + // suppress hicpp-signed-bitwise + // from OpenCV for (int i = 0; i < 3; i++) { std::vector > contours; cv::findContours(masks[i], contours, cv::RETR_LIST, cv::CHAIN_APPROX_NONE); @@ -90,7 +93,7 @@ bool ProjectorModule::define_project_func() // TODO(KYabuuchi) This will take into account ground tilt and camera vibration someday. project_func_ = [intrinsic_inv, q, t](const cv::Point & u) -> std::optional { - Eigen::Vector3f u3(u.x, u.y, 1); + Eigen::Vector3f u3(static_cast(u.x), static_cast(u.y), 1); Eigen::Vector3f u_bearing = (q * intrinsic_inv * u3).normalized(); if (u_bearing.z() > -0.01) return std::nullopt; float u_distance = -t.z() / u_bearing.z(); diff --git a/localization/yabloc/yabloc_pose_initializer/src/camera/semantic_segmentation.cpp b/localization/yabloc/yabloc_pose_initializer/src/camera/semantic_segmentation.cpp index 760ada6f8310a..917015269d3e2 100644 --- a/localization/yabloc/yabloc_pose_initializer/src/camera/semantic_segmentation.cpp +++ b/localization/yabloc/yabloc_pose_initializer/src/camera/semantic_segmentation.cpp @@ -53,7 +53,8 @@ cv::Mat SemanticSegmentation::convert_blob_to_image(const cv::Mat & blob) const int channels = blob.size[1]; const int height = blob.size[2]; const int width = blob.size[3]; - cv::Mat image = cv::Mat(height, width, CV_32FC4); + cv::Mat image = cv::Mat(height, width, CV_32FC4); // NOLINT + // suppress hicpp-signed-bitwise from OpenCV for (int h = 0; h < height; ++h) { for (int w = 0; w < width; ++w) { @@ -92,7 +93,8 @@ cv::Mat SemanticSegmentation::normalize(const cv::Mat & mask, double score_thres for (size_t i = 1; i < masks.size(); ++i) { cv::Mat bin_mask; cv::threshold(masks[i], bin_mask, score_threshold, 255, cv::THRESH_BINARY_INV); - bin_mask.convertTo(bin_mask, CV_8UC1); + bin_mask.convertTo(bin_mask, CV_8UC1); // NOLINT + // suppress hicpp-signed-bitwise from OpenCV bin_masks.push_back(255 - bin_mask); } @@ -109,11 +111,11 @@ cv::Mat SemanticSegmentation::draw_overlay(const cv::Mat & image, const cv::Mat void SemanticSegmentation::print_error_message(const rclcpp::Logger & logger) { - const std::string ERROR_MESSAGE = + const std::string error_message = R"(The yabloc_pose_initializer is not working correctly because the DNN model has not been downloaded correctly. Please check the README of yabloc_pose_initializer to know how download models.)"; - std::istringstream stream(ERROR_MESSAGE); + std::istringstream stream(error_message); std::string line; while (std::getline(stream, line)) { RCLCPP_ERROR_STREAM(logger, line); diff --git a/map/map_height_fitter/CMakeLists.txt b/map/map_height_fitter/CMakeLists.txt index 0dec2f6a1663a..9485ed3fa54cd 100644 --- a/map/map_height_fitter/CMakeLists.txt +++ b/map/map_height_fitter/CMakeLists.txt @@ -11,7 +11,7 @@ ament_auto_add_library(${PROJECT_NAME} SHARED ) target_link_libraries(map_height_fitter ${PCL_LIBRARIES}) -# When adding `lanelet2_extension` to package.xml, many warnings are generated. +# When adding `autoware_lanelet2_extension` to package.xml, many warnings are generated. # These are treated as errors in compile, so pedantic warnings are disabled for this package. target_compile_options(map_height_fitter PRIVATE -Wno-pedantic) diff --git a/map/map_height_fitter/package.xml b/map/map_height_fitter/package.xml index c10e65cdfaab1..36e5814365c3f 100644 --- a/map/map_height_fitter/package.xml +++ b/map/map_height_fitter/package.xml @@ -18,9 +18,9 @@ ament_cmake autoware_cmake + autoware_lanelet2_extension autoware_map_msgs geometry_msgs - lanelet2_extension libpcl-common pcl_conversions rclcpp diff --git a/map/map_height_fitter/src/map_height_fitter.cpp b/map/map_height_fitter/src/map_height_fitter.cpp index e01201f90f7e3..d6a20a636733d 100644 --- a/map/map_height_fitter/src/map_height_fitter.cpp +++ b/map/map_height_fitter/src/map_height_fitter.cpp @@ -14,8 +14,8 @@ #include "map_height_fitter/map_height_fitter.hpp" -#include -#include +#include +#include #include #include diff --git a/map/map_loader/README.md b/map/map_loader/README.md index a30bfa1a8f633..73b0164f0c8a7 100644 --- a/map/map_loader/README.md +++ b/map/map_loader/README.md @@ -151,7 +151,7 @@ Please see [tier4_autoware_msgs/msg/MapProjectorInfo.msg](https://github.com/tie {{ json_to_markdown("map/map_loader/schema/lanelet2_map_loader.schema.json") }} `use_waypoints` decides how to handle a centerline. -This flag enables to use the `overwriteLaneletsCenterlineWithWaypoints` function instead of `overwriteLaneletsCenterline`. Please see [the document of the lanelet2_extension package](https://github.com/autowarefoundation/autoware_common/blob/main/tmp/lanelet2_extension/docs/lanelet2_format_extension.md#centerline) in detail. +This flag enables to use the `overwriteLaneletsCenterlineWithWaypoints` function instead of `overwriteLaneletsCenterline`. Please see [the document of the autoware_lanelet2_extension package](https://github.com/autowarefoundation/autoware_lanelet2_extension/blob/main/autoware_lanelet2_extension/docs/lanelet2_format_extension.md#centerline) in detail. --- diff --git a/map/map_loader/package.xml b/map/map_loader/package.xml index a6f4ac7f81275..7c4d9eaf99556 100644 --- a/map/map_loader/package.xml +++ b/map/map_loader/package.xml @@ -20,13 +20,13 @@ ament_cmake_auto autoware_cmake + autoware_lanelet2_extension autoware_map_msgs component_interface_specs component_interface_utils fmt geography_utils geometry_msgs - lanelet2_extension libpcl-all-dev pcl_conversions rclcpp diff --git a/map/map_loader/src/lanelet2_map_loader/lanelet2_map_loader_node.cpp b/map/map_loader/src/lanelet2_map_loader/lanelet2_map_loader_node.cpp index ee89ad571b90a..7b712c7c281a2 100644 --- a/map/map_loader/src/lanelet2_map_loader/lanelet2_map_loader_node.cpp +++ b/map/map_loader/src/lanelet2_map_loader/lanelet2_map_loader_node.cpp @@ -36,12 +36,12 @@ #include "lanelet2_local_projector.hpp" #include +#include +#include +#include +#include +#include #include -#include -#include -#include -#include -#include #include #include diff --git a/map/map_loader/src/lanelet2_map_loader/lanelet2_map_visualization_node.cpp b/map/map_loader/src/lanelet2_map_loader/lanelet2_map_visualization_node.cpp index c81236bec86c0..87319222001d9 100644 --- a/map/map_loader/src/lanelet2_map_loader/lanelet2_map_visualization_node.cpp +++ b/map/map_loader/src/lanelet2_map_loader/lanelet2_map_visualization_node.cpp @@ -33,10 +33,10 @@ #include "map_loader/lanelet2_map_visualization_node.hpp" -#include -#include -#include -#include +#include +#include +#include +#include #include #include @@ -153,8 +153,8 @@ void Lanelet2MapVisualizationNode::onMapBin( setColor(&cl_speed_bumps, 0.56, 0.40, 0.27, 0.5); setColor(&cl_crosswalks, 0.80, 0.80, 0.0, 0.5); setColor(&cl_obstacle_polygons, 0.4, 0.27, 0.27, 0.5); - setColor(&cl_parking_lots, 0.5, 0.5, 0.0, 0.3); - setColor(&cl_parking_spaces, 1.0, 0.647, 0.0, 0.6); + setColor(&cl_parking_lots, 1.0, 1.0, 1.0, 0.2); + setColor(&cl_parking_spaces, 1.0, 1.0, 1.0, 0.3); setColor(&cl_lanelet_id, 0.5, 0.5, 0.5, 0.999); setColor(&cl_no_obstacle_segmentation_area, 0.37, 0.37, 0.27, 0.5); setColor(&cl_no_obstacle_segmentation_area_for_run_out, 0.37, 0.7, 0.27, 0.5); diff --git a/map/map_projection_loader/include/map_projection_loader/load_info_from_lanelet2_map.hpp b/map/map_projection_loader/include/map_projection_loader/load_info_from_lanelet2_map.hpp index 746f16e0f6b33..750b90fe495bd 100644 --- a/map/map_projection_loader/include/map_projection_loader/load_info_from_lanelet2_map.hpp +++ b/map/map_projection_loader/include/map_projection_loader/load_info_from_lanelet2_map.hpp @@ -15,10 +15,10 @@ #ifndef MAP_PROJECTION_LOADER__LOAD_INFO_FROM_LANELET2_MAP_HPP_ #define MAP_PROJECTION_LOADER__LOAD_INFO_FROM_LANELET2_MAP_HPP_ -#include -#include -#include -#include +#include +#include +#include +#include #include "tier4_map_msgs/msg/map_projector_info.hpp" diff --git a/map/map_projection_loader/package.xml b/map/map_projection_loader/package.xml index 7a930085cd7b1..b77ef18ac2293 100644 --- a/map/map_projection_loader/package.xml +++ b/map/map_projection_loader/package.xml @@ -17,9 +17,9 @@ ament_cmake_auto autoware_cmake + autoware_lanelet2_extension component_interface_specs component_interface_utils - lanelet2_extension rclcpp rclcpp_components tier4_map_msgs diff --git a/map/map_tf_generator/package.xml b/map/map_tf_generator/package.xml index 6f08da169309e..41c3364358f74 100644 --- a/map/map_tf_generator/package.xml +++ b/map/map_tf_generator/package.xml @@ -16,7 +16,7 @@ ament_cmake_auto autoware_cmake - lanelet2_extension + autoware_lanelet2_extension libpcl-all-dev pcl_conversions rclcpp diff --git a/map/map_tf_generator/src/vector_map_tf_generator_node.cpp b/map/map_tf_generator/src/vector_map_tf_generator_node.cpp index df3075bdce1fe..093b42037ed72 100644 --- a/map/map_tf_generator/src/vector_map_tf_generator_node.cpp +++ b/map/map_tf_generator/src/vector_map_tf_generator_node.cpp @@ -12,7 +12,7 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include +#include #include #include diff --git a/map/util/lanelet2_map_preprocessor/package.xml b/map/util/lanelet2_map_preprocessor/package.xml index e84bd8a80c202..b0c371ac9eeed 100644 --- a/map/util/lanelet2_map_preprocessor/package.xml +++ b/map/util/lanelet2_map_preprocessor/package.xml @@ -17,7 +17,7 @@ ament_cmake_auto autoware_cmake - lanelet2_extension + autoware_lanelet2_extension libpcl-all-dev rclcpp diff --git a/map/util/lanelet2_map_preprocessor/src/fix_lane_change_tags.cpp b/map/util/lanelet2_map_preprocessor/src/fix_lane_change_tags.cpp index a406e5381357d..cbd279af2b46f 100644 --- a/map/util/lanelet2_map_preprocessor/src/fix_lane_change_tags.cpp +++ b/map/util/lanelet2_map_preprocessor/src/fix_lane_change_tags.cpp @@ -12,9 +12,9 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include -#include -#include +#include +#include +#include #include #include diff --git a/map/util/lanelet2_map_preprocessor/src/fix_z_value_by_pcd.cpp b/map/util/lanelet2_map_preprocessor/src/fix_z_value_by_pcd.cpp index 758fee3addc06..14a33b01beee0 100644 --- a/map/util/lanelet2_map_preprocessor/src/fix_z_value_by_pcd.cpp +++ b/map/util/lanelet2_map_preprocessor/src/fix_z_value_by_pcd.cpp @@ -12,9 +12,9 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include -#include -#include +#include +#include +#include #include #include diff --git a/map/util/lanelet2_map_preprocessor/src/merge_close_lines.cpp b/map/util/lanelet2_map_preprocessor/src/merge_close_lines.cpp index d001bdc54a680..e0d071ea11dc1 100644 --- a/map/util/lanelet2_map_preprocessor/src/merge_close_lines.cpp +++ b/map/util/lanelet2_map_preprocessor/src/merge_close_lines.cpp @@ -12,9 +12,9 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include -#include -#include +#include +#include +#include #include #include diff --git a/map/util/lanelet2_map_preprocessor/src/merge_close_points.cpp b/map/util/lanelet2_map_preprocessor/src/merge_close_points.cpp index beb736e809275..e18a366003e10 100644 --- a/map/util/lanelet2_map_preprocessor/src/merge_close_points.cpp +++ b/map/util/lanelet2_map_preprocessor/src/merge_close_points.cpp @@ -12,9 +12,9 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include -#include -#include +#include +#include +#include #include #include diff --git a/map/util/lanelet2_map_preprocessor/src/remove_unreferenced_geometry.cpp b/map/util/lanelet2_map_preprocessor/src/remove_unreferenced_geometry.cpp index e6c4feb4cee9a..633dae4e20c1d 100644 --- a/map/util/lanelet2_map_preprocessor/src/remove_unreferenced_geometry.cpp +++ b/map/util/lanelet2_map_preprocessor/src/remove_unreferenced_geometry.cpp @@ -12,9 +12,9 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include -#include -#include +#include +#include +#include #include #include diff --git a/map/util/lanelet2_map_preprocessor/src/transform_maps.cpp b/map/util/lanelet2_map_preprocessor/src/transform_maps.cpp index 2317ddb7d9f95..d29df716b11e7 100644 --- a/map/util/lanelet2_map_preprocessor/src/transform_maps.cpp +++ b/map/util/lanelet2_map_preprocessor/src/transform_maps.cpp @@ -12,9 +12,9 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include -#include -#include +#include +#include +#include #include #include diff --git a/perception/autoware_crosswalk_traffic_light_estimator/package.xml b/perception/autoware_crosswalk_traffic_light_estimator/package.xml index 18b0b8a41264f..3eec0e121082a 100644 --- a/perception/autoware_crosswalk_traffic_light_estimator/package.xml +++ b/perception/autoware_crosswalk_traffic_light_estimator/package.xml @@ -12,11 +12,11 @@ ament_cmake_auto autoware_cmake + autoware_lanelet2_extension autoware_map_msgs autoware_perception_msgs autoware_planning_msgs autoware_universe_utils - lanelet2_extension rclcpp rclcpp_components tier4_perception_msgs diff --git a/perception/autoware_crosswalk_traffic_light_estimator/src/node.cpp b/perception/autoware_crosswalk_traffic_light_estimator/src/node.cpp index 2c4da0b95b161..068cfc02a6aae 100644 --- a/perception/autoware_crosswalk_traffic_light_estimator/src/node.cpp +++ b/perception/autoware_crosswalk_traffic_light_estimator/src/node.cpp @@ -13,8 +13,8 @@ // limitations under the License. #include "autoware_crosswalk_traffic_light_estimator/node.hpp" -#include -#include +#include +#include #include #include diff --git a/perception/autoware_map_based_prediction/package.xml b/perception/autoware_map_based_prediction/package.xml index 37bb7aafb01bf..9e60073de4be6 100644 --- a/perception/autoware_map_based_prediction/package.xml +++ b/perception/autoware_map_based_prediction/package.xml @@ -14,12 +14,12 @@ ament_cmake autoware_cmake + autoware_lanelet2_extension autoware_motion_utils autoware_perception_msgs autoware_universe_utils glog interpolation - lanelet2_extension rclcpp rclcpp_components tf2 diff --git a/perception/autoware_map_based_prediction/src/map_based_prediction_node.cpp b/perception/autoware_map_based_prediction/src/map_based_prediction_node.cpp index f505cb7cc5592..2b8358eb52e4e 100644 --- a/perception/autoware_map_based_prediction/src/map_based_prediction_node.cpp +++ b/perception/autoware_map_based_prediction/src/map_based_prediction_node.cpp @@ -20,10 +20,10 @@ #include #include #include +#include +#include +#include #include -#include -#include -#include #include @@ -1771,11 +1771,12 @@ std::vector MapBasedPredictionNode::getPredictedReferencePath( object.kinematics.acceleration_with_covariance.accel.linear.y) : 0.0; const double t_h = time_horizon; - const double λ = std::log(2) / acceleration_exponential_half_life_; + const double lambda = std::log(2) / acceleration_exponential_half_life_; auto get_search_distance_with_decaying_acc = [&]() -> double { const double acceleration_distance = - obj_acc * (1.0 / λ) * t_h + obj_acc * (1.0 / std::pow(λ, 2)) * (std::exp(-λ * t_h) - 1); + obj_acc * (1.0 / lambda) * t_h + + obj_acc * (1.0 / std::pow(lambda, 2)) * std::expm1(-lambda * t_h); double search_dist = acceleration_distance + obj_vel * t_h; return search_dist; }; @@ -1787,13 +1788,13 @@ std::vector MapBasedPredictionNode::getPredictedReferencePath( return obj_vel * t_h; } // Time to reach final speed - const double t_f = (-1.0 / λ) * std::log(1 - ((final_speed - obj_vel) * λ) / obj_acc); + const double t_f = (-1.0 / lambda) * std::log(1 - ((final_speed - obj_vel) * lambda) / obj_acc); // It is assumed the vehicle accelerates until final_speed is reached and // then continues at constant speed for the rest of the time horizon const double search_dist = // Distance covered while accelerating - obj_acc * (1.0 / λ) * t_f + obj_acc * (1.0 / std::pow(λ, 2)) * (std::exp(-λ * t_f) - 1) + - obj_vel * t_f + + obj_acc * (1.0 / lambda) * t_f + + obj_acc * (1.0 / std::pow(lambda, 2)) * std::expm1(-lambda * t_f) + obj_vel * t_f + // Distance covered at constant speed final_speed * (t_h - t_f); return search_dist; @@ -1807,7 +1808,7 @@ std::vector MapBasedPredictionNode::getPredictedReferencePath( const double legal_speed_limit = static_cast(limit.speedLimit.value()); double final_speed_after_acceleration = - obj_vel + obj_acc * (1.0 / λ) * (1.0 - std::exp(-λ * t_h)); + obj_vel + obj_acc * (1.0 / lambda) * (1.0 - std::exp(-lambda * t_h)); const double final_speed_limit = legal_speed_limit * speed_limit_multiplier_; const bool final_speed_surpasses_limit = final_speed_after_acceleration > final_speed_limit; diff --git a/perception/autoware_map_based_prediction/src/path_generator.cpp b/perception/autoware_map_based_prediction/src/path_generator.cpp index aeb1b0fedd33f..dac034a6b7a7d 100644 --- a/perception/autoware_map_based_prediction/src/path_generator.cpp +++ b/perception/autoware_map_based_prediction/src/path_generator.cpp @@ -421,7 +421,7 @@ FrenetPoint PathGenerator::getFrenetPoint( // Using a decaying acceleration model. Consult the README for more information about the model. const double t_h = duration; - const float λ = std::log(2) / acceleration_exponential_half_life_; + const float lambda = std::log(2) / acceleration_exponential_half_life_; auto have_same_sign = [](double a, double b) -> bool { return (a >= 0.0 && b >= 0.0) || (a < 0.0 && b < 0.0); @@ -434,7 +434,7 @@ FrenetPoint PathGenerator::getFrenetPoint( return v; } // Get velocity after time horizon - const auto terminal_velocity = v + a * (1.0 / λ) * (1 - std::exp(-λ * t_h)); + const auto terminal_velocity = v + a * (1.0 / lambda) * (1 - std::exp(-lambda * t_h)); // If vehicle is decelerating, make sure its speed does not change signs (we assume it will, at // most stop, not reverse its direction) @@ -443,15 +443,16 @@ FrenetPoint PathGenerator::getFrenetPoint( // if the velocities don't have the same sign, calculate when the vehicle reaches 0 speed -> // time t_stop - // 0 = Vo + acc(1/λ)(1-e^(-λt_stop)) - // e^(-λt_stop) = 1 - (-Vo* λ)/acc - // t_stop = (-1/λ)*ln(1 - (-Vo* λ)/acc) - // t_stop = (-1/λ)*ln(1 + (Vo* λ)/acc) - auto t_stop = (-1.0 / λ) * std::log(1 + (v * λ / a)); + // 0 = Vo + acc(1/lambda)(1-e^(-lambda t_stop)) + // e^(-lambda t_stop) = 1 - (-Vo* lambda)/acc + // t_stop = (-1/lambda)*ln(1 - (-Vo* lambda)/acc) + // t_stop = (-1/lambda)*ln(1 + (Vo* lambda)/acc) + auto t_stop = (-1.0 / lambda) * std::log1p(v * lambda / a); // Calculate the distance traveled until stopping auto distance_to_reach_zero_speed = - v * t_stop + a * t_stop * (1.0 / λ) + a * (1.0 / std::pow(λ, 2)) * (std::exp(-λ * t_h) - 1); + v * t_stop + a * t_stop * (1.0 / lambda) + + a * (1.0 / std::pow(lambda, 2)) * std::expm1(-lambda * t_h); // Output an equivalent constant speed return distance_to_reach_zero_speed / t_h; } @@ -461,17 +462,18 @@ FrenetPoint PathGenerator::getFrenetPoint( // assume it will continue accelerating (reckless driving) const bool object_has_surpassed_limit_already = v > speed_limit; if (terminal_velocity < speed_limit || object_has_surpassed_limit_already) - return v + a * (1.0 / λ) + (a / (t_h * std::pow(λ, 2))) * (std::exp(-λ * t_h) - 1); + return v + a * (1.0 / lambda) + (a / (t_h * std::pow(lambda, 2))) * std::expm1(-lambda * t_h); // It is assumed the vehicle accelerates until final_speed is reached and // then continues at constant speed for the rest of the time horizon // So, we calculate the time it takes to reach the speed limit and compute how far the vehicle // would go if it accelerated until reaching the speed limit, and then continued at a constant // speed. - const double t_f = (-1.0 / λ) * std::log(1 - ((speed_limit - v) * λ) / a); + const double t_f = (-1.0 / lambda) * std::log(1 - ((speed_limit - v) * lambda) / a); const double distance_covered = // Distance covered while accelerating - a * (1.0 / λ) * t_f + a * (1.0 / std::pow(λ, 2)) * (std::exp(-λ * t_f) - 1) + v * t_f + + a * (1.0 / lambda) * t_f + a * (1.0 / std::pow(lambda, 2)) * std::expm1(-lambda * t_f) + + v * t_f + // Distance covered at constant speed for the rest of the horizon time speed_limit * (t_h - t_f); return distance_covered / t_h; diff --git a/perception/bytetrack/lib/src/lapjv.cpp b/perception/bytetrack/lib/src/lapjv.cpp index ecf4c31b81ff8..1b8b39ccbb9f7 100644 --- a/perception/bytetrack/lib/src/lapjv.cpp +++ b/perception/bytetrack/lib/src/lapjv.cpp @@ -181,7 +181,8 @@ int_t _carr_dense( /** Find columns with minimum d[j] and put them on the SCAN list. */ -uint_t _find_dense(const uint_t n, uint_t lo, cost_t * d, int_t * cols, [[maybe_unused]] int_t * y) +uint_t _find_dense( + const uint_t n, uint_t lo, const cost_t * d, int_t * cols, [[maybe_unused]] int_t * y) { uint_t hi = lo + 1; cost_t mind = d[cols[lo]]; @@ -203,7 +204,7 @@ uint_t _find_dense(const uint_t n, uint_t lo, cost_t * d, int_t * cols, [[maybe_ // and try to decrease d of the TODO columns using the SCAN column. int_t _scan_dense( const uint_t n, cost_t * cost[], uint_t * plo, uint_t * phi, cost_t * d, int_t * cols, - int_t * pred, int_t * y, cost_t * v) + int_t * pred, const int_t * y, const cost_t * v) { uint_t lo = *plo; uint_t hi = *phi; diff --git a/perception/bytetrack/lib/src/strack.cpp b/perception/bytetrack/lib/src/strack.cpp index f2c5a40ad3c2e..81ba438faee44 100644 --- a/perception/bytetrack/lib/src/strack.cpp +++ b/perception/bytetrack/lib/src/strack.cpp @@ -112,12 +112,6 @@ void STrack::activate(int frame_id) this->track_id = this->next_id(); this->unique_id = boost::uuids::random_generator()(); - std::vector _tlwh_tmp(4); - _tlwh_tmp[0] = this->original_tlwh[0]; - _tlwh_tmp[1] = this->original_tlwh[1]; - _tlwh_tmp[2] = this->original_tlwh[2]; - _tlwh_tmp[3] = this->original_tlwh[3]; - std::vector xyah = tlwh_to_xyah(_tlwh_tmp); // init kf init_kalman_filter(); // reflect state @@ -132,7 +126,6 @@ void STrack::activate(int frame_id) void STrack::re_activate(STrack & new_track, int frame_id, bool new_id) { - std::vector xyah = tlwh_to_xyah(new_track.tlwh); // TODO(me): write kf update Eigen::MatrixXd measurement = Eigen::MatrixXd::Zero(_kf_parameters.dim_z, 1); measurement << new_track.tlwh[0], new_track.tlwh[1], new_track.tlwh[2], new_track.tlwh[3]; @@ -156,8 +149,6 @@ void STrack::update(STrack & new_track, int frame_id) this->frame_id = frame_id; this->tracklet_len++; - std::vector xyah = tlwh_to_xyah(new_track.tlwh); - // update // TODO(me): write update Eigen::MatrixXd measurement = Eigen::MatrixXd::Zero(_kf_parameters.dim_z, 1); diff --git a/perception/bytetrack/lib/src/utils.cpp b/perception/bytetrack/lib/src/utils.cpp index 64ad27cf36eba..e987bd36b8064 100644 --- a/perception/bytetrack/lib/src/utils.cpp +++ b/perception/bytetrack/lib/src/utils.cpp @@ -190,7 +190,6 @@ std::vector> ByteTracker::ious( // bbox_ious for (size_t k = 0; k < btlbrs.size(); k++) { - std::vector ious_tmp; float box_area = (btlbrs[k][2] - btlbrs[k][0] + 1) * (btlbrs[k][3] - btlbrs[k][1] + 1); for (size_t n = 0; n < atlbrs.size(); n++) { float iw = std::min(atlbrs[n][2], btlbrs[k][2]) - std::max(atlbrs[n][0], btlbrs[k][0]) + 1; @@ -277,8 +276,6 @@ double ByteTracker::lapjv( std::vector> cost_c; cost_c.assign(cost.begin(), cost.end()); - std::vector> cost_c_extended; - int n_rows = cost.size(); int n_cols = cost[0].size(); rowsol.resize(n_rows); @@ -296,6 +293,8 @@ double ByteTracker::lapjv( } if (extend_cost || cost_limit < LONG_MAX) { + std::vector> cost_c_extended; + n = n_rows + n_cols; cost_c_extended.resize(n); for (size_t i = 0; i < cost_c_extended.size(); i++) cost_c_extended[i].resize(n); diff --git a/perception/bytetrack/src/bytetrack_visualizer_node.cpp b/perception/bytetrack/src/bytetrack_visualizer_node.cpp index ce350b8c288da..cdb767568a510 100644 --- a/perception/bytetrack/src/bytetrack_visualizer_node.cpp +++ b/perception/bytetrack/src/bytetrack_visualizer_node.cpp @@ -128,14 +128,14 @@ void ByteTrackVisualizerNode::callback( } std::vector bboxes; - for (auto & feat_obj : rect_msg->feature_objects) { + for (const auto & feat_obj : rect_msg->feature_objects) { auto roi_msg = feat_obj.feature.roi; cv::Rect rect(roi_msg.x_offset, roi_msg.y_offset, roi_msg.width, roi_msg.height); bboxes.push_back(rect); } std::vector uuids; - for (auto & dynamic_obj : uuid_msg->objects) { + for (const auto & dynamic_obj : uuid_msg->objects) { auto uuid_msg = dynamic_obj.id.uuid; boost::uuids::uuid uuid_raw; std::copy(uuid_msg.begin(), uuid_msg.end(), uuid_raw.begin()); diff --git a/perception/cluster_merger/CMakeLists.txt b/perception/cluster_merger/CMakeLists.txt index 5ad0de572b44f..773cea6383f41 100644 --- a/perception/cluster_merger/CMakeLists.txt +++ b/perception/cluster_merger/CMakeLists.txt @@ -6,12 +6,12 @@ find_package(autoware_cmake REQUIRED) autoware_package() # Targets -ament_auto_add_library(cluster_merger_node_component SHARED - src/cluster_merger/node.cpp +ament_auto_add_library(${PROJECT_NAME} SHARED + src/cluster_merger_node.cpp ) -rclcpp_components_register_node(cluster_merger_node_component - PLUGIN "cluster_merger::ClusterMergerNode" +rclcpp_components_register_node(${PROJECT_NAME} + PLUGIN "autoware::cluster_merger::ClusterMergerNode" EXECUTABLE cluster_merger_node) diff --git a/perception/cluster_merger/src/cluster_merger/node.cpp b/perception/cluster_merger/src/cluster_merger_node.cpp similarity index 93% rename from perception/cluster_merger/src/cluster_merger/node.cpp rename to perception/cluster_merger/src/cluster_merger_node.cpp index 48bf953027510..ada95b905d320 100644 --- a/perception/cluster_merger/src/cluster_merger/node.cpp +++ b/perception/cluster_merger/src/cluster_merger_node.cpp @@ -12,14 +12,15 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "cluster_merger/node.hpp" +#include "cluster_merger_node.hpp" #include "object_recognition_utils/object_recognition_utils.hpp" #include #include #include -namespace cluster_merger + +namespace autoware::cluster_merger { ClusterMergerNode::ClusterMergerNode(const rclcpp::NodeOptions & node_options) @@ -67,7 +68,7 @@ void ClusterMergerNode::objectsCallback( transformed_objects1.feature_objects.end()); pub_objects_->publish(output_objects); } -} // namespace cluster_merger +} // namespace autoware::cluster_merger #include "rclcpp_components/register_node_macro.hpp" -RCLCPP_COMPONENTS_REGISTER_NODE(cluster_merger::ClusterMergerNode) +RCLCPP_COMPONENTS_REGISTER_NODE(autoware::cluster_merger::ClusterMergerNode) diff --git a/perception/cluster_merger/include/cluster_merger/node.hpp b/perception/cluster_merger/src/cluster_merger_node.hpp similarity index 92% rename from perception/cluster_merger/include/cluster_merger/node.hpp rename to perception/cluster_merger/src/cluster_merger_node.hpp index a60a76f1c2ef0..2213d413d7031 100644 --- a/perception/cluster_merger/include/cluster_merger/node.hpp +++ b/perception/cluster_merger/src/cluster_merger_node.hpp @@ -12,8 +12,8 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef CLUSTER_MERGER__NODE_HPP_ -#define CLUSTER_MERGER__NODE_HPP_ +#ifndef CLUSTER_MERGER_NODE_HPP_ +#define CLUSTER_MERGER_NODE_HPP_ #include "autoware/universe_utils/ros/transform_listener.hpp" #include "message_filters/subscriber.h" @@ -31,7 +31,7 @@ #include #include -namespace cluster_merger +namespace autoware::cluster_merger { using tier4_perception_msgs::msg::DetectedObjectsWithFeature; using tier4_perception_msgs::msg::DetectedObjectWithFeature; @@ -68,6 +68,6 @@ class ClusterMergerNode : public rclcpp::Node rclcpp::Publisher::SharedPtr pub_objects_; }; -} // namespace cluster_merger +} // namespace autoware::cluster_merger -#endif // CLUSTER_MERGER__NODE_HPP_ +#endif // CLUSTER_MERGER_NODE_HPP_ diff --git a/perception/detected_object_validation/CMakeLists.txt b/perception/detected_object_validation/CMakeLists.txt index d023c6c9df328..c416209d7d55d 100644 --- a/perception/detected_object_validation/CMakeLists.txt +++ b/perception/detected_object_validation/CMakeLists.txt @@ -88,6 +88,7 @@ rclcpp_components_register_node(occupancy_grid_based_validator if(BUILD_TESTING) ament_auto_add_gtest(detection_object_validation_tests test/test_utils.cpp + test/object_position_filter/test_object_position_filter.cpp ) endif() diff --git a/perception/detected_object_validation/include/detected_object_validation/detected_object_filter/object_lanelet_filter.hpp b/perception/detected_object_validation/include/detected_object_validation/detected_object_filter/object_lanelet_filter.hpp index 9b9894d56a424..3ffd8d18c9a67 100644 --- a/perception/detected_object_validation/include/detected_object_validation/detected_object_filter/object_lanelet_filter.hpp +++ b/perception/detected_object_validation/include/detected_object_validation/detected_object_filter/object_lanelet_filter.hpp @@ -20,7 +20,7 @@ #include #include #include -#include +#include #include #include diff --git a/perception/detected_object_validation/include/detected_object_validation/obstacle_pointcloud_based_validator/debugger.hpp b/perception/detected_object_validation/include/detected_object_validation/obstacle_pointcloud_based_validator/debugger.hpp index afb1f8a729567..d338580d95418 100644 --- a/perception/detected_object_validation/include/detected_object_validation/obstacle_pointcloud_based_validator/debugger.hpp +++ b/perception/detected_object_validation/include/detected_object_validation/obstacle_pointcloud_based_validator/debugger.hpp @@ -24,9 +24,6 @@ #include #include -#include -#include - namespace obstacle_pointcloud_based_validator { class Debugger diff --git a/perception/detected_object_validation/include/detected_object_validation/obstacle_pointcloud_based_validator/obstacle_pointcloud_based_validator.hpp b/perception/detected_object_validation/include/detected_object_validation/obstacle_pointcloud_based_validator/obstacle_pointcloud_based_validator.hpp index 47685dec7dbdc..a16bb63fd0c87 100644 --- a/perception/detected_object_validation/include/detected_object_validation/obstacle_pointcloud_based_validator/obstacle_pointcloud_based_validator.hpp +++ b/perception/detected_object_validation/include/detected_object_validation/obstacle_pointcloud_based_validator/obstacle_pointcloud_based_validator.hpp @@ -75,6 +75,8 @@ class Validator const autoware_perception_msgs::msg::DetectedObject & object) = 0; size_t getThresholdPointCloud(const autoware_perception_msgs::msg::DetectedObject & object); virtual pcl::PointCloud::Ptr getDebugNeighborPointCloud() = 0; + + virtual ~Validator() = default; }; class Validator2D : public Validator diff --git a/perception/detected_object_validation/package.xml b/perception/detected_object_validation/package.xml index 7abfbe6625f01..d8eb1257ba451 100644 --- a/perception/detected_object_validation/package.xml +++ b/perception/detected_object_validation/package.xml @@ -17,11 +17,12 @@ libopencv-dev + autoware_lanelet2_extension autoware_map_msgs autoware_perception_msgs + autoware_test_utils autoware_universe_utils geometry_msgs - lanelet2_extension message_filters nav_msgs object_recognition_utils diff --git a/perception/detected_object_validation/src/object_lanelet_filter.cpp b/perception/detected_object_validation/src/object_lanelet_filter.cpp index cc3a600783a8f..d1ba34f4a61b2 100644 --- a/perception/detected_object_validation/src/object_lanelet_filter.cpp +++ b/perception/detected_object_validation/src/object_lanelet_filter.cpp @@ -15,8 +15,8 @@ #include "detected_object_validation/detected_object_filter/object_lanelet_filter.hpp" #include -#include -#include +#include +#include #include #include diff --git a/perception/detected_object_validation/test/object_position_filter/test_object_position_filter.cpp b/perception/detected_object_validation/test/object_position_filter/test_object_position_filter.cpp new file mode 100644 index 0000000000000..03cebf91418ce --- /dev/null +++ b/perception/detected_object_validation/test/object_position_filter/test_object_position_filter.cpp @@ -0,0 +1,126 @@ +// Copyright 2024 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "detected_object_validation/detected_object_filter/object_position_filter.hpp" + +#include +#include + +#include + +#include + +using autoware_perception_msgs::msg::DetectedObject; +using autoware_perception_msgs::msg::DetectedObjects; +using autoware_perception_msgs::msg::ObjectClassification; +using object_position_filter::ObjectPositionFilterNode; + +std::shared_ptr generateTestManager() +{ + auto test_manager = std::make_shared(); + return test_manager; +} + +std::shared_ptr generateNode() +{ + auto node_options = rclcpp::NodeOptions{}; + const auto detected_object_validation_dir = + ament_index_cpp::get_package_share_directory("detected_object_validation"); + node_options.arguments( + {"--ros-args", "--params-file", + detected_object_validation_dir + "/config/object_position_filter.param.yaml"}); + return std::make_shared(node_options); +} + +TEST(DetectedObjectValidationTest, testObjectPositionFilterEmptyObject) +{ + rclcpp::init(0, nullptr); + const std::string input_topic = "/input/object"; + const std::string output_topic = "/output/object"; + auto test_manager = generateTestManager(); + auto test_target_node = generateNode(); + + int counter = 0; + auto callback = [&counter](const DetectedObjects::ConstSharedPtr msg) { + (void)msg; + ++counter; + }; + test_manager->set_subscriber(output_topic, callback); + + DetectedObjects msg; + test_manager->test_pub_msg(test_target_node, input_topic, msg); + EXPECT_GE(counter, 1); + rclcpp::shutdown(); +} + +TEST(DetectedObjectValidationTest, testObjectPositionFilterSeveralObjects) +{ + rclcpp::init(0, nullptr); + const std::string input_topic = "/input/object"; + const std::string output_topic = "/output/object"; + auto test_manager = generateTestManager(); + auto test_target_node = generateNode(); + + // Create a DetectedObjects message with several objects + DetectedObjects msg; + msg.header.frame_id = "base_link"; + + // Object 1: Inside bounds + { + DetectedObject object; + object.kinematics.pose_with_covariance.pose.position.x = 10.0; + object.kinematics.pose_with_covariance.pose.position.y = 5.0; + object.classification.resize(1); + object.classification[0].label = ObjectClassification::UNKNOWN; + msg.objects.push_back(object); + } + + // Object 2: Outside bounds (x-axis) + { + DetectedObject object; + object.kinematics.pose_with_covariance.pose.position.x = 110.0; + object.kinematics.pose_with_covariance.pose.position.y = 5.0; + object.classification.resize(1); + object.classification[0].label = ObjectClassification::UNKNOWN; + msg.objects.push_back(object); + } + + // Object 3: Outside bounds (y-axis) + { + DetectedObject object; + object.kinematics.pose_with_covariance.pose.position.x = 10.0; + object.kinematics.pose_with_covariance.pose.position.y = 60.0; + object.classification.resize(1); + object.classification[0].label = ObjectClassification::UNKNOWN; + msg.objects.push_back(object); + } + + // Object 4: Inside bounds + { + DetectedObject object; + object.kinematics.pose_with_covariance.pose.position.x = 20.0; + object.kinematics.pose_with_covariance.pose.position.y = -5.0; + object.classification.resize(1); + object.classification[0].label = ObjectClassification::UNKNOWN; + msg.objects.push_back(object); + } + + DetectedObjects latest_msg; + auto callback = [&latest_msg](const DetectedObjects::ConstSharedPtr msg) { latest_msg = *msg; }; + test_manager->set_subscriber(output_topic, callback); + + test_manager->test_pub_msg(test_target_node, input_topic, msg); + EXPECT_EQ(latest_msg.objects.size(), 2); + rclcpp::shutdown(); +} diff --git a/perception/detection_by_tracker/CMakeLists.txt b/perception/detection_by_tracker/CMakeLists.txt index 3813f1ba4707f..112187551d924 100644 --- a/perception/detection_by_tracker/CMakeLists.txt +++ b/perception/detection_by_tracker/CMakeLists.txt @@ -26,22 +26,22 @@ include_directories( # Generate exe file set(DETECTION_BY_TRACKER_SRC - src/detection_by_tracker_core.cpp - src/utils.cpp + src/detection_by_tracker_node.cpp + src/utils/utils.cpp ) -ament_auto_add_library(detection_by_tracker_node SHARED +ament_auto_add_library(${PROJECT_NAME} SHARED ${DETECTION_BY_TRACKER_SRC} ) -target_link_libraries(detection_by_tracker_node +target_link_libraries(${PROJECT_NAME} Eigen3::Eigen ${PCL_LIBRARIES} ) -rclcpp_components_register_node(detection_by_tracker_node - PLUGIN "DetectionByTracker" - EXECUTABLE detection_by_tracker +rclcpp_components_register_node(${PROJECT_NAME} + PLUGIN "autoware::detection_by_tracker::DetectionByTracker" + EXECUTABLE detection_by_tracker_node ) ament_auto_package( diff --git a/perception/detection_by_tracker/launch/detection_by_tracker.launch.xml b/perception/detection_by_tracker/launch/detection_by_tracker.launch.xml index 95e7cbf16b388..3fba5cb58f8f3 100644 --- a/perception/detection_by_tracker/launch/detection_by_tracker.launch.xml +++ b/perception/detection_by_tracker/launch/detection_by_tracker.launch.xml @@ -4,7 +4,7 @@ - + diff --git a/perception/detection_by_tracker/package.xml b/perception/detection_by_tracker/package.xml index 52d4d3fcdd7a2..50bd876ce0605 100644 --- a/perception/detection_by_tracker/package.xml +++ b/perception/detection_by_tracker/package.xml @@ -16,9 +16,7 @@ autoware_universe_utils eigen euclidean_cluster - libpcl-all-dev object_recognition_utils - pcl_conversions rclcpp rclcpp_components shape_estimation diff --git a/perception/detection_by_tracker/include/detection_by_tracker/debugger.hpp b/perception/detection_by_tracker/src/debugger/debugger.hpp similarity index 85% rename from perception/detection_by_tracker/include/detection_by_tracker/debugger.hpp rename to perception/detection_by_tracker/src/debugger/debugger.hpp index 0169082a2253a..242056c3ca316 100644 --- a/perception/detection_by_tracker/include/detection_by_tracker/debugger.hpp +++ b/perception/detection_by_tracker/src/debugger/debugger.hpp @@ -12,22 +12,23 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef DETECTION_BY_TRACKER__DEBUGGER_HPP_ -#define DETECTION_BY_TRACKER__DEBUGGER_HPP_ +#ifndef DEBUGGER__DEBUGGER_HPP_ +#define DEBUGGER__DEBUGGER_HPP_ + +#include "autoware/universe_utils/ros/debug_publisher.hpp" +#include "autoware/universe_utils/system/stop_watch.hpp" +#include "euclidean_cluster/euclidean_cluster.hpp" +#include "euclidean_cluster/utils.hpp" +#include "euclidean_cluster/voxel_grid_based_euclidean_cluster.hpp" +#include "shape_estimation/shape_estimator.hpp" -#include -#include -#include -#include -#include #include -#include -#include -#include +#include "autoware_perception_msgs/msg/detected_objects.hpp" +#include "autoware_perception_msgs/msg/tracked_objects.hpp" +#include "tier4_perception_msgs/msg/detected_objects_with_feature.hpp" #include #include -#include #include #include @@ -46,6 +47,8 @@ #include #include +namespace autoware::detection_by_tracker +{ class Debugger { public: @@ -117,5 +120,6 @@ class Debugger return objects; } }; +} // namespace autoware::detection_by_tracker -#endif // DETECTION_BY_TRACKER__DEBUGGER_HPP_ +#endif // DEBUGGER__DEBUGGER_HPP_ diff --git a/perception/detection_by_tracker/src/detection_by_tracker_core.cpp b/perception/detection_by_tracker/src/detection_by_tracker_node.cpp similarity index 98% rename from perception/detection_by_tracker/src/detection_by_tracker_core.cpp rename to perception/detection_by_tracker/src/detection_by_tracker_node.cpp index 4e502b359120d..c41d6002d9266 100644 --- a/perception/detection_by_tracker/src/detection_by_tracker_core.cpp +++ b/perception/detection_by_tracker/src/detection_by_tracker_node.cpp @@ -12,12 +12,16 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "detection_by_tracker/detection_by_tracker_core.hpp" +#define EIGEN_MPL2_ONLY + +#include "detection_by_tracker_node.hpp" +#include "autoware/universe_utils/geometry/geometry.hpp" +#include "autoware/universe_utils/math/unit_conversion.hpp" #include "object_recognition_utils/object_recognition_utils.hpp" -#include -#include +#include +#include #include #include @@ -25,10 +29,6 @@ #include #include -#define EIGEN_MPL2_ONLY -#include -#include - using Label = autoware_perception_msgs::msg::ObjectClassification; namespace { @@ -80,6 +80,9 @@ boost::optional getReferenceShapeSizeInfo( } } // namespace +namespace autoware::detection_by_tracker +{ + void TrackerHandler::onTrackedObjects( const autoware_perception_msgs::msg::TrackedObjects::ConstSharedPtr msg) { @@ -466,5 +469,7 @@ void DetectionByTracker::mergeOverSegmentedObjects( } } +} // namespace autoware::detection_by_tracker + #include -RCLCPP_COMPONENTS_REGISTER_NODE(DetectionByTracker) +RCLCPP_COMPONENTS_REGISTER_NODE(autoware::detection_by_tracker::DetectionByTracker) diff --git a/perception/detection_by_tracker/include/detection_by_tracker/detection_by_tracker_core.hpp b/perception/detection_by_tracker/src/detection_by_tracker_node.hpp similarity index 81% rename from perception/detection_by_tracker/include/detection_by_tracker/detection_by_tracker_core.hpp rename to perception/detection_by_tracker/src/detection_by_tracker_node.hpp index d6d030aadb000..4e58d53fa71aa 100644 --- a/perception/detection_by_tracker/include/detection_by_tracker/detection_by_tracker_core.hpp +++ b/perception/detection_by_tracker/src/detection_by_tracker_node.hpp @@ -12,24 +12,24 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef DETECTION_BY_TRACKER__DETECTION_BY_TRACKER_CORE_HPP_ -#define DETECTION_BY_TRACKER__DETECTION_BY_TRACKER_CORE_HPP_ +#ifndef DETECTION_BY_TRACKER_NODE_HPP_ +#define DETECTION_BY_TRACKER_NODE_HPP_ -#include "detection_by_tracker/debugger.hpp" -#include "detection_by_tracker/utils.hpp" +#include "autoware/universe_utils/ros/published_time_publisher.hpp" +#include "debugger/debugger.hpp" +#include "euclidean_cluster/euclidean_cluster.hpp" +#include "euclidean_cluster/utils.hpp" +#include "euclidean_cluster/voxel_grid_based_euclidean_cluster.hpp" +#include "shape_estimation/shape_estimator.hpp" +#include "utils/utils.hpp" -#include -#include -#include -#include #include -#include -#include -#include +#include "autoware_perception_msgs/msg/detected_objects.hpp" +#include "autoware_perception_msgs/msg/tracked_objects.hpp" +#include "tier4_perception_msgs/msg/detected_objects_with_feature.hpp" #include #include -#include #include #include @@ -48,6 +48,10 @@ #include #include #include + +namespace autoware::detection_by_tracker +{ + class TrackerHandler { private: @@ -82,7 +86,7 @@ class DetectionByTracker : public rclcpp::Node std::map max_search_distance_for_merger_; std::map max_search_distance_for_divider_; - detection_by_tracker::utils::TrackerIgnoreLabel tracker_ignore_; + utils::TrackerIgnoreLabel tracker_ignore_; std::unique_ptr published_time_publisher_; @@ -109,4 +113,6 @@ class DetectionByTracker : public rclcpp::Node tier4_perception_msgs::msg::DetectedObjectsWithFeature & out_objects); }; -#endif // DETECTION_BY_TRACKER__DETECTION_BY_TRACKER_CORE_HPP_ +} // namespace autoware::detection_by_tracker + +#endif // DETECTION_BY_TRACKER_NODE_HPP_ diff --git a/perception/detection_by_tracker/src/utils.cpp b/perception/detection_by_tracker/src/utils/utils.cpp similarity index 91% rename from perception/detection_by_tracker/src/utils.cpp rename to perception/detection_by_tracker/src/utils/utils.cpp index 51c6e5724543b..b269d703be7d4 100644 --- a/perception/detection_by_tracker/src/utils.cpp +++ b/perception/detection_by_tracker/src/utils/utils.cpp @@ -12,11 +12,11 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "detection_by_tracker/utils.hpp" +#include "utils.hpp" #include -namespace detection_by_tracker +namespace autoware::detection_by_tracker { namespace utils { @@ -30,4 +30,4 @@ bool TrackerIgnoreLabel::isIgnore(const uint8_t label) const (label == Label::BICYCLE && BICYCLE) || (label == Label::PEDESTRIAN && PEDESTRIAN); } } // namespace utils -} // namespace detection_by_tracker +} // namespace autoware::detection_by_tracker diff --git a/perception/detection_by_tracker/include/detection_by_tracker/utils.hpp b/perception/detection_by_tracker/src/utils/utils.hpp similarity index 82% rename from perception/detection_by_tracker/include/detection_by_tracker/utils.hpp rename to perception/detection_by_tracker/src/utils/utils.hpp index ed783b0343a14..b7041124fa931 100644 --- a/perception/detection_by_tracker/include/detection_by_tracker/utils.hpp +++ b/perception/detection_by_tracker/src/utils/utils.hpp @@ -12,12 +12,12 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef DETECTION_BY_TRACKER__UTILS_HPP_ -#define DETECTION_BY_TRACKER__UTILS_HPP_ +#ifndef UTILS__UTILS_HPP_ +#define UTILS__UTILS_HPP_ #include -namespace detection_by_tracker +namespace autoware::detection_by_tracker { namespace utils { @@ -34,6 +34,6 @@ struct TrackerIgnoreLabel bool isIgnore(const uint8_t label) const; }; // struct TrackerIgnoreLabel } // namespace utils -} // namespace detection_by_tracker +} // namespace autoware::detection_by_tracker -#endif // DETECTION_BY_TRACKER__UTILS_HPP_ +#endif // UTILS__UTILS_HPP_ diff --git a/perception/elevation_map_loader/package.xml b/perception/elevation_map_loader/package.xml index afbd88da84bde..08990b971b0a3 100644 --- a/perception/elevation_map_loader/package.xml +++ b/perception/elevation_map_loader/package.xml @@ -13,12 +13,12 @@ autoware_cmake autoware_grid_map_utils + autoware_lanelet2_extension autoware_map_msgs autoware_universe_utils grid_map_cv grid_map_pcl grid_map_ros - lanelet2_extension libpcl-all-dev pcl_conversions rclcpp diff --git a/perception/elevation_map_loader/src/elevation_map_loader_node.cpp b/perception/elevation_map_loader/src/elevation_map_loader_node.cpp index 3b249e45ae194..80f7d85fd53c8 100644 --- a/perception/elevation_map_loader/src/elevation_map_loader_node.cpp +++ b/perception/elevation_map_loader/src/elevation_map_loader_node.cpp @@ -15,13 +15,13 @@ #include "elevation_map_loader/elevation_map_loader_node.hpp" #include +#include +#include #include #include #include #include #include -#include -#include #include #include diff --git a/perception/ground_segmentation/CMakeLists.txt b/perception/ground_segmentation/CMakeLists.txt index 656ffec486c3c..f8e4f50a553a2 100644 --- a/perception/ground_segmentation/CMakeLists.txt +++ b/perception/ground_segmentation/CMakeLists.txt @@ -21,20 +21,20 @@ include_directories( ${GRID_MAP_INCLUDE_DIR} ) -ament_auto_add_library(ground_segmentation SHARED - src/ray_ground_filter_nodelet.cpp - src/ransac_ground_filter_nodelet.cpp - src/scan_ground_filter_nodelet.cpp +ament_auto_add_library(${PROJECT_NAME} SHARED + src/ray_ground_filter/node.cpp + src/ransac_ground_filter/node.cpp + src/scan_ground_filter/node.cpp ) -target_link_libraries(ground_segmentation +target_link_libraries(${PROJECT_NAME} ${Boost_LIBRARIES} ${OpenCV_LIBRARIES} ${PCL_LIBRARIES} ) if(OPENMP_FOUND) - set_target_properties(ground_segmentation PROPERTIES + set_target_properties(${PROJECT_NAME} PROPERTIES COMPILE_FLAGS ${OpenMP_CXX_FLAGS} LINK_FLAGS ${OpenMP_CXX_FLAGS} ) @@ -42,18 +42,18 @@ endif() # ========== Ground Filter ========== # -- Ray Ground Filter -- -rclcpp_components_register_node(ground_segmentation - PLUGIN "ground_segmentation::RayGroundFilterComponent" +rclcpp_components_register_node(${PROJECT_NAME} + PLUGIN "autoware::ground_segmentation::RayGroundFilterComponent" EXECUTABLE ray_ground_filter_node) # -- RANSAC Ground Filter -- -rclcpp_components_register_node(ground_segmentation - PLUGIN "ground_segmentation::RANSACGroundFilterComponent" +rclcpp_components_register_node(${PROJECT_NAME} + PLUGIN "autoware::ground_segmentation::RANSACGroundFilterComponent" EXECUTABLE ransac_ground_filter_node) # -- Scan Ground Filter -- -rclcpp_components_register_node(ground_segmentation - PLUGIN "ground_segmentation::ScanGroundFilterComponent" +rclcpp_components_register_node(${PROJECT_NAME} + PLUGIN "autoware::ground_segmentation::ScanGroundFilterComponent" EXECUTABLE scan_ground_filter_node) @@ -88,7 +88,7 @@ if(BUILD_TESTING) ) target_link_libraries(test_ray_ground_filter - ground_segmentation + ${PROJECT_NAME} ${YAML_CPP_LIBRARIES} ) @@ -97,7 +97,14 @@ if(BUILD_TESTING) ) target_link_libraries(test_scan_ground_filter - ground_segmentation + ${PROJECT_NAME} ${YAML_CPP_LIBRARIES} ) + ament_add_ros_isolated_gtest(test_ransac_ground_filter + test/test_ransac_ground_filter.cpp + ) + target_link_libraries(test_ransac_ground_filter + ground_segmentation + ${YAML_CPP_LIBRARIES}) + endif() diff --git a/perception/ground_segmentation/config/ground_segmentation.param.yaml b/perception/ground_segmentation/config/ground_segmentation.param.yaml index 594ef1fe974b2..ca077941f0185 100644 --- a/perception/ground_segmentation/config/ground_segmentation.param.yaml +++ b/perception/ground_segmentation/config/ground_segmentation.param.yaml @@ -16,7 +16,7 @@ negative: False common_ground_filter: - plugin: "ground_segmentation::ScanGroundFilterComponent" + plugin: "autoware::ground_segmentation::ScanGroundFilterComponent" parameters: global_slope_max_angle_deg: 10.0 local_slope_max_angle_deg: 13.0 # recommended 30.0 for non elevation_grid_mode diff --git a/perception/ground_segmentation/launch/scan_ground_filter.launch.py b/perception/ground_segmentation/launch/scan_ground_filter.launch.py index df8290997315e..990fcace3449a 100644 --- a/perception/ground_segmentation/launch/scan_ground_filter.launch.py +++ b/perception/ground_segmentation/launch/scan_ground_filter.launch.py @@ -36,7 +36,7 @@ def launch_setup(context, *args, **kwargs): nodes = [ ComposableNode( package="ground_segmentation", - plugin="ground_segmentation::ScanGroundFilterComponent", + plugin="autoware::ground_segmentation::ScanGroundFilterComponent", name="scan_ground_filter", remappings=[ ("input", LaunchConfiguration("input/pointcloud")), diff --git a/perception/ground_segmentation/src/ransac_ground_filter_nodelet.cpp b/perception/ground_segmentation/src/ransac_ground_filter/node.cpp similarity index 97% rename from perception/ground_segmentation/src/ransac_ground_filter_nodelet.cpp rename to perception/ground_segmentation/src/ransac_ground_filter/node.cpp index aa224e7adc5bf..25e29c5b9f21a 100644 --- a/perception/ground_segmentation/src/ransac_ground_filter_nodelet.cpp +++ b/perception/ground_segmentation/src/ransac_ground_filter/node.cpp @@ -12,7 +12,7 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "ground_segmentation/ransac_ground_filter_nodelet.hpp" +#include "node.hpp" #include @@ -54,15 +54,6 @@ Eigen::Vector3d getArbitraryOrthogonalVector(const Eigen::Vector3d & input) return unit_vec; } -ground_segmentation::PlaneBasis getPlaneBasis(const Eigen::Vector3d & plane_normal) -{ - ground_segmentation::PlaneBasis basis; - basis.e_z = plane_normal; - basis.e_x = getArbitraryOrthogonalVector(plane_normal); - basis.e_y = basis.e_x.cross(basis.e_z); - return basis; -} - geometry_msgs::msg::Pose getDebugPose(const Eigen::Affine3d & plane_affine) { geometry_msgs::msg::Pose debug_pose; @@ -78,8 +69,17 @@ geometry_msgs::msg::Pose getDebugPose(const Eigen::Affine3d & plane_affine) } } // namespace -namespace ground_segmentation +namespace autoware::ground_segmentation { +PlaneBasis getPlaneBasis(const Eigen::Vector3d & plane_normal) +{ + PlaneBasis basis; + basis.e_z = plane_normal; + basis.e_x = getArbitraryOrthogonalVector(plane_normal); + basis.e_y = basis.e_x.cross(basis.e_z); + return basis; +} + using pointcloud_preprocessor::get_param; RANSACGroundFilterComponent::RANSACGroundFilterComponent(const rclcpp::NodeOptions & options) @@ -203,7 +203,7 @@ Eigen::Affine3d RANSACGroundFilterComponent::getPlaneAffine( pcl::PointXYZ centroid_point; centroid.get(centroid_point); Eigen::Translation trans(centroid_point.x, centroid_point.y, centroid_point.z); - const ground_segmentation::PlaneBasis basis = getPlaneBasis(plane_normal); + const PlaneBasis basis = getPlaneBasis(plane_normal); Eigen::Matrix3d rot; rot << basis.e_x.x(), basis.e_y.x(), basis.e_z.x(), basis.e_x.y(), basis.e_y.y(), basis.e_z.y(), basis.e_x.z(), basis.e_y.z(), basis.e_z.z(); @@ -396,7 +396,7 @@ rcl_interfaces::msg::SetParametersResult RANSACGroundFilterComponent::paramCallb return result; } -} // namespace ground_segmentation +} // namespace autoware::ground_segmentation #include -RCLCPP_COMPONENTS_REGISTER_NODE(ground_segmentation::RANSACGroundFilterComponent) +RCLCPP_COMPONENTS_REGISTER_NODE(autoware::ground_segmentation::RANSACGroundFilterComponent) diff --git a/perception/ground_segmentation/include/ground_segmentation/ransac_ground_filter_nodelet.hpp b/perception/ground_segmentation/src/ransac_ground_filter/node.hpp similarity index 94% rename from perception/ground_segmentation/include/ground_segmentation/ransac_ground_filter_nodelet.hpp rename to perception/ground_segmentation/src/ransac_ground_filter/node.hpp index f2fc47a50ff62..ebdd28f26ebe8 100644 --- a/perception/ground_segmentation/include/ground_segmentation/ransac_ground_filter_nodelet.hpp +++ b/perception/ground_segmentation/src/ransac_ground_filter/node.hpp @@ -12,8 +12,8 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef GROUND_SEGMENTATION__RANSAC_GROUND_FILTER_NODELET_HPP_ -#define GROUND_SEGMENTATION__RANSAC_GROUND_FILTER_NODELET_HPP_ +#ifndef RANSAC_GROUND_FILTER__NODE_HPP_ +#define RANSAC_GROUND_FILTER__NODE_HPP_ #include "pointcloud_preprocessor/filter.hpp" @@ -38,7 +38,7 @@ #include #include -namespace ground_segmentation +namespace autoware::ground_segmentation { struct PlaneBasis { @@ -129,6 +129,6 @@ class RANSACGroundFilterComponent : public pointcloud_preprocessor::Filter EIGEN_MAKE_ALIGNED_OPERATOR_NEW explicit RANSACGroundFilterComponent(const rclcpp::NodeOptions & options); }; -} // namespace ground_segmentation +} // namespace autoware::ground_segmentation -#endif // GROUND_SEGMENTATION__RANSAC_GROUND_FILTER_NODELET_HPP_ +#endif // RANSAC_GROUND_FILTER__NODE_HPP_ diff --git a/perception/ground_segmentation/include/ground_segmentation/gencolors.hpp b/perception/ground_segmentation/src/ray_ground_filter/gencolors.hpp similarity index 96% rename from perception/ground_segmentation/include/ground_segmentation/gencolors.hpp rename to perception/ground_segmentation/src/ray_ground_filter/gencolors.hpp index 07637b1514b09..658e5718a01cc 100644 --- a/perception/ground_segmentation/include/ground_segmentation/gencolors.hpp +++ b/perception/ground_segmentation/src/ray_ground_filter/gencolors.hpp @@ -53,19 +53,18 @@ // //M*/ -#ifndef GROUND_SEGMENTATION__GENCOLORS_HPP_ -#define GROUND_SEGMENTATION__GENCOLORS_HPP_ +#ifndef RAY_GROUND_FILTER__GENCOLORS_HPP_ +#define RAY_GROUND_FILTER__GENCOLORS_HPP_ #include +#include #include #include #include -// #include -#include -namespace ray_ground_filter +namespace autoware::ray_ground_filter { using namespace cv; // NOLINT @@ -159,5 +158,5 @@ inline void generateColors(std::vector & colors, size_t count, size_t fa colors[i] = Scalar(c.x, c.y, c.z); } } -} // namespace ray_ground_filter -#endif // GROUND_SEGMENTATION__GENCOLORS_HPP_ +} // namespace autoware::ray_ground_filter +#endif // RAY_GROUND_FILTER__GENCOLORS_HPP_ diff --git a/perception/ground_segmentation/src/ray_ground_filter_nodelet.cpp b/perception/ground_segmentation/src/ray_ground_filter/node.cpp similarity index 97% rename from perception/ground_segmentation/src/ray_ground_filter_nodelet.cpp rename to perception/ground_segmentation/src/ray_ground_filter/node.cpp index c58f1c0e507e5..c75972d3c4f14 100644 --- a/perception/ground_segmentation/src/ray_ground_filter_nodelet.cpp +++ b/perception/ground_segmentation/src/ray_ground_filter/node.cpp @@ -29,12 +29,12 @@ * v1.0: amc-nu (abrahammonrroy@yahoo.com) */ -#include "ground_segmentation/ray_ground_filter_nodelet.hpp" +#include "node.hpp" #include #include -namespace ground_segmentation +namespace autoware::ground_segmentation { using pointcloud_preprocessor::get_param; @@ -250,8 +250,9 @@ void RayGroundFilterComponent::ClassifyPointCloud( // // Enable the dynamic reconfigure service // has_service = true; // srv_ = boost::make_shared< -// dynamic_reconfigure::Server >(nh); -// dynamic_reconfigure::Server::CallbackType f = +// dynamic_reconfigure::Server >(nh); +// dynamic_reconfigure::Server::CallbackType +// f = // boost::bind(&RayGroundFilterComponent::config_callback, this, _1, _2); // srv_->setCallback(f); // return (true); @@ -394,7 +395,7 @@ rcl_interfaces::msg::SetParametersResult RayGroundFilterComponent::paramCallback return result; } -} // namespace ground_segmentation +} // namespace autoware::ground_segmentation #include -RCLCPP_COMPONENTS_REGISTER_NODE(ground_segmentation::RayGroundFilterComponent) +RCLCPP_COMPONENTS_REGISTER_NODE(autoware::ground_segmentation::RayGroundFilterComponent) diff --git a/perception/ground_segmentation/include/ground_segmentation/ray_ground_filter_nodelet.hpp b/perception/ground_segmentation/src/ray_ground_filter/node.hpp similarity index 95% rename from perception/ground_segmentation/include/ground_segmentation/ray_ground_filter_nodelet.hpp rename to perception/ground_segmentation/src/ray_ground_filter/node.hpp index 6e2638e8566d8..8bb17cdaf8a09 100644 --- a/perception/ground_segmentation/include/ground_segmentation/ray_ground_filter_nodelet.hpp +++ b/perception/ground_segmentation/src/ray_ground_filter/node.hpp @@ -42,8 +42,8 @@ * v1.0: amc-nu (abrahammonrroy@yahoo.com) */ -#ifndef GROUND_SEGMENTATION__RAY_GROUND_FILTER_NODELET_HPP_ -#define GROUND_SEGMENTATION__RAY_GROUND_FILTER_NODELET_HPP_ +#ifndef RAY_GROUND_FILTER__NODE_HPP_ +#define RAY_GROUND_FILTER__NODE_HPP_ #include @@ -58,14 +58,7 @@ #include #endif -#include - -#include -#include -#include -// #include - -#include "ground_segmentation/gencolors.hpp" +#include "gencolors.hpp" #include "pointcloud_preprocessor/filter.hpp" #include @@ -76,11 +69,17 @@ #include #include +#include + +#include +#include +#include + namespace bg = boost::geometry; using Point = bg::model::d2::point_xy; using Polygon = bg::model::polygon; -namespace ground_segmentation +namespace autoware::ground_segmentation { class RayGroundFilterComponent : public pointcloud_preprocessor::Filter { @@ -206,6 +205,6 @@ class RayGroundFilterComponent : public pointcloud_preprocessor::Filter EIGEN_MAKE_ALIGNED_OPERATOR_NEW explicit RayGroundFilterComponent(const rclcpp::NodeOptions & options); }; -} // namespace ground_segmentation +} // namespace autoware::ground_segmentation -#endif // GROUND_SEGMENTATION__RAY_GROUND_FILTER_NODELET_HPP_ +#endif // RAY_GROUND_FILTER__NODE_HPP_ diff --git a/perception/ground_segmentation/src/scan_ground_filter_nodelet.cpp b/perception/ground_segmentation/src/scan_ground_filter/node.cpp similarity index 98% rename from perception/ground_segmentation/src/scan_ground_filter_nodelet.cpp rename to perception/ground_segmentation/src/scan_ground_filter/node.cpp index 7853aa400b6a5..26db036f79bbe 100644 --- a/perception/ground_segmentation/src/scan_ground_filter_nodelet.cpp +++ b/perception/ground_segmentation/src/scan_ground_filter/node.cpp @@ -12,18 +12,18 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "ground_segmentation/scan_ground_filter_nodelet.hpp" +#include "node.hpp" -#include -#include -#include -#include +#include "autoware/universe_utils/geometry/geometry.hpp" +#include "autoware/universe_utils/math/normalization.hpp" +#include "autoware/universe_utils/math/unit_conversion.hpp" +#include "autoware_vehicle_info_utils/vehicle_info_utils.hpp" #include #include #include -namespace ground_segmentation +namespace autoware::ground_segmentation { using autoware::universe_utils::calcDistance3d; using autoware::universe_utils::deg2rad; @@ -33,7 +33,7 @@ using autoware::vehicle_info_utils::VehicleInfoUtils; using pointcloud_preprocessor::get_param; ScanGroundFilterComponent::ScanGroundFilterComponent(const rclcpp::NodeOptions & options) -: Filter("ScanGroundFilter", options) +: pointcloud_preprocessor::Filter("ScanGroundFilter", options) { // set initial parameters { @@ -715,7 +715,7 @@ rcl_interfaces::msg::SetParametersResult ScanGroundFilterComponent::onParameter( return result; } -} // namespace ground_segmentation +} // namespace autoware::ground_segmentation #include -RCLCPP_COMPONENTS_REGISTER_NODE(ground_segmentation::ScanGroundFilterComponent) +RCLCPP_COMPONENTS_REGISTER_NODE(autoware::ground_segmentation::ScanGroundFilterComponent) diff --git a/perception/ground_segmentation/include/ground_segmentation/scan_ground_filter_nodelet.hpp b/perception/ground_segmentation/src/scan_ground_filter/node.hpp similarity index 97% rename from perception/ground_segmentation/include/ground_segmentation/scan_ground_filter_nodelet.hpp rename to perception/ground_segmentation/src/scan_ground_filter/node.hpp index 3ae41173a7a7d..0e6ed598053ba 100644 --- a/perception/ground_segmentation/include/ground_segmentation/scan_ground_filter_nodelet.hpp +++ b/perception/ground_segmentation/src/scan_ground_filter/node.hpp @@ -12,14 +12,13 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef GROUND_SEGMENTATION__SCAN_GROUND_FILTER_NODELET_HPP_ -#define GROUND_SEGMENTATION__SCAN_GROUND_FILTER_NODELET_HPP_ +#ifndef SCAN_GROUND_FILTER__NODE_HPP_ +#define SCAN_GROUND_FILTER__NODE_HPP_ +#include "autoware_vehicle_info_utils/vehicle_info.hpp" #include "pointcloud_preprocessor/filter.hpp" #include "pointcloud_preprocessor/transform_info.hpp" -#include - #include #include @@ -41,7 +40,7 @@ class ScanGroundFilterTest; -namespace ground_segmentation +namespace autoware::ground_segmentation { using autoware::vehicle_info_utils::VehicleInfo; @@ -291,6 +290,6 @@ class ScanGroundFilterComponent : public pointcloud_preprocessor::Filter // for test friend ScanGroundFilterTest; }; -} // namespace ground_segmentation +} // namespace autoware::ground_segmentation -#endif // GROUND_SEGMENTATION__SCAN_GROUND_FILTER_NODELET_HPP_ +#endif // SCAN_GROUND_FILTER__NODE_HPP_ diff --git a/perception/ground_segmentation/test/data/test.pcd b/perception/ground_segmentation/test/data/test.pcd index f9ad91ab980c1..2df8ca09a18f9 100644 --- a/perception/ground_segmentation/test/data/test.pcd +++ b/perception/ground_segmentation/test/data/test.pcd @@ -1,65547 +1,65547 @@ # .PCD v0.7 - Point Cloud Data file format VERSION 0.7 -FIELDS x y z -SIZE 4 4 4 -TYPE F F F -COUNT 1 1 1 +FIELDS x y z intensity +SIZE 4 4 4 4 +TYPE F F F F +COUNT 1 1 1 1 WIDTH 1024 HEIGHT 64 VIEWPOINT 0 0 0 1 0 0 0 POINTS 65536 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--21.784285 3.7592926 -1.9003711 --21.78042 3.8963959 -1.9021176 --21.767857 4.0321417 -1.9031656 --21.711397 4.1596332 -1.9003711 --21.650286 4.28581 -1.8972272 --21.61187 4.4161801 -1.8961792 --21.56097 4.5437751 -1.8940833 --21.618353 4.6945868 -1.9017683 --21.48609 4.8041067 -1.8925114 --21.44747 4.9338427 -1.8917254 --21.40225 5.0619102 -1.8904154 --21.361109 5.1907845 -1.8895422 --21.309525 5.3169332 -1.8877956 --21.273605 5.4468446 -1.8875335 --21.2446 5.5785394 -1.8879702 --21.224401 5.7126722 -1.8892802 --21.22064 5.8515334 -1.8921621 --21.237064 5.9965181 -1.8969651 --21.239109 6.1380525 -1.9005457 --21.258366 6.285203 -1.9057854 --21.276613 6.4328203 -1.9110253 --21.277668 6.5758977 -1.9147804 --21.291065 6.7234287 -1.9197582 --21.276905 6.8628097 -1.9222908 --21.263727 7.0028925 -1.924998 --21.248686 7.1427541 -1.9276179 --21.240265 7.2852802 -1.9309365 --21.257223 7.4371824 -1.9367002 --21.268423 7.5878921 -1.9420273 --21.281382 7.7400403 -1.9476163 --21.271786 7.8846693 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-1.7868426 --15.297755 13.839502 -1.7708613 --15.133945 13.861115 -1.7615169 --14.971908 13.882597 -1.7523474 --14.850854 13.940794 -1.7480682 --14.681269 13.952052 -1.7379379 --14.534363 13.983167 -1.730515 --14.423312 14.047758 -1.7274585 --14.32053 14.119907 -1.7254498 --14.242788 14.21666 -1.7265851 --14.172807 14.321486 -1.7287683 --14.103467 14.427412 -1.7311263 --14.000836 14.499345 -1.7293797 --13.890292 14.562634 -1.7266724 --13.792478 14.638888 -1.7256244 --13.934506 14.972635 -1.7554913 --13.851138 15.067388 -1.7566265 --14.882827 16.390343 -1.903253 --14.734087 16.427959 -1.8969651 --14.607907 16.489721 -1.8936467 --14.468406 16.535561 -1.8885815 --14.342571 16.59614 -1.8853503 --14.217834 16.657312 -1.8822938 --14.101233 16.727482 -1.8803725 --14.002153 16.818283 -1.8808966 --13.899328 16.904652 -1.8809838 --13.790945 16.984205 -1.8803725 --13.677748 17.057625 -1.8791499 --13.567261 17.134199 -1.878364 --13.455736 17.209276 -1.8774906 --13.340756 17.279688 -1.8761808 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9.569437 -2.020015 --13.372093 9.6643486 -2.022742 --13.330956 9.7596989 -2.0255928 --13.291543 9.8566818 -2.0288153 --13.237954 9.9434156 -2.0299308 --13.198014 10.040678 -2.0332775 --13.157379 10.137824 -2.0366242 --13.107443 10.228133 -2.0386074 --13.054558 10.316365 -2.0402186 --12.98404 10.390692 -2.039103 --12.935457 10.482661 -2.0415821 --12.877796 10.5675 -2.0426974 --12.830266 10.660905 -2.0455484 --12.790424 10.761151 -2.0497625 --12.757389 10.867744 -2.0552163 --12.723525 10.974337 -2.0606699 --12.679864 11.073088 -2.0646362 --12.633983 11.170402 -2.0683548 --12.57999 11.260949 -2.0708337 --12.526097 11.351921 -2.0734365 --12.465712 11.437287 -2.075048 --12.410566 11.527743 -2.0776508 --12.348996 11.612514 -2.0792623 --12.289009 11.699015 -2.0812454 --12.228425 11.785216 -2.0832286 --12.167245 11.871115 -2.0852118 --12.104059 11.955311 -2.086947 --12.044508 12.043388 -2.0894258 --11.987818 12.134715 -2.0925245 --11.929073 12.224378 -2.0953753 --11.862793 12.306631 -2.0969868 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1.6203023 -1.9431083 --13.130093 1.7004147 -1.9425163 --13.104712 1.7789506 -1.9402966 --13.080821 1.8575175 -1.9383729 --13.054502 1.9355679 -1.9361532 --13.036516 2.0147254 -1.9352652 --12.991687 2.0894928 -1.9303819 --12.969842 2.167701 -1.92905 --12.953374 2.2467341 -1.928606 --12.950051 2.3280983 -1.9302338 --12.942329 2.4087818 -1.9312698 --12.918565 2.4864659 -1.9299378 --12.896276 2.5643353 -1.928902 --12.872548 2.6418257 -1.9277182 --12.864803 2.722604 -1.92905 --12.851721 2.8023329 -1.929642 --12.836214 2.8815703 -1.9299378 --12.804801 2.9571607 -1.9278661 --12.746964 3.026304 -1.9217988 --12.741606 3.107743 -1.9238707 --12.719419 3.1851447 -1.9234266 --12.709215 3.2655916 -1.9249065 --12.653554 3.3341854 -1.9194312 --12.654818 3.417696 -1.9228348 --12.635515 3.4958079 -1.9231308 --12.606215 3.571115 -1.9219469 --12.600213 3.6530812 -1.9244626 --12.717886 3.7719576 -1.9463639 --12.582828 3.8160434 -1.928902 --12.601684 3.9063592 -1.9355612 --12.519966 3.9653873 -1.9265343 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-1.9365971 --11.576836 6.3708229 -1.9386688 --11.556523 6.4523458 -1.9419245 --11.50639 6.5172839 -1.9401486 --11.500484 6.6074734 -1.9460679 --11.463141 6.6799068 -1.9466598 --11.411722 6.7440786 -1.9448841 --11.388788 6.8251586 -1.9481397 --11.34416 6.8933697 -1.9476957 --11.306702 6.9659595 -1.9485836 --11.273811 7.0414929 -1.9503595 --11.235407 7.1137099 -1.9512473 --11.203198 7.1899943 -1.9533191 --11.16799 7.264533 -1.9549469 --11.147149 7.3487101 -1.9592383 --11.115005 7.4257607 -1.961606 --11.090524 7.5082388 -1.9654536 --11.051585 7.581183 -1.9667854 --11.026785 7.6640935 -1.970781 --11.00866 7.75211 -1.9761083 --10.963302 7.8212352 -1.9765522 --10.928731 7.8982067 -1.9790679 --10.880063 7.9651155 -1.9790679 --10.850844 8.0464535 -1.9827675 --10.801268 8.1128817 -1.9827675 --10.778045 8.1993656 -1.9877988 --10.735365 8.2713804 -1.9892787 --10.721834 8.3663025 -1.9963818 --10.657107 8.4215078 -1.9938661 --10.628397 8.5052795 -1.9983056 --10.579851 8.5734482 -1.9990455 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10.683999 -2.0508392 --8.9264545 10.740487 -2.0511351 --8.8798332 10.818756 -2.0557227 --8.8288507 10.892247 -2.0594223 --8.7698851 10.956253 -2.0613458 --8.7098522 11.019169 -2.0631218 --8.6518106 11.084879 -2.0654895 --8.5763884 11.128356 -2.0637136 --8.528904 11.208318 -2.068893 --8.470068 11.273882 -2.0714087 --8.4001436 11.324847 -2.0712607 --8.3334188 11.380166 -2.0720007 --8.2744417 11.446314 -2.0748122 --8.2235832 11.524242 -2.0798438 --8.1663837 11.593891 -2.0833952 --8.1024294 11.654338 -2.085319 --8.0358019 11.711173 -2.0866508 --7.9710007 11.770905 -2.0885746 --7.9030213 11.826172 -2.0897584 --7.835752 11.882698 -2.0912383 --7.7702441 11.94216 -2.0933101 --7.7037668 12.000432 -2.0952339 --7.6373959 12.059174 -2.0973055 --7.5695457 12.115874 -2.0990813 --7.5018058 12.173038 -2.1010051 --7.4310813 12.2256 -2.1021891 --7.3599939 12.277753 -2.1033728 --7.2890496 12.330344 -2.1047046 --7.2187324 12.384236 -2.1063325 --7.1445789 12.431726 -2.1069243 --7.0710897 12.480494 -2.1078122 --6.9982352 12.530555 -2.1089962 --6.9274163 12.584522 -2.11092 --6.8477111 12.622445 -2.1101799 --6.7682676 12.660735 -2.1095881 --6.7134967 12.745779 -2.1169872 --6.6734214 12.860475 -2.1294177 --6.5948353 12.902061 -2.1295657 --6.5093207 12.929914 -2.1274939 --6.4254699 12.960751 -2.126014 --6.3371477 12.982157 -2.1230545 --6.2440939 12.993162 -2.1184671 --6.1486073 12.998151 -2.1129916 --6.0595551 13.015907 -2.109736 --5.9672217 13.025868 -2.1052966 --5.8697133 13.023465 -2.0989335 --5.7732615 13.022165 -2.0928662 --5.6758614 13.017454 -2.086355 --5.5767884 13.007495 -2.0791037 --5.4761324 12.992254 -2.0711126 --5.3872275 13.003681 -2.0675611 --5.303236 13.026416 -2.0659335 --5.209621 13.024734 -2.0604579 --5.1097183 13.005825 -2.052319 --5.0269723 13.029551 -2.0511351 --4.9357224 13.030554 -2.0463998 --4.8408589 13.020732 -2.0400364 --4.7486749 13.016741 -2.0347092 --4.6597214 13.020492 -2.0307136 --4.5735197 13.031107 -2.0279019 --4.4851122 13.034609 -2.0240545 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3.9138277 -1.9215105 --4.3476791 3.9336271 -1.9180238 --4.321363 3.9583082 -1.9170729 --4.2872572 3.9757211 -1.9126352 --4.2517176 3.9915667 -1.9075637 --4.2463965 4.0358729 -1.9164389 --4.2324886 4.0723734 -1.9215105 --4.2094598 4.1002517 -1.9224614 --4.1862478 4.1280012 -1.9234123 --4.1615114 4.1542797 -1.9237293 --4.1292715 4.1729951 -1.9205596 --4.0969586 4.1914711 -1.9173899 --4.0639162 4.2090259 -1.9139031 --4.0242662 4.21947 -1.9072467 --4.0197635 4.2668629 -1.9177068 --3.9941533 4.2921414 -1.9180238 --3.9715931 4.3207579 -1.9199257 --3.9386308 4.3380241 -1.9167559 --3.9119391 4.3621097 -1.9167559 --3.8888736 4.3902903 -1.9186578 --3.8587394 4.4105005 -1.9170729 --3.830364 4.4326591 -1.9164389 --3.7993991 4.4517498 -1.9145371 --3.7555068 4.4554 -1.9059788 --3.7426627 4.4958525 -1.9135861 --3.7156086 4.519465 -1.9139031 --3.6830249 4.5362883 -1.9113673 --3.658088 4.5625014 -1.9129522 --3.6253138 4.5789127 -1.9104165 --3.600071 4.6048064 -1.9120013 --3.5787041 4.635818 -1.915805 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-3.9639957 -1.6663982 --2.9488714 -3.9315495 -1.660181 --2.9717958 -3.9118769 -1.6595266 --2.2902231 -2.9770503 -1.259012 --2.3107708 -2.9659226 -1.2603208 --2.324827 -2.9464965 -1.2580303 --3.0745742 -3.8473618 -1.6637805 --3.0856364 -3.8130014 -1.6569089 --3.1089745 -3.7939966 -1.6569089 --3.1418526 -3.7864819 -1.6621444 --3.1650269 -3.7671325 -1.6621444 --3.189307 -3.7490807 -1.6627988 --3.2196531 -3.7380309 -1.6667254 --3.2288616 -3.7025402 -1.6595266 --3.2446375 -3.6748683 -1.6559272 --3.2652352 -3.6527774 -1.6549456 --3.2799759 -3.6242678 -1.651019 --3.3098145 -3.612433 -1.6549456 --3.3332033 -3.5934415 -1.6556001 --3.3519545 -3.5694783 -1.6539639 --3.3672807 -3.541997 -1.6506917 --3.3928833 -3.5253532 -1.652655 --3.4052162 -3.4949951 -1.648074 --3.416641 -3.4639463 -1.6431657 --3.4358277 -3.4409115 -1.6421841 --3.4730024 -3.4357107 -1.6500373 --3.4818501 -3.402451 -1.6441474 --3.4938211 -3.3724935 -1.6398935 --3.517185 -3.3536 -1.6412024 --3.5445764 -3.3384328 -1.6444746 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-1.6984656 --4.2475142 -2.6940629 -1.7001016 --4.2767835 -2.6759667 -1.7053372 --4.3043818 -2.65662 -1.7099181 --4.3319035 -2.6370351 -1.7144992 --4.3512435 -2.6123502 -1.7158082 --4.3728833 -2.5889709 -1.7180986 --4.4099069 -2.5744727 -1.7266062 --4.421525 -2.5450094 -1.7249702 --4.4337702 -2.5159669 -1.7236613 --4.4565473 -2.4928646 -1.7266062 --4.5081744 -2.4855392 -1.7410039 --4.4909549 -2.440243 -1.7282424 --4.5616646 -2.4425139 -1.7501659 --4.5857601 -2.4193263 -1.7537653 --4.603034 -2.3924499 -1.754747 --4.6235156 -2.3671744 -1.7570375 --4.6328902 -2.3362088 -1.7550743 --4.6564488 -2.3123589 -1.7586737 --4.6739454 -2.2854035 -1.7599826 --4.7108712 -2.2677431 -1.7688174 --4.7272596 -2.2400084 -1.7697991 --4.7426276 -2.2117586 -1.7704535 --4.7595453 -2.1841927 -1.7717624 --4.7969747 -2.1658299 -1.7809244 --4.7289867 -2.1003189 -1.7501659 --4.7391872 -2.0701287 -1.7491844 --4.7613516 -2.0451109 -1.7527838 --4.8025403 -2.0279815 -1.7635819 --4.8463154 -2.0115094 -1.7753618 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--0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --5.4623103 0.97650236 -1.9482287 --5.4543648 1.0096576 -1.9475535 --5.4397464 1.0415134 -1.9445152 --5.4360232 1.0754193 -1.945528 --5.4173331 1.1063063 -1.9411393 --5.4058371 1.138556 -1.9394515 --5.4015093 1.172303 -1.9404641 --5.3932958 1.2052187 -1.9401267 --5.3876338 1.2387103 -1.9408017 --5.3808479 1.2719613 -1.9411393 --5.3793449 1.3065083 -1.9435025 --5.3785338 1.3413124 -1.9462032 --5.3665547 1.373356 -1.9448528 --5.3653107 1.4081699 -1.9475535 --5.3592963 1.4417976 -1.9485663 --5.3476262 1.4739044 -1.9475535 --5.3384819 1.5066891 -1.9475535 --5.3273282 1.5388947 -1.9468783 --5.3087583 1.5688865 -1.9435025 --5.2936254 1.5997975 -1.9414769 --5.2801118 1.6311374 -1.9401267 --5.2628226 1.6612386 -1.9374259 --5.2426758 1.6903229 -1.9337125 --0 0 -0 --5.2277842 1.7566295 -1.9360756 --5.2177978 1.7889787 -1.9364132 --5.1853971 1.8135046 -1.928311 --5.2220497 1.8623582 -1.9465408 --5.1972542 1.8895416 -1.9414769 --5.1793838 1.919107 -1.9391139 --5.1631074 1.9491889 -1.9374259 --5.1501718 1.9804947 -1.9370884 --5.1545763 2.0185785 -1.9435025 --5.1324754 2.0463345 -1.9397891 --5.106739 2.0724797 -1.9347252 --5.0878363 2.1012602 -1.9323621 --5.0783186 2.1338973 -1.9337125 --5.0564742 2.1613183 -1.9303366 --5.042254 2.1919284 -1.929999 --5.0304317 2.2235868 -1.9306742 --5.023562 2.2574971 -1.9333749 --5.0070472 2.2871056 -1.9323621 --4.994627 2.3185778 -1.9330373 --4.9769011 2.3475745 -1.9316869 --4.9649496 2.3792882 -1.9326997 --4.9443326 2.4068244 -1.9303366 --4.930315 2.4375339 -1.9306742 --4.9169483 2.4685845 -1.9313493 --4.9067407 2.5012724 -1.9333749 --4.8913012 2.5313325 -1.9333749 --4.8673434 2.5569203 -1.929999 --4.83495 2.5778818 -1.9232472 --4.8364277 2.6169055 -1.9303366 --4.8178039 2.6451731 -1.9293238 --4.8014827 2.6746848 -1.9293238 --4.7849808 2.7040958 -1.9293238 --4.7731991 2.7362134 -1.9313493 --4.7612028 2.7682881 -1.9333749 --4.7449379 2.7979283 -1.9337125 --4.7317204 2.8294051 -1.9354004 --4.7166853 2.859849 -1.9364132 --4.7014546 2.8902149 -1.9374259 --4.6788387 2.9160213 -1.9354004 --4.6568789 2.9421618 -1.9337125 --4.6379461 2.9701729 -1.9333749 --4.6148963 2.9955001 -1.9313493 --4.595643 3.023243 -1.9310117 --4.5707407 3.0472078 -1.928311 --4.547277 3.0720346 -1.9262855 --4.5267882 3.098815 -1.9256103 --4.5138769 3.1308239 -1.928311 --4.4868798 3.1530502 -1.9249351 --4.4705157 3.1827052 -1.9262855 --4.4516664 3.2106264 -1.9266231 --4.4372034 3.2417674 -1.9289862 --4.4179854 3.2694926 -1.9293238 --4.4031138 3.3004909 -1.9316869 --4.3872781 3.3308606 -1.9337125 --4.3660111 3.3571439 -1.9333749 --4.3364177 3.3769307 -1.9293238 --4.3133979 3.4017262 -1.928311 --4.2850885 3.4222553 -1.9249351 --4.2186289 3.4117882 -1.9040047 --4.2252903 3.4602699 -1.916833 --4.2047038 3.4867315 -1.9171706 --4.1882768 3.5167022 -1.9195337 --4.1694889 3.5447733 -1.9208841 --4.1433792 3.5666058 -1.9188585 --4.134191 3.6030931 -1.9249351 --4.1176519 3.6333742 -1.9276358 --4.0903659 3.6541817 -1.9252727 --4.0727544 3.6836305 -1.9276358 --4.0493813 3.7079155 -1.9272983 --4.0224118 3.728853 -1.9252727 --3.9953375 3.7495992 -1.9232472 --3.9715729 3.7733953 -1.9229096 --3.9463096 3.7957358 -1.9218968 --3.9215961 3.8185654 -1.9212216 --3.8994329 3.8438773 -1.9218968 --3.8831036 3.8750448 -1.9256103 --3.859916 3.8994672 -1.9259479 --3.8207796 3.9075992 -1.9181833 --3.7947693 3.9289384 -1.9171706 --3.7699404 3.9514678 -1.916833 --3.7494974 3.9786353 -1.9188585 --3.7243731 4.0008779 -1.9185209 --3.7010283 4.0250406 -1.9191961 --3.6813247 4.0532479 -1.9218968 --3.6626713 4.0827661 -1.9252727 --3.6338053 4.1009359 -1.9232472 --3.6110539 4.1259885 -1.9245975 --3.5869005 4.1494923 -1.9252727 --3.5625947 4.1728544 -1.9259479 --3.5332825 4.1903181 -1.9239224 --3.5062997 4.2104726 -1.9232472 --3.4642539 4.2122793 -1.9141324 --3.448432 4.2458768 -1.9198713 --3.4211369 4.2654877 -1.9191961 --3.3761966 4.2627878 -1.9083933 --3.3638957 4.3012176 -1.9164954 --3.3368652 4.321032 -1.9161578 --3.3120012 4.3436708 -1.9171706 --3.2858529 4.3646631 -1.9175082 --3.2533944 4.3771882 -1.9141324 --3.2270319 4.3978271 -1.91447 --3.2044048 4.4236431 -1.9171706 --3.1772015 4.443222 -1.9171706 --3.1498785 4.4626331 -1.9171706 --3.1111364 4.4656582 -1.9100813 --3.0932779 4.4986243 -1.9161578 --3.0656166 4.5175195 -1.9161578 --3.0388875 4.5378089 -1.916833 --3.009949 4.5547991 -1.9161578 --2.9819446 4.5731821 -1.9161578 --2.9477158 4.5818939 -1.9121068 --2.9190419 4.5991001 -1.9117692 --2.8882694 4.6129355 -1.9100813 --2.8579319 4.6273675 -1.9087309 --2.8294849 4.6448159 -1.9087309 --2.8014166 4.6628971 -1.9090685 --2.7741923 4.6824279 -1.9100813 --2.7477837 4.7034254 -1.9117692 --2.7249808 4.7308002 -1.9161578 --2.6945074 4.7449756 -1.915145 --2.6708601 4.7712755 -1.9191961 --2.6374407 4.7801566 -1.9161578 --2.6031134 4.7871537 -1.9124444 --2.5754697 4.8063545 -1.9137948 --2.5454903 4.8212342 -1.9134572 --2.5158598 4.8367624 -1.9134572 --2.4869933 4.8537836 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-3.3119035 -1.3249412 --1.627273 -3.2910242 -1.3204182 --1.6558548 -3.2977316 -1.3273766 --1.6828755 -3.3008711 -1.3329433 --1.7117217 -3.3071325 -1.3399017 --1.7393988 -3.3106742 -1.3458163 --1.7817494 -3.3412952 -1.3632121 --2.1919718 -4.0499992 -1.6669443 --2.2127173 -4.0290785 -1.663813 --2.246985 -4.0326219 -1.6711192 --2.2730708 -4.0212069 -1.672163 --2.2879043 -3.9901068 -1.6648567 --2.3161163 -3.9824758 -1.6676401 --2.3338377 -3.9568858 -1.6627692 --2.3696272 -3.9617984 -1.6711192 --2.400701 -3.958405 -1.6759901 --2.4254355 -3.9443986 -1.676338 --2.4381802 -3.9111464 -1.6683359 --2.4676442 -3.9048288 -1.672163 --2.492569 -3.8911927 -1.6728588 --2.5148659 -3.8734667 -1.671815 --2.5380702 -3.8571799 -1.6714672 --2.5471203 -3.8196974 -1.6617255 --2.5841625 -3.8241901 -1.6707714 --2.6155264 -3.8198636 -1.6759901 --2.6244812 -3.7829485 -1.6665963 --2.6320035 -3.7445345 -1.6565067 --2.6679862 -3.7466395 -1.6648567 --2.6909249 -3.7301984 -1.6648567 --2.711549 -3.7105899 -1.663465 --2.7298019 -3.687855 -1.6606817 --2.7456276 -3.6620362 -1.6565067 --2.7652099 -3.6413879 -1.6547672 --2.7840705 -3.6198943 -1.6526796 --2.8021953 -3.5975671 -1.6502442 --2.3299377 -2.954025 -1.3541663 --2.3433368 -2.9338145 -1.351383 --2.365423 -2.9244821 -1.3538184 --2.9068739 -3.5487425 -1.6603338 --2.9226067 -3.5236249 -1.6568546 --2.9477911 -3.5099325 -1.6589421 --2.9577293 -3.4782121 -1.6523317 --2.9796274 -3.4607086 -1.6526796 --3.0075843 -3.4501328 -1.6565067 --3.0342824 -3.4379385 -1.6596379 --3.0534456 -3.4171591 -1.6585943 --3.0579979 -3.3802869 -1.6495483 --3.0742476 -3.3566213 -1.647113 --3.1005242 -3.3438807 -1.6502442 --3.122267 -3.32616 -1.6509401 --3.1464932 -3.3110161 -1.6530275 --3.1641495 -3.2889454 -1.6516359 --3.1698761 -3.2546966 -1.6439817 --3.2003212 -3.2458642 -1.6495483 --3.2135525 -3.2195325 -1.6460692 --3.2352462 -3.2017317 -1.647113 --3.2595248 -3.1864069 -1.6495483 --3.280364 -3.1676488 -1.6502442 --3.2977028 -3.1455207 -1.6492004 --3.3271792 -3.1348681 -1.6544192 --3.3484111 -3.1163096 -1.655463 --3.3716114 -3.0995114 -1.6575505 --3.3891778 -3.0775051 -1.6568546 --3.4191666 -3.0666645 -1.6624213 --3.4337075 -3.041898 -1.6603338 --3.4699504 -3.0362017 -1.6690317 --3.4956098 -3.0209818 -1.6725109 --3.5147936 -3.0000846 -1.6728588 --3.5446069 -2.9881272 -1.6784256 --3.5693607 -2.971719 -1.6815568 --3.5831819 -2.9461904 -1.6794692 --3.612839 -2.9336016 -1.6850359 --3.6293082 -2.9102058 -1.6843401 --3.6500371 -2.8902092 -1.6857318 --3.671396 -2.870645 -1.6874714 --3.689683 -2.8486366 -1.6878192 --3.705601 -2.8248069 -1.6871234 --3.7266099 -2.8048348 -1.688863 --3.7460079 -2.7835929 -1.6899068 --3.772846 -2.7677622 -1.6944298 --3.7927954 -2.7467592 -1.6958214 --3.8217828 -2.732156 -1.7013881 --3.8400073 -2.7097352 -1.7020839 --3.8504062 -2.6818373 -1.6993005 --3.8737435 -2.6629393 -1.7024318 --3.8962178 -2.6433296 -1.7052152 --3.9193761 -2.6240656 -1.7083464 --3.9487033 -2.6087494 -1.7142611 --3.9717057 -2.5890777 -1.7173923 --3.9914603 -2.5671937 -1.7191318 --4.0095124 -2.5441606 -1.7201756 --4.0322008 -2.5239861 -1.7233069 --4.0635686 -2.5090444 -1.7302653 --4.0860949 -2.4884508 -1.7333965 --4.1117363 -2.469605 -1.7379194 --4.1397204 -2.4519708 -1.743486 --4.1644034 -2.4321952 -1.7476611 --4.1833115 -2.4089355 -1.7494007 --4.2151375 -2.3929377 -1.756707 --4.2526517 -2.379843 -1.7664486 --4.2679949 -2.354157 -1.7667966 --4.2716503 -2.3221064 -1.7622737 --4.2874708 -2.2967372 -1.7629695 --4.3288474 -2.2848217 -1.7744508 --4.3602548 -2.2672963 -1.781757 --4.3757539 -2.241354 -1.7824528 --4.4028182 -2.2212162 -1.7880195 --4.4138451 -2.1929083 -1.7869759 --4.3952122 -2.1501365 -1.773755 --4.3745317 -2.106863 -1.7598382 --4.3780575 -2.0755663 -1.7560111 --4.3847623 -2.0458903 -1.7535757 --4.4262047 -2.0322411 -1.7654049 --4.4590988 -2.0143023 -1.773755 --4.509932 -2.0040264 -1.7894112 --4.5522141 -1.9894497 -1.8015883 --4.6160221 -1.983678 -1.8224634 --4.6721554 -1.9739153 -1.8402072 --4.7049608 -1.953835 -1.8485572 --4.8132205 -1.9642277 -1.887176 --4.8852305 -1.9587182 -1.9111824 --4.9503722 -1.9496512 -1.9324054 --4.9910998 -1.9303937 -1.9438866 --5.0282698 -1.9093788 -1.9539763 --5.0583405 -1.8853621 -1.9612825 --5.0777388 -1.8571858 -1.9644138 --5.0908036 -1.8266274 -1.9651096 --5.1107602 -1.7984666 -1.9685888 --5.1172676 -1.7655457 -1.9668492 --5.140439 -1.7383156 -1.9717201 --5.1581283 -1.7090992 -1.9745034 --5.1640592 -1.6759712 -1.9727638 --5.2010522 -1.6527659 -1.9832014 --5.1860275 -1.6130306 -1.9734596 --5.1814756 -1.5768152 -1.967893 --5.1937494 -1.5457944 -1.9689367 --5.2058372 -1.5146822 -1.9699805 --5.2285604 -1.4865543 -1.9751992 --5.2312598 -1.4526898 -1.9727638 --5.2445993 -1.4217896 -1.9745034 --5.2432542 -1.3869475 -1.9706763 --5.2552972 -1.3556854 -1.972068 --5.2616978 -1.322957 -1.9713721 --5.2678952 -1.2902013 -1.9706763 --5.2748003 -1.2576367 -1.9703283 --5.2796779 -1.2246125 -1.9692847 --5.290751 -1.1930181 -1.9706763 --5.2961397 -1.1601313 -1.9699805 --5.3068266 -1.1283921 -1.9713721 --5.3099775 -1.0950521 -1.9699805 --5.3156772 -1.0622666 -1.9696325 --5.3220954 -1.0296302 -1.9696325 --5.32094 -0.99557728 -1.9668492 --5.3241811 -0.96241015 -1.9658054 --5.3364511 -0.93084979 -1.9682409 --5.3522329 -0.89979601 -1.972068 --5.3687592 -0.86873311 -1.976243 --0 -0 -0 --5.408689 -0.80720687 -1.9873763 --5.4385991 -0.77758205 -1.9967701 --5.464633 -0.74711323 -2.0047722 --0 -0 -0 --0 -0 -0 --0 -0 -0 --0 -0 -0 --0 -0 -0 --0 -0 -0 --0 -0 -0 --0 -0 -0 --0 -0 -0 --0 -0 -0 --0 -0 -0 --0 -0 -0 --0 -0 -0 --0 -0 -0 --0 -0 -0 --0 -0 -0 --0 -0 -0 --0 -0 -0 --0 -0 -0 --0 -0 -0 --5.3409567 -0.037536558 -1.9400595 --0 -0 -0 --5.3428903 0.028018259 -1.9407554 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --5.2527013 -0.13438906 -1.9715911 --5.2328877 -0.10175557 -1.9637177 --0 -0 -0 --0 -0 -0 --0 -0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --0 0 -0 --5.1187916 1.0126848 -1.9576337 --5.1005878 1.0416461 -1.9529814 --5.0886178 1.0717689 -1.950834 --5.0782957 1.1021799 -1.9494026 --5.0668807 1.1323011 -1.9476131 --5.0571074 1.1627421 -1.9465395 --5.0526042 1.1943942 -1.9476131 --5.0442729 1.2251537 -1.9472553 --5.0430026 1.2576629 -1.9497604 --5.0460467 1.2913597 -1.954055 --5.0298991 1.3201644 -1.950834 --5.0189991 1.3502749 -1.9497604 --5.01332 1.3817897 -1.950834 --5.0020509 1.4117641 -1.9497604 --4.9771466 1.4377662 -1.9433186 --4.9736037 1.4698658 -1.9454659 --4.9591284 1.4987352 -1.9433186 --4.9471622 1.528309 -1.942245 --4.93502 1.5577924 -1.9411713 --4.921814 1.586898 -1.9397398 --4.9102101 1.6164839 -1.9390241 --4.9010844 1.6468793 -1.939382 --4.8917704 1.6772236 -1.9397398 --4.887557 1.7093647 -1.942245 --4.8831334 1.7415172 -1.9447501 --4.8749886 1.7724037 -1.9458238 --4.8657727 1.8029314 -1.9465395 --4.8581128 1.8340722 -1.947971 --4.8345656 1.8591553 -1.9429607 --0 0 -0 --4.7975087 1.9128108 -1.9372346 --4.7804904 1.9401052 -1.9350874 --4.7650437 1.9679757 -1.9336559 --4.7554612 1.9982615 -1.9347295 --4.7491212 2.0299714 -1.9372346 --4.7331505 2.0575845 -1.9358032 --4.7289772 2.0903623 -1.939382 --4.7058411 2.1147473 -1.9350874 --4.6953249 2.1447456 -1.936161 --4.6812296 2.1731222 -1.9358032 --4.6441598 2.1906524 -1.9257826 --4.6390514 2.2231419 -1.9293613 --4.6127291 2.2454352 -1.9239931 --4.6072354 2.2778339 -1.9275719 --4.5798197 2.2993579 -1.9218459 --0 0 -0 --4.5603809 2.3601065 -1.9257826 --4.5466404 2.3884783 -1.9261404 --4.5368433 2.4189665 -1.9282877 --0 0 -0 --4.515821 2.4794011 -1.9322244 --4.5062332 2.5102434 -1.9347295 --4.488307 2.5364678 -1.9336559 --4.4734693 2.5644243 -1.9340137 --4.4592643 2.5927632 -1.9347295 --4.4408584 2.6186533 -1.9336559 --4.4239058 2.6453733 -1.933298 --4.4051952 2.671016 -1.9322244 --4.3887234 2.6979954 -1.9322244 --4.3736711 2.7258611 -1.9329401 --4.3584418 2.7536433 -1.9336559 --4.3430367 2.7813416 -1.9343716 --0 0 -0 --4.3070183 2.8334014 -1.9336559 --4.2918825 2.8613293 -1.9347295 --4.2726974 2.8865645 -1.9340137 --4.2495112 2.9090352 -1.9315087 --4.2308145 2.9345229 -1.9311507 --4.2066169 2.9561329 -1.9282877 --4.191442 2.9840543 -1.9297192 --4.1677513 3.0058935 -1.927214 --4.1560154 3.0363667 -1.9304349 --4.1365557 3.061255 -1.9300771 --4.1049919 3.0770588 -1.9239931 --4.105372 3.1168675 -1.933298 --4.0824685 3.1391535 -1.9315087 --4.0609198 3.1624229 -1.9304349 --4.0443721 3.1895933 -1.9318665 --4.0218062 3.2120185 -1.9304349 --4.0005698 3.235461 -1.9297192 --3.9690824 3.2504821 -1.9239931 --3.9648774 3.2878866 -1.9318665 --3.946774 3.3139539 -1.9329401 --3.9277887 3.339318 -1.9336559 --3.9114716 3.3670104 -1.9358032 --3.8907382 3.3909473 -1.9358032 --3.8656573 3.4110501 -1.9336559 --3.8446548 3.434705 -1.9336559 --3.8221223 3.4569781 -1.9329401 --3.8008387 3.480365 -1.9329401 --3.7746181 3.4991777 -1.9304349 --3.7476292 3.5171609 -1.9275719 --3.7219157 3.536231 -1.9254247 --3.6974556 3.5564127 -1.9239931 --3.673557 3.5770788 -1.9229195 --3.6541996 3.6021738 -1.924351 --3.6320283 3.6245277 -1.924351 --3.6149759 3.6520538 -1.927214 --3.588582 3.6701605 -1.9250668 --3.5640485 3.6900959 -1.9239931 --3.5419841 3.7125704 -1.924351 --3.5152946 3.7301569 -1.9222038 --3.4936132 3.7530229 -1.9229195 --3.4660947 3.7695775 -1.9204143 --3.446667 3.7949212 -1.9225616 --3.4245641 3.8173881 -1.9232774 --3.4072697 3.8453174 -1.9268562 --3.3823812 3.8647463 -1.9261404 --3.360436 3.8875465 -1.927214 --3.3359127 3.9073825 -1.9268562 --3.3088648 3.9242086 -1.9250668 --3.2811384 3.9401326 -1.9229195 --3.255121 3.9580276 -1.9218459 --3.2307737 3.9779263 -1.9218459 --3.2103949 4.0027738 -1.924351 --3.1875131 4.0245929 -1.9254247 --3.166786 4.0492234 -1.9279298 --3.1424515 4.0693173 -1.9282877 --3.1196885 4.0914927 -1.9297192 --3.0950863 4.1113033 -1.9300771 --3.0597742 4.1167283 -1.9236352 --3.0333519 4.1339197 -1.9229195 --3.0008078 4.1426239 -1.9182671 --2.9693575 4.1526036 -1.9143304 --2.9427445 4.1692195 -1.9136146 --0 0 -0 --2.8887146 4.2011704 -1.9118253 --2.8681262 4.2265425 -1.9154041 --2.8452566 4.2487164 -1.9175513 --2.820678 4.2684317 -1.918625 --2.7939241 4.2848763 -1.9182671 --2.7685907 4.3035097 -1.9189829 --2.7371283 4.3125329 -1.9154041 --2.7071464 4.3237066 -1.9128989 --2.679584 4.3386469 -1.9121832 --2.6470821 4.345438 -1.9078885 --2.6290262 4.3760052 -1.9143304 --2.6021259 4.3920546 -1.9143304 --2.5770123 4.4111629 -1.9157619 --2.5540924 4.4341741 -1.9189829 --2.5296037 4.4546337 -1.9211301 --2.5031354 4.4717007 -1.9218459 --2.4742968 4.4845228 -1.9207722 --2.4467337 4.4996204 -1.9207722 --2.41555 4.5079646 -1.9179091 --2.3847919 4.5169215 -1.9154041 --2.3540149 4.5256705 -1.9128989 --2.3274791 4.5425224 -1.9139725 --2.2966177 4.5508828 -1.9114674 --2.2657413 4.5590358 -1.9089622 --2.2438824 4.5854325 -1.9143304 --2.2148938 4.5974321 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a/perception/ground_segmentation/test/test_ransac_ground_filter.cpp b/perception/ground_segmentation/test/test_ransac_ground_filter.cpp new file mode 100644 index 0000000000000..fb66ba9e05b6d --- /dev/null +++ b/perception/ground_segmentation/test/test_ransac_ground_filter.cpp @@ -0,0 +1,228 @@ +// Copyright 2024 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "../src/ransac_ground_filter/node.hpp" +#include "ament_index_cpp/get_package_share_directory.hpp" +#include "tf2_ros/transform_broadcaster.h" + +#include + +#include "geometry_msgs/msg/transform_stamped.hpp" +#include +#include + +#include + +#ifdef ROS_DISTRO_GALACTIC +#include +#include +#else +#include +#include +#endif +#include + +void setPointCloud2Fields(sensor_msgs::msg::PointCloud2 & pointcloud) +{ + pointcloud.fields.resize(4); + pointcloud.fields[0].name = "x"; + pointcloud.fields[1].name = "y"; + pointcloud.fields[2].name = "z"; + pointcloud.fields[3].name = "intensity"; + pointcloud.fields[0].offset = 0; + pointcloud.fields[1].offset = 4; + pointcloud.fields[2].offset = 8; + pointcloud.fields[3].offset = 12; + pointcloud.fields[0].datatype = sensor_msgs::msg::PointField::FLOAT32; + pointcloud.fields[1].datatype = sensor_msgs::msg::PointField::FLOAT32; + pointcloud.fields[2].datatype = sensor_msgs::msg::PointField::FLOAT32; + pointcloud.fields[3].datatype = sensor_msgs::msg::PointField::FLOAT32; + pointcloud.fields[0].count = 1; + pointcloud.fields[1].count = 1; + pointcloud.fields[2].count = 1; + pointcloud.fields[3].count = 1; + pointcloud.height = 1; + pointcloud.point_step = 16; + pointcloud.is_bigendian = false; + pointcloud.is_dense = true; + pointcloud.header.frame_id = "base_link"; + pointcloud.header.stamp.sec = 0; + pointcloud.header.stamp.nanosec = 0; +} + +class RansacGroundFilterTestSuite : public ::testing::Test +{ +protected: + void SetUp() { rclcpp::init(0, nullptr); } + void TearDown() { (void)rclcpp::shutdown(); } +}; + +class RansacGroundFilterTest : public autoware::ground_segmentation::RANSACGroundFilterComponent +{ +public: + explicit RansacGroundFilterTest(const rclcpp::NodeOptions & options) + : RANSACGroundFilterComponent(options) + { + input_pointcloud_pub_ = this->create_publisher( + "/test_ransac_ground_filter/input_cloud", 1); + + output_pointcloud_pub_ = this->create_publisher( + "/test_ransac_ground_filter/output_cloud", 1); + } + + ~RansacGroundFilterTest() {} + + void filter(const PointCloud2ConstPtr & input, const IndicesPtr & indices, PointCloud2 & output) + { + return RANSACGroundFilterComponent::filter(input, indices, output); + } + + rclcpp::Publisher::SharedPtr input_pointcloud_pub_; + rclcpp::Publisher::SharedPtr output_pointcloud_pub_; +}; + +void convertPCL2PointCloud2( + const pcl::PointCloud & pcl_cloud, sensor_msgs::msg::PointCloud2 & cloud) +{ + cloud.height = 1; + cloud.width = pcl_cloud.size(); + cloud.is_dense = true; + cloud.is_bigendian = false; + cloud.point_step = 16; + cloud.row_step = cloud.point_step * cloud.width; + cloud.fields.resize(4); + cloud.fields[0].name = "x"; + cloud.fields[0].offset = 0; + cloud.fields[0].datatype = sensor_msgs::msg::PointField::FLOAT32; + cloud.fields[0].count = 1; + cloud.fields[1].name = "y"; + cloud.fields[1].offset = 4; + cloud.fields[1].datatype = sensor_msgs::msg::PointField::FLOAT32; + cloud.fields[1].count = 1; + cloud.fields[2].name = "z"; + cloud.fields[2].offset = 8; + cloud.fields[2].datatype = sensor_msgs::msg::PointField::FLOAT32; + cloud.fields[2].count = 1; + cloud.fields[3].name = "intensity"; + cloud.fields[3].offset = 12; + cloud.fields[3].datatype = sensor_msgs::msg::PointField::FLOAT32; + cloud.fields[3].count = 1; + cloud.data.resize(cloud.row_step * cloud.height); + for (size_t i = 0; i < pcl_cloud.size(); ++i) { + memcpy( + &cloud.data[i * cloud.point_step + cloud.fields[0].offset], &pcl_cloud[i].x, sizeof(float)); + memcpy( + &cloud.data[i * cloud.point_step + cloud.fields[1].offset], &pcl_cloud[i].y, sizeof(float)); + memcpy( + &cloud.data[i * cloud.point_step + cloud.fields[2].offset], &pcl_cloud[i].z, sizeof(float)); + memcpy( + &cloud.data[i * cloud.point_step + cloud.fields[3].offset], &pcl_cloud[i].intensity, + sizeof(float)); + } +} + +TEST_F(RansacGroundFilterTestSuite, TestCase1) +{ + const auto share_dir = ament_index_cpp::get_package_share_directory("ground_segmentation"); + const auto config_path = share_dir + "/config/ransac_ground_filter.param.yaml"; + YAML::Node config = YAML::LoadFile(config_path); + auto params = config["/**"]["ros__parameters"]; + + std::string base_frame = params["base_frame"].as(); + std::string unit_axis = params["unit_axis"].as(); + int max_iterations = params["max_iterations"].as(); + int min_trial = params["min_trial"].as(); + int min_points = params["min_points"].as(); + double outlier_threshold = params["outlier_threshold"].as(); + double plane_slope_threshold = params["plane_slope_threshold"].as(); + double voxel_size_x = params["voxel_size_x"].as(); + double voxel_size_y = params["voxel_size_y"].as(); + double voxel_size_z = params["voxel_size_z"].as(); + double height_threshold = params["height_threshold"].as(); + bool debug = params["debug"].as(); + + const auto pcd_path = share_dir + "/data/test.pcd"; + pcl::PointCloud cloud; + pcl::io::loadPCDFile(pcd_path, cloud); + + sensor_msgs::msg::PointCloud2::SharedPtr origin_input_msg_ptr(new sensor_msgs::msg::PointCloud2); + convertPCL2PointCloud2(cloud, *origin_input_msg_ptr); + origin_input_msg_ptr->header.frame_id = "velodyne_top"; + + // input cloud frame MUST be base_link + sensor_msgs::msg::PointCloud2::SharedPtr input_msg_ptr(new sensor_msgs::msg::PointCloud2); + + geometry_msgs::msg::TransformStamped t; + t.header.frame_id = "base_link"; + t.child_frame_id = "velodyne_top"; + t.transform.translation.x = 0.6; + t.transform.translation.y = 0; + t.transform.translation.z = 2; + tf2::Quaternion q; + q.setRPY(0.0, 0.0, 0.0); + t.transform.rotation.x = q.x(); + t.transform.rotation.y = q.y(); + t.transform.rotation.z = q.z(); + t.transform.rotation.w = q.w(); + + tf2::doTransform(*origin_input_msg_ptr, *input_msg_ptr, t); + std::vector parameters; + parameters.emplace_back("base_frame", base_frame); + parameters.emplace_back("unit_axis", unit_axis); + parameters.emplace_back("max_iterations", max_iterations); + parameters.emplace_back("min_trial", min_trial); + parameters.emplace_back("min_points", min_points); + parameters.emplace_back("outlier_threshold", outlier_threshold); + parameters.emplace_back("plane_slope_threshold", plane_slope_threshold); + parameters.emplace_back("voxel_size_x", voxel_size_x); + parameters.emplace_back("voxel_size_y", voxel_size_y); + parameters.emplace_back("voxel_size_z", voxel_size_z); + parameters.emplace_back("height_threshold", height_threshold); + parameters.emplace_back("debug", debug); + + rclcpp::NodeOptions node_options; + node_options.parameter_overrides(parameters); + auto ransac_ground_filter_test = std::make_shared(node_options); + ransac_ground_filter_test->input_pointcloud_pub_->publish(*input_msg_ptr); + + sensor_msgs::msg::PointCloud2 out_cloud; + ransac_ground_filter_test->filter(input_msg_ptr, nullptr, out_cloud); + ransac_ground_filter_test->output_pointcloud_pub_->publish(out_cloud); + std::cout << "out_cloud.width: " << out_cloud.width << std::endl; + std::cout << "out_cloud.height: " << out_cloud.height << std::endl; + std::cout << "out_cloud.row_step: " << out_cloud.row_step << std::endl; + std::cout << "out_cloud.point_step: " << out_cloud.point_step << std::endl; + std::cout << "out_cloud.is_dense: " << out_cloud.is_dense << std::endl; + std::cout << "out_cloud.is_bigendian: " << out_cloud.is_bigendian << std::endl; + std::cout << "out_cloud.data.size(): " << out_cloud.data.size() << std::endl; + + // check out_cloud + int effect_num = 0; + int total_num = 0; + const float min_no_ground_point_z = 0.1; // z in base_frame + for (sensor_msgs::PointCloud2ConstIterator iter_x(out_cloud, "x"), iter_y(out_cloud, "y"), + iter_z(out_cloud, "z"), iter_intensity(out_cloud, "intensity"); + iter_x != iter_x.end(); ++iter_x, ++iter_y, ++iter_z, ++iter_intensity) { + const float z = *iter_z; + total_num += 1; + if (z > min_no_ground_point_z) { + effect_num += 1; + } + } + + const float percent = 1.0 * effect_num / total_num; + std::cout << "effect_num=" << effect_num << ",total_num=" << total_num + << ",percentage:" << percent << std::endl; + EXPECT_GE(percent, 0.8); +} diff --git a/perception/ground_segmentation/test/test_ray_ground_filter.cpp b/perception/ground_segmentation/test/test_ray_ground_filter.cpp index 0c1db47a5ae09..4233b8bb18ebe 100644 --- a/perception/ground_segmentation/test/test_ray_ground_filter.cpp +++ b/perception/ground_segmentation/test/test_ray_ground_filter.cpp @@ -12,11 +12,10 @@ // See the License for the specific language governing permissions and // limitations under the License. +#include "../src/ray_ground_filter/node.hpp" #include "ament_index_cpp/get_package_share_directory.hpp" #include "tf2_ros/transform_broadcaster.h" -#include - #include "geometry_msgs/msg/transform_stamped.hpp" #include @@ -37,7 +36,7 @@ class RayGroundFilterComponentTestSuite : public ::testing::Test void TearDown() { (void)rclcpp::shutdown(); } }; // sanity_check -class RayGroundFilterComponentTest : public ground_segmentation::RayGroundFilterComponent +class RayGroundFilterComponentTest : public autoware::ground_segmentation::RayGroundFilterComponent { public: explicit RayGroundFilterComponentTest(const rclcpp::NodeOptions & options) @@ -61,6 +60,46 @@ class RayGroundFilterComponentTest : public ground_segmentation::RayGroundFilter rclcpp::Publisher::SharedPtr output_pointcloud_pub_; }; +void convertPCL2PointCloud2( + const pcl::PointCloud & pcl_cloud, sensor_msgs::msg::PointCloud2 & cloud) +{ + cloud.height = 1; + cloud.width = pcl_cloud.size(); + cloud.is_dense = true; + cloud.is_bigendian = false; + cloud.point_step = 16; + cloud.row_step = cloud.point_step * cloud.width; + cloud.fields.resize(4); + cloud.fields[0].name = "x"; + cloud.fields[0].offset = 0; + cloud.fields[0].datatype = sensor_msgs::msg::PointField::FLOAT32; + cloud.fields[0].count = 1; + cloud.fields[1].name = "y"; + cloud.fields[1].offset = 4; + cloud.fields[1].datatype = sensor_msgs::msg::PointField::FLOAT32; + cloud.fields[1].count = 1; + cloud.fields[2].name = "z"; + cloud.fields[2].offset = 8; + cloud.fields[2].datatype = sensor_msgs::msg::PointField::FLOAT32; + cloud.fields[2].count = 1; + cloud.fields[3].name = "intensity"; + cloud.fields[3].offset = 12; + cloud.fields[3].datatype = sensor_msgs::msg::PointField::FLOAT32; + cloud.fields[3].count = 1; + cloud.data.resize(cloud.row_step * cloud.height); + for (size_t i = 0; i < pcl_cloud.size(); ++i) { + memcpy( + &cloud.data[i * cloud.point_step + cloud.fields[0].offset], &pcl_cloud[i].x, sizeof(float)); + memcpy( + &cloud.data[i * cloud.point_step + cloud.fields[1].offset], &pcl_cloud[i].y, sizeof(float)); + memcpy( + &cloud.data[i * cloud.point_step + cloud.fields[2].offset], &pcl_cloud[i].z, sizeof(float)); + memcpy( + &cloud.data[i * cloud.point_step + cloud.fields[3].offset], &pcl_cloud[i].intensity, + sizeof(float)); + } +} + TEST_F(RayGroundFilterComponentTestSuite, TestCase1) { const auto share_dir = ament_index_cpp::get_package_share_directory("ground_segmentation"); @@ -83,11 +122,11 @@ TEST_F(RayGroundFilterComponentTestSuite, TestCase1) double reclass_distance_threshold_ = params["reclass_distance_threshold"].as(); const auto pcd_path = share_dir + "/data/test.pcd"; - pcl::PointCloud cloud; - pcl::io::loadPCDFile(pcd_path, cloud); + pcl::PointCloud cloud; + pcl::io::loadPCDFile(pcd_path, cloud); sensor_msgs::msg::PointCloud2::SharedPtr origin_input_msg_ptr(new sensor_msgs::msg::PointCloud2); - pcl::toROSMsg(cloud, *origin_input_msg_ptr); + convertPCL2PointCloud2(cloud, *origin_input_msg_ptr); origin_input_msg_ptr->header.frame_id = "velodyne_top"; // input cloud frame MUST be base_link @@ -141,8 +180,8 @@ TEST_F(RayGroundFilterComponentTestSuite, TestCase1) int total_num = 0; const float min_no_ground_point_z = 0.1; // z in base_frame for (sensor_msgs::PointCloud2ConstIterator iter_x(out_cloud, "x"), iter_y(out_cloud, "y"), - iter_z(out_cloud, "z"); - iter_x != iter_x.end(); ++iter_x, ++iter_y, ++iter_z) { + iter_z(out_cloud, "z"), iter_intensity(out_cloud, "intensity"); + iter_x != iter_x.end(); ++iter_x, ++iter_y, ++iter_z, ++iter_intensity) { const float z = *iter_z; total_num += 1; if (z > min_no_ground_point_z) { diff --git a/perception/ground_segmentation/test/test_scan_ground_filter.cpp b/perception/ground_segmentation/test/test_scan_ground_filter.cpp index e48bd36d8c54e..f5faecd59d6d3 100644 --- a/perception/ground_segmentation/test/test_scan_ground_filter.cpp +++ b/perception/ground_segmentation/test/test_scan_ground_filter.cpp @@ -12,11 +12,10 @@ // See the License for the specific language governing permissions and // limitations under the License. +#include "../src/scan_ground_filter/node.hpp" #include "ament_index_cpp/get_package_share_directory.hpp" #include "tf2_ros/transform_broadcaster.h" -#include - #include #include @@ -31,6 +30,45 @@ #include +void convertPCL2PointCloud2( + const pcl::PointCloud & pcl_cloud, sensor_msgs::msg::PointCloud2 & cloud) +{ + cloud.height = 1; + cloud.width = pcl_cloud.size(); + cloud.is_dense = true; + cloud.is_bigendian = false; + cloud.point_step = 16; + cloud.row_step = cloud.point_step * cloud.width; + cloud.fields.resize(4); + cloud.fields[0].name = "x"; + cloud.fields[0].offset = 0; + cloud.fields[0].datatype = sensor_msgs::msg::PointField::FLOAT32; + cloud.fields[0].count = 1; + cloud.fields[1].name = "y"; + cloud.fields[1].offset = 4; + cloud.fields[1].datatype = sensor_msgs::msg::PointField::FLOAT32; + cloud.fields[1].count = 1; + cloud.fields[2].name = "z"; + cloud.fields[2].offset = 8; + cloud.fields[2].datatype = sensor_msgs::msg::PointField::FLOAT32; + cloud.fields[2].count = 1; + cloud.fields[3].name = "intensity"; + cloud.fields[3].offset = 12; + cloud.fields[3].datatype = sensor_msgs::msg::PointField::FLOAT32; + cloud.fields[3].count = 1; + cloud.data.resize(cloud.row_step * cloud.height); + for (size_t i = 0; i < pcl_cloud.size(); ++i) { + memcpy( + &cloud.data[i * cloud.point_step + cloud.fields[0].offset], &pcl_cloud[i].x, sizeof(float)); + memcpy( + &cloud.data[i * cloud.point_step + cloud.fields[1].offset], &pcl_cloud[i].y, sizeof(float)); + memcpy( + &cloud.data[i * cloud.point_step + cloud.fields[2].offset], &pcl_cloud[i].z, sizeof(float)); + memcpy( + &cloud.data[i * cloud.point_step + cloud.fields[3].offset], &pcl_cloud[i].intensity, + sizeof(float)); + } +} class ScanGroundFilterTest : public ::testing::Test { protected: @@ -87,16 +125,17 @@ class ScanGroundFilterTest : public ::testing::Test options.parameter_overrides(parameters); - scan_ground_filter_ = std::make_shared(options); + scan_ground_filter_ = + std::make_shared(options); // read pcd to pointcloud sensor_msgs::msg::PointCloud2::SharedPtr origin_input_msg_ptr = std::make_shared(); const auto share_dir = ament_index_cpp::get_package_share_directory("ground_segmentation"); const auto pcd_path = share_dir + "/data/test.pcd"; - pcl::PointCloud cloud; - pcl::io::loadPCDFile(pcd_path, cloud); - pcl::toROSMsg(cloud, *origin_input_msg_ptr); + pcl::PointCloud cloud; + pcl::io::loadPCDFile(pcd_path, cloud); + convertPCL2PointCloud2(cloud, *origin_input_msg_ptr); origin_input_msg_ptr->header.frame_id = "velodyne_top"; // input cloud frame MUST be base_link @@ -122,7 +161,7 @@ class ScanGroundFilterTest : public ::testing::Test ~ScanGroundFilterTest() override { rclcpp::shutdown(); } public: - std::shared_ptr scan_ground_filter_; + std::shared_ptr scan_ground_filter_; rclcpp::Node::SharedPtr dummy_node_; rclcpp::Publisher::SharedPtr input_pointcloud_pub_; rclcpp::Publisher::SharedPtr output_pointcloud_pub_; diff --git a/perception/image_projection_based_fusion/CMakeLists.txt b/perception/image_projection_based_fusion/CMakeLists.txt index 0f4cb7112f74a..b6dfbaf402cc8 100644 --- a/perception/image_projection_based_fusion/CMakeLists.txt +++ b/perception/image_projection_based_fusion/CMakeLists.txt @@ -127,6 +127,7 @@ if(TRT_AVAIL AND CUDA_AVAIL AND CUDNN_AVAIL) ament_auto_add_library(pointpainting_lib SHARED src/pointpainting_fusion/node.cpp + src/pointpainting_fusion/pointcloud_densification.cpp src/pointpainting_fusion/pointpainting_trt.cpp src/pointpainting_fusion/voxel_generator.cpp ) diff --git a/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/preprocess/pointcloud_densification.hpp b/perception/image_projection_based_fusion/include/image_projection_based_fusion/pointpainting_fusion/pointcloud_densification.hpp similarity index 59% rename from perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/preprocess/pointcloud_densification.hpp rename to perception/image_projection_based_fusion/include/image_projection_based_fusion/pointpainting_fusion/pointcloud_densification.hpp index 70ec420666a3f..03609eb18e689 100644 --- a/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/preprocess/pointcloud_densification.hpp +++ b/perception/image_projection_based_fusion/include/image_projection_based_fusion/pointpainting_fusion/pointcloud_densification.hpp @@ -1,4 +1,4 @@ -// Copyright 2021-2022 AutoCore Ltd., TIER IV, Inc. +// Copyright 2024 TIER IV, Inc. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -12,10 +12,8 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef LIDAR_CENTERPOINT_TVM__PREPROCESS__POINTCLOUD_DENSIFICATION_HPP_ -#define LIDAR_CENTERPOINT_TVM__PREPROCESS__POINTCLOUD_DENSIFICATION_HPP_ - -#include +#ifndef IMAGE_PROJECTION_BASED_FUSION__POINTPAINTING_FUSION__POINTCLOUD_DENSIFICATION_HPP_ +#define IMAGE_PROJECTION_BASED_FUSION__POINTPAINTING_FUSION__POINTCLOUD_DENSIFICATION_HPP_ #include #include @@ -25,43 +23,24 @@ #include #endif +#include + #include #include #include -namespace autoware -{ -namespace perception -{ -namespace lidar_centerpoint_tvm +namespace image_projection_based_fusion { -class LIDAR_CENTERPOINT_TVM_LOCAL DensificationParam -{ -public: - DensificationParam(const std::string & world_frame_id, const uint32_t num_past_frames) - : world_frame_id_(std::move(world_frame_id)), - pointcloud_cache_size_(num_past_frames + /*current frame*/ 1) - { - } - - std::string world_frame_id() const { return world_frame_id_; } - uint32_t pointcloud_cache_size() const { return pointcloud_cache_size_; } - -private: - std::string world_frame_id_; - uint32_t pointcloud_cache_size_{1}; -}; - struct PointCloudWithTransform { sensor_msgs::msg::PointCloud2 pointcloud_msg; Eigen::Affine3f affine_past2world; }; -class LIDAR_CENTERPOINT_TVM_LOCAL PointCloudDensification +class PointCloudDensification { public: - explicit PointCloudDensification(const DensificationParam & param); + explicit PointCloudDensification(const centerpoint::DensificationParam & param); bool enqueuePointCloud( const sensor_msgs::msg::PointCloud2 & input_pointcloud_msg, const tf2_ros::Buffer & tf_buffer); @@ -76,19 +55,18 @@ class LIDAR_CENTERPOINT_TVM_LOCAL PointCloudDensification { return iter == pointcloud_cache_.end(); } + unsigned int pointcloud_cache_size() const { return param_.pointcloud_cache_size(); } private: void enqueue(const sensor_msgs::msg::PointCloud2 & msg, const Eigen::Affine3f & affine); void dequeue(); - DensificationParam param_; + centerpoint::DensificationParam param_; double current_timestamp_{0.0}; Eigen::Affine3f affine_world2current_; std::list pointcloud_cache_; }; -} // namespace lidar_centerpoint_tvm -} // namespace perception -} // namespace autoware +} // namespace image_projection_based_fusion -#endif // LIDAR_CENTERPOINT_TVM__PREPROCESS__POINTCLOUD_DENSIFICATION_HPP_ +#endif // IMAGE_PROJECTION_BASED_FUSION__POINTPAINTING_FUSION__POINTCLOUD_DENSIFICATION_HPP_ diff --git a/perception/image_projection_based_fusion/include/image_projection_based_fusion/pointpainting_fusion/voxel_generator.hpp b/perception/image_projection_based_fusion/include/image_projection_based_fusion/pointpainting_fusion/voxel_generator.hpp old mode 100755 new mode 100644 index d0f44b9d58706..4cdca8e49ac7e --- a/perception/image_projection_based_fusion/include/image_projection_based_fusion/pointpainting_fusion/voxel_generator.hpp +++ b/perception/image_projection_based_fusion/include/image_projection_based_fusion/pointpainting_fusion/voxel_generator.hpp @@ -15,19 +15,35 @@ #ifndef IMAGE_PROJECTION_BASED_FUSION__POINTPAINTING_FUSION__VOXEL_GENERATOR_HPP_ #define IMAGE_PROJECTION_BASED_FUSION__POINTPAINTING_FUSION__VOXEL_GENERATOR_HPP_ +#include +#include #include #include +#include #include namespace image_projection_based_fusion { -class VoxelGenerator : public centerpoint::VoxelGenerator + +class VoxelGenerator { public: - using centerpoint::VoxelGenerator::VoxelGenerator; + explicit VoxelGenerator( + const centerpoint::DensificationParam & param, const centerpoint::CenterPointConfig & config); + + bool enqueuePointCloud( + const sensor_msgs::msg::PointCloud2 & input_pointcloud_msg, const tf2_ros::Buffer & tf_buffer); + + std::size_t generateSweepPoints(std::vector & points); + +protected: + std::unique_ptr pd_ptr_{nullptr}; - std::size_t generateSweepPoints(std::vector & points) override; + centerpoint::CenterPointConfig config_; + std::array range_; + std::array grid_size_; + std::array recip_voxel_size_; }; } // namespace image_projection_based_fusion diff --git a/perception/image_projection_based_fusion/include/image_projection_based_fusion/roi_detected_object_fusion/node.hpp b/perception/image_projection_based_fusion/include/image_projection_based_fusion/roi_detected_object_fusion/node.hpp index 65e03c6bc6316..d61ced593de78 100644 --- a/perception/image_projection_based_fusion/include/image_projection_based_fusion/roi_detected_object_fusion/node.hpp +++ b/perception/image_projection_based_fusion/include/image_projection_based_fusion/roi_detected_object_fusion/node.hpp @@ -18,6 +18,8 @@ #include "autoware/universe_utils/ros/debug_publisher.hpp" #include "image_projection_based_fusion/fusion_node.hpp" +#include + #include "autoware_perception_msgs/msg/object_classification.hpp" #include @@ -45,7 +47,8 @@ class RoiDetectedObjectFusionNode std::map generateDetectedObjectRoIs( const DetectedObjects & input_object_msg, const double image_width, const double image_height, - const Eigen::Affine3d & object2camera_affine, const Eigen::Matrix4d & camera_projection); + const Eigen::Affine3d & object2camera_affine, + const image_geometry::PinholeCameraModel & pinhole_camera_model); void fuseObjectsOnImage( const DetectedObjects & input_object_msg, diff --git a/perception/image_projection_based_fusion/include/image_projection_based_fusion/roi_pointcloud_fusion/node.hpp b/perception/image_projection_based_fusion/include/image_projection_based_fusion/roi_pointcloud_fusion/node.hpp index 4cbc0990352e6..4d4fd8d2dac42 100644 --- a/perception/image_projection_based_fusion/include/image_projection_based_fusion/roi_pointcloud_fusion/node.hpp +++ b/perception/image_projection_based_fusion/include/image_projection_based_fusion/roi_pointcloud_fusion/node.hpp @@ -17,6 +17,8 @@ #include "image_projection_based_fusion/fusion_node.hpp" +#include + #include #include namespace image_projection_based_fusion diff --git a/perception/image_projection_based_fusion/include/image_projection_based_fusion/segmentation_pointcloud_fusion/node.hpp b/perception/image_projection_based_fusion/include/image_projection_based_fusion/segmentation_pointcloud_fusion/node.hpp index c458c17bed793..26bcfd5ed802a 100644 --- a/perception/image_projection_based_fusion/include/image_projection_based_fusion/segmentation_pointcloud_fusion/node.hpp +++ b/perception/image_projection_based_fusion/include/image_projection_based_fusion/segmentation_pointcloud_fusion/node.hpp @@ -17,6 +17,8 @@ #include "image_projection_based_fusion/fusion_node.hpp" +#include + #include #include diff --git a/perception/image_projection_based_fusion/include/image_projection_based_fusion/utils/utils.hpp b/perception/image_projection_based_fusion/include/image_projection_based_fusion/utils/utils.hpp index ffa1666396a1d..107e0d6c179a8 100644 --- a/perception/image_projection_based_fusion/include/image_projection_based_fusion/utils/utils.hpp +++ b/perception/image_projection_based_fusion/include/image_projection_based_fusion/utils/utils.hpp @@ -42,6 +42,7 @@ #include "autoware_perception_msgs/msg/shape.hpp" #include "tier4_perception_msgs/msg/detected_object_with_feature.hpp" +#include #include #include #include @@ -60,6 +61,10 @@ struct PointData float distance; size_t orig_index; }; + +Eigen::Vector2d calcRawImageProjectedPoint( + const image_geometry::PinholeCameraModel & pinhole_camera_model, const cv::Point3d & point3d); + std::optional getTransformStamped( const tf2_ros::Buffer & tf_buffer, const std::string & target_frame_id, const std::string & source_frame_id, const rclcpp::Time & time); diff --git a/perception/image_projection_based_fusion/package.xml b/perception/image_projection_based_fusion/package.xml index 8d5a2ef1fe519..00ffdc8fd5528 100644 --- a/perception/image_projection_based_fusion/package.xml +++ b/perception/image_projection_based_fusion/package.xml @@ -21,6 +21,7 @@ autoware_universe_utils cv_bridge euclidean_cluster + image_geometry image_transport lidar_centerpoint message_filters diff --git a/perception/image_projection_based_fusion/src/pointpainting_fusion/node.cpp b/perception/image_projection_based_fusion/src/pointpainting_fusion/node.cpp index 7973c22cc1d78..a7c83ac7614a8 100644 --- a/perception/image_projection_based_fusion/src/pointpainting_fusion/node.cpp +++ b/perception/image_projection_based_fusion/src/pointpainting_fusion/node.cpp @@ -307,9 +307,9 @@ void PointPaintingFusionNode::fuseOnSingleImage( const auto class_offset = painted_pointcloud_msg.fields.at(4).offset; const auto p_step = painted_pointcloud_msg.point_step; // projection matrix - Eigen::Matrix3f camera_projection; // use only x,y,z - camera_projection << camera_info.p.at(0), camera_info.p.at(1), camera_info.p.at(2), - camera_info.p.at(4), camera_info.p.at(5), camera_info.p.at(6); + image_geometry::PinholeCameraModel pinhole_camera_model; + pinhole_camera_model.fromCameraInfo(camera_info); + Eigen::Vector3f point_lidar, point_camera; /** dc : don't care @@ -342,15 +342,15 @@ dc | dc dc dc dc ||zc| continue; } // project - Eigen::Vector3f normalized_projected_point = camera_projection * Eigen::Vector3f(p_x, p_y, p_z); + Eigen::Vector2d projected_point = + calcRawImageProjectedPoint(pinhole_camera_model, cv::Point3d(p_x, p_y, p_z)); + // iterate 2d bbox for (const auto & feature_object : objects) { sensor_msgs::msg::RegionOfInterest roi = feature_object.feature.roi; // paint current point if it is inside bbox int label2d = feature_object.object.classification.front().label; - if ( - !isUnknown(label2d) && - isInsideBbox(normalized_projected_point.x(), normalized_projected_point.y(), roi, p_z)) { + if (!isUnknown(label2d) && isInsideBbox(projected_point.x(), projected_point.y(), roi, p_z)) { data = &painted_pointcloud_msg.data[0]; auto p_class = reinterpret_cast(&output[stride + class_offset]); for (const auto & cls : isClassTable_) { @@ -361,7 +361,7 @@ dc | dc dc dc dc ||zc| #if 0 // Parallelizing loop don't support push_back if (debugger_) { - debug_image_points.push_back(normalized_projected_point); + debug_image_points.push_back(projected_point); } #endif } diff --git a/perception/lidar_centerpoint_tvm/lib/preprocess/pointcloud_densification.cpp b/perception/image_projection_based_fusion/src/pointpainting_fusion/pointcloud_densification.cpp similarity index 80% rename from perception/lidar_centerpoint_tvm/lib/preprocess/pointcloud_densification.cpp rename to perception/image_projection_based_fusion/src/pointpainting_fusion/pointcloud_densification.cpp index 5562e963d177b..9edf6a73cd59b 100644 --- a/perception/lidar_centerpoint_tvm/lib/preprocess/pointcloud_densification.cpp +++ b/perception/image_projection_based_fusion/src/pointpainting_fusion/pointcloud_densification.cpp @@ -1,4 +1,4 @@ -// Copyright 2021-2022 AutoCore Ltd., TIER IV, Inc. +// Copyright 2024 TIER IV, Inc. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. @@ -12,7 +12,7 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "lidar_centerpoint_tvm/preprocess/pointcloud_densification.hpp" +#include "image_projection_based_fusion/pointpainting_fusion/pointcloud_densification.hpp" #include @@ -28,13 +28,8 @@ #include #include -namespace autoware +namespace { -namespace perception -{ -namespace lidar_centerpoint_tvm -{ - boost::optional getTransform( const tf2_ros::Buffer & tf_buffer, const std::string & target_frame_id, const std::string & source_frame_id, const rclcpp::Time & time) @@ -45,7 +40,7 @@ boost::optional getTransform( target_frame_id, source_frame_id, time, rclcpp::Duration::from_seconds(0.5)); return transform_stamped.transform; } catch (tf2::TransformException & ex) { - RCLCPP_WARN_STREAM(rclcpp::get_logger("lidar_centerpoint_tvm"), ex.what()); + RCLCPP_WARN_STREAM(rclcpp::get_logger("lidar_centerpoint"), ex.what()); return boost::none; } } @@ -57,18 +52,12 @@ Eigen::Affine3f transformToEigen(const geometry_msgs::msg::Transform & t) return a; } -} // namespace lidar_centerpoint_tvm -} // namespace perception -} // namespace autoware +} // namespace -namespace autoware -{ -namespace perception +namespace image_projection_based_fusion { -namespace lidar_centerpoint_tvm -{ - -PointCloudDensification::PointCloudDensification(const DensificationParam & param) : param_(param) +PointCloudDensification::PointCloudDensification(const centerpoint::DensificationParam & param) +: param_(param) { } @@ -89,6 +78,7 @@ bool PointCloudDensification::enqueuePointCloud( } else { enqueue(pointcloud_msg, Eigen::Affine3f::Identity()); } + dequeue(); return true; @@ -110,6 +100,4 @@ void PointCloudDensification::dequeue() } } -} // namespace lidar_centerpoint_tvm -} // namespace perception -} // namespace autoware +} // namespace image_projection_based_fusion diff --git a/perception/image_projection_based_fusion/src/pointpainting_fusion/voxel_generator.cpp b/perception/image_projection_based_fusion/src/pointpainting_fusion/voxel_generator.cpp index ea10cb1237436..cb3fc33d3e022 100755 --- a/perception/image_projection_based_fusion/src/pointpainting_fusion/voxel_generator.cpp +++ b/perception/image_projection_based_fusion/src/pointpainting_fusion/voxel_generator.cpp @@ -18,6 +18,32 @@ namespace image_projection_based_fusion { + +VoxelGenerator::VoxelGenerator( + const centerpoint::DensificationParam & param, const centerpoint::CenterPointConfig & config) +: config_(config) +{ + pd_ptr_ = std::make_unique(param); + range_[0] = config.range_min_x_; + range_[1] = config.range_min_y_; + range_[2] = config.range_min_z_; + range_[3] = config.range_max_x_; + range_[4] = config.range_max_y_; + range_[5] = config.range_max_z_; + grid_size_[0] = config.grid_size_x_; + grid_size_[1] = config.grid_size_y_; + grid_size_[2] = config.grid_size_z_; + recip_voxel_size_[0] = 1 / config.voxel_size_x_; + recip_voxel_size_[1] = 1 / config.voxel_size_y_; + recip_voxel_size_[2] = 1 / config.voxel_size_z_; +} + +bool VoxelGenerator::enqueuePointCloud( + const sensor_msgs::msg::PointCloud2 & input_pointcloud_msg, const tf2_ros::Buffer & tf_buffer) +{ + return pd_ptr_->enqueuePointCloud(input_pointcloud_msg, tf_buffer); +} + size_t VoxelGenerator::generateSweepPoints(std::vector & points) { Eigen::Vector3f point_current, point_past; diff --git a/perception/image_projection_based_fusion/src/roi_cluster_fusion/node.cpp b/perception/image_projection_based_fusion/src/roi_cluster_fusion/node.cpp index bc786dd460d12..51987cbbcab84 100644 --- a/perception/image_projection_based_fusion/src/roi_cluster_fusion/node.cpp +++ b/perception/image_projection_based_fusion/src/roi_cluster_fusion/node.cpp @@ -83,10 +83,8 @@ void RoiClusterFusionNode::fuseOnSingleImage( const DetectedObjectsWithFeature & input_roi_msg, const sensor_msgs::msg::CameraInfo & camera_info, DetectedObjectsWithFeature & output_cluster_msg) { - Eigen::Matrix4d projection; - projection << camera_info.p.at(0), camera_info.p.at(1), camera_info.p.at(2), camera_info.p.at(3), - camera_info.p.at(4), camera_info.p.at(5), camera_info.p.at(6), camera_info.p.at(7), - camera_info.p.at(8), camera_info.p.at(9), camera_info.p.at(10), camera_info.p.at(11); + image_geometry::PinholeCameraModel pinhole_camera_model; + pinhole_camera_model.fromCameraInfo(camera_info); // get transform from cluster frame id to camera optical frame id geometry_msgs::msg::TransformStamped transform_stamped; @@ -133,23 +131,19 @@ void RoiClusterFusionNode::fuseOnSingleImage( continue; } - Eigen::Vector4d projected_point = - projection * Eigen::Vector4d(*iter_x, *iter_y, *iter_z, 1.0); - Eigen::Vector2d normalized_projected_point = Eigen::Vector2d( - projected_point.x() / projected_point.z(), projected_point.y() / projected_point.z()); + Eigen::Vector2d projected_point = + calcRawImageProjectedPoint(pinhole_camera_model, cv::Point3d(*iter_x, *iter_y, *iter_z)); if ( - 0 <= static_cast(normalized_projected_point.x()) && - static_cast(normalized_projected_point.x()) <= - static_cast(camera_info.width) - 1 && - 0 <= static_cast(normalized_projected_point.y()) && - static_cast(normalized_projected_point.y()) <= - static_cast(camera_info.height) - 1) { - min_x = std::min(static_cast(normalized_projected_point.x()), min_x); - min_y = std::min(static_cast(normalized_projected_point.y()), min_y); - max_x = std::max(static_cast(normalized_projected_point.x()), max_x); - max_y = std::max(static_cast(normalized_projected_point.y()), max_y); - projected_points.push_back(normalized_projected_point); - if (debugger_) debugger_->obstacle_points_.push_back(normalized_projected_point); + 0 <= static_cast(projected_point.x()) && + static_cast(projected_point.x()) <= static_cast(camera_info.width) - 1 && + 0 <= static_cast(projected_point.y()) && + static_cast(projected_point.y()) <= static_cast(camera_info.height) - 1) { + min_x = std::min(static_cast(projected_point.x()), min_x); + min_y = std::min(static_cast(projected_point.y()), min_y); + max_x = std::max(static_cast(projected_point.x()), max_x); + max_y = std::max(static_cast(projected_point.y()), max_y); + projected_points.push_back(projected_point); + if (debugger_) debugger_->obstacle_points_.push_back(projected_point); } } if (projected_points.empty()) { diff --git a/perception/image_projection_based_fusion/src/roi_detected_object_fusion/node.cpp b/perception/image_projection_based_fusion/src/roi_detected_object_fusion/node.cpp index 762d3e0ee51b1..7d85ecb2f9200 100644 --- a/perception/image_projection_based_fusion/src/roi_detected_object_fusion/node.cpp +++ b/perception/image_projection_based_fusion/src/roi_detected_object_fusion/node.cpp @@ -86,15 +86,12 @@ void RoiDetectedObjectFusionNode::fuseOnSingleImage( object2camera_affine = transformToEigen(transform_stamped_optional.value().transform); } - Eigen::Matrix4d camera_projection; - camera_projection << camera_info.p.at(0), camera_info.p.at(1), camera_info.p.at(2), - camera_info.p.at(3), camera_info.p.at(4), camera_info.p.at(5), camera_info.p.at(6), - camera_info.p.at(7), camera_info.p.at(8), camera_info.p.at(9), camera_info.p.at(10), - camera_info.p.at(11); + image_geometry::PinholeCameraModel pinhole_camera_model; + pinhole_camera_model.fromCameraInfo(camera_info); const auto object_roi_map = generateDetectedObjectRoIs( input_object_msg, static_cast(camera_info.width), - static_cast(camera_info.height), object2camera_affine, camera_projection); + static_cast(camera_info.height), object2camera_affine, pinhole_camera_model); fuseObjectsOnImage(input_object_msg, input_roi_msg.feature_objects, object_roi_map); if (debugger_) { @@ -109,7 +106,8 @@ void RoiDetectedObjectFusionNode::fuseOnSingleImage( std::map RoiDetectedObjectFusionNode::generateDetectedObjectRoIs( const DetectedObjects & input_object_msg, const double image_width, const double image_height, - const Eigen::Affine3d & object2camera_affine, const Eigen::Matrix4d & camera_projection) + const Eigen::Affine3d & object2camera_affine, + const image_geometry::PinholeCameraModel & pinhole_camera_model) { std::map object_roi_map; int64_t timestamp_nsec = @@ -148,13 +146,8 @@ RoiDetectedObjectFusionNode::generateDetectedObjectRoIs( continue; } - Eigen::Vector2d proj_point; - { - Eigen::Vector4d proj_point_hom = - camera_projection * Eigen::Vector4d(point.x(), point.y(), point.z(), 1.0); - proj_point = Eigen::Vector2d( - proj_point_hom.x() / (proj_point_hom.z()), proj_point_hom.y() / (proj_point_hom.z())); - } + Eigen::Vector2d proj_point = calcRawImageProjectedPoint( + pinhole_camera_model, cv::Point3d(point.x(), point.y(), point.z())); min_x = std::min(proj_point.x(), min_x); min_y = std::min(proj_point.y(), min_y); diff --git a/perception/image_projection_based_fusion/src/roi_pointcloud_fusion/node.cpp b/perception/image_projection_based_fusion/src/roi_pointcloud_fusion/node.cpp index ea5c2005bdd46..7f19402d9e565 100644 --- a/perception/image_projection_based_fusion/src/roi_pointcloud_fusion/node.cpp +++ b/perception/image_projection_based_fusion/src/roi_pointcloud_fusion/node.cpp @@ -101,10 +101,9 @@ void RoiPointCloudFusionNode::fuseOnSingleImage( } // transform pointcloud to camera optical frame id - Eigen::Matrix4d projection; - projection << camera_info.p.at(0), camera_info.p.at(1), camera_info.p.at(2), camera_info.p.at(3), - camera_info.p.at(4), camera_info.p.at(5), camera_info.p.at(6), camera_info.p.at(7), - camera_info.p.at(8), camera_info.p.at(9), camera_info.p.at(10), camera_info.p.at(11); + image_geometry::PinholeCameraModel pinhole_camera_model; + pinhole_camera_model.fromCameraInfo(camera_info); + geometry_msgs::msg::TransformStamped transform_stamped; { const auto transform_stamped_optional = getTransformStamped( @@ -143,10 +142,8 @@ void RoiPointCloudFusionNode::fuseOnSingleImage( if (transformed_z <= 0.0) { continue; } - Eigen::Vector4d projected_point = - projection * Eigen::Vector4d(transformed_x, transformed_y, transformed_z, 1.0); - Eigen::Vector2d normalized_projected_point = Eigen::Vector2d( - projected_point.x() / projected_point.z(), projected_point.y() / projected_point.z()); + Eigen::Vector2d projected_point = calcRawImageProjectedPoint( + pinhole_camera_model, cv::Point3d(transformed_x, transformed_y, transformed_z)); for (std::size_t i = 0; i < output_objs.size(); ++i) { auto & feature_obj = output_objs.at(i); const auto & check_roi = feature_obj.feature.roi; @@ -156,10 +153,9 @@ void RoiPointCloudFusionNode::fuseOnSingleImage( continue; } if ( - check_roi.x_offset <= normalized_projected_point.x() && - check_roi.y_offset <= normalized_projected_point.y() && - check_roi.x_offset + check_roi.width >= normalized_projected_point.x() && - check_roi.y_offset + check_roi.height >= normalized_projected_point.y()) { + check_roi.x_offset <= projected_point.x() && check_roi.y_offset <= projected_point.y() && + check_roi.x_offset + check_roi.width >= projected_point.x() && + check_roi.y_offset + check_roi.height >= projected_point.y()) { std::memcpy( &cluster.data[clusters_data_size.at(i)], &input_pointcloud_msg.data[offset], point_step); clusters_data_size.at(i) += point_step; diff --git a/perception/image_projection_based_fusion/src/segmentation_pointcloud_fusion/node.cpp b/perception/image_projection_based_fusion/src/segmentation_pointcloud_fusion/node.cpp index 0096b91f7a3b6..a3117125a46b1 100644 --- a/perception/image_projection_based_fusion/src/segmentation_pointcloud_fusion/node.cpp +++ b/perception/image_projection_based_fusion/src/segmentation_pointcloud_fusion/node.cpp @@ -65,11 +65,13 @@ void SegmentPointCloudFusionNode::fuseOnSingleImage( if (mask.cols == 0 || mask.rows == 0) { return; } - Eigen::Matrix4d projection; - projection << camera_info.p.at(0), camera_info.p.at(1), camera_info.p.at(2), camera_info.p.at(3), - camera_info.p.at(4), camera_info.p.at(5), camera_info.p.at(6), camera_info.p.at(7), - camera_info.p.at(8), camera_info.p.at(9), camera_info.p.at(10), camera_info.p.at(11), 0.0, 0.0, - 0.0, 1.0; + const int orig_width = camera_info.width; + const int orig_height = camera_info.height; + // resize mask to the same size as the camera image + cv::resize(mask, mask, cv::Size(orig_width, orig_height), 0, 0, cv::INTER_NEAREST); + image_geometry::PinholeCameraModel pinhole_camera_model; + pinhole_camera_model.fromCameraInfo(camera_info); + geometry_msgs::msg::TransformStamped transform_stamped; // transform pointcloud from frame id to camera optical frame id { @@ -113,14 +115,11 @@ void SegmentPointCloudFusionNode::fuseOnSingleImage( continue; } - Eigen::Vector4d projected_point = - projection * Eigen::Vector4d(transformed_x, transformed_y, transformed_z, 1.0); - Eigen::Vector2d normalized_projected_point = Eigen::Vector2d( - projected_point.x() / projected_point.z(), projected_point.y() / projected_point.z()); + Eigen::Vector2d projected_point = calcRawImageProjectedPoint( + pinhole_camera_model, cv::Point3d(transformed_x, transformed_y, transformed_z)); - bool is_inside_image = - normalized_projected_point.x() > 0 && normalized_projected_point.x() < camera_info.width && - normalized_projected_point.y() > 0 && normalized_projected_point.y() < camera_info.height; + bool is_inside_image = projected_point.x() > 0 && projected_point.x() < camera_info.width && + projected_point.y() > 0 && projected_point.y() < camera_info.height; if (!is_inside_image) { copyPointCloud( input_pointcloud_msg, point_step, global_offset, output_cloud, output_pointcloud_size); @@ -129,8 +128,7 @@ void SegmentPointCloudFusionNode::fuseOnSingleImage( // skip filtering pointcloud where semantic id out of the defined list uint8_t semantic_id = mask.at( - static_cast(normalized_projected_point.y()), - static_cast(normalized_projected_point.x())); + static_cast(projected_point.y()), static_cast(projected_point.x())); if (static_cast(semantic_id) >= filter_semantic_label_target_.size()) { copyPointCloud( input_pointcloud_msg, point_step, global_offset, output_cloud, output_pointcloud_size); diff --git a/perception/image_projection_based_fusion/src/utils/utils.cpp b/perception/image_projection_based_fusion/src/utils/utils.cpp index 2e997f3e8e60f..aeec2886a801a 100644 --- a/perception/image_projection_based_fusion/src/utils/utils.cpp +++ b/perception/image_projection_based_fusion/src/utils/utils.cpp @@ -13,8 +13,18 @@ // limitations under the License. #include "image_projection_based_fusion/utils/utils.hpp" + namespace image_projection_based_fusion { +Eigen::Vector2d calcRawImageProjectedPoint( + const image_geometry::PinholeCameraModel & pinhole_camera_model, const cv::Point3d & point3d) +{ + const cv::Point2d rectified_image_point = pinhole_camera_model.project3dToPixel(point3d); + + const cv::Point2d raw_image_point = pinhole_camera_model.unrectifyPoint(rectified_image_point); + + return Eigen::Vector2d(raw_image_point.x, raw_image_point.y); +} std::optional getTransformStamped( const tf2_ros::Buffer & tf_buffer, const std::string & target_frame_id, diff --git a/perception/lidar_apollo_instance_segmentation/package.xml b/perception/lidar_apollo_instance_segmentation/package.xml index 1157b7575933c..cf6f5a514aa0d 100755 --- a/perception/lidar_apollo_instance_segmentation/package.xml +++ b/perception/lidar_apollo_instance_segmentation/package.xml @@ -4,6 +4,7 @@ lidar_apollo_instance_segmentation 0.1.0 lidar_apollo_instance_segmentation + Yoshi Ri Yukihiro Saito Apache License 2.0 diff --git a/perception/lidar_apollo_segmentation_tvm/include/lidar_apollo_segmentation_tvm/feature_generator.hpp b/perception/lidar_apollo_segmentation_tvm/include/lidar_apollo_segmentation_tvm/feature_generator.hpp index 5cecda966d198..84cf7957d2e7e 100644 --- a/perception/lidar_apollo_segmentation_tvm/include/lidar_apollo_segmentation_tvm/feature_generator.hpp +++ b/perception/lidar_apollo_segmentation_tvm/include/lidar_apollo_segmentation_tvm/feature_generator.hpp @@ -37,8 +37,6 @@ namespace lidar_apollo_segmentation_tvm class LIDAR_APOLLO_SEGMENTATION_TVM_LOCAL FeatureGenerator { private: - const bool use_intensity_feature_; - const bool use_constant_feature_; const float min_height_; const float max_height_; std::shared_ptr map_ptr_; diff --git a/perception/lidar_apollo_segmentation_tvm/include/lidar_apollo_segmentation_tvm/feature_map.hpp b/perception/lidar_apollo_segmentation_tvm/include/lidar_apollo_segmentation_tvm/feature_map.hpp index 349dfc7f1f0ad..f78f11c8aed56 100644 --- a/perception/lidar_apollo_segmentation_tvm/include/lidar_apollo_segmentation_tvm/feature_map.hpp +++ b/perception/lidar_apollo_segmentation_tvm/include/lidar_apollo_segmentation_tvm/feature_map.hpp @@ -15,7 +15,7 @@ #ifndef LIDAR_APOLLO_SEGMENTATION_TVM__FEATURE_MAP_HPP_ #define LIDAR_APOLLO_SEGMENTATION_TVM__FEATURE_MAP_HPP_ -#include +#include #include namespace autoware @@ -45,6 +45,7 @@ struct FeatureMapInterface virtual void initializeMap(std::vector & map) = 0; virtual void resetMap(std::vector & map) = 0; explicit FeatureMapInterface(int32_t _channels, int32_t _width, int32_t _height, int32_t _range); + virtual ~FeatureMapInterface() {} }; /// \brief FeatureMap with no extra feature channels. diff --git a/perception/lidar_apollo_segmentation_tvm/include/lidar_apollo_segmentation_tvm/lidar_apollo_segmentation_tvm.hpp b/perception/lidar_apollo_segmentation_tvm/include/lidar_apollo_segmentation_tvm/lidar_apollo_segmentation_tvm.hpp index 7f8c5f51f73a6..044f319c4d335 100644 --- a/perception/lidar_apollo_segmentation_tvm/include/lidar_apollo_segmentation_tvm/lidar_apollo_segmentation_tvm.hpp +++ b/perception/lidar_apollo_segmentation_tvm/include/lidar_apollo_segmentation_tvm/lidar_apollo_segmentation_tvm.hpp @@ -164,12 +164,7 @@ class LIDAR_APOLLO_SEGMENTATION_TVM_PUBLIC ApolloLidarSegmentation tvm_utility::Version version_check() const; private: - const int32_t range_; - const float score_threshold_; const float z_offset_; - const float objectness_thresh_; - const int32_t min_pts_num_; - const float height_thresh_; const pcl::PointCloud::Ptr pcl_pointcloud_ptr_; // Earliest supported model version. const std::array model_version_from{2, 0, 0}; diff --git a/perception/lidar_apollo_segmentation_tvm/package.xml b/perception/lidar_apollo_segmentation_tvm/package.xml index 643a331e54a5a..ca4f3d2645cb9 100755 --- a/perception/lidar_apollo_segmentation_tvm/package.xml +++ b/perception/lidar_apollo_segmentation_tvm/package.xml @@ -5,7 +5,7 @@ 0.1.0 lidar_apollo_segmentation_tvm Ambroise Vincent - Xinyu Wang + Yoshi Ri Apache 2.0 ament_cmake_auto diff --git a/perception/lidar_apollo_segmentation_tvm/src/feature_generator.cpp b/perception/lidar_apollo_segmentation_tvm/src/feature_generator.cpp index edf8276065163..fd038efc60ffb 100644 --- a/perception/lidar_apollo_segmentation_tvm/src/feature_generator.cpp +++ b/perception/lidar_apollo_segmentation_tvm/src/feature_generator.cpp @@ -35,10 +35,7 @@ inline float normalizeIntensity(float intensity) FeatureGenerator::FeatureGenerator( const int32_t width, const int32_t height, const int32_t range, const bool use_intensity_feature, const bool use_constant_feature, const float min_height, const float max_height) -: use_intensity_feature_(use_intensity_feature), - use_constant_feature_(use_constant_feature), - min_height_(min_height), - max_height_(max_height) +: min_height_(min_height), max_height_(max_height) { // select feature map type if (use_constant_feature && use_intensity_feature) { diff --git a/perception/lidar_apollo_segmentation_tvm/src/lidar_apollo_segmentation_tvm.cpp b/perception/lidar_apollo_segmentation_tvm/src/lidar_apollo_segmentation_tvm.cpp index e78cd57b3a4a1..9d41770eb5331 100644 --- a/perception/lidar_apollo_segmentation_tvm/src/lidar_apollo_segmentation_tvm.cpp +++ b/perception/lidar_apollo_segmentation_tvm/src/lidar_apollo_segmentation_tvm.cpp @@ -109,12 +109,7 @@ ApolloLidarSegmentation::ApolloLidarSegmentation( int32_t range, float score_threshold, bool use_intensity_feature, bool use_constant_feature, float z_offset, float min_height, float max_height, float objectness_thresh, int32_t min_pts_num, float height_thresh, const std::string & data_path) -: range_(range), - score_threshold_(score_threshold), - z_offset_(z_offset), - objectness_thresh_(objectness_thresh), - min_pts_num_(min_pts_num), - height_thresh_(height_thresh), +: z_offset_(z_offset), pcl_pointcloud_ptr_(new pcl::PointCloud), PreP(std::make_shared( config, range, use_intensity_feature, use_constant_feature, min_height, max_height)), diff --git a/perception/lidar_apollo_segmentation_tvm/src/log_table.cpp b/perception/lidar_apollo_segmentation_tvm/src/log_table.cpp index a4b967de2e9bc..de2f1734e0733 100644 --- a/perception/lidar_apollo_segmentation_tvm/src/log_table.cpp +++ b/perception/lidar_apollo_segmentation_tvm/src/log_table.cpp @@ -46,7 +46,7 @@ float calcApproximateLog(float num) return log_table.data[integer_num]; } } - return std::log(1.0f + num); + return std::log1p(num); } } // namespace lidar_apollo_segmentation_tvm } // namespace perception diff --git a/perception/lidar_apollo_segmentation_tvm_nodes/package.xml b/perception/lidar_apollo_segmentation_tvm_nodes/package.xml index b6c6cb261eb29..7d3ac6fbe5ac7 100755 --- a/perception/lidar_apollo_segmentation_tvm_nodes/package.xml +++ b/perception/lidar_apollo_segmentation_tvm_nodes/package.xml @@ -5,7 +5,7 @@ 0.1.0 lidar_apollo_segmentation_tvm_nodes Ambroise Vincent - Xinyu Wang + Yoshi Ri Apache 2.0 ament_cmake diff --git a/perception/lidar_centerpoint/CMakeLists.txt b/perception/lidar_centerpoint/CMakeLists.txt index 8a7f4c6107c67..915224a50d53f 100644 --- a/perception/lidar_centerpoint/CMakeLists.txt +++ b/perception/lidar_centerpoint/CMakeLists.txt @@ -159,49 +159,52 @@ if(TRT_AVAIL AND CUDA_AVAIL AND CUDNN_AVAIL) ament_auto_add_gtest(test_nms test/test_nms.cpp ) - ament_auto_add_gtest(test_voxel_generator - test/test_voxel_generator.cpp - ) - - add_executable(test_preprocess_kernel - test/test_preprocess_kernel.cpp - lib/utils.cpp - ) - - target_include_directories(test_preprocess_kernel PUBLIC - ${test_preprocess_kernel_SOURCE_DIR} - ) - - target_link_libraries(test_preprocess_kernel - centerpoint_cuda_lib - gtest - gtest_main - ) - - ament_add_test(test_preprocess_kernel - GENERATE_RESULT_FOR_RETURN_CODE_ZERO - COMMAND "$" - ) - - add_executable(test_postprocess_kernel - test/test_postprocess_kernel.cpp - lib/utils.cpp - ) - - target_include_directories(test_postprocess_kernel PUBLIC - ${test_postprocess_kernel_SOURCE_DIR} - ) - - target_link_libraries(test_postprocess_kernel - centerpoint_cuda_lib - gtest - gtest_main - ) - ament_add_test(test_postprocess_kernel - GENERATE_RESULT_FOR_RETURN_CODE_ZERO - COMMAND "$" - ) + # Temporary disabled, tracked by: + # https://github.com/autowarefoundation/autoware.universe/issues/7724 +# ament_auto_add_gtest(test_voxel_generator +# test/test_voxel_generator.cpp +# ) +# +# add_executable(test_preprocess_kernel +# test/test_preprocess_kernel.cpp +# lib/utils.cpp +# ) +# +# target_include_directories(test_preprocess_kernel PUBLIC +# ${test_preprocess_kernel_SOURCE_DIR} +# ) +# +# target_link_libraries(test_preprocess_kernel +# centerpoint_cuda_lib +# gtest +# gtest_main +# ) +# +# ament_add_test(test_preprocess_kernel +# GENERATE_RESULT_FOR_RETURN_CODE_ZERO +# COMMAND "$" +# ) +# +# add_executable(test_postprocess_kernel +# test/test_postprocess_kernel.cpp +# lib/utils.cpp +# ) +# +# target_include_directories(test_postprocess_kernel PUBLIC +# ${test_postprocess_kernel_SOURCE_DIR} +# ) +# +# target_link_libraries(test_postprocess_kernel +# centerpoint_cuda_lib +# gtest +# gtest_main +# ) +# +# ament_add_test(test_postprocess_kernel +# GENERATE_RESULT_FOR_RETURN_CODE_ZERO +# COMMAND "$" +# ) endif() diff --git a/perception/lidar_centerpoint/include/lidar_centerpoint/centerpoint_trt.hpp b/perception/lidar_centerpoint/include/lidar_centerpoint/centerpoint_trt.hpp index 8cf250be0c049..55157f70fcfc3 100644 --- a/perception/lidar_centerpoint/include/lidar_centerpoint/centerpoint_trt.hpp +++ b/perception/lidar_centerpoint/include/lidar_centerpoint/centerpoint_trt.hpp @@ -15,15 +15,14 @@ #ifndef LIDAR_CENTERPOINT__CENTERPOINT_TRT_HPP_ #define LIDAR_CENTERPOINT__CENTERPOINT_TRT_HPP_ -#include -#include -#include -#include - -#include - -#include -#include +#include "lidar_centerpoint/cuda_utils.hpp" +#include "lidar_centerpoint/network/network_trt.hpp" +#include "lidar_centerpoint/postprocess/postprocess_kernel.hpp" +#include "lidar_centerpoint/preprocess/voxel_generator.hpp" +#include "pcl/point_cloud.h" +#include "pcl/point_types.h" + +#include "sensor_msgs/msg/point_cloud2.hpp" #include #include diff --git a/perception/lidar_centerpoint/include/lidar_centerpoint/cuda_utils.hpp b/perception/lidar_centerpoint/include/lidar_centerpoint/cuda_utils.hpp index 5ab26d75a0a41..efe3bbb9a5482 100644 --- a/perception/lidar_centerpoint/include/lidar_centerpoint/cuda_utils.hpp +++ b/perception/lidar_centerpoint/include/lidar_centerpoint/cuda_utils.hpp @@ -42,7 +42,7 @@ #ifndef LIDAR_CENTERPOINT__CUDA_UTILS_HPP_ #define LIDAR_CENTERPOINT__CUDA_UTILS_HPP_ -#include +#include "cuda_runtime_api.h" #include #include diff --git a/perception/lidar_centerpoint/include/lidar_centerpoint/detection_class_remapper.hpp b/perception/lidar_centerpoint/include/lidar_centerpoint/detection_class_remapper.hpp index cd1a0e58e115e..d961dd998af76 100644 --- a/perception/lidar_centerpoint/include/lidar_centerpoint/detection_class_remapper.hpp +++ b/perception/lidar_centerpoint/include/lidar_centerpoint/detection_class_remapper.hpp @@ -17,10 +17,10 @@ #include -#include -#include -#include -#include +#include "autoware_perception_msgs/msg/detected_object_kinematics.hpp" +#include "autoware_perception_msgs/msg/detected_objects.hpp" +#include "autoware_perception_msgs/msg/object_classification.hpp" +#include "autoware_perception_msgs/msg/shape.hpp" #include diff --git a/perception/lidar_centerpoint/include/lidar_centerpoint/network/network_trt.hpp b/perception/lidar_centerpoint/include/lidar_centerpoint/network/network_trt.hpp index 639aa0ba8bad3..1bf77248efe08 100644 --- a/perception/lidar_centerpoint/include/lidar_centerpoint/network/network_trt.hpp +++ b/perception/lidar_centerpoint/include/lidar_centerpoint/network/network_trt.hpp @@ -15,8 +15,8 @@ #ifndef LIDAR_CENTERPOINT__NETWORK__NETWORK_TRT_HPP_ #define LIDAR_CENTERPOINT__NETWORK__NETWORK_TRT_HPP_ -#include -#include +#include "lidar_centerpoint/centerpoint_config.hpp" +#include "lidar_centerpoint/network/tensorrt_wrapper.hpp" #include diff --git a/perception/lidar_centerpoint/include/lidar_centerpoint/network/scatter_kernel.hpp b/perception/lidar_centerpoint/include/lidar_centerpoint/network/scatter_kernel.hpp index 3dd00c25fd9e7..ad5e222ce01e8 100644 --- a/perception/lidar_centerpoint/include/lidar_centerpoint/network/scatter_kernel.hpp +++ b/perception/lidar_centerpoint/include/lidar_centerpoint/network/scatter_kernel.hpp @@ -15,8 +15,8 @@ #ifndef LIDAR_CENTERPOINT__NETWORK__SCATTER_KERNEL_HPP_ #define LIDAR_CENTERPOINT__NETWORK__SCATTER_KERNEL_HPP_ -#include -#include +#include "cuda.h" +#include "cuda_runtime_api.h" namespace centerpoint { diff --git a/perception/lidar_centerpoint/include/lidar_centerpoint/network/tensorrt_wrapper.hpp b/perception/lidar_centerpoint/include/lidar_centerpoint/network/tensorrt_wrapper.hpp index 7f55ab6f5e414..4af5aac7ff7fe 100644 --- a/perception/lidar_centerpoint/include/lidar_centerpoint/network/tensorrt_wrapper.hpp +++ b/perception/lidar_centerpoint/include/lidar_centerpoint/network/tensorrt_wrapper.hpp @@ -15,10 +15,9 @@ #ifndef LIDAR_CENTERPOINT__NETWORK__TENSORRT_WRAPPER_HPP_ #define LIDAR_CENTERPOINT__NETWORK__TENSORRT_WRAPPER_HPP_ -#include -#include - -#include +#include "NvInfer.h" +#include "lidar_centerpoint/centerpoint_config.hpp" +#include "tensorrt_common/tensorrt_common.hpp" #include #include @@ -32,7 +31,7 @@ class TensorRTWrapper public: explicit TensorRTWrapper(const CenterPointConfig & config); - ~TensorRTWrapper(); + virtual ~TensorRTWrapper(); bool init( const std::string & onnx_path, const std::string & engine_path, const std::string & precision); diff --git a/perception/lidar_centerpoint/include/lidar_centerpoint/node.hpp b/perception/lidar_centerpoint/include/lidar_centerpoint/node.hpp index 4ccfa908e12d7..154ff97cdb887 100644 --- a/perception/lidar_centerpoint/include/lidar_centerpoint/node.hpp +++ b/perception/lidar_centerpoint/include/lidar_centerpoint/node.hpp @@ -15,13 +15,13 @@ #ifndef LIDAR_CENTERPOINT__NODE_HPP_ #define LIDAR_CENTERPOINT__NODE_HPP_ +#include "lidar_centerpoint/centerpoint_trt.hpp" +#include "lidar_centerpoint/detection_class_remapper.hpp" #include "lidar_centerpoint/postprocess/non_maximum_suppression.hpp" #include #include #include -#include -#include #include #include diff --git a/perception/lidar_centerpoint/include/lidar_centerpoint/postprocess/circle_nms_kernel.hpp b/perception/lidar_centerpoint/include/lidar_centerpoint/postprocess/circle_nms_kernel.hpp index cd2d71d33806d..5b55636a3eec4 100644 --- a/perception/lidar_centerpoint/include/lidar_centerpoint/postprocess/circle_nms_kernel.hpp +++ b/perception/lidar_centerpoint/include/lidar_centerpoint/postprocess/circle_nms_kernel.hpp @@ -15,9 +15,8 @@ #ifndef LIDAR_CENTERPOINT__POSTPROCESS__CIRCLE_NMS_KERNEL_HPP_ #define LIDAR_CENTERPOINT__POSTPROCESS__CIRCLE_NMS_KERNEL_HPP_ -#include - -#include +#include "lidar_centerpoint/utils.hpp" +#include "thrust/device_vector.h" namespace centerpoint { diff --git a/perception/lidar_centerpoint/include/lidar_centerpoint/postprocess/postprocess_kernel.hpp b/perception/lidar_centerpoint/include/lidar_centerpoint/postprocess/postprocess_kernel.hpp index d790e896dea9a..e15d3022c947c 100644 --- a/perception/lidar_centerpoint/include/lidar_centerpoint/postprocess/postprocess_kernel.hpp +++ b/perception/lidar_centerpoint/include/lidar_centerpoint/postprocess/postprocess_kernel.hpp @@ -15,12 +15,11 @@ #ifndef LIDAR_CENTERPOINT__POSTPROCESS__POSTPROCESS_KERNEL_HPP_ #define LIDAR_CENTERPOINT__POSTPROCESS__POSTPROCESS_KERNEL_HPP_ -#include -#include - -#include -#include -#include +#include "cuda.h" +#include "cuda_runtime_api.h" +#include "lidar_centerpoint/centerpoint_config.hpp" +#include "lidar_centerpoint/utils.hpp" +#include "thrust/device_vector.h" #include diff --git a/perception/lidar_centerpoint/include/lidar_centerpoint/preprocess/pointcloud_densification.hpp b/perception/lidar_centerpoint/include/lidar_centerpoint/preprocess/pointcloud_densification.hpp index 209fc74e6c58f..c1d6a6b060955 100644 --- a/perception/lidar_centerpoint/include/lidar_centerpoint/preprocess/pointcloud_densification.hpp +++ b/perception/lidar_centerpoint/include/lidar_centerpoint/preprocess/pointcloud_densification.hpp @@ -15,17 +15,19 @@ #ifndef LIDAR_CENTERPOINT__PREPROCESS__POINTCLOUD_DENSIFICATION_HPP_ #define LIDAR_CENTERPOINT__PREPROCESS__POINTCLOUD_DENSIFICATION_HPP_ -#include -#include -#ifdef ROS_DISTRO_GALACTIC -#include -#else -#include -#endif +#include "tf2_ros/buffer.h" +#include "tf2_ros/transform_listener.h" #include #include #include +#ifdef ROS_DISTRO_GALACTIC +#include "tf2_sensor_msgs/tf2_sensor_msgs.h" +#else +#include "tf2_sensor_msgs/tf2_sensor_msgs.hpp" +#endif + +#include "lidar_centerpoint/cuda_utils.hpp" namespace centerpoint { @@ -48,7 +50,10 @@ class DensificationParam struct PointCloudWithTransform { - sensor_msgs::msg::PointCloud2 pointcloud_msg; + cuda::unique_ptr points_d{nullptr}; + std_msgs::msg::Header header; + size_t num_points{0}; + size_t point_step{0}; Eigen::Affine3f affine_past2world; }; @@ -58,7 +63,8 @@ class PointCloudDensification explicit PointCloudDensification(const DensificationParam & param); bool enqueuePointCloud( - const sensor_msgs::msg::PointCloud2 & input_pointcloud_msg, const tf2_ros::Buffer & tf_buffer); + const sensor_msgs::msg::PointCloud2 & input_pointcloud_msg, const tf2_ros::Buffer & tf_buffer, + cudaStream_t stream); double getCurrentTimestamp() const { return current_timestamp_; } Eigen::Affine3f getAffineWorldToCurrent() const { return affine_world2current_; } @@ -73,7 +79,8 @@ class PointCloudDensification unsigned int pointcloud_cache_size() const { return param_.pointcloud_cache_size(); } private: - void enqueue(const sensor_msgs::msg::PointCloud2 & msg, const Eigen::Affine3f & affine); + void enqueue( + const sensor_msgs::msg::PointCloud2 & msg, const Eigen::Affine3f & affine, cudaStream_t stream); void dequeue(); DensificationParam param_; diff --git a/perception/lidar_centerpoint/include/lidar_centerpoint/preprocess/preprocess_kernel.hpp b/perception/lidar_centerpoint/include/lidar_centerpoint/preprocess/preprocess_kernel.hpp index 9488b67475509..3abcd89cb5c55 100644 --- a/perception/lidar_centerpoint/include/lidar_centerpoint/preprocess/preprocess_kernel.hpp +++ b/perception/lidar_centerpoint/include/lidar_centerpoint/preprocess/preprocess_kernel.hpp @@ -15,11 +15,15 @@ #ifndef LIDAR_CENTERPOINT__PREPROCESS__PREPROCESS_KERNEL_HPP_ #define LIDAR_CENTERPOINT__PREPROCESS__PREPROCESS_KERNEL_HPP_ -#include -#include +#include "cuda.h" +#include "cuda_runtime_api.h" namespace centerpoint { +cudaError_t generateSweepPoints_launch( + const float * input_points, size_t points_size, int input_point_step, float time_lag, + const float * transform, int num_features, float * output_points, cudaStream_t stream); + cudaError_t generateVoxels_random_launch( const float * points, size_t points_size, float min_x_range, float max_x_range, float min_y_range, float max_y_range, float min_z_range, float max_z_range, float pillar_x_size, float pillar_y_size, diff --git a/perception/lidar_centerpoint/include/lidar_centerpoint/preprocess/voxel_generator.hpp b/perception/lidar_centerpoint/include/lidar_centerpoint/preprocess/voxel_generator.hpp index be15d51e91715..3ade5e677cdbe 100644 --- a/perception/lidar_centerpoint/include/lidar_centerpoint/preprocess/voxel_generator.hpp +++ b/perception/lidar_centerpoint/include/lidar_centerpoint/preprocess/voxel_generator.hpp @@ -15,10 +15,10 @@ #ifndef LIDAR_CENTERPOINT__PREPROCESS__VOXEL_GENERATOR_HPP_ #define LIDAR_CENTERPOINT__PREPROCESS__VOXEL_GENERATOR_HPP_ -#include -#include +#include "lidar_centerpoint/centerpoint_config.hpp" +#include "lidar_centerpoint/preprocess/pointcloud_densification.hpp" -#include +#include "sensor_msgs/msg/point_cloud2.hpp" #include #include @@ -31,10 +31,11 @@ class VoxelGeneratorTemplate explicit VoxelGeneratorTemplate( const DensificationParam & param, const CenterPointConfig & config); - virtual std::size_t generateSweepPoints(std::vector & points) = 0; + virtual std::size_t generateSweepPoints(float * d_points, cudaStream_t stream) = 0; bool enqueuePointCloud( - const sensor_msgs::msg::PointCloud2 & input_pointcloud_msg, const tf2_ros::Buffer & tf_buffer); + const sensor_msgs::msg::PointCloud2 & input_pointcloud_msg, const tf2_ros::Buffer & tf_buffer, + cudaStream_t stream); protected: std::unique_ptr pd_ptr_{nullptr}; @@ -50,7 +51,7 @@ class VoxelGenerator : public VoxelGeneratorTemplate public: using VoxelGeneratorTemplate::VoxelGeneratorTemplate; - std::size_t generateSweepPoints(std::vector & points) override; + std::size_t generateSweepPoints(float * d_points, cudaStream_t stream) override; }; } // namespace centerpoint diff --git a/perception/lidar_centerpoint/include/lidar_centerpoint/ros_utils.hpp b/perception/lidar_centerpoint/include/lidar_centerpoint/ros_utils.hpp index e33d86cd0aba7..484fbcfd36657 100644 --- a/perception/lidar_centerpoint/include/lidar_centerpoint/ros_utils.hpp +++ b/perception/lidar_centerpoint/include/lidar_centerpoint/ros_utils.hpp @@ -17,12 +17,9 @@ // ros packages cannot be included from cuda. -#include +#include "lidar_centerpoint/utils.hpp" -#include -#include -#include -#include +#include "autoware_perception_msgs/msg/detected_objects.hpp" #include #include diff --git a/perception/lidar_centerpoint/lib/centerpoint_trt.cpp b/perception/lidar_centerpoint/lib/centerpoint_trt.cpp index f7b0fe5e0c58f..6f379167b0e71 100644 --- a/perception/lidar_centerpoint/lib/centerpoint_trt.cpp +++ b/perception/lidar_centerpoint/lib/centerpoint_trt.cpp @@ -14,10 +14,11 @@ #include "lidar_centerpoint/centerpoint_trt.hpp" +#include "lidar_centerpoint/centerpoint_config.hpp" +#include "lidar_centerpoint/network/scatter_kernel.hpp" +#include "lidar_centerpoint/preprocess/preprocess_kernel.hpp" + #include -#include -#include -#include #include #include @@ -130,14 +131,11 @@ bool CenterPointTRT::detect( bool CenterPointTRT::preprocess( const sensor_msgs::msg::PointCloud2 & input_pointcloud_msg, const tf2_ros::Buffer & tf_buffer) { - bool is_success = vg_ptr_->enqueuePointCloud(input_pointcloud_msg, tf_buffer); + bool is_success = vg_ptr_->enqueuePointCloud(input_pointcloud_msg, tf_buffer, stream_); if (!is_success) { return false; } - const auto count = vg_ptr_->generateSweepPoints(points_); - CHECK_CUDA_ERROR(cudaMemcpyAsync( - points_d_.get(), points_.data(), count * config_.point_feature_size_ * sizeof(float), - cudaMemcpyHostToDevice, stream_)); + const auto count = vg_ptr_->generateSweepPoints(points_d_.get(), stream_); CHECK_CUDA_ERROR(cudaMemsetAsync(num_voxels_d_.get(), 0, sizeof(unsigned int), stream_)); CHECK_CUDA_ERROR( cudaMemsetAsync(voxels_buffer_d_.get(), 0, voxels_buffer_size_ * sizeof(float), stream_)); diff --git a/perception/lidar_centerpoint/lib/detection_class_remapper.cpp b/perception/lidar_centerpoint/lib/detection_class_remapper.cpp index ecfbb4360ecf0..9805fc7a661d1 100644 --- a/perception/lidar_centerpoint/lib/detection_class_remapper.cpp +++ b/perception/lidar_centerpoint/lib/detection_class_remapper.cpp @@ -12,7 +12,7 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include +#include "lidar_centerpoint/detection_class_remapper.hpp" namespace centerpoint { diff --git a/perception/lidar_centerpoint/lib/network/scatter_kernel.cu b/perception/lidar_centerpoint/lib/network/scatter_kernel.cu index 09cc83bf606fe..f139e230426ca 100644 --- a/perception/lidar_centerpoint/lib/network/scatter_kernel.cu +++ b/perception/lidar_centerpoint/lib/network/scatter_kernel.cu @@ -13,8 +13,7 @@ // limitations under the License. #include "lidar_centerpoint/network/scatter_kernel.hpp" - -#include +#include "lidar_centerpoint/utils.hpp" namespace { diff --git a/perception/lidar_centerpoint/lib/network/tensorrt_wrapper.cpp b/perception/lidar_centerpoint/lib/network/tensorrt_wrapper.cpp index 91fcce9a80f78..bdfde42a12a02 100644 --- a/perception/lidar_centerpoint/lib/network/tensorrt_wrapper.cpp +++ b/perception/lidar_centerpoint/lib/network/tensorrt_wrapper.cpp @@ -14,7 +14,7 @@ #include "lidar_centerpoint/network/tensorrt_wrapper.hpp" -#include +#include "NvOnnxParser.h" #include #include diff --git a/perception/lidar_centerpoint/lib/postprocess/circle_nms_kernel.cu b/perception/lidar_centerpoint/lib/postprocess/circle_nms_kernel.cu index 398e75a55c44b..c208eefe135fb 100644 --- a/perception/lidar_centerpoint/lib/postprocess/circle_nms_kernel.cu +++ b/perception/lidar_centerpoint/lib/postprocess/circle_nms_kernel.cu @@ -21,12 +21,10 @@ Written by Shaoshuai Shi All Rights Reserved 2019-2020. */ +#include "lidar_centerpoint/cuda_utils.hpp" #include "lidar_centerpoint/postprocess/circle_nms_kernel.hpp" - -#include -#include - -#include +#include "lidar_centerpoint/utils.hpp" +#include "thrust/host_vector.h" namespace { diff --git a/perception/lidar_centerpoint/lib/postprocess/non_maximum_suppression.cpp b/perception/lidar_centerpoint/lib/postprocess/non_maximum_suppression.cpp index 66e53310be263..16f4fc89bbcd6 100644 --- a/perception/lidar_centerpoint/lib/postprocess/non_maximum_suppression.cpp +++ b/perception/lidar_centerpoint/lib/postprocess/non_maximum_suppression.cpp @@ -14,9 +14,10 @@ #include "lidar_centerpoint/postprocess/non_maximum_suppression.hpp" -#include "autoware/universe_utils/geometry/geometry.hpp" -#include "object_recognition_utils/geometry.hpp" -#include "object_recognition_utils/object_recognition_utils.hpp" +#include +#include +#include + namespace centerpoint { diff --git a/perception/lidar_centerpoint/lib/postprocess/postprocess_kernel.cu b/perception/lidar_centerpoint/lib/postprocess/postprocess_kernel.cu index 34bbd2811041c..12835bab038a6 100644 --- a/perception/lidar_centerpoint/lib/postprocess/postprocess_kernel.cu +++ b/perception/lidar_centerpoint/lib/postprocess/postprocess_kernel.cu @@ -13,12 +13,10 @@ // limitations under the License. #include "lidar_centerpoint/postprocess/circle_nms_kernel.hpp" - -#include - -#include -#include -#include +#include "lidar_centerpoint/postprocess/postprocess_kernel.hpp" +#include "thrust/count.h" +#include "thrust/device_vector.h" +#include "thrust/sort.h" namespace { diff --git a/perception/lidar_centerpoint/lib/preprocess/pointcloud_densification.cpp b/perception/lidar_centerpoint/lib/preprocess/pointcloud_densification.cpp index 66c348fda50b4..2859d58d8f669 100644 --- a/perception/lidar_centerpoint/lib/preprocess/pointcloud_densification.cpp +++ b/perception/lidar_centerpoint/lib/preprocess/pointcloud_densification.cpp @@ -14,20 +14,19 @@ #include "lidar_centerpoint/preprocess/pointcloud_densification.hpp" -#include +#include "pcl_conversions/pcl_conversions.h" +#include "pcl_ros/transforms.hpp" -#include +#include "boost/optional.hpp" -#include +#include +#include #ifdef ROS_DISTRO_GALACTIC -#include +#include "tf2_eigen/tf2_eigen.h" #else -#include +#include "tf2_eigen/tf2_eigen.hpp" #endif -#include -#include - namespace { boost::optional getTransform( @@ -61,7 +60,8 @@ PointCloudDensification::PointCloudDensification(const DensificationParam & para } bool PointCloudDensification::enqueuePointCloud( - const sensor_msgs::msg::PointCloud2 & pointcloud_msg, const tf2_ros::Buffer & tf_buffer) + const sensor_msgs::msg::PointCloud2 & pointcloud_msg, const tf2_ros::Buffer & tf_buffer, + cudaStream_t stream) { const auto header = pointcloud_msg.header; @@ -73,9 +73,9 @@ bool PointCloudDensification::enqueuePointCloud( } auto affine_world2current = transformToEigen(transform_world2current.get()); - enqueue(pointcloud_msg, affine_world2current); + enqueue(pointcloud_msg, affine_world2current, stream); } else { - enqueue(pointcloud_msg, Eigen::Affine3f::Identity()); + enqueue(pointcloud_msg, Eigen::Affine3f::Identity(), stream); } dequeue(); @@ -84,12 +84,24 @@ bool PointCloudDensification::enqueuePointCloud( } void PointCloudDensification::enqueue( - const sensor_msgs::msg::PointCloud2 & msg, const Eigen::Affine3f & affine_world2current) + const sensor_msgs::msg::PointCloud2 & msg, const Eigen::Affine3f & affine_world2current, + cudaStream_t stream) { affine_world2current_ = affine_world2current; current_timestamp_ = rclcpp::Time(msg.header.stamp).seconds(); - PointCloudWithTransform pointcloud = {msg, affine_world2current.inverse()}; - pointcloud_cache_.push_front(pointcloud); + + assert(sizeof(uint8_t) * msg.width * msg.height * msg.point_step % sizeof(float) == 0); + auto points_d = cuda::make_unique( + sizeof(uint8_t) * msg.width * msg.height * msg.point_step / sizeof(float)); + CHECK_CUDA_ERROR(cudaMemcpyAsync( + points_d.get(), msg.data.data(), sizeof(uint8_t) * msg.width * msg.height * msg.point_step, + cudaMemcpyHostToDevice, stream)); + + PointCloudWithTransform pointcloud = { + std::move(points_d), msg.header, msg.width * msg.height, msg.point_step, + affine_world2current.inverse()}; + + pointcloud_cache_.push_front(std::move(pointcloud)); } void PointCloudDensification::dequeue() diff --git a/perception/lidar_centerpoint/lib/preprocess/preprocess_kernel.cu b/perception/lidar_centerpoint/lib/preprocess/preprocess_kernel.cu index 218aaee125c02..4e78176739c26 100644 --- a/perception/lidar_centerpoint/lib/preprocess/preprocess_kernel.cu +++ b/perception/lidar_centerpoint/lib/preprocess/preprocess_kernel.cu @@ -28,9 +28,11 @@ * limitations under the License. */ +#include "lidar_centerpoint/cuda_utils.hpp" #include "lidar_centerpoint/preprocess/preprocess_kernel.hpp" +#include "lidar_centerpoint/utils.hpp" -#include +#include namespace { @@ -41,6 +43,51 @@ const std::size_t ENCODER_IN_FEATURE_SIZE = 9; // the same as encoder_in_featur namespace centerpoint { + +__global__ void generateSweepPoints_kernel( + const float * input_points, size_t points_size, int input_point_step, float time_lag, + const float * transform_array, int num_features, float * output_points) +{ + int point_idx = blockIdx.x * blockDim.x + threadIdx.x; + if (point_idx >= points_size) return; + + const float input_x = input_points[point_idx * input_point_step + 0]; + const float input_y = input_points[point_idx * input_point_step + 1]; + const float input_z = input_points[point_idx * input_point_step + 2]; + + // transform_array is expected to be column-major + output_points[point_idx * num_features + 0] = transform_array[0] * input_x + + transform_array[4] * input_y + + transform_array[8] * input_z + transform_array[12]; + output_points[point_idx * num_features + 1] = transform_array[1] * input_x + + transform_array[5] * input_y + + transform_array[9] * input_z + transform_array[13]; + output_points[point_idx * num_features + 2] = transform_array[2] * input_x + + transform_array[6] * input_y + + transform_array[10] * input_z + transform_array[14]; + output_points[point_idx * num_features + 3] = time_lag; +} + +cudaError_t generateSweepPoints_launch( + const float * input_points, size_t points_size, int input_point_step, float time_lag, + const float * transform_array, int num_features, float * output_points, cudaStream_t stream) +{ + auto transform_d = cuda::make_unique(16); + CHECK_CUDA_ERROR(cudaMemcpyAsync( + transform_d.get(), transform_array, 16 * sizeof(float), cudaMemcpyHostToDevice, stream)); + + dim3 blocks((points_size + 256 - 1) / 256); + dim3 threads(256); + assert(num_features == 4); + + generateSweepPoints_kernel<<>>( + input_points, points_size, input_point_step, time_lag, transform_d.get(), num_features, + output_points); + + cudaError_t err = cudaGetLastError(); + return err; +} + __global__ void generateVoxels_random_kernel( const float * points, size_t points_size, float min_x_range, float max_x_range, float min_y_range, float max_y_range, float min_z_range, float max_z_range, float pillar_x_size, float pillar_y_size, diff --git a/perception/lidar_centerpoint/lib/preprocess/voxel_generator.cpp b/perception/lidar_centerpoint/lib/preprocess/voxel_generator.cpp index 07a1a2de725f5..e90474fa07327 100644 --- a/perception/lidar_centerpoint/lib/preprocess/voxel_generator.cpp +++ b/perception/lidar_centerpoint/lib/preprocess/voxel_generator.cpp @@ -14,7 +14,12 @@ #include "lidar_centerpoint/preprocess/voxel_generator.hpp" -#include +#include "lidar_centerpoint/centerpoint_trt.hpp" +#include "lidar_centerpoint/preprocess/preprocess_kernel.hpp" + +#include "sensor_msgs/point_cloud2_iterator.hpp" + +#include namespace centerpoint { @@ -38,35 +43,40 @@ VoxelGeneratorTemplate::VoxelGeneratorTemplate( } bool VoxelGeneratorTemplate::enqueuePointCloud( - const sensor_msgs::msg::PointCloud2 & input_pointcloud_msg, const tf2_ros::Buffer & tf_buffer) + const sensor_msgs::msg::PointCloud2 & input_pointcloud_msg, const tf2_ros::Buffer & tf_buffer, + cudaStream_t stream) { - return pd_ptr_->enqueuePointCloud(input_pointcloud_msg, tf_buffer); + return pd_ptr_->enqueuePointCloud(input_pointcloud_msg, tf_buffer, stream); } -std::size_t VoxelGenerator::generateSweepPoints(std::vector & points) +std::size_t VoxelGenerator::generateSweepPoints(float * points_d, cudaStream_t stream) { - Eigen::Vector3f point_current, point_past; - size_t point_counter{}; + size_t point_counter = 0; for (auto pc_cache_iter = pd_ptr_->getPointCloudCacheIter(); !pd_ptr_->isCacheEnd(pc_cache_iter); pc_cache_iter++) { - auto pc_msg = pc_cache_iter->pointcloud_msg; + auto sweep_num_points = pc_cache_iter->num_points; + auto point_step = pc_cache_iter->point_step; auto affine_past2current = pd_ptr_->getAffineWorldToCurrent() * pc_cache_iter->affine_past2world; float time_lag = static_cast( - pd_ptr_->getCurrentTimestamp() - rclcpp::Time(pc_msg.header.stamp).seconds()); - - for (sensor_msgs::PointCloud2ConstIterator x_iter(pc_msg, "x"), y_iter(pc_msg, "y"), - z_iter(pc_msg, "z"); - x_iter != x_iter.end(); ++x_iter, ++y_iter, ++z_iter) { - point_past << *x_iter, *y_iter, *z_iter; - point_current = affine_past2current * point_past; + pd_ptr_->getCurrentTimestamp() - rclcpp::Time(pc_cache_iter->header.stamp).seconds()); - points.at(point_counter * config_.point_feature_size_) = point_current.x(); - points.at(point_counter * config_.point_feature_size_ + 1) = point_current.y(); - points.at(point_counter * config_.point_feature_size_ + 2) = point_current.z(); - points.at(point_counter * config_.point_feature_size_ + 3) = time_lag; - ++point_counter; + if (point_counter + sweep_num_points > CAPACITY_POINT) { + RCLCPP_WARN_STREAM( + rclcpp::get_logger("lidar_centerpoint"), + "Requested number of points exceeds the maximum capacity. Current points = " + << point_counter); + break; } + + static_assert(std::is_same::value); + static_assert(!Eigen::Matrix4f::IsRowMajor, "matrices should be col-major."); + generateSweepPoints_launch( + pc_cache_iter->points_d.get(), sweep_num_points, point_step / sizeof(float), time_lag, + affine_past2current.matrix().data(), config_.point_feature_size_, + points_d + config_.point_feature_size_ * point_counter, stream); + + point_counter += sweep_num_points; } return point_counter; } diff --git a/perception/lidar_centerpoint/package.xml b/perception/lidar_centerpoint/package.xml index 280e2219fb1f1..4c6fa471c297a 100644 --- a/perception/lidar_centerpoint/package.xml +++ b/perception/lidar_centerpoint/package.xml @@ -16,7 +16,6 @@ autoware_universe_utils object_recognition_utils pcl_ros - python3-open3d rclcpp rclcpp_components tensorrt_common diff --git a/perception/lidar_centerpoint/src/node.cpp b/perception/lidar_centerpoint/src/node.cpp index f9687a6342c9c..8bf62e3e0168b 100644 --- a/perception/lidar_centerpoint/src/node.cpp +++ b/perception/lidar_centerpoint/src/node.cpp @@ -14,17 +14,7 @@ #include "lidar_centerpoint/node.hpp" -#include -#include -#include -#include -#include - -#ifdef ROS_DISTRO_GALACTIC -#include -#else -#include -#endif +#include "pcl_ros/transforms.hpp" #include #include @@ -33,6 +23,17 @@ #include #include +#ifdef ROS_DISTRO_GALACTIC +#include "tf2_geometry_msgs/tf2_geometry_msgs.h" +#else +#include "tf2_geometry_msgs/tf2_geometry_msgs.hpp" +#endif + +#include "lidar_centerpoint/centerpoint_config.hpp" +#include "lidar_centerpoint/preprocess/pointcloud_densification.hpp" +#include "lidar_centerpoint/ros_utils.hpp" +#include "lidar_centerpoint/utils.hpp" + namespace centerpoint { LidarCenterPointNode::LidarCenterPointNode(const rclcpp::NodeOptions & node_options) diff --git a/perception/lidar_centerpoint/test/test_postprocess_kernel.cpp b/perception/lidar_centerpoint/test/test_postprocess_kernel.cpp index fde4fcbee3d6c..e75aff416b8aa 100644 --- a/perception/lidar_centerpoint/test/test_postprocess_kernel.cpp +++ b/perception/lidar_centerpoint/test/test_postprocess_kernel.cpp @@ -94,12 +94,12 @@ TEST_F(PostprocessKernelTest, SingleDetectionTest) constexpr float detection_x = 70.f; constexpr float detection_y = -38.4f; constexpr float detection_z = 1.0; - constexpr float detection_log_w = std::log(7.0); - constexpr float detection_log_l = std::log(1.0); - constexpr float detection_log_h = std::log(2.0); + const float detection_log_w = std::log(7.0); + const float detection_log_l = std::log(1.0); + const float detection_log_h = std::log(2.0); constexpr float detection_yaw = M_PI_4; - constexpr float detection_yaw_sin = std::sin(detection_yaw); - constexpr float detection_yaw_cos = std::sin(detection_yaw); + const float detection_yaw_sin = std::sin(detection_yaw); + const float detection_yaw_cos = std::sin(detection_yaw); constexpr float detection_vel_x = 5.0; constexpr float detection_vel_y = -5.0; @@ -240,9 +240,9 @@ TEST_F(PostprocessKernelTest, CircleNMSTest) constexpr float detection_x = 70.f; constexpr float detection_y = -38.4f; constexpr float detection_z = 1.0; - constexpr float detection_log_w = std::log(7.0); - constexpr float detection_log_l = std::log(1.0); - constexpr float detection_log_h = std::log(2.0); + const float detection_log_w = std::log(7.0); + const float detection_log_l = std::log(1.0); + const float detection_log_h = std::log(2.0); constexpr float detection_yaw1_sin = 0.0; constexpr float detection_yaw1_cos = 1.0; constexpr float detection_yaw2_sin = 1.0; diff --git a/perception/lidar_centerpoint/test/test_postprocess_kernel.hpp b/perception/lidar_centerpoint/test/test_postprocess_kernel.hpp index d0c9123da9e77..8f35b98119afe 100644 --- a/perception/lidar_centerpoint/test/test_postprocess_kernel.hpp +++ b/perception/lidar_centerpoint/test/test_postprocess_kernel.hpp @@ -31,17 +31,17 @@ class PostprocessKernelTest : public testing::Test void SetUp() override; void TearDown() override; - cudaStream_t stream_{nullptr}; + cudaStream_t stream_{}; - std::unique_ptr postprocess_cuda_ptr_; - std::unique_ptr config_ptr_; + std::unique_ptr postprocess_cuda_ptr_{}; + std::unique_ptr config_ptr_{}; - cuda::unique_ptr head_out_heatmap_d_; - cuda::unique_ptr head_out_offset_d_; - cuda::unique_ptr head_out_z_d_; - cuda::unique_ptr head_out_dim_d_; - cuda::unique_ptr head_out_rot_d_; - cuda::unique_ptr head_out_vel_d_; + cuda::unique_ptr head_out_heatmap_d_{}; + cuda::unique_ptr head_out_offset_d_{}; + cuda::unique_ptr head_out_z_d_{}; + cuda::unique_ptr head_out_dim_d_{}; + cuda::unique_ptr head_out_rot_d_{}; + cuda::unique_ptr head_out_vel_d_{}; }; } // namespace centerpoint diff --git a/perception/lidar_centerpoint/test/test_preprocess_kernel.hpp b/perception/lidar_centerpoint/test/test_preprocess_kernel.hpp index 57ff51966a417..1a3e850026886 100644 --- a/perception/lidar_centerpoint/test/test_preprocess_kernel.hpp +++ b/perception/lidar_centerpoint/test/test_preprocess_kernel.hpp @@ -28,7 +28,7 @@ class PreprocessKernelTest : public testing::Test void SetUp() override; void TearDown() override; - cudaStream_t stream_{nullptr}; + cudaStream_t stream_{}; std::size_t max_voxel_size_{}; std::size_t max_point_in_voxel_size_{}; diff --git a/perception/lidar_centerpoint/test/test_voxel_generator.cpp b/perception/lidar_centerpoint/test/test_voxel_generator.cpp index 5b0b3cd112e6c..6bded105536dc 100644 --- a/perception/lidar_centerpoint/test/test_voxel_generator.cpp +++ b/perception/lidar_centerpoint/test/test_voxel_generator.cpp @@ -14,9 +14,9 @@ #include "test_voxel_generator.hpp" -#include +#include "gtest/gtest.h" -#include +#include "sensor_msgs/point_cloud2_iterator.hpp" void VoxelGeneratorTest::SetUp() { @@ -95,6 +95,8 @@ void VoxelGeneratorTest::SetUp() transform2_ = transform1_; transform2_.header.stamp = cloud2_->header.stamp; transform2_.transform.translation.x = world_origin_x + delta_pointcloud_x_; + + cudaStreamCreate(&stream_); } void VoxelGeneratorTest::TearDown() @@ -117,8 +119,17 @@ TEST_F(VoxelGeneratorTest, SingleFrame) points.resize(capacity_ * config.point_feature_size_); std::fill(points.begin(), points.end(), std::nan("")); - bool status1 = voxel_generator.enqueuePointCloud(*cloud1_, *tf2_buffer_); - std::size_t generated_points_num = voxel_generator.generateSweepPoints(points); + auto points_d = cuda::make_unique(capacity_ * config.point_feature_size_); + cudaMemcpy( + points_d.get(), points.data(), capacity_ * config.point_feature_size_ * sizeof(float), + cudaMemcpyHostToDevice); + + bool status1 = voxel_generator.enqueuePointCloud(*cloud1_, *tf2_buffer_, stream_); + std::size_t generated_points_num = voxel_generator.generateSweepPoints(points_d.get(), stream_); + + cudaMemcpy( + points.data(), points_d.get(), capacity_ * config.point_feature_size_ * sizeof(float), + cudaMemcpyDeviceToHost); EXPECT_TRUE(status1); EXPECT_EQ(points_per_pointcloud_, generated_points_num); @@ -155,9 +166,18 @@ TEST_F(VoxelGeneratorTest, TwoFramesNoTf) points.resize(capacity_ * config.point_feature_size_); std::fill(points.begin(), points.end(), std::nan("")); - bool status1 = voxel_generator.enqueuePointCloud(*cloud1_, *tf2_buffer_); - bool status2 = voxel_generator.enqueuePointCloud(*cloud2_, *tf2_buffer_); - std::size_t generated_points_num = voxel_generator.generateSweepPoints(points); + auto points_d = cuda::make_unique(capacity_ * config.point_feature_size_); + cudaMemcpy( + points_d.get(), points.data(), capacity_ * config.point_feature_size_ * sizeof(float), + cudaMemcpyHostToDevice); + + bool status1 = voxel_generator.enqueuePointCloud(*cloud1_, *tf2_buffer_, stream_); + bool status2 = voxel_generator.enqueuePointCloud(*cloud2_, *tf2_buffer_, stream_); + std::size_t generated_points_num = voxel_generator.generateSweepPoints(points_d.get(), stream_); + + cudaMemcpy( + points.data(), points_d.get(), capacity_ * config.point_feature_size_ * sizeof(float), + cudaMemcpyDeviceToHost); EXPECT_FALSE(status1); EXPECT_FALSE(status2); @@ -180,12 +200,21 @@ TEST_F(VoxelGeneratorTest, TwoFrames) points.resize(capacity_ * config.point_feature_size_); std::fill(points.begin(), points.end(), std::nan("")); + auto points_d = cuda::make_unique(capacity_ * config.point_feature_size_); + cudaMemcpy( + points_d.get(), points.data(), capacity_ * config.point_feature_size_ * sizeof(float), + cudaMemcpyHostToDevice); + tf2_buffer_->setTransform(transform1_, "authority1"); tf2_buffer_->setTransform(transform2_, "authority1"); - bool status1 = voxel_generator.enqueuePointCloud(*cloud1_, *tf2_buffer_); - bool status2 = voxel_generator.enqueuePointCloud(*cloud2_, *tf2_buffer_); - std::size_t generated_points_num = voxel_generator.generateSweepPoints(points); + bool status1 = voxel_generator.enqueuePointCloud(*cloud1_, *tf2_buffer_, stream_); + bool status2 = voxel_generator.enqueuePointCloud(*cloud2_, *tf2_buffer_, stream_); + std::size_t generated_points_num = voxel_generator.generateSweepPoints(points_d.get(), stream_); + + cudaMemcpy( + points.data(), points_d.get(), capacity_ * config.point_feature_size_ * sizeof(float), + cudaMemcpyDeviceToHost); EXPECT_TRUE(status1); EXPECT_TRUE(status2); diff --git a/perception/lidar_centerpoint/test/test_voxel_generator.hpp b/perception/lidar_centerpoint/test/test_voxel_generator.hpp index 8fb7d8dffa44d..48355b8a331ba 100644 --- a/perception/lidar_centerpoint/test/test_voxel_generator.hpp +++ b/perception/lidar_centerpoint/test/test_voxel_generator.hpp @@ -33,34 +33,36 @@ class VoxelGeneratorTest : public testing::Test void TearDown() override; // These need to be public so that they can be accessed in the test cases - rclcpp::Node::SharedPtr node_; + rclcpp::Node::SharedPtr node_{}; - std::unique_ptr cloud1_, cloud2_; - geometry_msgs::msg::TransformStamped transform1_, transform2_; + std::unique_ptr cloud1_{}, cloud2_{}; + geometry_msgs::msg::TransformStamped transform1_{}, transform2_{}; - std::unique_ptr densification_param_ptr_; - std::unique_ptr config_ptr_; + std::unique_ptr densification_param_ptr_{}; + std::unique_ptr config_ptr_{}; - std::unique_ptr tf2_buffer_; + std::unique_ptr tf2_buffer_{}; - std::string world_frame_; - std::string lidar_frame_; - std::size_t points_per_pointcloud_; - std::size_t capacity_; - double delta_pointcloud_x_; + std::string world_frame_{}; + std::string lidar_frame_{}; + std::size_t points_per_pointcloud_{}; + std::size_t capacity_{}; + double delta_pointcloud_x_{}; - std::size_t class_size_; - float point_feature_size_; - std::size_t max_voxel_size_; + std::size_t class_size_{}; + float point_feature_size_{}; + std::size_t max_voxel_size_{}; - std::vector point_cloud_range_; - std::vector voxel_size_; - std::size_t downsample_factor_; - std::size_t encoder_in_feature_size_; - float score_threshold_; - float circle_nms_dist_threshold_; - std::vector yaw_norm_thresholds_; - bool has_variance_; + std::vector point_cloud_range_{}; + std::vector voxel_size_{}; + std::size_t downsample_factor_{}; + std::size_t encoder_in_feature_size_{}; + float score_threshold_{}; + float circle_nms_dist_threshold_{}; + std::vector yaw_norm_thresholds_{}; + bool has_variance_{}; + + cudaStream_t stream_{}; }; #endif // TEST_VOXEL_GENERATOR_HPP_ diff --git a/perception/lidar_centerpoint_tvm/.gitignore b/perception/lidar_centerpoint_tvm/.gitignore deleted file mode 100644 index e69de29bb2d1d..0000000000000 diff --git a/perception/lidar_centerpoint_tvm/CMakeLists.txt b/perception/lidar_centerpoint_tvm/CMakeLists.txt deleted file mode 100644 index e9da98d4ae3c4..0000000000000 --- a/perception/lidar_centerpoint_tvm/CMakeLists.txt +++ /dev/null @@ -1,100 +0,0 @@ -cmake_minimum_required(VERSION 3.14) -project(lidar_centerpoint_tvm) - -find_package(autoware_cmake REQUIRED) -autoware_package() - -set(tvm_runtime_DIR ${tvm_vendor_DIR}) -find_package(tvm_runtime CONFIG REQUIRED) - -# Gather neural network information. -set(${PROJECT_NAME}_BACKEND llvm CACHE STRING "${PROJECT_NAME} neural network backend") -set(MODEL_NAME_ENCODER centerpoint_encoder) - -# Get neural network. -set(NN_DEPENDENCY_ENCODER "") -get_neural_network(${MODEL_NAME_ENCODER} ${${PROJECT_NAME}_BACKEND} NN_DEPENDENCY_ENCODER) - -set(MODEL_NAME_BACKBONE centerpoint_backbone) - -# Get neural network. -set(NN_DEPENDENCY_BACKBONE "") -get_neural_network(${MODEL_NAME_BACKBONE} ${${PROJECT_NAME}_BACKEND} NN_DEPENDENCY_BACKBONE) - -if((NOT NN_DEPENDENCY_ENCODER STREQUAL "") AND (NOT NN_DEPENDENCY_BACKBONE STREQUAL "")) - ## centerpoint_tvm ## - ament_auto_add_library(${PROJECT_NAME} SHARED - data/models/${MODEL_NAME_ENCODER}/inference_engine_tvm_config.hpp - data/models/${MODEL_NAME_BACKBONE}/inference_engine_tvm_config.hpp - data/models/${MODEL_NAME_BACKBONE}/preprocessing_inference_engine_tvm_config.hpp - lib/centerpoint_tvm.cpp - lib/utils.cpp - lib/ros_utils.cpp - lib/preprocess/pointcloud_densification.cpp - lib/preprocess/voxel_generator.cpp - lib/preprocess/generate_features.cpp - lib/postprocess/circle_nms.cpp - lib/postprocess/generate_detected_boxes.cpp - ) - - add_dependencies(${PROJECT_NAME} ${NN_DEPENDENCY_ENCODER}) - add_dependencies(${PROJECT_NAME} ${NN_DEPENDENCY_BACKBONE}) - - target_compile_options(${PROJECT_NAME} PRIVATE "-Wno-sign-conversion" "-Wno-conversion") - - target_include_directories(${PROJECT_NAME} SYSTEM PUBLIC - "${tvm_vendor_INCLUDE_DIRS}" - ) - - target_link_libraries(${PROJECT_NAME} - ${tvm_runtime_LIBRARIES} - ) - - target_include_directories(${PROJECT_NAME} PRIVATE - data/models - ) - - ## node ## - ament_auto_add_library(lidar_centerpoint_tvm_component SHARED - src/node.cpp - ) - - target_link_libraries(lidar_centerpoint_tvm_component - ${PROJECT_NAME} - ) - - rclcpp_components_register_node(lidar_centerpoint_tvm_component - PLUGIN "autoware::perception::lidar_centerpoint_tvm::LidarCenterPointTVMNode" - EXECUTABLE lidar_centerpoint_tvm_node - ) - - ## single inference node ## - ament_auto_add_library(single_inference_lidar_centerpoint_tvm_component SHARED - src/single_inference_node.cpp - ) - - target_link_libraries(single_inference_lidar_centerpoint_tvm_component - ${tvm_runtime_LIBRARIES} - ) - - - rclcpp_components_register_node(single_inference_lidar_centerpoint_tvm_component - PLUGIN "autoware::perception::lidar_centerpoint_tvm::SingleInferenceLidarCenterPointNode" - EXECUTABLE single_inference_lidar_centerpoint_tvm_node - ) - - install(PROGRAMS - scripts/lidar_centerpoint_visualizer.py - DESTINATION lib/${PROJECT_NAME} - ) - - ament_export_dependencies(ament_cmake_python) - - ament_auto_package(INSTALL_TO_SHARE - launch - config - ) -else() - message(WARNING "Neural network not found, skipping package.") - ament_auto_package() -endif() diff --git a/perception/lidar_centerpoint_tvm/README.md b/perception/lidar_centerpoint_tvm/README.md deleted file mode 100644 index 26d588d4483ad..0000000000000 --- a/perception/lidar_centerpoint_tvm/README.md +++ /dev/null @@ -1,77 +0,0 @@ -# lidar_centerpoint_tvm - -## Design - -### Usage - -lidar_centerpoint_tvm is a package for detecting dynamic 3D objects using TVM compiled centerpoint module for different backends. To use this package, replace `lidar_centerpoint` with `lidar_centerpoint_tvm` in perception launch files(for example, `lidar_based_detection.launch.xml` is lidar based detection is chosen.). - -#### Neural network - -This package will not build without a neural network for its inference. -The network is provided by the `tvm_utility` package. -See its design page for more information on how to enable downloading pre-compiled networks (by setting the `DOWNLOAD_ARTIFACTS` cmake variable), or how to handle user-compiled networks. - -#### Backend - -The backend used for the inference can be selected by setting the `lidar_centerpoint_tvm_BACKEND` cmake variable. -The current available options are `llvm` for a CPU backend, and `vulkan` or `opencl` for a GPU backend. -It defaults to `llvm`. - -### Inputs / Outputs - -### Input - -| Name | Type | Description | -| -------------------- | ------------------------------- | ---------------- | -| `~/input/pointcloud` | `sensor_msgs::msg::PointCloud2` | input pointcloud | - -### Output - -| Name | Type | Description | -| -------------------------- | ------------------------------------------------ | -------------------- | -| `~/output/objects` | `autoware_perception_msgs::msg::DetectedObjects` | detected objects | -| `debug/cyclic_time_ms` | `tier4_debug_msgs::msg::Float64Stamped` | cyclic time (msg) | -| `debug/processing_time_ms` | `tier4_debug_msgs::msg::Float64Stamped` | processing time (ms) | - -## Parameters - -### Core Parameters - -| Name | Type | Default Value | Description | -| ------------------------------- | ------ | ------------- | ----------------------------------------------------------- | -| `score_threshold` | float | `0.1` | detected objects with score less than threshold are ignored | -| `densification_world_frame_id` | string | `map` | the world frame id to fuse multi-frame pointcloud | -| `densification_num_past_frames` | int | `1` | the number of past frames to fuse with the current frame | - -### Bounding Box - -The lidar segmentation node establishes a bounding box for the detected obstacles. -The `L-fit` method of fitting a bounding box to a cluster is used for that. - -### Limitation and Known Issue - -Due to an accuracy issue of `centerpoint` model, `vulkan` cannot be used at the moment. -As for 'llvm' backend, real-time performance cannot be achieved. - -### Scatter Implementation - -Scatter function can be implemented using either TVMScript or C++. For C++ implementation, please refer to - -## Reference - -[1] Yin, Tianwei, Xingyi Zhou, and Philipp Krähenbühl. "Center-based 3d object detection and tracking." arXiv preprint arXiv:2006.11275 (2020). - -[2] Lang, Alex H., et al. "PointPillars: Fast encoders for object detection from point clouds." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019. - -[3] - -[4] - -[5] - -## Related issues - - - -- #908: Run Lidar Centerpoint with TVM diff --git a/perception/lidar_centerpoint_tvm/config/centerpoint.param.yaml b/perception/lidar_centerpoint_tvm/config/centerpoint.param.yaml deleted file mode 100644 index 34dce71975985..0000000000000 --- a/perception/lidar_centerpoint_tvm/config/centerpoint.param.yaml +++ /dev/null @@ -1,10 +0,0 @@ -/**: - ros__parameters: - class_names: ["CAR", "PEDESTRIAN", "BICYCLE"] - rename_car_to_truck_and_bus: true - point_feature_size: 4 - max_voxel_size: 40000 - point_cloud_range: [-89.6, -89.6, -3.0, 89.6, 89.6, 5.0] - voxel_size: [0.32, 0.32, 8.0] - downsample_factor: 1 - encoder_in_feature_size: 9 diff --git a/perception/lidar_centerpoint_tvm/data/models/centerpoint_backbone/inference_engine_tvm_config.hpp b/perception/lidar_centerpoint_tvm/data/models/centerpoint_backbone/inference_engine_tvm_config.hpp deleted file mode 100644 index 5d832027c7591..0000000000000 --- a/perception/lidar_centerpoint_tvm/data/models/centerpoint_backbone/inference_engine_tvm_config.hpp +++ /dev/null @@ -1,61 +0,0 @@ -// Copyright 2021 Arm Limited and Contributors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "tvm_utility/pipeline.hpp" - -#ifndef PERCEPTION__LIDAR_CENTERPOINT_TVM__DATA__MODELS__CENTERPOINT_BACKBONE__INFERENCE_ENGINE_TVM_CONFIG_HPP_ // NOLINT -#define PERCEPTION__LIDAR_CENTERPOINT_TVM__DATA__MODELS__CENTERPOINT_BACKBONE__INFERENCE_ENGINE_TVM_CONFIG_HPP_ // NOLINT - -namespace model_zoo -{ -namespace perception -{ -namespace lidar_obstacle_detection -{ -namespace centerpoint_backbone -{ -namespace onnx_centerpoint_backbone -{ - -static const tvm_utility::pipeline::InferenceEngineTVMConfig config{ - {3, 0, 0}, // modelzoo_version - - "centerpoint_backbone", // network_name - "llvm", // network_backend - - "./deploy_lib.so", // network_module_path - "./deploy_graph.json", // network_graph_path - "./deploy_param.params", // network_params_path - - // cspell: ignore DLCPU - kDLCPU, // tvm_device_type - 0, // tvm_device_id - - {{"spatial_features", kDLFloat, 32, 1, {1, 32, 560, 560}}}, // network_inputs - - {{"heatmap", kDLFloat, 32, 1, {1, 3, 560, 560}}, - {"reg", kDLFloat, 32, 1, {1, 2, 560, 560}}, - {"height", kDLFloat, 32, 1, {1, 1, 560, 560}}, - {"dim", kDLFloat, 32, 1, {1, 3, 560, 560}}, - {"rot", kDLFloat, 32, 1, {1, 2, 560, 560}}, - {"vel", kDLFloat, 32, 1, {1, 2, 560, 560}}} // network_outputs -}; - -} // namespace onnx_centerpoint_backbone -} // namespace centerpoint_backbone -} // namespace lidar_obstacle_detection -} // namespace perception -} // namespace model_zoo -// NOLINTNEXTLINE -#endif // PERCEPTION__LIDAR_CENTERPOINT_TVM__DATA__MODELS__CENTERPOINT_BACKBONE__INFERENCE_ENGINE_TVM_CONFIG_HPP_ diff --git a/perception/lidar_centerpoint_tvm/data/models/centerpoint_backbone/preprocessing_inference_engine_tvm_config.hpp b/perception/lidar_centerpoint_tvm/data/models/centerpoint_backbone/preprocessing_inference_engine_tvm_config.hpp deleted file mode 100644 index 0cae27b49572c..0000000000000 --- a/perception/lidar_centerpoint_tvm/data/models/centerpoint_backbone/preprocessing_inference_engine_tvm_config.hpp +++ /dev/null @@ -1,60 +0,0 @@ -// Copyright 2021 Arm Limited and Contributors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "tvm_utility/pipeline.hpp" - -#ifndef PERCEPTION__LIDAR_CENTERPOINT_TVM__DATA__MODELS__CENTERPOINT_BACKBONE__PREPROCESSING_INFERENCE_ENGINE_TVM_CONFIG_HPP_ // NOLINT -#define PERCEPTION__LIDAR_CENTERPOINT_TVM__DATA__MODELS__CENTERPOINT_BACKBONE__PREPROCESSING_INFERENCE_ENGINE_TVM_CONFIG_HPP_ // NOLINT - -namespace model_zoo -{ -namespace perception -{ -namespace lidar_obstacle_detection -{ -namespace centerpoint_backbone -{ -namespace onnx_centerpoint_backbone -{ -namespace preprocessing -{ - -static const tvm_utility::pipeline::InferenceEngineTVMConfig config{ - {3, 0, 0}, // modelzoo_version - - "centerpoint_backbone", // network_name - "llvm", // network_backend - - "./preprocess.so", // network_module_path - "./", // network_graph_path - "./", // network_params_path - - // cspell: ignore DLCPU - kDLCPU, // tvm_device_type - 0, // tvm_device_id - - {{"pillar_features", kDLFloat, 32, 1, {40000, 1, 32}}, - {"coords", kDLInt, 32, 1, {40000, 3}}}, // network_inputs - - {{"spatial_features", kDLFloat, 32, 1, {1, 32, 560, 560}}} // network_outputs -}; - -} // namespace preprocessing -} // namespace onnx_centerpoint_backbone -} // namespace centerpoint_backbone -} // namespace lidar_obstacle_detection -} // namespace perception -} // namespace model_zoo -// NOLINTNEXTLINE -#endif // PERCEPTION__LIDAR_CENTERPOINT_TVM__DATA__MODELS__CENTERPOINT_BACKBONE__PREPROCESSING_INFERENCE_ENGINE_TVM_CONFIG_HPP_ diff --git a/perception/lidar_centerpoint_tvm/data/models/centerpoint_encoder/inference_engine_tvm_config.hpp b/perception/lidar_centerpoint_tvm/data/models/centerpoint_encoder/inference_engine_tvm_config.hpp deleted file mode 100644 index 3e423dbe99877..0000000000000 --- a/perception/lidar_centerpoint_tvm/data/models/centerpoint_encoder/inference_engine_tvm_config.hpp +++ /dev/null @@ -1,56 +0,0 @@ -// Copyright 2021 Arm Limited and Contributors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "tvm_utility/pipeline.hpp" - -#ifndef PERCEPTION__LIDAR_CENTERPOINT_TVM__DATA__MODELS__CENTERPOINT_ENCODER__INFERENCE_ENGINE_TVM_CONFIG_HPP_ // NOLINT -#define PERCEPTION__LIDAR_CENTERPOINT_TVM__DATA__MODELS__CENTERPOINT_ENCODER__INFERENCE_ENGINE_TVM_CONFIG_HPP_ // NOLINT - -namespace model_zoo -{ -namespace perception -{ -namespace lidar_obstacle_detection -{ -namespace centerpoint_encoder -{ -namespace onnx_centerpoint_encoder -{ - -static const tvm_utility::pipeline::InferenceEngineTVMConfig config{ - {3, 0, 0}, // modelzoo_version - - "centerpoint_encoder", // network_name - "llvm", // network_backend - - "./deploy_lib.so", // network_module_path - "./deploy_graph.json", // network_graph_path - "./deploy_param.params", // network_params_path - - // cspell: ignore DLCPU - kDLCPU, // tvm_device_type - 0, // tvm_device_id - - {{"input_features", kDLFloat, 32, 1, {40000, 32, 9}}}, // network_inputs - - {{"pillar_features", kDLFloat, 32, 1, {40000, 1, 32}}} // network_outputs -}; - -} // namespace onnx_centerpoint_encoder -} // namespace centerpoint_encoder -} // namespace lidar_obstacle_detection -} // namespace perception -} // namespace model_zoo -// NOLINTNEXTLINE -#endif // PERCEPTION__LIDAR_CENTERPOINT_TVM__DATA__MODELS__CENTERPOINT_ENCODER__INFERENCE_ENGINE_TVM_CONFIG_HPP_ diff --git a/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/centerpoint_config.hpp b/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/centerpoint_config.hpp deleted file mode 100644 index 5825decbb78d6..0000000000000 --- a/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/centerpoint_config.hpp +++ /dev/null @@ -1,126 +0,0 @@ -// Copyright 2021-2022 AutoCore Ltd., TIER IV, Inc. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef LIDAR_CENTERPOINT_TVM__CENTERPOINT_CONFIG_HPP_ -#define LIDAR_CENTERPOINT_TVM__CENTERPOINT_CONFIG_HPP_ - -#include -#include - -namespace autoware -{ -namespace perception -{ -namespace lidar_centerpoint_tvm -{ -class CenterPointConfig -{ -public: - explicit CenterPointConfig( - const std::size_t class_size, const float point_feature_size, const std::size_t max_voxel_size, - const std::vector & point_cloud_range, const std::vector & voxel_size, - const std::size_t downsample_factor, const std::size_t encoder_in_feature_size, - const float score_threshold, const float circle_nms_dist_threshold, - const float yaw_norm_threshold) - { - class_size_ = class_size; - point_feature_size_ = point_feature_size; - max_voxel_size_ = max_voxel_size; - if (point_cloud_range.size() == 6) { - range_min_x_ = static_cast(point_cloud_range[0]); - range_min_y_ = static_cast(point_cloud_range[1]); - range_min_z_ = static_cast(point_cloud_range[2]); - range_max_x_ = static_cast(point_cloud_range[3]); - range_max_y_ = static_cast(point_cloud_range[4]); - range_max_z_ = static_cast(point_cloud_range[5]); - } - if (voxel_size.size() == 3) { - voxel_size_x_ = static_cast(voxel_size[0]); - voxel_size_y_ = static_cast(voxel_size[1]); - voxel_size_z_ = static_cast(voxel_size[2]); - } - downsample_factor_ = downsample_factor; - encoder_in_feature_size_ = encoder_in_feature_size; - - if (score_threshold > 0 && score_threshold < 1) { - score_threshold_ = score_threshold; - } - - if (circle_nms_dist_threshold > 0) { - circle_nms_dist_threshold_ = circle_nms_dist_threshold; - } - - if (yaw_norm_threshold >= 0 && yaw_norm_threshold < 1) { - yaw_norm_threshold_ = yaw_norm_threshold; - } - - grid_size_x_ = static_cast((range_max_x_ - range_min_x_) / voxel_size_x_); - grid_size_y_ = static_cast((range_max_y_ - range_min_y_) / voxel_size_y_); - grid_size_z_ = static_cast((range_max_z_ - range_min_z_) / voxel_size_z_); - offset_x_ = range_min_x_ + voxel_size_x_ / 2; - offset_y_ = range_min_y_ + voxel_size_y_ / 2; - offset_z_ = range_min_z_ + voxel_size_z_ / 2; - down_grid_size_x_ = grid_size_x_ / downsample_factor_; - down_grid_size_y_ = grid_size_y_ / downsample_factor_; - }; - - // input params - std::size_t class_size_{3}; - const std::size_t point_dim_size_{3}; // x, y and z - std::size_t point_feature_size_{4}; // x, y, z and time lag - std::size_t max_point_in_voxel_size_{32}; - std::size_t max_voxel_size_{40000}; - float range_min_x_{-89.6f}; - float range_min_y_{-89.6f}; - float range_min_z_{-3.0f}; - float range_max_x_{89.6f}; - float range_max_y_{89.6f}; - float range_max_z_{5.0f}; - float voxel_size_x_{0.32f}; - float voxel_size_y_{0.32f}; - float voxel_size_z_{8.0f}; - - // network params - const std::size_t batch_size_{1}; - std::size_t downsample_factor_{2}; - std::size_t encoder_in_feature_size_{9}; - const std::size_t encoder_out_feature_size_{32}; - const std::size_t head_out_size_{6}; - const std::size_t head_out_offset_size_{2}; - const std::size_t head_out_z_size_{1}; - const std::size_t head_out_dim_size_{3}; - const std::size_t head_out_rot_size_{2}; - const std::size_t head_out_vel_size_{2}; - - // post-process params - float score_threshold_{0.35f}; - float circle_nms_dist_threshold_{1.5f}; - float yaw_norm_threshold_{0.0f}; - - // calculated params - std::size_t grid_size_x_ = (range_max_x_ - range_min_x_) / voxel_size_x_; - std::size_t grid_size_y_ = (range_max_y_ - range_min_y_) / voxel_size_y_; - std::size_t grid_size_z_ = (range_max_z_ - range_min_z_) / voxel_size_z_; - float offset_x_ = range_min_x_ + voxel_size_x_ / 2; - float offset_y_ = range_min_y_ + voxel_size_y_ / 2; - float offset_z_ = range_min_z_ + voxel_size_z_ / 2; - std::size_t down_grid_size_x_ = grid_size_x_ / downsample_factor_; - std::size_t down_grid_size_y_ = grid_size_y_ / downsample_factor_; -}; - -} // namespace lidar_centerpoint_tvm -} // namespace perception -} // namespace autoware - -#endif // LIDAR_CENTERPOINT_TVM__CENTERPOINT_CONFIG_HPP_ diff --git a/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/centerpoint_tvm.hpp b/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/centerpoint_tvm.hpp deleted file mode 100644 index 865ca7d4253bf..0000000000000 --- a/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/centerpoint_tvm.hpp +++ /dev/null @@ -1,186 +0,0 @@ -// Copyright 2021-2022 AutoCore Ltd., TIER IV, Inc., Arm Ltd. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef LIDAR_CENTERPOINT_TVM__CENTERPOINT_TVM_HPP_ -#define LIDAR_CENTERPOINT_TVM__CENTERPOINT_TVM_HPP_ - -#include -#include -#include -#include - -#include - -#include -#include -#include -#include -#include -#include - -#include -#include -#include -#include - -namespace autoware -{ -namespace perception -{ -namespace lidar_centerpoint_tvm -{ -using tvm_utility::pipeline::TVMArrayContainer; -using tvm_utility::pipeline::TVMArrayContainerVector; - -struct MixedInputs -{ - // The number of non-empty voxel - std::size_t num_voxels; - // The voxel features (point info in each voxel) or pillar features. - std::vector features; - // The number of points in each voxel. - std::vector num_points_per_voxel; - // The index from voxel number to its 3D position - std::vector coords; -}; - -class LIDAR_CENTERPOINT_TVM_LOCAL TVMScatterIE : public tvm_utility::pipeline::InferenceEngine -{ -public: - explicit TVMScatterIE( - tvm_utility::pipeline::InferenceEngineTVMConfig config, const std::string & pkg_name, - const std::string & data_path, const std::string & function_name); - TVMArrayContainerVector schedule(const TVMArrayContainerVector & input); - void set_coords(TVMArrayContainer coords) { coords_ = coords; } - -private: - tvm_utility::pipeline::InferenceEngineTVMConfig config_; - TVMArrayContainer coords_; - TVMArrayContainerVector output_; - tvm::runtime::PackedFunc scatter_function; -}; - -class LIDAR_CENTERPOINT_TVM_LOCAL VoxelEncoderPreProcessor -: public tvm_utility::pipeline::PreProcessor -{ -public: - /// \brief Constructor. - /// \param[in] config The TVM configuration. - /// \param[in] config_mod The centerpoint model configuration. - explicit VoxelEncoderPreProcessor( - const tvm_utility::pipeline::InferenceEngineTVMConfig & config, - const CenterPointConfig & config_mod); - - /// \brief Convert the voxel_features to encoder_in_features (a TVM array). - /// \param[in] voxel_inputs The voxel features related input - /// \return A TVM array containing the encoder_in_features. - /// \throw std::runtime_error If the features are incorrectly configured. - TVMArrayContainerVector schedule(const MixedInputs & voxel_inputs); - -private: - const int64_t max_voxel_size; - const int64_t max_point_in_voxel_size; - const int64_t encoder_in_feature_size; - const int64_t input_datatype_bytes; - const CenterPointConfig config_detail; - std::vector encoder_in_features; - TVMArrayContainer output; -}; - -class LIDAR_CENTERPOINT_TVM_LOCAL BackboneNeckHeadPostProcessor -: public tvm_utility::pipeline::PostProcessor> -{ -public: - /// \brief Constructor. - /// \param[in] config The TVM configuration. - /// \param[in] config_mod The centerpoint model configuration. - explicit BackboneNeckHeadPostProcessor( - const tvm_utility::pipeline::InferenceEngineTVMConfig & config, - const CenterPointConfig & config_mod); - - /// \brief Copy the inference result. - /// \param[in] input The result of the inference engine. - /// \return The inferred data. - std::vector schedule(const TVMArrayContainerVector & input); - -private: - const int64_t output_datatype_bytes; - const CenterPointConfig config_detail; - std::vector head_out_heatmap; - std::vector head_out_offset; - std::vector head_out_z; - std::vector head_out_dim; - std::vector head_out_rot; - std::vector head_out_vel; -}; - -class LIDAR_CENTERPOINT_TVM_PUBLIC CenterPointTVM -{ -public: - /// \brief Constructor - /// \param[in] dense_param The densification parameter used to constructing vg_ptr. - /// \param[in] config The CenterPoint model configuration. - explicit CenterPointTVM( - const DensificationParam & densification_param, const CenterPointConfig & config, - const std::string & data_path); - - ~CenterPointTVM(); - - bool detect( - const sensor_msgs::msg::PointCloud2 & input_pointcloud_msg, const tf2_ros::Buffer & tf_buffer, - std::vector & det_boxes3d); - -protected: - void initPtr(); - - virtual bool preprocess( - const sensor_msgs::msg::PointCloud2 & input_pointcloud_msg, const tf2_ros::Buffer & tf_buffer); - - using VE_PrePT = VoxelEncoderPreProcessor; - using IET = tvm_utility::pipeline::InferenceEngineTVM; - using BNH_PostPT = BackboneNeckHeadPostProcessor; - using TSE = TVMScatterIE; - using TSP = tvm_utility::pipeline::TowStagePipeline; - - tvm_utility::pipeline::InferenceEngineTVMConfig config_ve; - tvm_utility::pipeline::InferenceEngineTVMConfig config_bnh; - - // Voxel Encoder Pipeline. - std::shared_ptr VE_PreP; - std::shared_ptr VE_IE; - - // Backbone Neck Head Pipeline. - std::shared_ptr BNH_IE; - std::shared_ptr BNH_PostP; - - std::shared_ptr scatter_ie; - std::shared_ptr> - TSP_pipeline; - - // Variables - std::unique_ptr vg_ptr_{nullptr}; - - CenterPointConfig config_; - std::size_t num_voxels_{0}; - std::shared_ptr> voxels_; - std::shared_ptr> coordinates_; - std::shared_ptr> num_points_per_voxel_; - TVMArrayContainer coords_tvm_; -}; - -} // namespace lidar_centerpoint_tvm -} // namespace perception -} // namespace autoware - -#endif // LIDAR_CENTERPOINT_TVM__CENTERPOINT_TVM_HPP_ diff --git a/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/node.hpp b/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/node.hpp deleted file mode 100644 index 88a43139908a7..0000000000000 --- a/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/node.hpp +++ /dev/null @@ -1,71 +0,0 @@ -// Copyright 2021-2022 AutoCore Ltd., TIER IV, Inc. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef LIDAR_CENTERPOINT_TVM__NODE_HPP_ -#define LIDAR_CENTERPOINT_TVM__NODE_HPP_ - -#include -#include -#include -#include -#include - -#include -#include -#include -#include -#include - -#include -#include -#include - -namespace autoware -{ -namespace perception -{ -namespace lidar_centerpoint_tvm -{ -class LIDAR_CENTERPOINT_TVM_PUBLIC LidarCenterPointTVMNode : public rclcpp::Node -{ -public: - explicit LidarCenterPointTVMNode(const rclcpp::NodeOptions & node_options); - -private: - void pointCloudCallback(const sensor_msgs::msg::PointCloud2::ConstSharedPtr input_pointcloud_msg); - - tf2_ros::Buffer tf_buffer_; - tf2_ros::TransformListener tf_listener_{tf_buffer_}; - - rclcpp::Subscription::SharedPtr pointcloud_sub_; - rclcpp::Publisher::SharedPtr objects_pub_; - - float score_threshold_{0.0}; - std::vector class_names_; - bool rename_car_to_truck_and_bus_{false}; - bool has_twist_{false}; - - std::unique_ptr detector_ptr_{nullptr}; - - // debugger - std::unique_ptr> stop_watch_ptr_{ - nullptr}; - std::unique_ptr debug_publisher_ptr_{nullptr}; -}; - -} // namespace lidar_centerpoint_tvm -} // namespace perception -} // namespace autoware - -#endif // LIDAR_CENTERPOINT_TVM__NODE_HPP_ diff --git a/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/postprocess/circle_nms.hpp b/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/postprocess/circle_nms.hpp deleted file mode 100644 index 3a0847f00bd21..0000000000000 --- a/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/postprocess/circle_nms.hpp +++ /dev/null @@ -1,37 +0,0 @@ -// Copyright 2022 AutoCore Ltd. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef LIDAR_CENTERPOINT_TVM__POSTPROCESS__CIRCLE_NMS_HPP_ -#define LIDAR_CENTERPOINT_TVM__POSTPROCESS__CIRCLE_NMS_HPP_ - -#include - -#include - -namespace autoware -{ -namespace perception -{ -namespace lidar_centerpoint_tvm -{ -// Non-maximum suppression (NMS) uses the distance on the xy plane instead of -// intersection over union (IoU) to suppress overlapped objects. -std::size_t circleNMS( - std::vector & boxes3d, const float dist_thresh, std::vector & keep_mask); - -} // namespace lidar_centerpoint_tvm -} // namespace perception -} // namespace autoware - -#endif // LIDAR_CENTERPOINT_TVM__POSTPROCESS__CIRCLE_NMS_HPP_ diff --git a/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/postprocess/generate_detected_boxes.hpp b/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/postprocess/generate_detected_boxes.hpp deleted file mode 100644 index ba91addc5e02e..0000000000000 --- a/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/postprocess/generate_detected_boxes.hpp +++ /dev/null @@ -1,39 +0,0 @@ -// Copyright 2022 AutoCore Ltd., TIER IV, Inc. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef LIDAR_CENTERPOINT_TVM__POSTPROCESS__GENERATE_DETECTED_BOXES_HPP_ -#define LIDAR_CENTERPOINT_TVM__POSTPROCESS__GENERATE_DETECTED_BOXES_HPP_ - -#include -#include - -#include - -namespace autoware -{ -namespace perception -{ -namespace lidar_centerpoint_tvm -{ -void generateDetectedBoxes3D( - const std::vector & out_heatmap, const std::vector & out_offset, - const std::vector & out_z, const std::vector & out_dim, - const std::vector & out_rot, const std::vector & out_vel, - const CenterPointConfig & config, std::vector & det_boxes3d); - -} // namespace lidar_centerpoint_tvm -} // namespace perception -} // namespace autoware - -#endif // LIDAR_CENTERPOINT_TVM__POSTPROCESS__GENERATE_DETECTED_BOXES_HPP_ diff --git a/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/preprocess/generate_features.hpp b/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/preprocess/generate_features.hpp deleted file mode 100644 index d799dc0bfac57..0000000000000 --- a/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/preprocess/generate_features.hpp +++ /dev/null @@ -1,38 +0,0 @@ -// Copyright 2021-2022 AutoCore Ltd., TIER IV, Inc. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef LIDAR_CENTERPOINT_TVM__PREPROCESS__GENERATE_FEATURES_HPP_ -#define LIDAR_CENTERPOINT_TVM__PREPROCESS__GENERATE_FEATURES_HPP_ - -#include - -#include -#include - -namespace autoware -{ -namespace perception -{ -namespace lidar_centerpoint_tvm -{ -void generateFeatures( - const std::vector & voxel_features, const std::vector & voxel_num_points, - const std::vector & coords, const std::size_t num_voxels, - const CenterPointConfig & config, std::vector & features); - -} // namespace lidar_centerpoint_tvm -} // namespace perception -} // namespace autoware - -#endif // LIDAR_CENTERPOINT_TVM__PREPROCESS__GENERATE_FEATURES_HPP_ diff --git a/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/preprocess/voxel_generator.hpp b/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/preprocess/voxel_generator.hpp deleted file mode 100644 index 7ef49841c680b..0000000000000 --- a/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/preprocess/voxel_generator.hpp +++ /dev/null @@ -1,77 +0,0 @@ -// Copyright 2021-2022 AutoCore Ltd., TIER IV, Inc. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef LIDAR_CENTERPOINT_TVM__PREPROCESS__VOXEL_GENERATOR_HPP_ -#define LIDAR_CENTERPOINT_TVM__PREPROCESS__VOXEL_GENERATOR_HPP_ - -#include -#include -#include - -#include - -#include -#include - -namespace autoware -{ -namespace perception -{ -namespace lidar_centerpoint_tvm -{ -class LIDAR_CENTERPOINT_TVM_LOCAL VoxelGeneratorTemplate -{ -public: - explicit VoxelGeneratorTemplate( - const DensificationParam & param, const CenterPointConfig & config); - - virtual std::size_t pointsToVoxels( - std::vector & voxels, std::vector & coordinates, - std::vector & num_points_per_voxel) = 0; - - bool enqueuePointCloud( - const sensor_msgs::msg::PointCloud2 & input_pointcloud_msg, const tf2_ros::Buffer & tf_buffer); - -protected: - std::unique_ptr pd_ptr_{nullptr}; - - CenterPointConfig config_; - std::array range_; - std::array grid_size_; - std::array recip_voxel_size_; -}; - -class LIDAR_CENTERPOINT_TVM_LOCAL VoxelGenerator : public VoxelGeneratorTemplate -{ -public: - using VoxelGeneratorTemplate::VoxelGeneratorTemplate; - - /** - * @brief Traverse all the lidar points and put each point into the corresponding voxel. - * - * @param voxels To store point info in each voxel - * @param coordinates To store the index from voxel number to its 3D position - * @param num_points_per_voxel To store the number of points in each voxel - * @return The number of non-empty voxel - */ - std::size_t pointsToVoxels( - std::vector & voxels, std::vector & coordinates, - std::vector & num_points_per_voxel) override; -}; - -} // namespace lidar_centerpoint_tvm -} // namespace perception -} // namespace autoware - -#endif // LIDAR_CENTERPOINT_TVM__PREPROCESS__VOXEL_GENERATOR_HPP_ diff --git a/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/ros_utils.hpp b/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/ros_utils.hpp deleted file mode 100644 index a33430a1b09ba..0000000000000 --- a/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/ros_utils.hpp +++ /dev/null @@ -1,47 +0,0 @@ -// Copyright 2022 AutoCore Ltd., TIER IV, Inc. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef LIDAR_CENTERPOINT_TVM__ROS_UTILS_HPP_ -#define LIDAR_CENTERPOINT_TVM__ROS_UTILS_HPP_ - -#include - -#include -#include -#include -#include - -#include -#include - -namespace autoware -{ -namespace perception -{ -namespace lidar_centerpoint_tvm -{ -void box3DToDetectedObject( - const Box3D & box3d, const std::vector & class_names, - const bool rename_car_to_truck_and_bus, const bool has_twist, - autoware_perception_msgs::msg::DetectedObject & obj); - -uint8_t getSemanticType(const std::string & class_name); - -bool isCarLikeVehicleLabel(const uint8_t label); - -} // namespace lidar_centerpoint_tvm -} // namespace perception -} // namespace autoware - -#endif // LIDAR_CENTERPOINT_TVM__ROS_UTILS_HPP_ diff --git a/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/single_inference_node.hpp b/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/single_inference_node.hpp deleted file mode 100644 index 0a4fcb27ac202..0000000000000 --- a/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/single_inference_node.hpp +++ /dev/null @@ -1,66 +0,0 @@ -// Copyright 2022 TIER IV, Inc. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef LIDAR_CENTERPOINT_TVM__SINGLE_INFERENCE_NODE_HPP_ -#define LIDAR_CENTERPOINT_TVM__SINGLE_INFERENCE_NODE_HPP_ - -#include -#include -#include -#include - -#include -#include -#include -#include - -#include -#include -#include - -namespace autoware -{ -namespace perception -{ -namespace lidar_centerpoint_tvm -{ -class SingleInferenceLidarCenterPointNode : public rclcpp::Node -{ -public: - explicit SingleInferenceLidarCenterPointNode(const rclcpp::NodeOptions & node_options); - -private: - void detect(const std::string & pcd_path, const std::string & detections_path); - std::vector getVertices( - const autoware_perception_msgs::msg::Shape & shape, const Eigen::Affine3d & pose) const; - void dumpDetectionsAsMesh( - const autoware_perception_msgs::msg::DetectedObjects & objects_msg, - const std::string & output_path) const; - - tf2_ros::Buffer tf_buffer_; - tf2_ros::TransformListener tf_listener_{tf_buffer_}; - - float score_threshold_{0.0}; - std::vector class_names_; - bool rename_car_to_truck_and_bus_{false}; - bool has_twist_{false}; - - std::unique_ptr detector_ptr_{nullptr}; -}; - -} // namespace lidar_centerpoint_tvm -} // namespace perception -} // namespace autoware - -#endif // LIDAR_CENTERPOINT_TVM__SINGLE_INFERENCE_NODE_HPP_ diff --git a/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/utils.hpp b/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/utils.hpp deleted file mode 100644 index 259deef53f189..0000000000000 --- a/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/utils.hpp +++ /dev/null @@ -1,50 +0,0 @@ -// Copyright 2022 AutoCore Ltd., TIER IV, Inc. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef LIDAR_CENTERPOINT_TVM__UTILS_HPP_ -#define LIDAR_CENTERPOINT_TVM__UTILS_HPP_ - -#include -#include - -namespace autoware -{ -namespace perception -{ -namespace lidar_centerpoint_tvm -{ -struct Box3D -{ - // initializer not allowed for __shared__ variable - int32_t label; - float score; - float x; - float y; - float z; - float length; - float width; - float height; - float yaw; - float vel_x; - float vel_y; -}; - -// cspell: ignore divup -std::size_t divup(const std::size_t a, const std::size_t b); - -} // namespace lidar_centerpoint_tvm -} // namespace perception -} // namespace autoware - -#endif // LIDAR_CENTERPOINT_TVM__UTILS_HPP_ diff --git a/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/visibility_control.hpp b/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/visibility_control.hpp deleted file mode 100644 index a4afc54afc5fb..0000000000000 --- a/perception/lidar_centerpoint_tvm/include/lidar_centerpoint_tvm/visibility_control.hpp +++ /dev/null @@ -1,36 +0,0 @@ -// Copyright 2021-2022 Arm Ltd., AutoCore Ltd. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef LIDAR_CENTERPOINT_TVM__VISIBILITY_CONTROL_HPP_ -#define LIDAR_CENTERPOINT_TVM__VISIBILITY_CONTROL_HPP_ - -#if defined(__WIN32) -#if defined(LIDAR_CENTERPOINT_TVM_BUILDING_DLL) || defined(LIDAR_CENTERPOINT_TVM_EXPORTS) -#define LIDAR_CENTERPOINT_TVM_PUBLIC __declspec(dllexport) -#define LIDAR_CENTERPOINT_TVM__LOCAL -#else -#define LIDAR_CENTERPOINT_TVM_PUBLIC __declspec(dllimport) -#define LIDAR_CENTERPOINT_TVM__LOCAL -#endif -#elif defined(__linux__) -#define LIDAR_CENTERPOINT_TVM_PUBLIC __attribute__((visibility("default"))) -#define LIDAR_CENTERPOINT_TVM_LOCAL __attribute__((visibility("hidden"))) -#elif defined(__APPLE__) -#define LIDAR_CENTERPOINT_TVM_PUBLIC __attribute__((visibility("default"))) -#define LIDAR_CENTERPOINT_TVM_LOCAL __attribute__((visibility("hidden"))) -#else -#error "Unsupported Build Configuration" -#endif - -#endif // LIDAR_CENTERPOINT_TVM__VISIBILITY_CONTROL_HPP_ diff --git a/perception/lidar_centerpoint_tvm/launch/lidar_centerpoint_tvm.launch.xml b/perception/lidar_centerpoint_tvm/launch/lidar_centerpoint_tvm.launch.xml deleted file mode 100644 index 2bd6e3aa15c21..0000000000000 --- a/perception/lidar_centerpoint_tvm/launch/lidar_centerpoint_tvm.launch.xml +++ /dev/null @@ -1,26 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/perception/lidar_centerpoint_tvm/launch/single_inference_lidar_centerpoint_tvm.launch.xml b/perception/lidar_centerpoint_tvm/launch/single_inference_lidar_centerpoint_tvm.launch.xml deleted file mode 100644 index c2e0801fbd11c..0000000000000 --- a/perception/lidar_centerpoint_tvm/launch/single_inference_lidar_centerpoint_tvm.launch.xml +++ /dev/null @@ -1,33 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/perception/lidar_centerpoint_tvm/lib/centerpoint_tvm.cpp b/perception/lidar_centerpoint_tvm/lib/centerpoint_tvm.cpp deleted file mode 100644 index 91f727448b76a..0000000000000 --- a/perception/lidar_centerpoint_tvm/lib/centerpoint_tvm.cpp +++ /dev/null @@ -1,246 +0,0 @@ -// Copyright 2021-2022 AutoCore Ltd., TIER IV, Inc., Arm Ltd. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "lidar_centerpoint_tvm/centerpoint_tvm.hpp" - -#include -#include -#include -#include -#include -#include -#include - -#include -#include -#include -#include - -auto config_en = model_zoo::perception::lidar_obstacle_detection::centerpoint_encoder:: - onnx_centerpoint_encoder::config; -auto config_bk = model_zoo::perception::lidar_obstacle_detection::centerpoint_backbone:: - onnx_centerpoint_backbone::config; -auto config_scatter = model_zoo::perception::lidar_obstacle_detection::centerpoint_backbone:: - onnx_centerpoint_backbone::preprocessing::config; - -namespace autoware -{ -namespace perception -{ -namespace lidar_centerpoint_tvm -{ - -TVMScatterIE::TVMScatterIE( - tvm_utility::pipeline::InferenceEngineTVMConfig config, const std::string & pkg_name, - const std::string & data_path, const std::string & function_name) -: config_(config) -{ - std::string network_prefix = data_path + "/" + pkg_name + "/models/" + config.network_name + "/"; - std::string network_module_path = network_prefix + config.network_module_path; - - std::ifstream module(network_module_path); - if (!module.good()) { - throw std::runtime_error("File " + network_module_path + " specified in config.hpp not found"); - } - module.close(); - tvm::runtime::Module runtime_mod = tvm::runtime::Module::LoadFromFile(network_module_path); - - scatter_function = runtime_mod.GetFunction(function_name); - - for (auto & output_config : config.network_outputs) { - output_.push_back(TVMArrayContainer( - output_config.node_shape, output_config.tvm_dtype_code, output_config.tvm_dtype_bits, - output_config.tvm_dtype_lanes, config.tvm_device_type, config.tvm_device_id)); - } -} - -TVMArrayContainerVector TVMScatterIE::schedule(const TVMArrayContainerVector & input) -{ - scatter_function(input[0].getArray(), coords_.getArray(), output_[0].getArray()); - - return output_; -} - -VoxelEncoderPreProcessor::VoxelEncoderPreProcessor( - const tvm_utility::pipeline::InferenceEngineTVMConfig & config, - const CenterPointConfig & config_mod) -: max_voxel_size(config.network_inputs[0].node_shape[0]), - max_point_in_voxel_size(config.network_inputs[0].node_shape[1]), - encoder_in_feature_size(config.network_inputs[0].node_shape[2]), - input_datatype_bytes(config.network_inputs[0].tvm_dtype_bits / 8), - config_detail(config_mod) -{ - encoder_in_features.resize(max_voxel_size * max_point_in_voxel_size * encoder_in_feature_size); - // Allocate input variable - std::vector shape_x{1, max_voxel_size, max_point_in_voxel_size, encoder_in_feature_size}; - TVMArrayContainer x{ - shape_x, - config.network_inputs[0].tvm_dtype_code, - config.network_inputs[0].tvm_dtype_bits, - config.network_inputs[0].tvm_dtype_lanes, - config.tvm_device_type, - config.tvm_device_id}; - output = x; -} - -TVMArrayContainerVector VoxelEncoderPreProcessor::schedule(const MixedInputs & voxel_inputs) -{ - // generate encoder_in_features from the voxels - generateFeatures( - voxel_inputs.features, voxel_inputs.num_points_per_voxel, voxel_inputs.coords, - voxel_inputs.num_voxels, config_detail, encoder_in_features); - - TVMArrayCopyFromBytes( - output.getArray(), encoder_in_features.data(), - max_voxel_size * max_point_in_voxel_size * encoder_in_feature_size * input_datatype_bytes); - - return {output}; -} - -BackboneNeckHeadPostProcessor::BackboneNeckHeadPostProcessor( - const tvm_utility::pipeline::InferenceEngineTVMConfig & config, - const CenterPointConfig & config_mod) -: output_datatype_bytes(config.network_outputs[0].tvm_dtype_bits / 8), config_detail(config_mod) -{ - head_out_heatmap.resize( - config.network_outputs[0].node_shape[1] * config.network_outputs[0].node_shape[2] * - config.network_outputs[0].node_shape[3]); - head_out_offset.resize( - config.network_outputs[1].node_shape[1] * config.network_outputs[1].node_shape[2] * - config.network_outputs[1].node_shape[3]); - head_out_z.resize( - config.network_outputs[2].node_shape[1] * config.network_outputs[2].node_shape[2] * - config.network_outputs[2].node_shape[3]); - head_out_dim.resize( - config.network_outputs[3].node_shape[1] * config.network_outputs[3].node_shape[2] * - config.network_outputs[3].node_shape[3]); - head_out_rot.resize( - config.network_outputs[4].node_shape[1] * config.network_outputs[4].node_shape[2] * - config.network_outputs[4].node_shape[3]); - head_out_vel.resize( - config.network_outputs[5].node_shape[1] * config.network_outputs[5].node_shape[2] * - config.network_outputs[5].node_shape[3]); -} - -std::vector BackboneNeckHeadPostProcessor::schedule(const TVMArrayContainerVector & input) -{ - TVMArrayCopyToBytes( - input[0].getArray(), head_out_heatmap.data(), head_out_heatmap.size() * output_datatype_bytes); - TVMArrayCopyToBytes( - input[1].getArray(), head_out_offset.data(), head_out_offset.size() * output_datatype_bytes); - TVMArrayCopyToBytes( - input[2].getArray(), head_out_z.data(), head_out_z.size() * output_datatype_bytes); - TVMArrayCopyToBytes( - input[3].getArray(), head_out_dim.data(), head_out_dim.size() * output_datatype_bytes); - TVMArrayCopyToBytes( - input[4].getArray(), head_out_rot.data(), head_out_rot.size() * output_datatype_bytes); - TVMArrayCopyToBytes( - input[5].getArray(), head_out_vel.data(), head_out_vel.size() * output_datatype_bytes); - - std::vector det_boxes3d; - - generateDetectedBoxes3D( - head_out_heatmap, head_out_offset, head_out_z, head_out_dim, head_out_rot, head_out_vel, - config_detail, det_boxes3d); - - return det_boxes3d; -} - -CenterPointTVM::CenterPointTVM( - const DensificationParam & densification_param, const CenterPointConfig & config, - const std::string & data_path) -: config_ve(config_en), - config_bnh(config_bk), - VE_PreP(std::make_shared(config_en, config)), - VE_IE(std::make_shared(config_en, "lidar_centerpoint_tvm", data_path)), - BNH_IE(std::make_shared(config_bk, "lidar_centerpoint_tvm", data_path)), - BNH_PostP(std::make_shared(config_bk, config)), - scatter_ie(std::make_shared(config_scatter, "lidar_centerpoint_tvm", data_path, "scatter")), - TSP_pipeline(std::make_shared(VE_PreP, VE_IE, scatter_ie, BNH_IE, BNH_PostP)), - config_(config) -{ - vg_ptr_ = std::make_unique(densification_param, config_); - initPtr(); -} - -CenterPointTVM::~CenterPointTVM() -{ -} - -void CenterPointTVM::initPtr() -{ - const auto voxels_size = - config_.max_voxel_size_ * config_.max_point_in_voxel_size_ * config_.point_feature_size_; - const auto coordinates_size = config_.max_voxel_size_ * config_.point_dim_size_; - - voxels_ = std::make_shared>(voxels_size); - coordinates_ = std::make_shared>(coordinates_size); - num_points_per_voxel_ = std::make_shared>(config_.max_voxel_size_); - std::vector shape_coords{ - config_scatter.network_inputs[1].node_shape[0], config_scatter.network_inputs[1].node_shape[1]}; - coords_tvm_ = TVMArrayContainer( - shape_coords, config_scatter.network_inputs[1].tvm_dtype_code, - config_scatter.network_inputs[1].tvm_dtype_bits, - config_scatter.network_inputs[1].tvm_dtype_lanes, config_scatter.tvm_device_type, - config_scatter.tvm_device_id); -} - -bool CenterPointTVM::detect( - const sensor_msgs::msg::PointCloud2 & input_pointcloud_msg, const tf2_ros::Buffer & tf_buffer, - std::vector & det_boxes3d) -{ - std::fill(voxels_->begin(), voxels_->end(), 0); - std::fill(coordinates_->begin(), coordinates_->end(), -1); - std::fill(num_points_per_voxel_->begin(), num_points_per_voxel_->end(), 0); - - if (!preprocess(input_pointcloud_msg, tf_buffer)) { - RCLCPP_WARN_STREAM( - rclcpp::get_logger("lidar_centerpoint"), "Fail to preprocess and skip to detect."); - return false; - } - - MixedInputs voxel_inputs{num_voxels_, *voxels_, *num_points_per_voxel_, *coordinates_}; - auto bnh_output = TSP_pipeline->schedule(voxel_inputs); - - det_boxes3d = bnh_output; - if (det_boxes3d.size() == 0) { - RCLCPP_WARN_STREAM(rclcpp::get_logger("lidar_centerpoint_tvm"), "No detected boxes."); - } - - return true; -} - -bool CenterPointTVM::preprocess( - const sensor_msgs::msg::PointCloud2 & input_pointcloud_msg, const tf2_ros::Buffer & tf_buffer) -{ - bool is_success = vg_ptr_->enqueuePointCloud(input_pointcloud_msg, tf_buffer); - if (!is_success) { - return false; - } - num_voxels_ = vg_ptr_->pointsToVoxels(*voxels_, *coordinates_, *num_points_per_voxel_); - if (num_voxels_ == 0) { - return false; - } - - TVMArrayCopyFromBytes( - coords_tvm_.getArray(), coordinates_->data(), coordinates_->size() * sizeof(int32_t)); - - scatter_ie->set_coords(coords_tvm_); - - return true; -} - -} // namespace lidar_centerpoint_tvm -} // namespace perception -} // namespace autoware diff --git a/perception/lidar_centerpoint_tvm/lib/postprocess/circle_nms.cpp b/perception/lidar_centerpoint_tvm/lib/postprocess/circle_nms.cpp deleted file mode 100644 index 0ab51347be772..0000000000000 --- a/perception/lidar_centerpoint_tvm/lib/postprocess/circle_nms.cpp +++ /dev/null @@ -1,65 +0,0 @@ -// Copyright 2022 AutoCore Ltd. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "lidar_centerpoint_tvm/postprocess/circle_nms.hpp" - -#include - -#include - -namespace autoware -{ -namespace perception -{ -namespace lidar_centerpoint_tvm -{ - -inline float dist2dPow(const Box3D & a, const Box3D & b) -{ - return powf(a.x - b.x, 2) + powf(a.y - b.y, 2); -} - -std::size_t circleNMS( - std::vector & boxes3d, const float dist_thresh, std::vector & keep_mask) -{ - // params: boxes3d, vector sorted by score from largest to smallest - const auto num_boxes3d = boxes3d.size(); - const float dist2d_pow_thresh = powf(dist_thresh, 2); - - // generate keep_mask - std::size_t num_to_keep = 0; - std::vector suppress(num_boxes3d); // suppress[i]=true mean i-th box should be suppressed - - // std::uint64_t * suppress_ptr = & suppress.front(); - for (std::size_t i = 0; i < num_boxes3d; i++) { - if (suppress[i]) { - keep_mask[i] = false; - } else { - keep_mask[i] = true; - num_to_keep++; - for (std::size_t j = i + 1; j < num_boxes3d; j++) { - // if the j-th box has been suppressed, continue - if (suppress[j]) continue; - // if the j-th box is in the circle of i-th, set j-th box to be suppressed - if (dist2dPow(boxes3d[i], boxes3d[j]) < dist2d_pow_thresh) suppress[j] = true; - } - } - } - - return num_to_keep; -} - -} // namespace lidar_centerpoint_tvm -} // namespace perception -} // namespace autoware diff --git a/perception/lidar_centerpoint_tvm/lib/postprocess/generate_detected_boxes.cpp b/perception/lidar_centerpoint_tvm/lib/postprocess/generate_detected_boxes.cpp deleted file mode 100644 index cbfe2d0c66669..0000000000000 --- a/perception/lidar_centerpoint_tvm/lib/postprocess/generate_detected_boxes.cpp +++ /dev/null @@ -1,176 +0,0 @@ -// Copyright 2022 AutoCore Ltd., TIER IV, Inc. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "lidar_centerpoint_tvm/postprocess/generate_detected_boxes.hpp" - -#include - -#include -#include -#include -#include - -namespace -{ -const std::size_t THREAD_NUM_POST = 32; -} // namespace - -namespace autoware -{ -namespace perception -{ -namespace lidar_centerpoint_tvm -{ - -struct is_score_greater -{ - explicit is_score_greater(float t) : t_(t) {} - bool operator()(const Box3D & b) { return b.score > t_; } - -private: - float t_{0.0}; -}; - -struct is_kept -{ - bool operator()(const bool keep) { return keep; } -}; - -struct score_greater -{ - bool operator()(const Box3D & lb, const Box3D & rb) { return lb.score > rb.score; } -}; - -inline float sigmoid(float x) -{ - return 1.0f / (1.0f + expf(-x)); -} - -void generateBoxes3D_worker( - const std::vector & out_heatmap, const std::vector & out_offset, - const std::vector & out_z, const std::vector & out_dim, - const std::vector & out_rot, const std::vector & out_vel, - const CenterPointConfig & config, std::vector & boxes3d, std::size_t thread_idx, - std::size_t grids_per_thread) -{ - // generate boxes3d from the outputs of the network. - // shape of out_*: (N, DOWN_GRID_SIZE_Y, DOWN_GRID_SIZE_X) - // heatmap: N = class_size, offset: N = 2, z: N = 1, dim: N = 3, rot: N = 2, vel: N = 2 - for (std::size_t idx = 0; idx < grids_per_thread; idx++) { - std::size_t grid_idx = thread_idx * grids_per_thread + idx; - const auto down_grid_size = config.down_grid_size_y_ * config.down_grid_size_x_; - if (grid_idx >= down_grid_size) { - return; - } - - const auto yi = grid_idx / config.down_grid_size_x_; - const auto xi = grid_idx % config.down_grid_size_x_; - - int32_t label = -1; - float max_score = -1; - for (std::size_t ci = 0; ci < config.class_size_; ci++) { - float score = sigmoid(out_heatmap[down_grid_size * ci + grid_idx]); - if (score > max_score) { - label = ci; - max_score = score; - } - } - - const float offset_x = out_offset[down_grid_size * 0 + grid_idx]; - const float offset_y = out_offset[down_grid_size * 1 + grid_idx]; - const float x = - config.voxel_size_x_ * config.downsample_factor_ * (xi + offset_x) + config.range_min_x_; - const float y = - config.voxel_size_y_ * config.downsample_factor_ * (yi + offset_y) + config.range_min_y_; - const float z = out_z[grid_idx]; - const float w = out_dim[down_grid_size * 0 + grid_idx]; - const float l = out_dim[down_grid_size * 1 + grid_idx]; - const float h = out_dim[down_grid_size * 2 + grid_idx]; - const float yaw_sin = out_rot[down_grid_size * 0 + grid_idx]; - const float yaw_cos = out_rot[down_grid_size * 1 + grid_idx]; - const float yaw_norm = sqrtf(yaw_sin * yaw_sin + yaw_cos * yaw_cos); - const float vel_x = out_vel[down_grid_size * 0 + grid_idx]; - const float vel_y = out_vel[down_grid_size * 1 + grid_idx]; - - boxes3d[grid_idx].label = label; - boxes3d[grid_idx].score = yaw_norm >= config.yaw_norm_threshold_ ? max_score : 0.f; - boxes3d[grid_idx].x = x; - boxes3d[grid_idx].y = y; - boxes3d[grid_idx].z = z; - boxes3d[grid_idx].length = expf(l); - boxes3d[grid_idx].width = expf(w); - boxes3d[grid_idx].height = expf(h); - boxes3d[grid_idx].yaw = atan2f(yaw_sin, yaw_cos); - boxes3d[grid_idx].vel_x = vel_x; - boxes3d[grid_idx].vel_y = vel_y; - } -} - -// cspell: ignore divup -void generateDetectedBoxes3D( - const std::vector & out_heatmap, const std::vector & out_offset, - const std::vector & out_z, const std::vector & out_dim, - const std::vector & out_rot, const std::vector & out_vel, - const CenterPointConfig & config, std::vector & det_boxes3d) -{ - std::vector threadPool; - const auto down_grid_size = config.down_grid_size_y_ * config.down_grid_size_x_; - std::vector boxes3d(down_grid_size); - std::size_t grids_per_thread = divup(down_grid_size, THREAD_NUM_POST); - for (std::size_t idx = 0; idx < THREAD_NUM_POST; idx++) { - std::thread worker( - generateBoxes3D_worker, std::ref(out_heatmap), std::ref(out_offset), std::ref(out_z), - std::ref(out_dim), std::ref(out_rot), std::ref(out_vel), std::ref(config), std::ref(boxes3d), - idx, grids_per_thread); - threadPool.push_back(std::move(worker)); - } - for (std::size_t idx = 0; idx < THREAD_NUM_POST; idx++) { - threadPool[idx].join(); - } - - // suppress by score - const auto num_det_boxes3d = - std::count_if(boxes3d.begin(), boxes3d.end(), is_score_greater(config.score_threshold_)); - if (num_det_boxes3d == 0) { - // construct boxes3d failed - std::cerr << "lidar_centerpoint_tvm: construct boxes3d failed" << std::endl; - } - std::vector det_boxes3d_no_nms(num_det_boxes3d); - std::copy_if( - boxes3d.begin(), boxes3d.end(), det_boxes3d_no_nms.begin(), - is_score_greater(config.score_threshold_)); - - // sort by score - std::sort(det_boxes3d_no_nms.begin(), det_boxes3d_no_nms.end(), score_greater()); - - // suppress by NMS - std::vector final_keep_mask(num_det_boxes3d); - const auto num_final_det_boxes3d = - circleNMS(det_boxes3d_no_nms, config.circle_nms_dist_threshold_, final_keep_mask); - - det_boxes3d.resize(num_final_det_boxes3d); - std::size_t box_id = 0; - for (std::size_t idx = 0; idx < final_keep_mask.size(); idx++) { - if (final_keep_mask[idx]) { - det_boxes3d[box_id] = det_boxes3d_no_nms[idx]; - box_id++; - } - } - // std::copy_if(det_boxes3d_no_nms.begin(), det_boxes3d_no_nms.end(), final_keep_mask.begin(), - // det_boxes3d.begin(), is_kept()); -} - -} // namespace lidar_centerpoint_tvm -} // namespace perception -} // namespace autoware diff --git a/perception/lidar_centerpoint_tvm/lib/preprocess/generate_features.cpp b/perception/lidar_centerpoint_tvm/lib/preprocess/generate_features.cpp deleted file mode 100644 index 9b98adc2def4e..0000000000000 --- a/perception/lidar_centerpoint_tvm/lib/preprocess/generate_features.cpp +++ /dev/null @@ -1,131 +0,0 @@ -// Copyright 2021-2022 AutoCore Ltd., TIER IV, Inc. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "lidar_centerpoint_tvm/preprocess/generate_features.hpp" - -#include - -#include - -namespace -{ -const std::size_t THREAD_NUM_VFE = 4; -} // namespace - -namespace autoware -{ -namespace perception -{ -namespace lidar_centerpoint_tvm -{ - -void generateFeatures_worker( - const std::vector & voxel_features, const std::vector & voxel_num_points, - const std::vector & coords, const std::size_t num_voxels, - const CenterPointConfig & config, std::vector & features, std::size_t thread_idx, - std::size_t pillars_per_thread) -{ - for (std::size_t idx = 0; idx < pillars_per_thread; idx++) { - std::size_t pillar_idx = thread_idx * pillars_per_thread + idx; - if (pillar_idx >= num_voxels) return; - - // voxel/pillar information - float points_sum[3] = {0.0, 0.0, 0.0}; // sum of x, y, z in the voxel - int32_t coordinate[3] = { - coords[pillar_idx * 3], coords[pillar_idx * 3 + 1], - coords[pillar_idx * 3 + 2]}; // 3D position(z,y,x) of the voxel - std::size_t points_count = voxel_num_points[pillar_idx]; // number of points in the voxel - - for (std::size_t i = 0; i < config.max_point_in_voxel_size_; i++) { - std::size_t point_idx = - pillar_idx * config.max_point_in_voxel_size_ * config.point_feature_size_ + - i * config.point_feature_size_; - for (std::size_t j = 0; j < config.point_feature_size_; j++) { - // point (x, y, z, intensity) - if (i < points_count && j < 3) points_sum[j] += voxel_features[point_idx + j]; - } - } - - // calculate voxel mean - float mean[3] = { - points_sum[0] / points_count, points_sum[1] / points_count, points_sum[2] / points_count}; - // calculate offset - float x_offset = coordinate[2] * config.voxel_size_x_ + config.offset_x_; - float y_offset = coordinate[1] * config.voxel_size_y_ + config.offset_y_; - // float z_offset = coordinate[0] * config.voxel_size_z_ + config.offset_z_; - - // build the encoder_in_features - for (std::size_t i = 0; i < config.max_point_in_voxel_size_; i++) { - // feature_idx - std::size_t feature_idx = - pillar_idx * config.max_point_in_voxel_size_ * config.encoder_in_feature_size_ + - i * config.encoder_in_feature_size_; - std::size_t point_idx = - pillar_idx * config.max_point_in_voxel_size_ * config.point_feature_size_ + - i * config.point_feature_size_; - if (i < points_count) { - features[feature_idx + 0] = voxel_features[point_idx + 0]; - features[feature_idx + 1] = voxel_features[point_idx + 1]; - features[feature_idx + 2] = voxel_features[point_idx + 2]; - features[feature_idx + 3] = voxel_features[point_idx + 3]; - // feature-mean - features[feature_idx + 4] = voxel_features[point_idx + 0] - mean[0]; - features[feature_idx + 5] = voxel_features[point_idx + 1] - mean[1]; - features[feature_idx + 6] = voxel_features[point_idx + 2] - mean[2]; - // feature-offset - features[feature_idx + 7] = voxel_features[point_idx + 0] - x_offset; - features[feature_idx + 8] = voxel_features[point_idx + 1] - y_offset; - - } else { - features[feature_idx + 0] = 0; - features[feature_idx + 1] = 0; - features[feature_idx + 2] = 0; - features[feature_idx + 3] = 0; - // feature-mean - features[feature_idx + 4] = 0; - features[feature_idx + 5] = 0; - features[feature_idx + 6] = 0; - // feature-offset - features[feature_idx + 7] = 0; - features[feature_idx + 8] = 0; - } - } - } -} - -// cspell: ignore divup -void generateFeatures( - const std::vector & voxel_features, const std::vector & voxel_num_points, - const std::vector & coords, const std::size_t num_voxels, - const CenterPointConfig & config, std::vector & features) -{ - // voxel_features (float): max_voxel_size*max_point_in_voxel_size*point_feature_size - // voxel_num_points (int): max_voxel_size - // coords (int): max_voxel_size*point_dim_size - std::vector threadPool; - std::size_t pillars_per_thread = divup(config.max_voxel_size_, THREAD_NUM_VFE); - for (std::size_t idx = 0; idx < THREAD_NUM_VFE; idx++) { - std::thread worker( - generateFeatures_worker, std::ref(voxel_features), std::ref(voxel_num_points), - std::ref(coords), num_voxels, std::ref(config), std::ref(features), idx, pillars_per_thread); - threadPool.push_back(std::move(worker)); - } - for (std::size_t idx = 0; idx < THREAD_NUM_VFE; idx++) { - threadPool[idx].join(); - } -} - -} // namespace lidar_centerpoint_tvm -} // namespace perception -} // namespace autoware diff --git a/perception/lidar_centerpoint_tvm/lib/preprocess/voxel_generator.cpp b/perception/lidar_centerpoint_tvm/lib/preprocess/voxel_generator.cpp deleted file mode 100644 index 11ae8e7064b1c..0000000000000 --- a/perception/lidar_centerpoint_tvm/lib/preprocess/voxel_generator.cpp +++ /dev/null @@ -1,138 +0,0 @@ -// Copyright 2021-2022 AutoCore Ltd., TIER IV, Inc. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "lidar_centerpoint_tvm/preprocess/voxel_generator.hpp" - -#include - -namespace autoware -{ -namespace perception -{ -namespace lidar_centerpoint_tvm -{ - -VoxelGeneratorTemplate::VoxelGeneratorTemplate( - const DensificationParam & param, const CenterPointConfig & config) -: config_(config) -{ - pd_ptr_ = std::make_unique(param); - range_[0] = config.range_min_x_; - range_[1] = config.range_min_y_; - range_[2] = config.range_min_z_; - range_[3] = config.range_max_x_; - range_[4] = config.range_max_y_; - range_[5] = config.range_max_z_; - grid_size_[0] = config.grid_size_x_; - grid_size_[1] = config.grid_size_y_; - grid_size_[2] = config.grid_size_z_; - recip_voxel_size_[0] = 1 / config.voxel_size_x_; - recip_voxel_size_[1] = 1 / config.voxel_size_y_; - recip_voxel_size_[2] = 1 / config.voxel_size_z_; -} - -bool VoxelGeneratorTemplate::enqueuePointCloud( - const sensor_msgs::msg::PointCloud2 & input_pointcloud_msg, const tf2_ros::Buffer & tf_buffer) -{ - return pd_ptr_->enqueuePointCloud(input_pointcloud_msg, tf_buffer); -} - -std::size_t VoxelGenerator::pointsToVoxels( - std::vector & voxels, std::vector & coordinates, - std::vector & num_points_per_voxel) -{ - // voxels (float): (max_voxel_size * max_point_in_voxel_size * point_feature_size), point info in - // each voxel coordinates (int): (max_voxel_size * point_dim_size), the index from voxel to its 3D - // position num_points_per_voxel (float): (max_voxel_size), the number of points in each voxel - - const std::size_t grid_size = config_.grid_size_z_ * config_.grid_size_y_ * config_.grid_size_x_; - std::vector coord_to_voxel_idx( - grid_size, -1); // the index from 3D position to the voxel id - - std::size_t voxel_cnt = 0; // @return - std::vector point; - point.resize(config_.point_feature_size_); - std::vector coord_zyx; // record which grid the zyx coord of current point belong to - coord_zyx.resize(config_.point_dim_size_); - bool out_of_range; - std::size_t point_cnt; - int32_t c, coord_idx, voxel_idx; - Eigen::Vector3f point_current, point_past; - - for (auto pc_cache_iter = pd_ptr_->getPointCloudCacheIter(); !pd_ptr_->isCacheEnd(pc_cache_iter); - pc_cache_iter++) { - auto pc_msg = pc_cache_iter->pointcloud_msg; - auto affine_past2current = - pd_ptr_->getAffineWorldToCurrent() * pc_cache_iter->affine_past2world; - float time_lag = static_cast( - pd_ptr_->getCurrentTimestamp() - rclcpp::Time(pc_msg.header.stamp).seconds()); - - for (sensor_msgs::PointCloud2ConstIterator x_iter(pc_msg, "x"), y_iter(pc_msg, "y"), - z_iter(pc_msg, "z"); - x_iter != x_iter.end(); ++x_iter, ++y_iter, ++z_iter) { - point_past << *x_iter, *y_iter, *z_iter; - point_current = affine_past2current * point_past; - - point[0] = point_current.x(); - point[1] = point_current.y(); - point[2] = point_current.z(); - point[3] = time_lag; - - out_of_range = false; - for (std::size_t di = 0; di < config_.point_dim_size_; di++) { - c = static_cast((point[di] - range_[di]) * recip_voxel_size_[di]); - if (c < 0 || c >= grid_size_[di]) { - out_of_range = true; - break; - } - coord_zyx[config_.point_dim_size_ - di - 1] = c; - } - if (out_of_range) { - continue; - } - - coord_idx = coord_zyx[0] * config_.grid_size_y_ * config_.grid_size_x_ + - coord_zyx[1] * config_.grid_size_x_ + coord_zyx[2]; - voxel_idx = coord_to_voxel_idx[coord_idx]; - if (voxel_idx == -1) { - voxel_idx = voxel_cnt; - if (voxel_cnt >= config_.max_voxel_size_) { - continue; - } - - voxel_cnt++; - coord_to_voxel_idx[coord_idx] = voxel_idx; - for (std::size_t di = 0; di < config_.point_dim_size_; di++) { - coordinates[voxel_idx * config_.point_dim_size_ + di] = coord_zyx[di]; - } - } - - point_cnt = num_points_per_voxel[voxel_idx]; - if (point_cnt < config_.max_point_in_voxel_size_) { - for (std::size_t fi = 0; fi < config_.point_feature_size_; fi++) { - voxels - [voxel_idx * config_.max_point_in_voxel_size_ * config_.point_feature_size_ + - point_cnt * config_.point_feature_size_ + fi] = point[fi]; - } - num_points_per_voxel[voxel_idx]++; - } - } - } - - return voxel_cnt; -} - -} // namespace lidar_centerpoint_tvm -} // namespace perception -} // namespace autoware diff --git a/perception/lidar_centerpoint_tvm/lib/ros_utils.cpp b/perception/lidar_centerpoint_tvm/lib/ros_utils.cpp deleted file mode 100644 index 94639d71e66c0..0000000000000 --- a/perception/lidar_centerpoint_tvm/lib/ros_utils.cpp +++ /dev/null @@ -1,118 +0,0 @@ -// Copyright 2022 AutoCore Ltd., TIER IV, Inc. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "lidar_centerpoint_tvm/ros_utils.hpp" - -#include -#include - -namespace autoware -{ -namespace perception -{ -namespace lidar_centerpoint_tvm -{ - -using Label = autoware_perception_msgs::msg::ObjectClassification; - -void box3DToDetectedObject( - const Box3D & box3d, const std::vector & class_names, - const bool rename_car_to_truck_and_bus, const bool has_twist, - autoware_perception_msgs::msg::DetectedObject & obj) -{ - // TODO(yukke42): the value of classification confidence of DNN, not probability. - obj.existence_probability = box3d.score; - - // classification - autoware_perception_msgs::msg::ObjectClassification classification; - classification.probability = 1.0f; - if (box3d.label >= 0 && static_cast(box3d.label) < class_names.size()) { - classification.label = getSemanticType(class_names[box3d.label]); - } else { - classification.label = Label::UNKNOWN; - RCLCPP_WARN_STREAM( - rclcpp::get_logger("lidar_centerpoint"), "Unexpected label: UNKNOWN is set."); - } - - float l = box3d.length; - float w = box3d.width; - if (classification.label == Label::CAR && rename_car_to_truck_and_bus) { - // Note: object size is referred from multi_object_tracker - if ((w * l > 2.2 * 5.5) && (w * l <= 2.5 * 7.9)) { - classification.label = Label::TRUCK; - } else if (w * l > 2.5 * 7.9) { - classification.label = Label::BUS; - } - } - - if (isCarLikeVehicleLabel(classification.label)) { - obj.kinematics.orientation_availability = - autoware_perception_msgs::msg::DetectedObjectKinematics::SIGN_UNKNOWN; - } - - obj.classification.emplace_back(classification); - - // pose and shape - // mmdet3d yaw format to ros yaw format - float yaw = -box3d.yaw - autoware::universe_utils::pi / 2; - obj.kinematics.pose_with_covariance.pose.position = - autoware::universe_utils::createPoint(box3d.x, box3d.y, box3d.z); - obj.kinematics.pose_with_covariance.pose.orientation = - autoware::universe_utils::createQuaternionFromYaw(yaw); - obj.shape.type = autoware_perception_msgs::msg::Shape::BOUNDING_BOX; - obj.shape.dimensions = - autoware::universe_utils::createTranslation(box3d.length, box3d.width, box3d.height); - - // twist - if (has_twist) { - float vel_x = box3d.vel_x; - float vel_y = box3d.vel_y; - geometry_msgs::msg::Twist twist; - twist.linear.x = std::sqrt(std::pow(vel_x, 2) + std::pow(vel_y, 2)); - twist.angular.z = 2 * (std::atan2(vel_y, vel_x) - yaw); - obj.kinematics.twist_with_covariance.twist = twist; - obj.kinematics.has_twist = has_twist; - } -} - -uint8_t getSemanticType(const std::string & class_name) -{ - if (class_name == "CAR") { - return Label::CAR; - } else if (class_name == "TRUCK") { - return Label::TRUCK; - } else if (class_name == "BUS") { - return Label::BUS; - } else if (class_name == "TRAILER") { - return Label::TRAILER; - } else if (class_name == "BICYCLE") { - return Label::BICYCLE; - } else if (class_name == "MOTORBIKE") { - return Label::MOTORCYCLE; - } else if (class_name == "PEDESTRIAN") { - return Label::PEDESTRIAN; - } else { - return Label::UNKNOWN; - } -} - -bool isCarLikeVehicleLabel(const uint8_t label) -{ - return label == Label::CAR || label == Label::TRUCK || label == Label::BUS || - label == Label::TRAILER; -} - -} // namespace lidar_centerpoint_tvm -} // namespace perception -} // namespace autoware diff --git a/perception/lidar_centerpoint_tvm/lib/utils.cpp b/perception/lidar_centerpoint_tvm/lib/utils.cpp deleted file mode 100644 index caf9cb84fa1c7..0000000000000 --- a/perception/lidar_centerpoint_tvm/lib/utils.cpp +++ /dev/null @@ -1,41 +0,0 @@ -// Copyright 2022 AutoCore Ltd., TIER IV, Inc. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "lidar_centerpoint_tvm/utils.hpp" - -#include - -namespace autoware -{ -namespace perception -{ -namespace lidar_centerpoint_tvm -{ - -// cspell: ignore divup -std::size_t divup(const std::size_t a, const std::size_t b) -{ - if (a == 0) { - throw std::runtime_error("A dividend of divup isn't positive."); - } - if (b == 0) { - throw std::runtime_error("A divisor of divup isn't positive."); - } - - return (a + b - 1) / b; -} - -} // namespace lidar_centerpoint_tvm -} // namespace perception -} // namespace autoware diff --git a/perception/lidar_centerpoint_tvm/package.xml b/perception/lidar_centerpoint_tvm/package.xml deleted file mode 100644 index c71a27e4a1677..0000000000000 --- a/perception/lidar_centerpoint_tvm/package.xml +++ /dev/null @@ -1,32 +0,0 @@ - - - - lidar_centerpoint_tvm - 0.1.0 - lidar_centerpoint_tvm - Liu Zhengfei - Xinyu Wang - Apache 2.0 - - ament_cmake_auto - autoware_cmake - - autoware_perception_msgs - autoware_universe_utils - pcl_ros - rclcpp - rclcpp_components - tf2_eigen - tf2_geometry_msgs - tf2_sensor_msgs - tvm_utility - tvm_vendor - - ament_cmake_gtest - ament_lint_auto - autoware_lint_common - - - ament_cmake - - diff --git a/perception/lidar_centerpoint_tvm/scripts/lidar_centerpoint_visualizer.py b/perception/lidar_centerpoint_tvm/scripts/lidar_centerpoint_visualizer.py deleted file mode 100755 index 5a1135379615f..0000000000000 --- a/perception/lidar_centerpoint_tvm/scripts/lidar_centerpoint_visualizer.py +++ /dev/null @@ -1,55 +0,0 @@ -#!/usr/bin/env python -# Copyright 2022 TIER IV, Inc. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import os -import time - -import open3d as o3d -import rclpy -from rclpy.node import Node - - -def main(args=None): - rclpy.init(args=args) - - node = Node("lidar_centerpoint_visualizer") - node.declare_parameter("pcd_path", rclpy.Parameter.Type.STRING) - node.declare_parameter("detections_path", rclpy.Parameter.Type.STRING) - - pcd_path = node.get_parameter("pcd_path").get_parameter_value().string_value - detections_path = node.get_parameter("detections_path").get_parameter_value().string_value - - while not os.path.exists(pcd_path) and not os.path.exists(detections_path): - time.sleep(1.0) - - if not rclpy.ok(): - rclpy.shutdown() - return - - mesh = o3d.io.read_triangle_mesh(detections_path) - pcd = o3d.io.read_point_cloud(pcd_path) - - mesh_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=1.0, origin=[0, 0, 0]) - - detection_lines = o3d.geometry.LineSet.create_from_triangle_mesh(mesh) - detection_lines.paint_uniform_color([1.0, 0.0, 1.0]) - - o3d.visualization.draw_geometries([mesh_frame, pcd, detection_lines]) - - rclpy.shutdown() - - -if __name__ == "__main__": - main() diff --git a/perception/lidar_centerpoint_tvm/src/node.cpp b/perception/lidar_centerpoint_tvm/src/node.cpp deleted file mode 100644 index 9cf82b1c6bcae..0000000000000 --- a/perception/lidar_centerpoint_tvm/src/node.cpp +++ /dev/null @@ -1,154 +0,0 @@ -// Copyright 2021-2022 AutoCore Ltd., TIER IV, Inc. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "lidar_centerpoint_tvm/node.hpp" - -#include -#include -#include -#include -#include - -#ifdef ROS_DISTRO_GALACTIC -#include -#else -#include -#endif - -#include -#include -#include - -namespace autoware -{ -namespace perception -{ -namespace lidar_centerpoint_tvm -{ - -LidarCenterPointTVMNode::LidarCenterPointTVMNode(const rclcpp::NodeOptions & node_options) -: Node("lidar_centerpoint_tvm", node_options), tf_buffer_(this->get_clock()) -{ - const float score_threshold = - static_cast(this->declare_parameter("score_threshold", 0.35)); - const float circle_nms_dist_threshold = - static_cast(this->declare_parameter("circle_nms_dist_threshold", 1.5)); - const float yaw_norm_threshold = - static_cast(this->declare_parameter("yaw_norm_threshold", 0.0)); - const std::string densification_world_frame_id = - this->declare_parameter("densification_world_frame_id", "map"); - const int32_t densification_num_past_frames = - this->declare_parameter("densification_num_past_frames", 1); - - class_names_ = this->declare_parameter>("class_names"); - rename_car_to_truck_and_bus_ = this->declare_parameter("rename_car_to_truck_and_bus", false); - has_twist_ = this->declare_parameter("has_twist", false); - - const std::size_t point_feature_size = - static_cast(this->declare_parameter("point_feature_size")); - const std::size_t max_voxel_size = - static_cast(this->declare_parameter("max_voxel_size")); - const auto point_cloud_range = this->declare_parameter>("point_cloud_range"); - const auto voxel_size = this->declare_parameter>("voxel_size"); - const std::size_t downsample_factor = - static_cast(this->declare_parameter("downsample_factor")); - const std::size_t encoder_in_feature_size = - static_cast(this->declare_parameter("encoder_in_feature_size")); - const auto data_path = this->declare_parameter("data_path"); - - DensificationParam densification_param( - densification_world_frame_id, densification_num_past_frames); - - if (point_cloud_range.size() != 6) { - RCLCPP_WARN_STREAM( - rclcpp::get_logger("lidar_centerpoint"), - "The size of point_cloud_range != 6: use the default parameters."); - } - if (voxel_size.size() != 3) { - RCLCPP_WARN_STREAM( - rclcpp::get_logger("lidar_centerpoint"), - "The size of voxel_size != 3: use the default parameters."); - } - CenterPointConfig config( - class_names_.size(), point_feature_size, max_voxel_size, point_cloud_range, voxel_size, - downsample_factor, encoder_in_feature_size, score_threshold, circle_nms_dist_threshold, - yaw_norm_threshold); - detector_ptr_ = std::make_unique(densification_param, config, data_path); - - pointcloud_sub_ = this->create_subscription( - "~/input/pointcloud", rclcpp::SensorDataQoS{}.keep_last(1), - std::bind(&LidarCenterPointTVMNode::pointCloudCallback, this, std::placeholders::_1)); - objects_pub_ = this->create_publisher( - "~/output/objects", rclcpp::QoS{1}); - - // initialize debug tool - { - using autoware::universe_utils::DebugPublisher; - using autoware::universe_utils::StopWatch; - stop_watch_ptr_ = std::make_unique>(); - debug_publisher_ptr_ = std::make_unique(this, "lidar_centerpoint_tvm"); - stop_watch_ptr_->tic("cyclic_time"); - stop_watch_ptr_->tic("processing_time"); - } -} - -void LidarCenterPointTVMNode::pointCloudCallback( - const sensor_msgs::msg::PointCloud2::ConstSharedPtr input_pointcloud_msg) -{ - const auto objects_sub_count = - objects_pub_->get_subscription_count() + objects_pub_->get_intra_process_subscription_count(); - if (objects_sub_count < 1) { - return; - } - - if (stop_watch_ptr_) { - stop_watch_ptr_->toc("processing_time", true); - } - - std::vector det_boxes3d; - bool is_success = detector_ptr_->detect(*input_pointcloud_msg, tf_buffer_, det_boxes3d); - if (!is_success) { - return; - } - - autoware_perception_msgs::msg::DetectedObjects output_msg; - output_msg.header = input_pointcloud_msg->header; - for (const auto & box3d : det_boxes3d) { - autoware_perception_msgs::msg::DetectedObject obj; - box3DToDetectedObject(box3d, class_names_, rename_car_to_truck_and_bus_, has_twist_, obj); - output_msg.objects.emplace_back(obj); - } - - if (objects_sub_count > 0) { - objects_pub_->publish(output_msg); - } - - // add processing time for debug - if (debug_publisher_ptr_ && stop_watch_ptr_) { - const double cyclic_time_ms = stop_watch_ptr_->toc("cyclic_time", true); - const double processing_time_ms = stop_watch_ptr_->toc("processing_time", true); - debug_publisher_ptr_->publish( - "debug/cyclic_time_ms", cyclic_time_ms); - debug_publisher_ptr_->publish( - "debug/processing_time_ms", processing_time_ms); - } -} - -} // namespace lidar_centerpoint_tvm -} // namespace perception -} // namespace autoware - -#include -RCLCPP_COMPONENTS_REGISTER_NODE( - autoware::perception::lidar_centerpoint_tvm::LidarCenterPointTVMNode) diff --git a/perception/lidar_centerpoint_tvm/src/single_inference_node.cpp b/perception/lidar_centerpoint_tvm/src/single_inference_node.cpp deleted file mode 100644 index 224664e51ea2d..0000000000000 --- a/perception/lidar_centerpoint_tvm/src/single_inference_node.cpp +++ /dev/null @@ -1,239 +0,0 @@ -// Copyright 2022 TIER IV, Inc. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "lidar_centerpoint_tvm/single_inference_node.hpp" - -#include -#include -#include -#include -#include - -#include - -#include -#include -#ifdef ROS_DISTRO_GALACTIC -#include -#else -#include -#endif - -#include -#include -#include - -namespace autoware -{ -namespace perception -{ -namespace lidar_centerpoint_tvm -{ - -SingleInferenceLidarCenterPointNode::SingleInferenceLidarCenterPointNode( - const rclcpp::NodeOptions & node_options) -: Node("lidar_center_point_tvm", node_options), tf_buffer_(this->get_clock()) -{ - const float score_threshold = - static_cast(this->declare_parameter("score_threshold", 0.35)); - const float circle_nms_dist_threshold = - static_cast(this->declare_parameter("circle_nms_dist_threshold", 1.5)); - const float yaw_norm_threshold = - static_cast(this->declare_parameter("yaw_norm_threshold", 0.0)); - const std::string densification_world_frame_id = - this->declare_parameter("densification_world_frame_id", "map"); - const int densification_num_past_frames = - this->declare_parameter("densification_num_past_frames", 0); - - class_names_ = this->declare_parameter>("class_names"); - has_twist_ = this->declare_parameter("has_twist", false); - const std::size_t point_feature_size = - static_cast(this->declare_parameter("point_feature_size")); - const std::size_t max_voxel_size = - static_cast(this->declare_parameter("max_voxel_size")); - const auto point_cloud_range = this->declare_parameter>("point_cloud_range"); - const auto voxel_size = this->declare_parameter>("voxel_size"); - const std::size_t downsample_factor = - static_cast(this->declare_parameter("downsample_factor")); - const std::size_t encoder_in_feature_size = - static_cast(this->declare_parameter("encoder_in_feature_size")); - const auto pcd_path = this->declare_parameter("pcd_path"); - const auto detections_path = this->declare_parameter("detections_path"); - const auto data_path = this->declare_parameter("data_path"); - DensificationParam densification_param( - densification_world_frame_id, densification_num_past_frames); - - if (point_cloud_range.size() != 6) { - RCLCPP_WARN_STREAM( - rclcpp::get_logger("single_inference_lidar_centerpoint"), - "The size of point_cloud_range != 6: use the default parameters."); - } - if (voxel_size.size() != 3) { - RCLCPP_WARN_STREAM( - rclcpp::get_logger("single_inference_lidar_centerpoint"), - "The size of voxel_size != 3: use the default parameters."); - } - CenterPointConfig config( - class_names_.size(), point_feature_size, max_voxel_size, point_cloud_range, voxel_size, - downsample_factor, encoder_in_feature_size, score_threshold, circle_nms_dist_threshold, - yaw_norm_threshold); - detector_ptr_ = std::make_unique(densification_param, config, data_path); - - detect(pcd_path, detections_path); - exit(0); -} - -std::vector SingleInferenceLidarCenterPointNode::getVertices( - const autoware_perception_msgs::msg::Shape & shape, const Eigen::Affine3d & pose) const -{ - std::vector vertices; - Eigen::Vector3d vertex; - - vertex.x() = -shape.dimensions.x / 2.0; - vertex.y() = -shape.dimensions.y / 2.0; - vertex.z() = shape.dimensions.z / 2.0; - vertices.push_back(pose * vertex); - - vertex.x() = -shape.dimensions.x / 2.0; - vertex.y() = shape.dimensions.y / 2.0; - vertex.z() = shape.dimensions.z / 2.0; - vertices.push_back(pose * vertex); - - vertex.x() = -shape.dimensions.x / 2.0; - vertex.y() = shape.dimensions.y / 2.0; - vertex.z() = -shape.dimensions.z / 2.0; - vertices.push_back(pose * vertex); - - vertex.x() = shape.dimensions.x / 2.0; - vertex.y() = shape.dimensions.y / 2.0; - vertex.z() = shape.dimensions.z / 2.0; - vertices.push_back(pose * vertex); - - vertex.x() = shape.dimensions.x / 2.0; - vertex.y() = shape.dimensions.y / 2.0; - vertex.z() = -shape.dimensions.z / 2.0; - vertices.push_back(pose * vertex); - - vertex.x() = shape.dimensions.x / 2.0; - vertex.y() = -shape.dimensions.y / 2.0; - vertex.z() = shape.dimensions.z / 2.0; - vertices.push_back(pose * vertex); - - vertex.x() = shape.dimensions.x / 2.0; - vertex.y() = -shape.dimensions.y / 2.0; - vertex.z() = -shape.dimensions.z / 2.0; - vertices.push_back(pose * vertex); - - vertex.x() = -shape.dimensions.x / 2.0; - vertex.y() = -shape.dimensions.y / 2.0; - vertex.z() = -shape.dimensions.z / 2.0; - vertices.push_back(pose * vertex); - - return vertices; -} - -void SingleInferenceLidarCenterPointNode::detect( - const std::string & pcd_path, const std::string & detections_path) -{ - sensor_msgs::msg::PointCloud2 msg; - pcl::PointCloud::Ptr pc_ptr(new pcl::PointCloud()); - - pcl::io::loadPCDFile(pcd_path, *pc_ptr); - pcl::toROSMsg(*pc_ptr, msg); - msg.header.frame_id = "lidar_frame"; - - std::vector det_boxes3d; - bool is_success = detector_ptr_->detect(msg, tf_buffer_, det_boxes3d); - if (!is_success) { - return; - } - - autoware_perception_msgs::msg::DetectedObjects output_msg; - output_msg.header = msg.header; - for (const auto & box3d : det_boxes3d) { - autoware_perception_msgs::msg::DetectedObject obj; - box3DToDetectedObject(box3d, class_names_, rename_car_to_truck_and_bus_, has_twist_, obj); - output_msg.objects.emplace_back(obj); - } - - dumpDetectionsAsMesh(output_msg, detections_path); - - RCLCPP_INFO( - rclcpp::get_logger("single_inference_lidar_centerpoint_tvm"), - "The detection results were saved as meshes in %s", detections_path.c_str()); -} - -void SingleInferenceLidarCenterPointNode::dumpDetectionsAsMesh( - const autoware_perception_msgs::msg::DetectedObjects & objects_msg, - const std::string & output_path) const -{ - std::ofstream ofs(output_path, std::ofstream::out); - std::stringstream vertices_stream; - std::stringstream faces_stream; - int index = 0; - int num_detections = static_cast(objects_msg.objects.size()); - - ofs << "ply" << std::endl; - ofs << "format ascii 1.0" << std::endl; - ofs << "comment created by lidar_centerpoint" << std::endl; - ofs << "element vertex " << 8 * num_detections << std::endl; - ofs << "property float x" << std::endl; - ofs << "property float y" << std::endl; - ofs << "property float z" << std::endl; - ofs << "element face " << 12 * num_detections << std::endl; - ofs << "property list uchar uint vertex_indices" << std::endl; - ofs << "end_header" << std::endl; - - auto streamFace = [&faces_stream](int v1, int v2, int v3) { - faces_stream << std::to_string(3) << " " << std::to_string(v1) << " " << std::to_string(v2) - << " " << std::to_string(v3) << std::endl; - }; - - for (const auto & object : objects_msg.objects) { - Eigen::Affine3d pose_affine; - tf2::fromMsg(object.kinematics.pose_with_covariance.pose, pose_affine); - - std::vector vertices = getVertices(object.shape, pose_affine); - - for (const auto & vertex : vertices) { - vertices_stream << vertex.x() << " " << vertex.y() << " " << vertex.z() << std::endl; - } - - streamFace(index + 1, index + 3, index + 4); - streamFace(index + 3, index + 5, index + 6); - streamFace(index + 0, index + 7, index + 5); - streamFace(index + 7, index + 2, index + 4); - streamFace(index + 5, index + 3, index + 1); - streamFace(index + 7, index + 0, index + 2); - streamFace(index + 2, index + 1, index + 4); - streamFace(index + 4, index + 3, index + 6); - streamFace(index + 5, index + 7, index + 6); - streamFace(index + 6, index + 7, index + 4); - streamFace(index + 0, index + 5, index + 1); - index += 8; - } - - ofs << vertices_stream.str(); - ofs << faces_stream.str(); - - ofs.close(); -} - -} // namespace lidar_centerpoint_tvm -} // namespace perception -} // namespace autoware - -#include -RCLCPP_COMPONENTS_REGISTER_NODE( - autoware::perception::lidar_centerpoint_tvm::SingleInferenceLidarCenterPointNode) diff --git a/perception/multi_object_tracker/include/multi_object_tracker/data_association/data_association.hpp b/perception/multi_object_tracker/include/multi_object_tracker/data_association/data_association.hpp index 56f4c3ac45e52..c30423db02c26 100644 --- a/perception/multi_object_tracker/include/multi_object_tracker/data_association/data_association.hpp +++ b/perception/multi_object_tracker/include/multi_object_tracker/data_association/data_association.hpp @@ -19,19 +19,20 @@ #ifndef MULTI_OBJECT_TRACKER__DATA_ASSOCIATION__DATA_ASSOCIATION_HPP_ #define MULTI_OBJECT_TRACKER__DATA_ASSOCIATION__DATA_ASSOCIATION_HPP_ -#include -#include -#include -#include - #define EIGEN_MPL2_ONLY + #include "multi_object_tracker/data_association/solver/gnn_solver.hpp" #include "multi_object_tracker/tracker/tracker.hpp" #include #include -#include +#include "autoware_perception_msgs/msg/detected_objects.hpp" + +#include +#include +#include +#include class DataAssociation { diff --git a/perception/multi_object_tracker/include/multi_object_tracker/debugger/debug_object.hpp b/perception/multi_object_tracker/include/multi_object_tracker/debugger/debug_object.hpp index 7c1b814258bcf..08c9eb975d8e6 100644 --- a/perception/multi_object_tracker/include/multi_object_tracker/debugger/debug_object.hpp +++ b/perception/multi_object_tracker/include/multi_object_tracker/debugger/debug_object.hpp @@ -15,15 +15,15 @@ #ifndef MULTI_OBJECT_TRACKER__DEBUGGER__DEBUG_OBJECT_HPP_ #define MULTI_OBJECT_TRACKER__DEBUGGER__DEBUG_OBJECT_HPP_ +#include "autoware/universe_utils/ros/uuid_helper.hpp" #include "multi_object_tracker/tracker/model/tracker_base.hpp" -#include #include -#include "unique_identifier_msgs/msg/uuid.hpp" -#include -#include +#include "autoware_perception_msgs/msg/detected_objects.hpp" +#include "autoware_perception_msgs/msg/tracked_objects.hpp" #include +#include #include #include diff --git a/perception/multi_object_tracker/include/multi_object_tracker/debugger/debugger.hpp b/perception/multi_object_tracker/include/multi_object_tracker/debugger/debugger.hpp index 3019f16ada7c9..a1c516147b220 100644 --- a/perception/multi_object_tracker/include/multi_object_tracker/debugger/debugger.hpp +++ b/perception/multi_object_tracker/include/multi_object_tracker/debugger/debugger.hpp @@ -15,16 +15,16 @@ #ifndef MULTI_OBJECT_TRACKER__DEBUGGER__DEBUGGER_HPP_ #define MULTI_OBJECT_TRACKER__DEBUGGER__DEBUGGER_HPP_ +#include "autoware/universe_utils/ros/debug_publisher.hpp" +#include "autoware/universe_utils/ros/published_time_publisher.hpp" #include "multi_object_tracker/debugger/debug_object.hpp" -#include -#include #include #include #include -#include -#include +#include "autoware_perception_msgs/msg/detected_objects.hpp" +#include "autoware_perception_msgs/msg/tracked_objects.hpp" #include #include diff --git a/perception/multi_object_tracker/include/multi_object_tracker/multi_object_tracker_core.hpp b/perception/multi_object_tracker/include/multi_object_tracker/multi_object_tracker_core.hpp index 4c1ccced1bcf7..aff3cbd00eabe 100644 --- a/perception/multi_object_tracker/include/multi_object_tracker/multi_object_tracker_core.hpp +++ b/perception/multi_object_tracker/include/multi_object_tracker/multi_object_tracker_core.hpp @@ -27,8 +27,8 @@ #include -#include -#include +#include "autoware_perception_msgs/msg/detected_objects.hpp" +#include "autoware_perception_msgs/msg/tracked_objects.hpp" #include #include diff --git a/perception/multi_object_tracker/include/multi_object_tracker/processor/processor.hpp b/perception/multi_object_tracker/include/multi_object_tracker/processor/processor.hpp index b2f5f41c81f0a..6d0dbcd036e53 100644 --- a/perception/multi_object_tracker/include/multi_object_tracker/processor/processor.hpp +++ b/perception/multi_object_tracker/include/multi_object_tracker/processor/processor.hpp @@ -19,8 +19,8 @@ #include -#include -#include +#include "autoware_perception_msgs/msg/detected_objects.hpp" +#include "autoware_perception_msgs/msg/tracked_objects.hpp" #include #include diff --git a/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/bicycle_tracker.hpp b/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/bicycle_tracker.hpp index 36176b8202e72..63ad496b70ed9 100644 --- a/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/bicycle_tracker.hpp +++ b/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/bicycle_tracker.hpp @@ -19,10 +19,10 @@ #ifndef MULTI_OBJECT_TRACKER__TRACKER__MODEL__BICYCLE_TRACKER_HPP_ #define MULTI_OBJECT_TRACKER__TRACKER__MODEL__BICYCLE_TRACKER_HPP_ +#include "kalman_filter/kalman_filter.hpp" #include "multi_object_tracker/tracker/model/tracker_base.hpp" #include "multi_object_tracker/tracker/motion_model/bicycle_motion_model.hpp" - -#include +#include "multi_object_tracker/tracker/object_model/object_model.hpp" class BicycleTracker : public Tracker { @@ -30,13 +30,7 @@ class BicycleTracker : public Tracker autoware_perception_msgs::msg::DetectedObject object_; rclcpp::Logger logger_; -private: - struct EkfParams - { - double r_cov_x; - double r_cov_y; - double r_cov_yaw; - } ekf_params_; + object_model::ObjectModel object_model_ = object_model::bicycle; double z_; @@ -48,9 +42,7 @@ class BicycleTracker : public Tracker }; BoundingBox bounding_box_; -private: BicycleMotionModel motion_model_; - const char DIM = motion_model_.DIM; using IDX = BicycleMotionModel::IDX; public: @@ -63,15 +55,16 @@ class BicycleTracker : public Tracker bool measure( const autoware_perception_msgs::msg::DetectedObject & object, const rclcpp::Time & time, const geometry_msgs::msg::Transform & self_transform) override; - autoware_perception_msgs::msg::DetectedObject getUpdatingObject( - const autoware_perception_msgs::msg::DetectedObject & object, - const geometry_msgs::msg::Transform & self_transform); bool measureWithPose(const autoware_perception_msgs::msg::DetectedObject & object); bool measureWithShape(const autoware_perception_msgs::msg::DetectedObject & object); bool getTrackedObject( const rclcpp::Time & time, autoware_perception_msgs::msg::TrackedObject & object) const override; - virtual ~BicycleTracker() {} + +private: + autoware_perception_msgs::msg::DetectedObject getUpdatingObject( + const autoware_perception_msgs::msg::DetectedObject & object, + const geometry_msgs::msg::Transform & self_transform) const; }; #endif // MULTI_OBJECT_TRACKER__TRACKER__MODEL__BICYCLE_TRACKER_HPP_ diff --git a/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/big_vehicle_tracker.hpp b/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/big_vehicle_tracker.hpp index 8eec03c5dc847..7112e6a08ade1 100644 --- a/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/big_vehicle_tracker.hpp +++ b/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/big_vehicle_tracker.hpp @@ -19,10 +19,10 @@ #ifndef MULTI_OBJECT_TRACKER__TRACKER__MODEL__BIG_VEHICLE_TRACKER_HPP_ #define MULTI_OBJECT_TRACKER__TRACKER__MODEL__BIG_VEHICLE_TRACKER_HPP_ +#include "kalman_filter/kalman_filter.hpp" #include "multi_object_tracker/tracker/model/tracker_base.hpp" #include "multi_object_tracker/tracker/motion_model/bicycle_motion_model.hpp" - -#include +#include "multi_object_tracker/tracker/object_model/object_model.hpp" class BigVehicleTracker : public Tracker { @@ -30,14 +30,8 @@ class BigVehicleTracker : public Tracker autoware_perception_msgs::msg::DetectedObject object_; rclcpp::Logger logger_; -private: - struct EkfParams - { - double r_cov_x; - double r_cov_y; - double r_cov_yaw; - double r_cov_vel; - } ekf_params_; + object_model::ObjectModel object_model_ = object_model::big_vehicle; + double velocity_deviation_threshold_; double z_; @@ -51,9 +45,7 @@ class BigVehicleTracker : public Tracker BoundingBox bounding_box_; Eigen::Vector2d tracking_offset_; -private: BicycleMotionModel motion_model_; - const char DIM = motion_model_.DIM; using IDX = BicycleMotionModel::IDX; public: @@ -66,15 +58,16 @@ class BigVehicleTracker : public Tracker bool measure( const autoware_perception_msgs::msg::DetectedObject & object, const rclcpp::Time & time, const geometry_msgs::msg::Transform & self_transform) override; - autoware_perception_msgs::msg::DetectedObject getUpdatingObject( - const autoware_perception_msgs::msg::DetectedObject & object, - const geometry_msgs::msg::Transform & self_transform); bool measureWithPose(const autoware_perception_msgs::msg::DetectedObject & object); bool measureWithShape(const autoware_perception_msgs::msg::DetectedObject & object); bool getTrackedObject( const rclcpp::Time & time, autoware_perception_msgs::msg::TrackedObject & object) const override; - virtual ~BigVehicleTracker() {} + +private: + autoware_perception_msgs::msg::DetectedObject getUpdatingObject( + const autoware_perception_msgs::msg::DetectedObject & object, + const geometry_msgs::msg::Transform & self_transform); }; #endif // MULTI_OBJECT_TRACKER__TRACKER__MODEL__BIG_VEHICLE_TRACKER_HPP_ diff --git a/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/multiple_vehicle_tracker.hpp b/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/multiple_vehicle_tracker.hpp index b3d088da54721..75aec0b06d6b8 100644 --- a/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/multiple_vehicle_tracker.hpp +++ b/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/multiple_vehicle_tracker.hpp @@ -19,11 +19,11 @@ #ifndef MULTI_OBJECT_TRACKER__TRACKER__MODEL__MULTIPLE_VEHICLE_TRACKER_HPP_ #define MULTI_OBJECT_TRACKER__TRACKER__MODEL__MULTIPLE_VEHICLE_TRACKER_HPP_ +#include "kalman_filter/kalman_filter.hpp" #include "multi_object_tracker/tracker/model/big_vehicle_tracker.hpp" #include "multi_object_tracker/tracker/model/normal_vehicle_tracker.hpp" #include "multi_object_tracker/tracker/model/tracker_base.hpp" -#include #include class MultipleVehicleTracker : public Tracker diff --git a/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/normal_vehicle_tracker.hpp b/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/normal_vehicle_tracker.hpp index 107a3ef194afb..1165cecab258b 100644 --- a/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/normal_vehicle_tracker.hpp +++ b/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/normal_vehicle_tracker.hpp @@ -19,10 +19,10 @@ #ifndef MULTI_OBJECT_TRACKER__TRACKER__MODEL__NORMAL_VEHICLE_TRACKER_HPP_ #define MULTI_OBJECT_TRACKER__TRACKER__MODEL__NORMAL_VEHICLE_TRACKER_HPP_ +#include "kalman_filter/kalman_filter.hpp" #include "multi_object_tracker/tracker/model/tracker_base.hpp" #include "multi_object_tracker/tracker/motion_model/bicycle_motion_model.hpp" - -#include +#include "multi_object_tracker/tracker/object_model/object_model.hpp" class NormalVehicleTracker : public Tracker { @@ -30,14 +30,8 @@ class NormalVehicleTracker : public Tracker autoware_perception_msgs::msg::DetectedObject object_; rclcpp::Logger logger_; -private: - struct EkfParams - { - double r_cov_x; - double r_cov_y; - double r_cov_yaw; - double r_cov_vel; - } ekf_params_; + object_model::ObjectModel object_model_ = object_model::normal_vehicle; + double velocity_deviation_threshold_; double z_; @@ -51,9 +45,7 @@ class NormalVehicleTracker : public Tracker BoundingBox bounding_box_; Eigen::Vector2d tracking_offset_; -private: BicycleMotionModel motion_model_; - const char DIM = motion_model_.DIM; using IDX = BicycleMotionModel::IDX; public: @@ -66,15 +58,16 @@ class NormalVehicleTracker : public Tracker bool measure( const autoware_perception_msgs::msg::DetectedObject & object, const rclcpp::Time & time, const geometry_msgs::msg::Transform & self_transform) override; - autoware_perception_msgs::msg::DetectedObject getUpdatingObject( - const autoware_perception_msgs::msg::DetectedObject & object, - const geometry_msgs::msg::Transform & self_transform); bool measureWithPose(const autoware_perception_msgs::msg::DetectedObject & object); bool measureWithShape(const autoware_perception_msgs::msg::DetectedObject & object); bool getTrackedObject( const rclcpp::Time & time, autoware_perception_msgs::msg::TrackedObject & object) const override; - virtual ~NormalVehicleTracker() {} + +private: + autoware_perception_msgs::msg::DetectedObject getUpdatingObject( + const autoware_perception_msgs::msg::DetectedObject & object, + const geometry_msgs::msg::Transform & self_transform); }; #endif // MULTI_OBJECT_TRACKER__TRACKER__MODEL__NORMAL_VEHICLE_TRACKER_HPP_ diff --git a/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/pass_through_tracker.hpp b/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/pass_through_tracker.hpp index 605b3ae462cec..cd661dab52c6e 100644 --- a/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/pass_through_tracker.hpp +++ b/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/pass_through_tracker.hpp @@ -19,10 +19,9 @@ #ifndef MULTI_OBJECT_TRACKER__TRACKER__MODEL__PASS_THROUGH_TRACKER_HPP_ #define MULTI_OBJECT_TRACKER__TRACKER__MODEL__PASS_THROUGH_TRACKER_HPP_ +#include "kalman_filter/kalman_filter.hpp" #include "tracker_base.hpp" -#include - class PassThroughTracker : public Tracker { private: @@ -43,7 +42,6 @@ class PassThroughTracker : public Tracker bool getTrackedObject( const rclcpp::Time & time, autoware_perception_msgs::msg::TrackedObject & object) const override; - virtual ~PassThroughTracker() {} }; #endif // MULTI_OBJECT_TRACKER__TRACKER__MODEL__PASS_THROUGH_TRACKER_HPP_ diff --git a/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/pedestrian_and_bicycle_tracker.hpp b/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/pedestrian_and_bicycle_tracker.hpp index ad81c504d3719..3c3ac038b085e 100644 --- a/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/pedestrian_and_bicycle_tracker.hpp +++ b/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/pedestrian_and_bicycle_tracker.hpp @@ -19,12 +19,11 @@ #ifndef MULTI_OBJECT_TRACKER__TRACKER__MODEL__PEDESTRIAN_AND_BICYCLE_TRACKER_HPP_ #define MULTI_OBJECT_TRACKER__TRACKER__MODEL__PEDESTRIAN_AND_BICYCLE_TRACKER_HPP_ +#include "kalman_filter/kalman_filter.hpp" #include "multi_object_tracker/tracker/model/bicycle_tracker.hpp" #include "multi_object_tracker/tracker/model/pedestrian_tracker.hpp" #include "multi_object_tracker/tracker/model/tracker_base.hpp" -#include - class PedestrianAndBicycleTracker : public Tracker { private: diff --git a/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/pedestrian_tracker.hpp b/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/pedestrian_tracker.hpp index e5d718c4b15ee..c5be57f656eb5 100644 --- a/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/pedestrian_tracker.hpp +++ b/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/pedestrian_tracker.hpp @@ -19,10 +19,10 @@ #ifndef MULTI_OBJECT_TRACKER__TRACKER__MODEL__PEDESTRIAN_TRACKER_HPP_ #define MULTI_OBJECT_TRACKER__TRACKER__MODEL__PEDESTRIAN_TRACKER_HPP_ +#include "kalman_filter/kalman_filter.hpp" #include "multi_object_tracker/tracker/model/tracker_base.hpp" #include "multi_object_tracker/tracker/motion_model/ctrv_motion_model.hpp" - -#include +#include "multi_object_tracker/tracker/object_model/object_model.hpp" // cspell: ignore CTRV @@ -32,13 +32,7 @@ class PedestrianTracker : public Tracker autoware_perception_msgs::msg::DetectedObject object_; rclcpp::Logger logger_; -private: - struct EkfParams - { - double r_cov_x; - double r_cov_y; - double r_cov_yaw; - } ekf_params_; + object_model::ObjectModel object_model_ = object_model::pedestrian; double z_; @@ -56,9 +50,7 @@ class PedestrianTracker : public Tracker BoundingBox bounding_box_; Cylinder cylinder_; -private: CTRVMotionModel motion_model_; - const char DIM = motion_model_.DIM; using IDX = CTRVMotionModel::IDX; public: @@ -71,15 +63,16 @@ class PedestrianTracker : public Tracker bool measure( const autoware_perception_msgs::msg::DetectedObject & object, const rclcpp::Time & time, const geometry_msgs::msg::Transform & self_transform) override; - autoware_perception_msgs::msg::DetectedObject getUpdatingObject( - const autoware_perception_msgs::msg::DetectedObject & object, - const geometry_msgs::msg::Transform & self_transform); bool measureWithPose(const autoware_perception_msgs::msg::DetectedObject & object); bool measureWithShape(const autoware_perception_msgs::msg::DetectedObject & object); bool getTrackedObject( const rclcpp::Time & time, autoware_perception_msgs::msg::TrackedObject & object) const override; - virtual ~PedestrianTracker() {} + +private: + autoware_perception_msgs::msg::DetectedObject getUpdatingObject( + const autoware_perception_msgs::msg::DetectedObject & object, + const geometry_msgs::msg::Transform & self_transform) const; }; #endif // MULTI_OBJECT_TRACKER__TRACKER__MODEL__PEDESTRIAN_TRACKER_HPP_ diff --git a/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/tracker_base.hpp b/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/tracker_base.hpp index 56670e9b54b7e..44b3884c392e6 100644 --- a/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/tracker_base.hpp +++ b/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/tracker_base.hpp @@ -28,8 +28,8 @@ #include "autoware_perception_msgs/msg/detected_object.hpp" #include "autoware_perception_msgs/msg/tracked_object.hpp" -#include "geometry_msgs/msg/point.hpp" -#include "unique_identifier_msgs/msg/uuid.hpp" +#include +#include #include @@ -54,7 +54,7 @@ class Tracker const rclcpp::Time & time, const std::vector & classification, const size_t & channel_size); - virtual ~Tracker() {} + virtual ~Tracker() = default; void initializeExistenceProbabilities( const uint & channel_index, const float & existence_probability); diff --git a/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/unknown_tracker.hpp b/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/unknown_tracker.hpp index 43a1f2fd14842..ca8ecba160bd8 100644 --- a/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/unknown_tracker.hpp +++ b/perception/multi_object_tracker/include/multi_object_tracker/tracker/model/unknown_tracker.hpp @@ -19,18 +19,16 @@ #ifndef MULTI_OBJECT_TRACKER__TRACKER__MODEL__UNKNOWN_TRACKER_HPP_ #define MULTI_OBJECT_TRACKER__TRACKER__MODEL__UNKNOWN_TRACKER_HPP_ +#include "kalman_filter/kalman_filter.hpp" #include "multi_object_tracker/tracker/model/tracker_base.hpp" #include "multi_object_tracker/tracker/motion_model/cv_motion_model.hpp" -#include - class UnknownTracker : public Tracker { private: autoware_perception_msgs::msg::DetectedObject object_; rclcpp::Logger logger_; -private: struct EkfParams { double r_cov_x; @@ -41,9 +39,7 @@ class UnknownTracker : public Tracker double z_; -private: CVMotionModel motion_model_; - const char DIM = motion_model_.DIM; using IDX = CVMotionModel::IDX; public: @@ -64,7 +60,6 @@ class UnknownTracker : public Tracker bool getTrackedObject( const rclcpp::Time & time, autoware_perception_msgs::msg::TrackedObject & object) const override; - virtual ~UnknownTracker() {} }; #endif // MULTI_OBJECT_TRACKER__TRACKER__MODEL__UNKNOWN_TRACKER_HPP_ diff --git a/perception/multi_object_tracker/include/multi_object_tracker/tracker/motion_model/bicycle_motion_model.hpp b/perception/multi_object_tracker/include/multi_object_tracker/tracker/motion_model/bicycle_motion_model.hpp index 5127d0448835c..f6ce2842388c6 100644 --- a/perception/multi_object_tracker/include/multi_object_tracker/tracker/motion_model/bicycle_motion_model.hpp +++ b/perception/multi_object_tracker/include/multi_object_tracker/tracker/motion_model/bicycle_motion_model.hpp @@ -19,10 +19,10 @@ #ifndef MULTI_OBJECT_TRACKER__TRACKER__MOTION_MODEL__BICYCLE_MOTION_MODEL_HPP_ #define MULTI_OBJECT_TRACKER__TRACKER__MOTION_MODEL__BICYCLE_MOTION_MODEL_HPP_ +#include "kalman_filter/kalman_filter.hpp" #include "multi_object_tracker/tracker/motion_model/motion_model_base.hpp" #include -#include #include #ifdef ROS_DISTRO_GALACTIC @@ -47,6 +47,8 @@ class BicycleMotionModel : public MotionModel { double q_stddev_acc_long; double q_stddev_acc_lat; + double q_cov_acc_long; + double q_cov_acc_lat; double q_stddev_yaw_rate_min; double q_stddev_yaw_rate_max; double q_cov_slip_rate_min; diff --git a/perception/multi_object_tracker/include/multi_object_tracker/tracker/motion_model/ctrv_motion_model.hpp b/perception/multi_object_tracker/include/multi_object_tracker/tracker/motion_model/ctrv_motion_model.hpp index 6b071eddec7a9..657048079c46c 100644 --- a/perception/multi_object_tracker/include/multi_object_tracker/tracker/motion_model/ctrv_motion_model.hpp +++ b/perception/multi_object_tracker/include/multi_object_tracker/tracker/motion_model/ctrv_motion_model.hpp @@ -19,10 +19,10 @@ #ifndef MULTI_OBJECT_TRACKER__TRACKER__MOTION_MODEL__CTRV_MOTION_MODEL_HPP_ #define MULTI_OBJECT_TRACKER__TRACKER__MOTION_MODEL__CTRV_MOTION_MODEL_HPP_ +#include "kalman_filter/kalman_filter.hpp" #include "multi_object_tracker/tracker/motion_model/motion_model_base.hpp" #include -#include #include #ifdef ROS_DISTRO_GALACTIC diff --git a/perception/multi_object_tracker/include/multi_object_tracker/tracker/motion_model/cv_motion_model.hpp b/perception/multi_object_tracker/include/multi_object_tracker/tracker/motion_model/cv_motion_model.hpp index 59032706b00d6..421e413d7aab7 100644 --- a/perception/multi_object_tracker/include/multi_object_tracker/tracker/motion_model/cv_motion_model.hpp +++ b/perception/multi_object_tracker/include/multi_object_tracker/tracker/motion_model/cv_motion_model.hpp @@ -19,10 +19,10 @@ #ifndef MULTI_OBJECT_TRACKER__TRACKER__MOTION_MODEL__CV_MOTION_MODEL_HPP_ #define MULTI_OBJECT_TRACKER__TRACKER__MOTION_MODEL__CV_MOTION_MODEL_HPP_ +#include "kalman_filter/kalman_filter.hpp" #include "multi_object_tracker/tracker/motion_model/motion_model_base.hpp" #include -#include #include #ifdef ROS_DISTRO_GALACTIC diff --git a/perception/multi_object_tracker/include/multi_object_tracker/tracker/motion_model/motion_model_base.hpp b/perception/multi_object_tracker/include/multi_object_tracker/tracker/motion_model/motion_model_base.hpp index 1aca602ed66a3..e740612e96d4f 100644 --- a/perception/multi_object_tracker/include/multi_object_tracker/tracker/motion_model/motion_model_base.hpp +++ b/perception/multi_object_tracker/include/multi_object_tracker/tracker/motion_model/motion_model_base.hpp @@ -19,8 +19,9 @@ #ifndef MULTI_OBJECT_TRACKER__TRACKER__MOTION_MODEL__MOTION_MODEL_BASE_HPP_ #define MULTI_OBJECT_TRACKER__TRACKER__MOTION_MODEL__MOTION_MODEL_BASE_HPP_ +#include "kalman_filter/kalman_filter.hpp" + #include -#include #include #ifdef ROS_DISTRO_GALACTIC diff --git a/perception/multi_object_tracker/include/multi_object_tracker/tracker/object_model/object_model.hpp b/perception/multi_object_tracker/include/multi_object_tracker/tracker/object_model/object_model.hpp new file mode 100644 index 0000000000000..6a771344bb36b --- /dev/null +++ b/perception/multi_object_tracker/include/multi_object_tracker/tracker/object_model/object_model.hpp @@ -0,0 +1,305 @@ +// Copyright 2024 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +// +// +// Author: v1.0 Taekjin Lee +// + +#ifndef MULTI_OBJECT_TRACKER__TRACKER__OBJECT_MODEL__OBJECT_MODEL_HPP_ +#define MULTI_OBJECT_TRACKER__TRACKER__OBJECT_MODEL__OBJECT_MODEL_HPP_ + +#include + +namespace +{ +constexpr double const_g = 9.81; + +template +constexpr T sq(const T x) +{ + return x * x; +} + +template +constexpr T deg2rad(const T deg) +{ + return deg * static_cast(M_PI) / static_cast(180.0); +} + +// cspell: ignore kmph +template +constexpr T kmph2mps(const T kmph) +{ + return kmph * static_cast(1000.0) / static_cast(3600.0); +} + +} // namespace + +namespace object_model +{ + +enum class ObjectModelType { NormalVehicle, BigVehicle, Bicycle, Pedestrian, Unknown }; +struct ObjectSize +{ + double length{0.0}; // [m] + double width{0.0}; // [m] + double height{0.0}; // [m] +}; +struct ObjectSizeLimit +{ + double length_min{0.0}; // [m] + double length_max{0.0}; // [m] + double width_min{0.0}; // [m] + double width_max{0.0}; // [m] + double height_min{0.0}; // [m] + double height_max{0.0}; // [m] +}; +struct MotionProcessNoise +{ + double vel_long{0.0}; // [m/s] uncertain longitudinal velocity + double vel_lat{0.0}; // [m/s] uncertain lateral velocity + double yaw_rate{0.0}; // [rad/s] uncertain yaw rate + double yaw_rate_min{0.0}; // [rad/s] uncertain yaw rate, minimum + double yaw_rate_max{0.0}; // [rad/s] uncertain yaw rate, maximum + double acc_long{0.0}; // [m/s^2] uncertain longitudinal acceleration + double acc_lat{0.0}; // [m/s^2] uncertain lateral acceleration + double acc_turn{0.0}; // [rad/s^2] uncertain rotation acceleration +}; +struct MotionProcessLimit +{ + double acc_long_max{0.0}; // [m/s^2] + double acc_lat_max{0.0}; // [m/s^2] + double vel_long_max{0.0}; // [m/s] + double vel_lat_max{0.0}; // [m/s] + double yaw_rate_max{0.0}; // [rad/s] +}; +struct StateCovariance +{ + double pos_x{0.0}; // [m^2] + double pos_y{0.0}; // [m^2] + double yaw{0.0}; // [rad^2] + double yaw_rate{0.0}; // [rad^2/s^2] + double vel_long{0.0}; // [m^2/s^2] + double vel_lat{0.0}; // [m^2/s^2] + double acc_long{0.0}; // [m^2/s^4] + double acc_lat{0.0}; // [m^2/s^4] +}; +struct BicycleModelState +{ + double init_slip_angle_cov{0.0}; // [rad^2/s^2] initial slip angle covariance + double slip_angle_max{0.0}; // [rad] max slip angle + double slip_rate_stddev_min{0.0}; // [rad/s] uncertain slip angle change rate, minimum + double slip_rate_stddev_max{0.0}; // [rad/s] uncertain slip angle change rate, maximum + double wheel_pos_ratio_front{0.0}; // [-] + double wheel_pos_ratio_rear{0.0}; // [-] + double wheel_pos_front_min{0.0}; // [m] + double wheel_pos_rear_min{0.0}; // [m] +}; + +class ObjectModel +{ +public: + ObjectSize init_size; + ObjectSizeLimit size_limit; + MotionProcessNoise process_noise; + MotionProcessLimit process_limit; + StateCovariance initial_covariance; + StateCovariance measurement_covariance; + BicycleModelState bicycle_state; + + explicit ObjectModel(const ObjectModelType & type) + { + switch (type) { + case ObjectModelType::NormalVehicle: + init_size.length = 3.0; + init_size.width = 2.0; + init_size.height = 1.8; + size_limit.length_min = 1.0; + size_limit.length_max = 20.0; + size_limit.width_min = 1.0; + size_limit.width_max = 5.0; + size_limit.height_min = 1.0; + size_limit.height_max = 5.0; + + process_noise.acc_long = const_g * 0.35; + process_noise.acc_lat = const_g * 0.15; + process_noise.yaw_rate_min = deg2rad(1.5); + process_noise.yaw_rate_max = deg2rad(15.0); + + process_limit.acc_long_max = const_g; + process_limit.acc_lat_max = const_g; + process_limit.vel_long_max = kmph2mps(140.0); + + // initial covariance + initial_covariance.pos_x = sq(1.0); + initial_covariance.pos_y = sq(0.3); + initial_covariance.yaw = sq(deg2rad(25.0)); + initial_covariance.vel_long = sq(kmph2mps(1000.0)); + initial_covariance.vel_lat = sq(0.2); + + // measurement noise model + measurement_covariance.pos_x = sq(0.5); + measurement_covariance.pos_y = sq(0.4); + measurement_covariance.yaw = sq(deg2rad(20.0)); + measurement_covariance.vel_long = sq(1.0); + + // bicycle motion model + bicycle_state.init_slip_angle_cov = sq(deg2rad(5.0)); + bicycle_state.slip_angle_max = deg2rad(30.0); + bicycle_state.slip_rate_stddev_min = deg2rad(0.3); + bicycle_state.slip_rate_stddev_max = deg2rad(10.0); + bicycle_state.wheel_pos_ratio_front = 0.3; + bicycle_state.wheel_pos_ratio_rear = 0.25; + bicycle_state.wheel_pos_front_min = 1.0; + bicycle_state.wheel_pos_rear_min = 1.0; + break; + + case ObjectModelType::BigVehicle: + init_size.length = 6.0; + init_size.width = 2.0; + init_size.height = 2.0; + size_limit.length_min = 1.0; + size_limit.length_max = 35.0; + size_limit.width_min = 1.0; + size_limit.width_max = 10.0; + size_limit.height_min = 1.0; + size_limit.height_max = 10.0; + + process_noise.acc_long = const_g * 0.35; + process_noise.acc_lat = const_g * 0.15; + process_noise.yaw_rate_min = deg2rad(1.5); + process_noise.yaw_rate_max = deg2rad(15.0); + + process_limit.acc_long_max = const_g; + process_limit.acc_lat_max = const_g; + process_limit.vel_long_max = kmph2mps(140.0); + + // initial covariance + initial_covariance.pos_x = sq(1.5); + initial_covariance.pos_y = sq(0.5); + initial_covariance.yaw = sq(deg2rad(25.0)); + initial_covariance.vel_long = sq(kmph2mps(1000.0)); + initial_covariance.vel_lat = sq(0.2); + + // measurement noise model + measurement_covariance.pos_x = sq(0.5); + measurement_covariance.pos_y = sq(0.4); + measurement_covariance.yaw = sq(deg2rad(20.0)); + measurement_covariance.vel_long = sq(kmph2mps(10.0)); + + // bicycle motion model + bicycle_state.init_slip_angle_cov = sq(deg2rad(5.0)); + bicycle_state.slip_angle_max = deg2rad(30.0); + bicycle_state.slip_rate_stddev_min = deg2rad(0.3); + bicycle_state.slip_rate_stddev_max = deg2rad(10.0); + bicycle_state.wheel_pos_ratio_front = 0.3; + bicycle_state.wheel_pos_ratio_rear = 0.25; + bicycle_state.wheel_pos_front_min = 1.5; + bicycle_state.wheel_pos_rear_min = 1.5; + break; + + case ObjectModelType::Bicycle: + init_size.length = 1.0; + init_size.width = 0.5; + init_size.height = 1.7; + size_limit.length_min = 0.5; + size_limit.length_max = 8.0; + size_limit.width_min = 0.5; + size_limit.width_max = 3.0; + size_limit.height_min = 0.5; + size_limit.height_max = 2.5; + + process_noise.acc_long = const_g * 0.35; + process_noise.acc_lat = const_g * 0.15; + process_noise.yaw_rate_min = deg2rad(5.0); + process_noise.yaw_rate_max = deg2rad(15.0); + + process_limit.acc_long_max = const_g; + process_limit.acc_lat_max = const_g; + process_limit.vel_long_max = kmph2mps(120.0); + + // initial covariance + initial_covariance.pos_x = sq(0.8); + initial_covariance.pos_y = sq(0.5); + initial_covariance.yaw = sq(deg2rad(25.0)); + initial_covariance.vel_long = sq(kmph2mps(1000.0)); + initial_covariance.vel_lat = sq(0.2); + + // measurement noise model + measurement_covariance.pos_x = sq(0.5); + measurement_covariance.pos_y = sq(0.4); + measurement_covariance.yaw = sq(deg2rad(30.0)); + measurement_covariance.vel_long = sq(kmph2mps(10.0)); + + // bicycle motion model + bicycle_state.init_slip_angle_cov = sq(deg2rad(5.0)); + bicycle_state.slip_angle_max = deg2rad(30.0); + bicycle_state.slip_rate_stddev_min = deg2rad(1.0); + bicycle_state.slip_rate_stddev_max = deg2rad(10.0); + bicycle_state.wheel_pos_ratio_front = 0.3; + bicycle_state.wheel_pos_ratio_rear = 0.3; + bicycle_state.wheel_pos_front_min = 0.3; + bicycle_state.wheel_pos_rear_min = 0.3; + break; + + case ObjectModelType::Pedestrian: + init_size.length = 0.5; + init_size.width = 0.5; + init_size.height = 1.7; + size_limit.length_min = 0.3; + size_limit.length_max = 2.0; + size_limit.width_min = 0.3; + size_limit.width_max = 1.0; + size_limit.height_min = 0.6; + size_limit.height_max = 2.0; + + process_noise.vel_long = 0.5; + process_noise.vel_lat = 0.5; + process_noise.yaw_rate = deg2rad(20.0); + process_noise.acc_long = const_g * 0.3; + process_noise.acc_turn = deg2rad(30.0); + + process_limit.vel_long_max = kmph2mps(100.0); + process_limit.yaw_rate_max = deg2rad(30.0); + + // initial covariance + initial_covariance.pos_x = sq(2.0); + initial_covariance.pos_y = sq(2.0); + initial_covariance.yaw = sq(deg2rad(1000.0)); + initial_covariance.vel_long = sq(kmph2mps(120.0)); + initial_covariance.yaw_rate = sq(deg2rad(360.0)); + + // measurement noise model + measurement_covariance.pos_x = sq(0.4); + measurement_covariance.pos_y = sq(0.4); + measurement_covariance.yaw = sq(deg2rad(30.0)); + + break; + + default: + break; + } + } + virtual ~ObjectModel() = default; +}; + +// create static objects by using ObjectModel class +static const ObjectModel normal_vehicle(ObjectModelType::NormalVehicle); +static const ObjectModel big_vehicle(ObjectModelType::BigVehicle); +static const ObjectModel bicycle(ObjectModelType::Bicycle); +static const ObjectModel pedestrian(ObjectModelType::Pedestrian); + +} // namespace object_model + +#endif // MULTI_OBJECT_TRACKER__TRACKER__OBJECT_MODEL__OBJECT_MODEL_HPP_ diff --git a/perception/multi_object_tracker/include/multi_object_tracker/utils/utils.hpp b/perception/multi_object_tracker/include/multi_object_tracker/utils/utils.hpp index 259a58342c369..0bda7870ae2b9 100644 --- a/perception/multi_object_tracker/include/multi_object_tracker/utils/utils.hpp +++ b/perception/multi_object_tracker/include/multi_object_tracker/utils/utils.hpp @@ -22,9 +22,9 @@ #include #include -#include -#include -#include +#include "autoware_perception_msgs/msg/detected_object.hpp" +#include "autoware_perception_msgs/msg/shape.hpp" +#include "autoware_perception_msgs/msg/tracked_object.hpp" #include #include #include @@ -38,45 +38,6 @@ namespace utils { -enum MSG_COV_IDX { - X_X = 0, - X_Y = 1, - X_Z = 2, - X_ROLL = 3, - X_PITCH = 4, - X_YAW = 5, - Y_X = 6, - Y_Y = 7, - Y_Z = 8, - Y_ROLL = 9, - Y_PITCH = 10, - Y_YAW = 11, - Z_X = 12, - Z_Y = 13, - Z_Z = 14, - Z_ROLL = 15, - Z_PITCH = 16, - Z_YAW = 17, - ROLL_X = 18, - ROLL_Y = 19, - ROLL_Z = 20, - ROLL_ROLL = 21, - ROLL_PITCH = 22, - ROLL_YAW = 23, - PITCH_X = 24, - PITCH_Y = 25, - PITCH_Z = 26, - PITCH_ROLL = 27, - PITCH_PITCH = 28, - PITCH_YAW = 29, - YAW_X = 30, - YAW_Y = 31, - YAW_Z = 32, - YAW_ROLL = 33, - YAW_PITCH = 34, - YAW_YAW = 35 -}; - enum BBOX_IDX { FRONT_SURFACE = 0, RIGHT_SURFACE = 1, @@ -132,7 +93,8 @@ inline int getNearestCornerOrSurface( // (left) | | (right) // -- // x- (rear) - int xgrid, ygrid; + int xgrid = 0; + int ygrid = 0; const int labels[3][3] = { {BBOX_IDX::FRONT_L_CORNER, BBOX_IDX::FRONT_SURFACE, BBOX_IDX::FRONT_R_CORNER}, {BBOX_IDX::LEFT_SURFACE, BBOX_IDX::INSIDE, BBOX_IDX::RIGHT_SURFACE}, @@ -155,34 +117,6 @@ inline int getNearestCornerOrSurface( return labels[xgrid][ygrid]; // 0 to 7 + 1(null) value } -/** - * @brief Get the Nearest Corner or Surface from detected object - * @param object: input object - * @param yaw: object yaw angle (after solved front and back uncertainty) - * @param self_transform - * @return nearest corner or surface index - */ -inline int getNearestCornerOrSurfaceFromObject( - const autoware_perception_msgs::msg::DetectedObject & object, const double & yaw, - const geometry_msgs::msg::Transform & self_transform) -{ - // only work for BBOX shape - if (object.shape.type != autoware_perception_msgs::msg::Shape::BOUNDING_BOX) { - return BBOX_IDX::INVALID; - } - - // extract necessary information from input object - double x, y, width, length; - x = object.kinematics.pose_with_covariance.pose.position.x; - y = object.kinematics.pose_with_covariance.pose.position.y; - // yaw = tf2::getYaw(object.kinematics.pose_with_covariance.pose.orientation); //not use raw yaw - // now - width = object.shape.dimensions.y; - length = object.shape.dimensions.x; - - return getNearestCornerOrSurface(x, y, yaw, width, length, self_transform); -} - /** * @brief Calc bounding box center offset caused by shape change * @param dw: width update [m] = w_new - w_old @@ -225,30 +159,6 @@ inline Eigen::Vector2d calcOffsetVectorFromShapeChange( return offset; // do nothing if indx == INVALID or INSIDE } -/** - * @brief post-processing to recover bounding box center from tracking point and offset vector - * @param x: x of tracking point estimated with ekf - * @param y: y of tracking point estimated with ekf - * @param yaw: yaw of tracking point estimated with ekf - * @param dw: diff of width: w_estimated - w_input - * @param dl: diff of length: l_estimated - l_input - * @param indx: closest corner or surface index - * @param tracking_offset: tracking offset between bounding box center and tracking point - */ -inline Eigen::Vector2d recoverFromTrackingPoint( - const double x, const double y, const double yaw, const double dw, const double dl, - const int indx, const Eigen::Vector2d & tracking_offset) -{ - const Eigen::Vector2d tracking_point{x, y}; - const Eigen::Matrix2d R = Eigen::Rotation2Dd(yaw).toRotationMatrix(); - - const Eigen::Vector2d shape_change_offset = calcOffsetVectorFromShapeChange(dw, dl, indx); - - Eigen::Vector2d output_center = tracking_point - R * tracking_offset - R * shape_change_offset; - - return output_center; -} - /** * @brief Convert input object center to tracking point based on nearest corner information * 1. update anchor offset vector, 2. offset input bbox based on tracking_offset vector and @@ -274,9 +184,8 @@ inline void calcAnchorPointOffset( } // current object width and height - double w_n, l_n; - l_n = input_object.shape.dimensions.x; - w_n = input_object.shape.dimensions.y; + const double w_n = input_object.shape.dimensions.y; + const double l_n = input_object.shape.dimensions.x; // update offset const Eigen::Vector2d offset = calcOffsetVectorFromShapeChange(w_n - w, l_n - l, indx); @@ -304,27 +213,19 @@ inline bool convertConvexHullToBoundingBox( } // look for bounding box boundary - double max_x = 0; - double max_y = 0; - double min_x = 0; - double min_y = 0; - double max_z = 0; - for (size_t i = 0; i < input_object.shape.footprint.points.size(); ++i) { - const double foot_x = input_object.shape.footprint.points.at(i).x; - const double foot_y = input_object.shape.footprint.points.at(i).y; - const double foot_z = input_object.shape.footprint.points.at(i).z; - max_x = std::max(max_x, foot_x); - max_y = std::max(max_y, foot_y); - min_x = std::min(min_x, foot_x); - min_y = std::min(min_y, foot_y); - max_z = std::max(max_z, foot_z); + float max_x = 0; + float max_y = 0; + float min_x = 0; + float min_y = 0; + float max_z = 0; + for (const auto & point : input_object.shape.footprint.points) { + max_x = std::max(max_x, point.x); + max_y = std::max(max_y, point.y); + min_x = std::min(min_x, point.x); + min_y = std::min(min_y, point.y); + max_z = std::max(max_z, point.z); } - // calc bounding box state - const double length = max_x - min_x; - const double width = max_y - min_y; - const double height = max_z; - // calc new center const Eigen::Vector2d center{ input_object.kinematics.pose_with_covariance.pose.position.x, @@ -340,9 +241,9 @@ inline bool convertConvexHullToBoundingBox( output_object.kinematics.pose_with_covariance.pose.position.y = new_center.y(); output_object.shape.type = autoware_perception_msgs::msg::Shape::BOUNDING_BOX; - output_object.shape.dimensions.x = length; - output_object.shape.dimensions.y = width; - output_object.shape.dimensions.z = height; + output_object.shape.dimensions.x = max_x - min_x; + output_object.shape.dimensions.y = max_y - min_y; + output_object.shape.dimensions.z = max_z; return true; } diff --git a/perception/multi_object_tracker/src/data_association/data_association.cpp b/perception/multi_object_tracker/src/data_association/data_association.cpp index 013137aa8b82c..f7b40cd178528 100644 --- a/perception/multi_object_tracker/src/data_association/data_association.cpp +++ b/perception/multi_object_tracker/src/data_association/data_association.cpp @@ -176,7 +176,7 @@ Eigen::MatrixXd DataAssociation::calcScoreMatrix( bool passed_gate = true; // dist gate - if (passed_gate) { + { // passed_gate is always true if (max_dist < dist) passed_gate = false; } // area gate diff --git a/perception/multi_object_tracker/src/debugger/debug_object.cpp b/perception/multi_object_tracker/src/debugger/debug_object.cpp index e6a0b6242a168..fe6d3d5c75564 100644 --- a/perception/multi_object_tracker/src/debugger/debug_object.cpp +++ b/perception/multi_object_tracker/src/debugger/debug_object.cpp @@ -236,8 +236,9 @@ void TrackerObjectDebugger::draw( stream << std::fixed << std::setprecision(0) << object_data_front.existence_vector[i] * 100; existence_probability_text += channel_names_[i] + stream.str() + ":"; } - existence_probability_text = - existence_probability_text.substr(0, existence_probability_text.size() - 1); + if (!existence_probability_text.empty()) { + existence_probability_text.pop_back(); + } existence_probability_text += "\n" + object_data_front.uuid_str.substr(0, 6); text_marker.text = existence_probability_text; diff --git a/perception/multi_object_tracker/src/debugger/debugger.cpp b/perception/multi_object_tracker/src/debugger/debugger.cpp index a3ece17eab112..e692ae76468e4 100644 --- a/perception/multi_object_tracker/src/debugger/debugger.cpp +++ b/perception/multi_object_tracker/src/debugger/debugger.cpp @@ -98,7 +98,7 @@ void TrackerDebugger::checkDelay(diagnostic_updater::DiagnosticStatusWrapper & s stat.summary(diagnostic_msgs::msg::DiagnosticStatus::ERROR, "Measurement time is not set."); return; } - const double & delay = pipeline_latency_ms_; // [s] + const double & delay = pipeline_latency_ms_ / 1e3; // [s] if (delay == 0.0) { stat.summary(diagnostic_msgs::msg::DiagnosticStatus::OK, "Detection delay is not calculated."); diff --git a/perception/multi_object_tracker/src/multi_object_tracker_core.cpp b/perception/multi_object_tracker/src/multi_object_tracker_core.cpp index 54255e706e35a..97c3d93d191b2 100644 --- a/perception/multi_object_tracker/src/multi_object_tracker_core.cpp +++ b/perception/multi_object_tracker/src/multi_object_tracker_core.cpp @@ -13,6 +13,15 @@ // limitations under the License. // // +#define EIGEN_MPL2_ONLY + +#include "multi_object_tracker/multi_object_tracker_core.hpp" + +#include "multi_object_tracker/utils/utils.hpp" + +#include +#include +#include #include @@ -28,14 +37,6 @@ #include #include -#define EIGEN_MPL2_ONLY -#include "multi_object_tracker/multi_object_tracker_core.hpp" -#include "multi_object_tracker/utils/utils.hpp" - -#include -#include -#include - namespace { // Function to get the transform between two frames @@ -207,7 +208,7 @@ void MultiObjectTracker::onTrigger() { const rclcpp::Time current_time = this->now(); // get objects from the input manager and run process - std::vector> objects_list; + ObjectsList objects_list; const bool is_objects_ready = input_manager_->getObjects(current_time, objects_list); if (!is_objects_ready) return; diff --git a/perception/multi_object_tracker/src/processor/processor.cpp b/perception/multi_object_tracker/src/processor/processor.cpp index ff14f7849b161..50d1a021c5838 100644 --- a/perception/multi_object_tracker/src/processor/processor.cpp +++ b/perception/multi_object_tracker/src/processor/processor.cpp @@ -17,7 +17,7 @@ #include "multi_object_tracker/tracker/tracker.hpp" #include "object_recognition_utils/object_recognition_utils.hpp" -#include +#include "autoware_perception_msgs/msg/tracked_objects.hpp" #include diff --git a/perception/multi_object_tracker/src/tracker/model/bicycle_tracker.cpp b/perception/multi_object_tracker/src/tracker/model/bicycle_tracker.cpp index 05e06107c2247..a438c830596d7 100644 --- a/perception/multi_object_tracker/src/tracker/model/bicycle_tracker.cpp +++ b/perception/multi_object_tracker/src/tracker/model/bicycle_tracker.cpp @@ -15,14 +15,19 @@ // // Author: v1.0 Yukihiro Saito // +#define EIGEN_MPL2_ONLY #include "multi_object_tracker/tracker/model/bicycle_tracker.hpp" +#include "autoware/universe_utils/geometry/boost_polygon_utils.hpp" +#include "autoware/universe_utils/math/normalization.hpp" +#include "autoware/universe_utils/math/unit_conversion.hpp" +#include "autoware/universe_utils/ros/msg_covariance.hpp" #include "multi_object_tracker/utils/utils.hpp" +#include "object_recognition_utils/object_recognition_utils.hpp" -#include -#include -#include +#include +#include #include #include @@ -34,11 +39,6 @@ #else #include #endif -#include "object_recognition_utils/object_recognition_utils.hpp" - -#define EIGEN_MPL2_ONLY -#include -#include using Label = autoware_perception_msgs::msg::ObjectClassification; @@ -55,45 +55,36 @@ BicycleTracker::BicycleTracker( // initialize existence probability initializeExistenceProbabilities(channel_index, object.existence_probability); - // Initialize parameters - // measurement noise covariance: detector uncertainty + ego vehicle motion uncertainty - double r_stddev_x = 0.5; // in vehicle coordinate [m] - double r_stddev_y = 0.4; // in vehicle coordinate [m] - double r_stddev_yaw = autoware::universe_utils::deg2rad(30); // in map coordinate [rad] - ekf_params_.r_cov_x = std::pow(r_stddev_x, 2.0); - ekf_params_.r_cov_y = std::pow(r_stddev_y, 2.0); - ekf_params_.r_cov_yaw = std::pow(r_stddev_yaw, 2.0); - // OBJECT SHAPE MODEL if (object.shape.type == autoware_perception_msgs::msg::Shape::BOUNDING_BOX) { bounding_box_ = { object.shape.dimensions.x, object.shape.dimensions.y, object.shape.dimensions.z}; } else { - bounding_box_ = {1.0, 0.5, 1.7}; + bounding_box_ = { + object_model_.init_size.length, object_model_.init_size.width, + object_model_.init_size.height}; // default value } // set maximum and minimum size - constexpr double max_size = 5.0; - constexpr double min_size = 0.3; - bounding_box_.length = std::min(std::max(bounding_box_.length, min_size), max_size); - bounding_box_.width = std::min(std::max(bounding_box_.width, min_size), max_size); - bounding_box_.height = std::min(std::max(bounding_box_.height, min_size), max_size); + bounding_box_.length = std::clamp( + bounding_box_.length, object_model_.size_limit.length_min, object_model_.size_limit.length_max); + bounding_box_.width = std::clamp( + bounding_box_.width, object_model_.size_limit.width_min, object_model_.size_limit.width_max); + bounding_box_.height = std::clamp( + bounding_box_.height, object_model_.size_limit.height_min, object_model_.size_limit.height_max); // Set motion model parameters { - constexpr double q_stddev_acc_long = - 9.8 * 0.35; // [m/(s*s)] uncertain longitudinal acceleration - constexpr double q_stddev_acc_lat = 9.8 * 0.15; // [m/(s*s)] uncertain lateral acceleration - constexpr double q_stddev_yaw_rate_min = 5.0; // [deg/s] uncertain yaw change rate, minimum - constexpr double q_stddev_yaw_rate_max = 15.0; // [deg/s] uncertain yaw change rate, maximum - constexpr double q_stddev_slip_rate_min = - 1.0; // [deg/s] uncertain slip angle change rate, minimum - constexpr double q_stddev_slip_rate_max = - 10.0; // [deg/s] uncertain slip angle change rate, maximum - constexpr double q_max_slip_angle = 30; // [deg] max slip angle - constexpr double lf_ratio = 0.3; // [-] ratio of front wheel position - constexpr double lf_min = 0.3; // [m] minimum front wheel position - constexpr double lr_ratio = 0.3; // [-] ratio of rear wheel position - constexpr double lr_min = 0.3; // [m] minimum rear wheel position + const double q_stddev_acc_long = object_model_.process_noise.acc_long; + const double q_stddev_acc_lat = object_model_.process_noise.acc_lat; + const double q_stddev_yaw_rate_min = object_model_.process_noise.yaw_rate_min; + const double q_stddev_yaw_rate_max = object_model_.process_noise.yaw_rate_max; + const double q_stddev_slip_rate_min = object_model_.bicycle_state.slip_rate_stddev_min; + const double q_stddev_slip_rate_max = object_model_.bicycle_state.slip_rate_stddev_max; + const double q_max_slip_angle = object_model_.bicycle_state.slip_angle_max; + const double lf_ratio = object_model_.bicycle_state.wheel_pos_ratio_front; + const double lf_min = object_model_.bicycle_state.wheel_pos_front_min; + const double lr_ratio = object_model_.bicycle_state.wheel_pos_ratio_rear; + const double lr_min = object_model_.bicycle_state.wheel_pos_rear_min; motion_model_.setMotionParams( q_stddev_acc_long, q_stddev_acc_lat, q_stddev_yaw_rate_min, q_stddev_yaw_rate_max, q_stddev_slip_rate_min, q_stddev_slip_rate_max, q_max_slip_angle, lf_ratio, lf_min, lr_ratio, @@ -102,59 +93,47 @@ BicycleTracker::BicycleTracker( // Set motion limits { - constexpr double max_vel = autoware::universe_utils::kmph2mps(80); // [m/s] maximum velocity - constexpr double max_slip = 30; // [deg] maximum slip angle + const double max_vel = object_model_.process_limit.vel_long_max; + const double max_slip = object_model_.bicycle_state.slip_angle_max; motion_model_.setMotionLimits(max_vel, max_slip); // maximum velocity and slip angle } // Set initial state { + using autoware::universe_utils::xyzrpy_covariance_index::XYZRPY_COV_IDX; const double x = object.kinematics.pose_with_covariance.pose.position.x; const double y = object.kinematics.pose_with_covariance.pose.position.y; const double yaw = tf2::getYaw(object.kinematics.pose_with_covariance.pose.orientation); - auto pose_cov = object.kinematics.pose_with_covariance.covariance; - double vel = 0.0; - double vel_cov; - const double & length = bounding_box_.length; - - if (object.kinematics.has_twist) { - vel = object.kinematics.twist_with_covariance.twist.linear.x; - } + auto pose_cov = object.kinematics.pose_with_covariance.covariance; if (!object.kinematics.has_position_covariance) { // initial state covariance - constexpr double p0_stddev_x = 0.8; // in object coordinate [m] - constexpr double p0_stddev_y = 0.5; // in object coordinate [m] - constexpr double p0_stddev_yaw = - autoware::universe_utils::deg2rad(25); // in map coordinate [rad] - constexpr double p0_cov_x = p0_stddev_x * p0_stddev_x; - constexpr double p0_cov_y = p0_stddev_y * p0_stddev_y; - constexpr double p0_cov_yaw = p0_stddev_yaw * p0_stddev_yaw; + const auto & p0_cov_x = object_model_.initial_covariance.pos_x; + const auto & p0_cov_y = object_model_.initial_covariance.pos_y; + const auto & p0_cov_yaw = object_model_.initial_covariance.yaw; const double cos_yaw = std::cos(yaw); const double sin_yaw = std::sin(yaw); const double sin_2yaw = std::sin(2.0 * yaw); - pose_cov[utils::MSG_COV_IDX::X_X] = - p0_cov_x * cos_yaw * cos_yaw + p0_cov_y * sin_yaw * sin_yaw; - pose_cov[utils::MSG_COV_IDX::X_Y] = 0.5 * (p0_cov_x - p0_cov_y) * sin_2yaw; - pose_cov[utils::MSG_COV_IDX::Y_Y] = - p0_cov_x * sin_yaw * sin_yaw + p0_cov_y * cos_yaw * cos_yaw; - pose_cov[utils::MSG_COV_IDX::Y_X] = pose_cov[utils::MSG_COV_IDX::X_Y]; - pose_cov[utils::MSG_COV_IDX::YAW_YAW] = p0_cov_yaw; + pose_cov[XYZRPY_COV_IDX::X_X] = p0_cov_x * cos_yaw * cos_yaw + p0_cov_y * sin_yaw * sin_yaw; + pose_cov[XYZRPY_COV_IDX::X_Y] = 0.5 * (p0_cov_x - p0_cov_y) * sin_2yaw; + pose_cov[XYZRPY_COV_IDX::Y_Y] = p0_cov_x * sin_yaw * sin_yaw + p0_cov_y * cos_yaw * cos_yaw; + pose_cov[XYZRPY_COV_IDX::Y_X] = pose_cov[XYZRPY_COV_IDX::X_Y]; + pose_cov[XYZRPY_COV_IDX::YAW_YAW] = p0_cov_yaw; } - if (!object.kinematics.has_twist_covariance) { - constexpr double p0_stddev_vel = - autoware::universe_utils::kmph2mps(1000); // in object coordinate [m/s] - vel_cov = std::pow(p0_stddev_vel, 2.0); - } else { - vel_cov = object.kinematics.twist_with_covariance.covariance[utils::MSG_COV_IDX::X_X]; + double vel = 0.0; + double vel_cov = object_model_.initial_covariance.vel_long; + if (object.kinematics.has_twist) { + vel = object.kinematics.twist_with_covariance.twist.linear.x; + } + if (object.kinematics.has_twist_covariance) { + vel_cov = object.kinematics.twist_with_covariance.covariance[XYZRPY_COV_IDX::X_X]; } const double slip = 0.0; - const double p0_stddev_slip = - autoware::universe_utils::deg2rad(5); // in object coordinate [rad/s] - const double slip_cov = std::pow(p0_stddev_slip, 2.0); + const double slip_cov = object_model_.bicycle_state.init_slip_angle_cov; + const double & length = bounding_box_.length; // initialize motion model motion_model_.initialize(time, x, y, yaw, pose_cov, vel, vel_cov, slip, slip_cov, length); @@ -168,9 +147,9 @@ bool BicycleTracker::predict(const rclcpp::Time & time) autoware_perception_msgs::msg::DetectedObject BicycleTracker::getUpdatingObject( const autoware_perception_msgs::msg::DetectedObject & object, - const geometry_msgs::msg::Transform & /*self_transform*/) + const geometry_msgs::msg::Transform & /*self_transform*/) const { - autoware_perception_msgs::msg::DetectedObject updating_object; + autoware_perception_msgs::msg::DetectedObject updating_object = object; // OBJECT SHAPE MODEL // convert to bounding box if input is convex shape @@ -178,19 +157,42 @@ autoware_perception_msgs::msg::DetectedObject BicycleTracker::getUpdatingObject( if (!utils::convertConvexHullToBoundingBox(object, updating_object)) { updating_object = object; } - } else { - updating_object = object; } // UNCERTAINTY MODEL if (!object.kinematics.has_position_covariance) { + // measurement noise covariance + auto r_cov_x = object_model_.measurement_covariance.pos_x; + auto r_cov_y = object_model_.measurement_covariance.pos_y; + + // yaw angle fix + const double pose_yaw = tf2::getYaw(object.kinematics.pose_with_covariance.pose.orientation); + const bool is_yaw_available = + object.kinematics.orientation_availability != + autoware_perception_msgs::msg::DetectedObjectKinematics::UNAVAILABLE; + // fill covariance matrix + using autoware::universe_utils::xyzrpy_covariance_index::XYZRPY_COV_IDX; auto & pose_cov = updating_object.kinematics.pose_with_covariance.covariance; - pose_cov[utils::MSG_COV_IDX::X_X] = ekf_params_.r_cov_x; // x - x - pose_cov[utils::MSG_COV_IDX::X_Y] = 0; // x - y - pose_cov[utils::MSG_COV_IDX::Y_X] = 0; // y - x - pose_cov[utils::MSG_COV_IDX::Y_Y] = ekf_params_.r_cov_y; // y - y - pose_cov[utils::MSG_COV_IDX::YAW_YAW] = ekf_params_.r_cov_yaw; // yaw - yaw + const double cos_yaw = std::cos(pose_yaw); + const double sin_yaw = std::sin(pose_yaw); + const double sin_2yaw = std::sin(2.0 * pose_yaw); + pose_cov[XYZRPY_COV_IDX::X_X] = + r_cov_x * cos_yaw * cos_yaw + r_cov_y * sin_yaw * sin_yaw; // x - x + pose_cov[XYZRPY_COV_IDX::X_Y] = 0.5 * (r_cov_x - r_cov_y) * sin_2yaw; // x - y + pose_cov[XYZRPY_COV_IDX::Y_Y] = + r_cov_x * sin_yaw * sin_yaw + r_cov_y * cos_yaw * cos_yaw; // y - y + pose_cov[XYZRPY_COV_IDX::Y_X] = pose_cov[XYZRPY_COV_IDX::X_Y]; // y - x + pose_cov[XYZRPY_COV_IDX::X_YAW] = 0.0; // x - yaw + pose_cov[XYZRPY_COV_IDX::Y_YAW] = 0.0; // y - yaw + pose_cov[XYZRPY_COV_IDX::YAW_X] = 0.0; // yaw - x + pose_cov[XYZRPY_COV_IDX::YAW_Y] = 0.0; // yaw - y + pose_cov[XYZRPY_COV_IDX::YAW_YAW] = object_model_.measurement_covariance.yaw; // yaw - yaw + if (!is_yaw_available) { + pose_cov[XYZRPY_COV_IDX::YAW_YAW] *= 1e3; // yaw is not available, multiply large value + } + auto & twist_cov = updating_object.kinematics.twist_with_covariance.covariance; + twist_cov[XYZRPY_COV_IDX::X_X] = object_model_.measurement_covariance.vel_long; // vel - vel } return updating_object; @@ -229,8 +231,7 @@ bool BicycleTracker::measureWithPose(const autoware_perception_msgs::msg::Detect bool BicycleTracker::measureWithShape(const autoware_perception_msgs::msg::DetectedObject & object) { - autoware_perception_msgs::msg::DetectedObject bbox_object; - if (!object.shape.type == autoware_perception_msgs::msg::Shape::BOUNDING_BOX) { + if (object.shape.type != autoware_perception_msgs::msg::Shape::BOUNDING_BOX) { // do not update shape if the input is not a bounding box return false; } @@ -239,28 +240,29 @@ bool BicycleTracker::measureWithShape(const autoware_perception_msgs::msg::Detec constexpr double size_max = 30.0; // [m] constexpr double size_min = 0.1; // [m] if ( - bbox_object.shape.dimensions.x > size_max || bbox_object.shape.dimensions.y > size_max || - bbox_object.shape.dimensions.z > size_max) { + object.shape.dimensions.x > size_max || object.shape.dimensions.y > size_max || + object.shape.dimensions.z > size_max) { return false; } else if ( - bbox_object.shape.dimensions.x < size_min || bbox_object.shape.dimensions.y < size_min || - bbox_object.shape.dimensions.z < size_min) { + object.shape.dimensions.x < size_min || object.shape.dimensions.y < size_min || + object.shape.dimensions.z < size_min) { return false; } // update object size constexpr double gain = 0.1; constexpr double gain_inv = 1.0 - gain; - bounding_box_.length = gain_inv * bounding_box_.length + gain * bbox_object.shape.dimensions.x; - bounding_box_.width = gain_inv * bounding_box_.width + gain * bbox_object.shape.dimensions.y; - bounding_box_.height = gain_inv * bounding_box_.height + gain * bbox_object.shape.dimensions.z; + bounding_box_.length = gain_inv * bounding_box_.length + gain * object.shape.dimensions.x; + bounding_box_.width = gain_inv * bounding_box_.width + gain * object.shape.dimensions.y; + bounding_box_.height = gain_inv * bounding_box_.height + gain * object.shape.dimensions.z; // set maximum and minimum size - constexpr double max_size = 5.0; - constexpr double min_size = 0.3; - bounding_box_.length = std::min(std::max(bounding_box_.length, min_size), max_size); - bounding_box_.width = std::min(std::max(bounding_box_.width, min_size), max_size); - bounding_box_.height = std::min(std::max(bounding_box_.height, min_size), max_size); + bounding_box_.length = std::clamp( + bounding_box_.length, object_model_.size_limit.length_min, object_model_.size_limit.length_max); + bounding_box_.width = std::clamp( + bounding_box_.width, object_model_.size_limit.width_min, object_model_.size_limit.width_max); + bounding_box_.height = std::clamp( + bounding_box_.height, object_model_.size_limit.height_min, object_model_.size_limit.height_max); // update motion model motion_model_.updateExtendedState(bounding_box_.length); diff --git a/perception/multi_object_tracker/src/tracker/model/big_vehicle_tracker.cpp b/perception/multi_object_tracker/src/tracker/model/big_vehicle_tracker.cpp index 4414d8a21ca01..90d33e65b46bd 100644 --- a/perception/multi_object_tracker/src/tracker/model/big_vehicle_tracker.cpp +++ b/perception/multi_object_tracker/src/tracker/model/big_vehicle_tracker.cpp @@ -15,14 +15,19 @@ // // Author: v1.0 Yukihiro Saito // +#define EIGEN_MPL2_ONLY #include "multi_object_tracker/tracker/model/big_vehicle_tracker.hpp" +#include "autoware/universe_utils/geometry/boost_polygon_utils.hpp" +#include "autoware/universe_utils/math/normalization.hpp" +#include "autoware/universe_utils/math/unit_conversion.hpp" +#include "autoware/universe_utils/ros/msg_covariance.hpp" #include "multi_object_tracker/utils/utils.hpp" +#include "object_recognition_utils/object_recognition_utils.hpp" -#include -#include -#include +#include +#include #include #include @@ -34,11 +39,6 @@ #else #include #endif -#include "object_recognition_utils/object_recognition_utils.hpp" - -#define EIGEN_MPL2_ONLY -#include -#include using Label = autoware_perception_msgs::msg::ObjectClassification; @@ -56,17 +56,6 @@ BigVehicleTracker::BigVehicleTracker( // initialize existence probability initializeExistenceProbabilities(channel_index, object.existence_probability); - // Initialize parameters - // measurement noise covariance: detector uncertainty + ego vehicle motion uncertainty - float r_stddev_x = 0.5; // in vehicle coordinate [m] - float r_stddev_y = 0.4; // in vehicle coordinate [m] - float r_stddev_yaw = autoware::universe_utils::deg2rad(20); // in map coordinate [rad] - float r_stddev_vel = 1.0; // in object coordinate [m/s] - ekf_params_.r_cov_x = std::pow(r_stddev_x, 2.0); - ekf_params_.r_cov_y = std::pow(r_stddev_y, 2.0); - ekf_params_.r_cov_yaw = std::pow(r_stddev_yaw, 2.0); - ekf_params_.r_cov_vel = std::pow(r_stddev_vel, 2.0); - // velocity deviation threshold // if the predicted velocity is close to the observed velocity, // the observed velocity is used as the measurement. @@ -82,7 +71,9 @@ BigVehicleTracker::BigVehicleTracker( RCLCPP_WARN( logger_, "BigVehicleTracker::BigVehicleTracker: Failed to convert convex hull to bounding box."); - bounding_box_ = {6.0, 2.0, 2.0}; // default value + bounding_box_ = { + object_model_.init_size.length, object_model_.init_size.width, + object_model_.init_size.height}; // default value } else { bounding_box_ = { bbox_object.shape.dimensions.x, bbox_object.shape.dimensions.y, @@ -90,28 +81,26 @@ BigVehicleTracker::BigVehicleTracker( } } // set maximum and minimum size - constexpr double max_size = 30.0; - constexpr double min_size = 1.0; - bounding_box_.length = std::min(std::max(bounding_box_.length, min_size), max_size); - bounding_box_.width = std::min(std::max(bounding_box_.width, min_size), max_size); - bounding_box_.height = std::min(std::max(bounding_box_.height, min_size), max_size); + bounding_box_.length = std::clamp( + bounding_box_.length, object_model_.size_limit.length_min, object_model_.size_limit.length_max); + bounding_box_.width = std::clamp( + bounding_box_.width, object_model_.size_limit.width_min, object_model_.size_limit.width_max); + bounding_box_.height = std::clamp( + bounding_box_.height, object_model_.size_limit.height_min, object_model_.size_limit.height_max); // Set motion model parameters { - constexpr double q_stddev_acc_long = - 9.8 * 0.35; // [m/(s*s)] uncertain longitudinal acceleration - constexpr double q_stddev_acc_lat = 9.8 * 0.15; // [m/(s*s)] uncertain lateral acceleration - constexpr double q_stddev_yaw_rate_min = 1.5; // [deg/s] uncertain yaw change rate, minimum - constexpr double q_stddev_yaw_rate_max = 15.0; // [deg/s] uncertain yaw change rate, maximum - constexpr double q_stddev_slip_rate_min = - 0.3; // [deg/s] uncertain slip angle change rate, minimum - constexpr double q_stddev_slip_rate_max = - 10.0; // [deg/s] uncertain slip angle change rate, maximum - constexpr double q_max_slip_angle = 30; // [deg] max slip angle - constexpr double lf_ratio = 0.3; // [-] ratio of front wheel position - constexpr double lf_min = 1.5; // [m] minimum front wheel position - constexpr double lr_ratio = 0.25; // [-] ratio of rear wheel position - constexpr double lr_min = 1.5; // [m] minimum rear wheel position + const double q_stddev_acc_long = object_model_.process_noise.acc_long; + const double q_stddev_acc_lat = object_model_.process_noise.acc_lat; + const double q_stddev_yaw_rate_min = object_model_.process_noise.yaw_rate_min; + const double q_stddev_yaw_rate_max = object_model_.process_noise.yaw_rate_max; + const double q_stddev_slip_rate_min = object_model_.bicycle_state.slip_rate_stddev_min; + const double q_stddev_slip_rate_max = object_model_.bicycle_state.slip_rate_stddev_max; + const double q_max_slip_angle = object_model_.bicycle_state.slip_angle_max; + const double lf_ratio = object_model_.bicycle_state.wheel_pos_ratio_front; + const double lf_min = object_model_.bicycle_state.wheel_pos_front_min; + const double lr_ratio = object_model_.bicycle_state.wheel_pos_ratio_rear; + const double lr_min = object_model_.bicycle_state.wheel_pos_rear_min; motion_model_.setMotionParams( q_stddev_acc_long, q_stddev_acc_lat, q_stddev_yaw_rate_min, q_stddev_yaw_rate_max, q_stddev_slip_rate_min, q_stddev_slip_rate_max, q_max_slip_angle, lf_ratio, lf_min, lr_ratio, @@ -120,59 +109,47 @@ BigVehicleTracker::BigVehicleTracker( // Set motion limits { - constexpr double max_vel = autoware::universe_utils::kmph2mps(100); // [m/s] maximum velocity - constexpr double max_slip = 30; // [deg] maximum slip angle + const double max_vel = object_model_.process_limit.vel_long_max; + const double max_slip = object_model_.bicycle_state.slip_angle_max; motion_model_.setMotionLimits(max_vel, max_slip); // maximum velocity and slip angle } // Set initial state { + using autoware::universe_utils::xyzrpy_covariance_index::XYZRPY_COV_IDX; const double x = object.kinematics.pose_with_covariance.pose.position.x; const double y = object.kinematics.pose_with_covariance.pose.position.y; const double yaw = tf2::getYaw(object.kinematics.pose_with_covariance.pose.orientation); - auto pose_cov = object.kinematics.pose_with_covariance.covariance; - double vel = 0.0; - double vel_cov; - const double & length = bounding_box_.length; - - if (object.kinematics.has_twist) { - vel = object.kinematics.twist_with_covariance.twist.linear.x; - } + auto pose_cov = object.kinematics.pose_with_covariance.covariance; if (!object.kinematics.has_position_covariance) { // initial state covariance - constexpr double p0_stddev_x = 1.5; // in object coordinate [m] - constexpr double p0_stddev_y = 0.5; // in object coordinate [m] - constexpr double p0_stddev_yaw = - autoware::universe_utils::deg2rad(25); // in map coordinate [rad] - constexpr double p0_cov_x = p0_stddev_x * p0_stddev_x; - constexpr double p0_cov_y = p0_stddev_y * p0_stddev_y; - constexpr double p0_cov_yaw = p0_stddev_yaw * p0_stddev_yaw; + const auto & p0_cov_x = object_model_.initial_covariance.pos_x; + const auto & p0_cov_y = object_model_.initial_covariance.pos_y; + const auto & p0_cov_yaw = object_model_.initial_covariance.yaw; const double cos_yaw = std::cos(yaw); const double sin_yaw = std::sin(yaw); const double sin_2yaw = std::sin(2.0 * yaw); - pose_cov[utils::MSG_COV_IDX::X_X] = - p0_cov_x * cos_yaw * cos_yaw + p0_cov_y * sin_yaw * sin_yaw; - pose_cov[utils::MSG_COV_IDX::X_Y] = 0.5 * (p0_cov_x - p0_cov_y) * sin_2yaw; - pose_cov[utils::MSG_COV_IDX::Y_Y] = - p0_cov_x * sin_yaw * sin_yaw + p0_cov_y * cos_yaw * cos_yaw; - pose_cov[utils::MSG_COV_IDX::Y_X] = pose_cov[utils::MSG_COV_IDX::X_Y]; - pose_cov[utils::MSG_COV_IDX::YAW_YAW] = p0_cov_yaw; + pose_cov[XYZRPY_COV_IDX::X_X] = p0_cov_x * cos_yaw * cos_yaw + p0_cov_y * sin_yaw * sin_yaw; + pose_cov[XYZRPY_COV_IDX::X_Y] = 0.5 * (p0_cov_x - p0_cov_y) * sin_2yaw; + pose_cov[XYZRPY_COV_IDX::Y_Y] = p0_cov_x * sin_yaw * sin_yaw + p0_cov_y * cos_yaw * cos_yaw; + pose_cov[XYZRPY_COV_IDX::Y_X] = pose_cov[XYZRPY_COV_IDX::X_Y]; + pose_cov[XYZRPY_COV_IDX::YAW_YAW] = p0_cov_yaw; } - if (!object.kinematics.has_twist_covariance) { - constexpr double p0_stddev_vel = - autoware::universe_utils::kmph2mps(1000); // in object coordinate [m/s] - vel_cov = std::pow(p0_stddev_vel, 2.0); - } else { - vel_cov = object.kinematics.twist_with_covariance.covariance[utils::MSG_COV_IDX::X_X]; + double vel = 0.0; + double vel_cov = object_model_.initial_covariance.vel_long; + if (object.kinematics.has_twist) { + vel = object.kinematics.twist_with_covariance.twist.linear.x; + } + if (object.kinematics.has_twist_covariance) { + vel_cov = object.kinematics.twist_with_covariance.covariance[XYZRPY_COV_IDX::X_X]; } const double slip = 0.0; - const double p0_stddev_slip = - autoware::universe_utils::deg2rad(5); // in object coordinate [rad/s] - const double slip_cov = std::pow(p0_stddev_slip, 2.0); + const double slip_cov = object_model_.bicycle_state.init_slip_angle_cov; + const double & length = bounding_box_.length; // initialize motion model motion_model_.initialize(time, x, y, yaw, pose_cov, vel, vel_cov, slip, slip_cov, length); @@ -190,14 +167,9 @@ autoware_perception_msgs::msg::DetectedObject BigVehicleTracker::getUpdatingObje { autoware_perception_msgs::msg::DetectedObject updating_object = object; - // current (predicted) state - const double tracked_x = motion_model_.getStateElement(IDX::X); - const double tracked_y = motion_model_.getStateElement(IDX::Y); - const double tracked_yaw = motion_model_.getStateElement(IDX::YAW); - // OBJECT SHAPE MODEL // convert to bounding box if input is convex shape - autoware_perception_msgs::msg::DetectedObject bbox_object; + autoware_perception_msgs::msg::DetectedObject bbox_object = object; if (object.shape.type != autoware_perception_msgs::msg::Shape::BOUNDING_BOX) { if (!utils::convertConvexHullToBoundingBox(object, bbox_object)) { RCLCPP_WARN( @@ -205,10 +177,13 @@ autoware_perception_msgs::msg::DetectedObject BigVehicleTracker::getUpdatingObje "BigVehicleTracker::getUpdatingObject: Failed to convert convex hull to bounding box."); bbox_object = object; } - } else { - bbox_object = object; } + // current (predicted) state + const double tracked_x = motion_model_.getStateElement(IDX::X); + const double tracked_y = motion_model_.getStateElement(IDX::Y); + const double tracked_yaw = motion_model_.getStateElement(IDX::YAW); + // get offset measurement const int nearest_corner_index = utils::getNearestCornerOrSurface( tracked_x, tracked_y, tracked_yaw, bounding_box_.width, bounding_box_.length, self_transform); @@ -219,50 +194,45 @@ autoware_perception_msgs::msg::DetectedObject BigVehicleTracker::getUpdatingObje // UNCERTAINTY MODEL if (!object.kinematics.has_position_covariance) { // measurement noise covariance - float r_cov_x; - float r_cov_y; - using Label = autoware_perception_msgs::msg::ObjectClassification; + auto r_cov_x = object_model_.measurement_covariance.pos_x; + auto r_cov_y = object_model_.measurement_covariance.pos_y; const uint8_t label = object_recognition_utils::getHighestProbLabel(object.classification); - if (utils::isLargeVehicleLabel(label)) { - r_cov_x = ekf_params_.r_cov_x; - r_cov_y = ekf_params_.r_cov_y; - } else if (label == Label::CAR) { + if (label == autoware_perception_msgs::msg::ObjectClassification::CAR) { // if label is changed, enlarge the measurement noise covariance constexpr float r_stddev_x = 2.0; // [m] constexpr float r_stddev_y = 2.0; // [m] - r_cov_x = std::pow(r_stddev_x, 2.0); - r_cov_y = std::pow(r_stddev_y, 2.0); - } else { - r_cov_x = ekf_params_.r_cov_x; - r_cov_y = ekf_params_.r_cov_y; + r_cov_x = r_stddev_x * r_stddev_x; + r_cov_y = r_stddev_y * r_stddev_y; } // yaw angle fix - double pose_yaw = tf2::getYaw(object.kinematics.pose_with_covariance.pose.orientation); - bool is_yaw_available = object.kinematics.orientation_availability != - autoware_perception_msgs::msg::DetectedObjectKinematics::UNAVAILABLE; + const double pose_yaw = tf2::getYaw(object.kinematics.pose_with_covariance.pose.orientation); + const bool is_yaw_available = + object.kinematics.orientation_availability != + autoware_perception_msgs::msg::DetectedObjectKinematics::UNAVAILABLE; // fill covariance matrix + using autoware::universe_utils::xyzrpy_covariance_index::XYZRPY_COV_IDX; auto & pose_cov = updating_object.kinematics.pose_with_covariance.covariance; const double cos_yaw = std::cos(pose_yaw); const double sin_yaw = std::sin(pose_yaw); - const double sin_2yaw = std::sin(2.0f * pose_yaw); - pose_cov[utils::MSG_COV_IDX::X_X] = - r_cov_x * cos_yaw * cos_yaw + r_cov_y * sin_yaw * sin_yaw; // x - x - pose_cov[utils::MSG_COV_IDX::X_Y] = 0.5f * (r_cov_x - r_cov_y) * sin_2yaw; // x - y - pose_cov[utils::MSG_COV_IDX::Y_Y] = - r_cov_x * sin_yaw * sin_yaw + r_cov_y * cos_yaw * cos_yaw; // y - y - pose_cov[utils::MSG_COV_IDX::Y_X] = pose_cov[utils::MSG_COV_IDX::X_Y]; // y - x - pose_cov[utils::MSG_COV_IDX::X_YAW] = 0.0; // x - yaw - pose_cov[utils::MSG_COV_IDX::Y_YAW] = 0.0; // y - yaw - pose_cov[utils::MSG_COV_IDX::YAW_X] = 0.0; // yaw - x - pose_cov[utils::MSG_COV_IDX::YAW_Y] = 0.0; // yaw - y - pose_cov[utils::MSG_COV_IDX::YAW_YAW] = ekf_params_.r_cov_yaw; // yaw - yaw + const double sin_2yaw = std::sin(2.0 * pose_yaw); + pose_cov[XYZRPY_COV_IDX::X_X] = + r_cov_x * cos_yaw * cos_yaw + r_cov_y * sin_yaw * sin_yaw; // x - x + pose_cov[XYZRPY_COV_IDX::X_Y] = 0.5 * (r_cov_x - r_cov_y) * sin_2yaw; // x - y + pose_cov[XYZRPY_COV_IDX::Y_Y] = + r_cov_x * sin_yaw * sin_yaw + r_cov_y * cos_yaw * cos_yaw; // y - y + pose_cov[XYZRPY_COV_IDX::Y_X] = pose_cov[XYZRPY_COV_IDX::X_Y]; // y - x + pose_cov[XYZRPY_COV_IDX::X_YAW] = 0.0; // x - yaw + pose_cov[XYZRPY_COV_IDX::Y_YAW] = 0.0; // y - yaw + pose_cov[XYZRPY_COV_IDX::YAW_X] = 0.0; // yaw - x + pose_cov[XYZRPY_COV_IDX::YAW_Y] = 0.0; // yaw - y + pose_cov[XYZRPY_COV_IDX::YAW_YAW] = object_model_.measurement_covariance.yaw; // yaw - yaw if (!is_yaw_available) { - pose_cov[utils::MSG_COV_IDX::YAW_YAW] *= 1e3; // yaw is not available, multiply large value + pose_cov[XYZRPY_COV_IDX::YAW_YAW] *= 1e3; // yaw is not available, multiply large value } auto & twist_cov = updating_object.kinematics.twist_with_covariance.covariance; - twist_cov[utils::MSG_COV_IDX::X_X] = ekf_params_.r_cov_vel; // vel - vel + twist_cov[XYZRPY_COV_IDX::X_X] = object_model_.measurement_covariance.vel_long; // vel - vel } return updating_object; @@ -314,34 +284,35 @@ bool BigVehicleTracker::measureWithPose( bool BigVehicleTracker::measureWithShape( const autoware_perception_msgs::msg::DetectedObject & object) { - if (!object.shape.type == autoware_perception_msgs::msg::Shape::BOUNDING_BOX) { + if (object.shape.type != autoware_perception_msgs::msg::Shape::BOUNDING_BOX) { // do not update shape if the input is not a bounding box return false; } // check object size abnormality - constexpr double size_max = 40.0; // [m] + constexpr double size_max = 35.0; // [m] constexpr double size_min = 1.0; // [m] - if (object.shape.dimensions.x > size_max || object.shape.dimensions.y > size_max) { - return false; - } else if (object.shape.dimensions.x < size_min || object.shape.dimensions.y < size_min) { + bool is_size_valid = + (object.shape.dimensions.x <= size_max && object.shape.dimensions.y <= size_max && + object.shape.dimensions.x >= size_min && object.shape.dimensions.y >= size_min); + if (!is_size_valid) { return false; } + // update object size constexpr double gain = 0.5; constexpr double gain_inv = 1.0 - gain; - - // update object size bounding_box_.length = gain_inv * bounding_box_.length + gain * object.shape.dimensions.x; bounding_box_.width = gain_inv * bounding_box_.width + gain * object.shape.dimensions.y; bounding_box_.height = gain_inv * bounding_box_.height + gain * object.shape.dimensions.z; // set maximum and minimum size - constexpr double max_size = 30.0; - constexpr double min_size = 1.0; - bounding_box_.length = std::min(std::max(bounding_box_.length, min_size), max_size); - bounding_box_.width = std::min(std::max(bounding_box_.width, min_size), max_size); - bounding_box_.height = std::min(std::max(bounding_box_.height, min_size), max_size); + bounding_box_.length = std::clamp( + bounding_box_.length, object_model_.size_limit.length_min, object_model_.size_limit.length_max); + bounding_box_.width = std::clamp( + bounding_box_.width, object_model_.size_limit.width_min, object_model_.size_limit.width_max); + bounding_box_.height = std::clamp( + bounding_box_.height, object_model_.size_limit.height_min, object_model_.size_limit.height_max); // update motion model motion_model_.updateExtendedState(bounding_box_.length); diff --git a/perception/multi_object_tracker/src/tracker/model/normal_vehicle_tracker.cpp b/perception/multi_object_tracker/src/tracker/model/normal_vehicle_tracker.cpp index 1fff1a13add65..67282893083c1 100644 --- a/perception/multi_object_tracker/src/tracker/model/normal_vehicle_tracker.cpp +++ b/perception/multi_object_tracker/src/tracker/model/normal_vehicle_tracker.cpp @@ -15,14 +15,19 @@ // // Author: v1.0 Yukihiro Saito // +#define EIGEN_MPL2_ONLY #include "multi_object_tracker/tracker/model/normal_vehicle_tracker.hpp" +#include "autoware/universe_utils/geometry/boost_polygon_utils.hpp" +#include "autoware/universe_utils/math/normalization.hpp" +#include "autoware/universe_utils/math/unit_conversion.hpp" +#include "autoware/universe_utils/ros/msg_covariance.hpp" #include "multi_object_tracker/utils/utils.hpp" +#include "object_recognition_utils/object_recognition_utils.hpp" -#include -#include -#include +#include +#include #include #include @@ -34,11 +39,6 @@ #else #include #endif -#include "object_recognition_utils/object_recognition_utils.hpp" - -#define EIGEN_MPL2_ONLY -#include -#include using Label = autoware_perception_msgs::msg::ObjectClassification; @@ -56,17 +56,6 @@ NormalVehicleTracker::NormalVehicleTracker( // initialize existence probability initializeExistenceProbabilities(channel_index, object.existence_probability); - // Initialize parameters - // measurement noise covariance: detector uncertainty + ego vehicle motion uncertainty - float r_stddev_x = 0.5; // in vehicle coordinate [m] - float r_stddev_y = 0.4; // in vehicle coordinate [m] - float r_stddev_yaw = autoware::universe_utils::deg2rad(20); // in map coordinate [rad] - float r_stddev_vel = 1.0; // in object coordinate [m/s] - ekf_params_.r_cov_x = std::pow(r_stddev_x, 2.0); - ekf_params_.r_cov_y = std::pow(r_stddev_y, 2.0); - ekf_params_.r_cov_yaw = std::pow(r_stddev_yaw, 2.0); - ekf_params_.r_cov_vel = std::pow(r_stddev_vel, 2.0); - // velocity deviation threshold // if the predicted velocity is close to the observed velocity, // the observed velocity is used as the measurement. @@ -83,7 +72,9 @@ NormalVehicleTracker::NormalVehicleTracker( logger_, "NormalVehicleTracker::NormalVehicleTracker: Failed to convert convex hull to bounding " "box."); - bounding_box_ = {3.0, 2.0, 1.8}; // default value + bounding_box_ = { + object_model_.init_size.length, object_model_.init_size.width, + object_model_.init_size.height}; // default value } else { bounding_box_ = { bbox_object.shape.dimensions.x, bbox_object.shape.dimensions.y, @@ -91,28 +82,26 @@ NormalVehicleTracker::NormalVehicleTracker( } } // set maximum and minimum size - constexpr double max_size = 20.0; - constexpr double min_size = 1.0; - bounding_box_.length = std::min(std::max(bounding_box_.length, min_size), max_size); - bounding_box_.width = std::min(std::max(bounding_box_.width, min_size), max_size); - bounding_box_.height = std::min(std::max(bounding_box_.height, min_size), max_size); + bounding_box_.length = std::clamp( + bounding_box_.length, object_model_.size_limit.length_min, object_model_.size_limit.length_max); + bounding_box_.width = std::clamp( + bounding_box_.width, object_model_.size_limit.width_min, object_model_.size_limit.width_max); + bounding_box_.height = std::clamp( + bounding_box_.height, object_model_.size_limit.height_min, object_model_.size_limit.height_max); // Set motion model parameters { - constexpr double q_stddev_acc_long = - 9.8 * 0.35; // [m/(s*s)] uncertain longitudinal acceleration - constexpr double q_stddev_acc_lat = 9.8 * 0.15; // [m/(s*s)] uncertain lateral acceleration - constexpr double q_stddev_yaw_rate_min = 1.5; // [deg/s] uncertain yaw change rate, minimum - constexpr double q_stddev_yaw_rate_max = 15.0; // [deg/s] uncertain yaw change rate, maximum - constexpr double q_stddev_slip_rate_min = - 0.3; // [deg/s] uncertain slip angle change rate, minimum - constexpr double q_stddev_slip_rate_max = - 10.0; // [deg/s] uncertain slip angle change rate, maximum - constexpr double q_max_slip_angle = 30; // [deg] max slip angle - constexpr double lf_ratio = 0.3; // [-] ratio of front wheel position - constexpr double lf_min = 1.0; // [m] minimum front wheel position - constexpr double lr_ratio = 0.25; // [-] ratio of rear wheel position - constexpr double lr_min = 1.0; // [m] minimum rear wheel position + const double q_stddev_acc_long = object_model_.process_noise.acc_long; + const double q_stddev_acc_lat = object_model_.process_noise.acc_lat; + const double q_stddev_yaw_rate_min = object_model_.process_noise.yaw_rate_min; + const double q_stddev_yaw_rate_max = object_model_.process_noise.yaw_rate_max; + const double q_stddev_slip_rate_min = object_model_.bicycle_state.slip_rate_stddev_min; + const double q_stddev_slip_rate_max = object_model_.bicycle_state.slip_rate_stddev_max; + const double q_max_slip_angle = object_model_.bicycle_state.slip_angle_max; + const double lf_ratio = object_model_.bicycle_state.wheel_pos_ratio_front; + const double lf_min = object_model_.bicycle_state.wheel_pos_front_min; + const double lr_ratio = object_model_.bicycle_state.wheel_pos_ratio_rear; + const double lr_min = object_model_.bicycle_state.wheel_pos_rear_min; motion_model_.setMotionParams( q_stddev_acc_long, q_stddev_acc_lat, q_stddev_yaw_rate_min, q_stddev_yaw_rate_max, q_stddev_slip_rate_min, q_stddev_slip_rate_max, q_max_slip_angle, lf_ratio, lf_min, lr_ratio, @@ -121,59 +110,47 @@ NormalVehicleTracker::NormalVehicleTracker( // Set motion limits { - constexpr double max_vel = autoware::universe_utils::kmph2mps(100); // [m/s] maximum velocity - constexpr double max_slip = 30; // [deg] maximum slip angle + const double max_vel = object_model_.process_limit.vel_long_max; + const double max_slip = object_model_.bicycle_state.slip_angle_max; motion_model_.setMotionLimits(max_vel, max_slip); // maximum velocity and slip angle } // Set initial state { + using autoware::universe_utils::xyzrpy_covariance_index::XYZRPY_COV_IDX; const double x = object.kinematics.pose_with_covariance.pose.position.x; const double y = object.kinematics.pose_with_covariance.pose.position.y; const double yaw = tf2::getYaw(object.kinematics.pose_with_covariance.pose.orientation); - auto pose_cov = object.kinematics.pose_with_covariance.covariance; - double vel = 0.0; - double vel_cov; - const double & length = bounding_box_.length; - - if (object.kinematics.has_twist) { - vel = object.kinematics.twist_with_covariance.twist.linear.x; - } + auto pose_cov = object.kinematics.pose_with_covariance.covariance; if (!object.kinematics.has_position_covariance) { // initial state covariance - constexpr double p0_stddev_x = 1.0; // in object coordinate [m] - constexpr double p0_stddev_y = 0.3; // in object coordinate [m] - constexpr double p0_stddev_yaw = - autoware::universe_utils::deg2rad(25); // in map coordinate [rad] - constexpr double p0_cov_x = p0_stddev_x * p0_stddev_x; - constexpr double p0_cov_y = p0_stddev_y * p0_stddev_y; - constexpr double p0_cov_yaw = p0_stddev_yaw * p0_stddev_yaw; + const auto & p0_cov_x = object_model_.initial_covariance.pos_x; + const auto & p0_cov_y = object_model_.initial_covariance.pos_y; + const auto & p0_cov_yaw = object_model_.initial_covariance.yaw; const double cos_yaw = std::cos(yaw); const double sin_yaw = std::sin(yaw); const double sin_2yaw = std::sin(2.0 * yaw); - pose_cov[utils::MSG_COV_IDX::X_X] = - p0_cov_x * cos_yaw * cos_yaw + p0_cov_y * sin_yaw * sin_yaw; - pose_cov[utils::MSG_COV_IDX::X_Y] = 0.5 * (p0_cov_x - p0_cov_y) * sin_2yaw; - pose_cov[utils::MSG_COV_IDX::Y_Y] = - p0_cov_x * sin_yaw * sin_yaw + p0_cov_y * cos_yaw * cos_yaw; - pose_cov[utils::MSG_COV_IDX::Y_X] = pose_cov[utils::MSG_COV_IDX::X_Y]; - pose_cov[utils::MSG_COV_IDX::YAW_YAW] = p0_cov_yaw; + pose_cov[XYZRPY_COV_IDX::X_X] = p0_cov_x * cos_yaw * cos_yaw + p0_cov_y * sin_yaw * sin_yaw; + pose_cov[XYZRPY_COV_IDX::X_Y] = 0.5 * (p0_cov_x - p0_cov_y) * sin_2yaw; + pose_cov[XYZRPY_COV_IDX::Y_Y] = p0_cov_x * sin_yaw * sin_yaw + p0_cov_y * cos_yaw * cos_yaw; + pose_cov[XYZRPY_COV_IDX::Y_X] = pose_cov[XYZRPY_COV_IDX::X_Y]; + pose_cov[XYZRPY_COV_IDX::YAW_YAW] = p0_cov_yaw; } - if (!object.kinematics.has_twist_covariance) { - constexpr double p0_stddev_vel = - autoware::universe_utils::kmph2mps(1000); // in object coordinate [m/s] - vel_cov = std::pow(p0_stddev_vel, 2.0); - } else { - vel_cov = object.kinematics.twist_with_covariance.covariance[utils::MSG_COV_IDX::X_X]; + double vel = 0.0; + double vel_cov = object_model_.initial_covariance.vel_long; + if (object.kinematics.has_twist) { + vel = object.kinematics.twist_with_covariance.twist.linear.x; + } + if (object.kinematics.has_twist_covariance) { + vel_cov = object.kinematics.twist_with_covariance.covariance[XYZRPY_COV_IDX::X_X]; } const double slip = 0.0; - const double p0_stddev_slip = - autoware::universe_utils::deg2rad(5); // in object coordinate [rad/s] - const double slip_cov = std::pow(p0_stddev_slip, 2.0); + const double slip_cov = object_model_.bicycle_state.init_slip_angle_cov; + const double & length = bounding_box_.length; // initialize motion model motion_model_.initialize(time, x, y, yaw, pose_cov, vel, vel_cov, slip, slip_cov, length); @@ -191,14 +168,9 @@ autoware_perception_msgs::msg::DetectedObject NormalVehicleTracker::getUpdatingO { autoware_perception_msgs::msg::DetectedObject updating_object = object; - // current (predicted) state - const double tracked_x = motion_model_.getStateElement(IDX::X); - const double tracked_y = motion_model_.getStateElement(IDX::Y); - const double tracked_yaw = motion_model_.getStateElement(IDX::YAW); - // OBJECT SHAPE MODEL // convert to bounding box if input is convex shape - autoware_perception_msgs::msg::DetectedObject bbox_object; + autoware_perception_msgs::msg::DetectedObject bbox_object = object; if (object.shape.type != autoware_perception_msgs::msg::Shape::BOUNDING_BOX) { if (!utils::convertConvexHullToBoundingBox(object, bbox_object)) { RCLCPP_WARN( @@ -206,11 +178,13 @@ autoware_perception_msgs::msg::DetectedObject NormalVehicleTracker::getUpdatingO "NormalVehicleTracker::getUpdatingObject: Failed to convert convex hull to bounding box."); bbox_object = object; } - - } else { - bbox_object = object; } + // current (predicted) state + const double tracked_x = motion_model_.getStateElement(IDX::X); + const double tracked_y = motion_model_.getStateElement(IDX::Y); + const double tracked_yaw = motion_model_.getStateElement(IDX::YAW); + // get offset measurement const int nearest_corner_index = utils::getNearestCornerOrSurface( tracked_x, tracked_y, tracked_yaw, bounding_box_.width, bounding_box_.length, self_transform); @@ -221,50 +195,45 @@ autoware_perception_msgs::msg::DetectedObject NormalVehicleTracker::getUpdatingO // UNCERTAINTY MODEL if (!object.kinematics.has_position_covariance) { // measurement noise covariance - float r_cov_x; - float r_cov_y; - using Label = autoware_perception_msgs::msg::ObjectClassification; + auto r_cov_x = object_model_.measurement_covariance.pos_x; + auto r_cov_y = object_model_.measurement_covariance.pos_y; const uint8_t label = object_recognition_utils::getHighestProbLabel(object.classification); - if (label == Label::CAR) { - r_cov_x = ekf_params_.r_cov_x; - r_cov_y = ekf_params_.r_cov_y; - } else if (utils::isLargeVehicleLabel(label)) { + if (utils::isLargeVehicleLabel(label)) { // if label is changed, enlarge the measurement noise covariance constexpr float r_stddev_x = 2.0; // [m] constexpr float r_stddev_y = 2.0; // [m] - r_cov_x = std::pow(r_stddev_x, 2.0); - r_cov_y = std::pow(r_stddev_y, 2.0); - } else { - r_cov_x = ekf_params_.r_cov_x; - r_cov_y = ekf_params_.r_cov_y; + r_cov_x = r_stddev_x * r_stddev_x; + r_cov_y = r_stddev_y * r_stddev_y; } // yaw angle fix - double pose_yaw = tf2::getYaw(object.kinematics.pose_with_covariance.pose.orientation); - bool is_yaw_available = object.kinematics.orientation_availability != - autoware_perception_msgs::msg::DetectedObjectKinematics::UNAVAILABLE; + const double pose_yaw = tf2::getYaw(object.kinematics.pose_with_covariance.pose.orientation); + const bool is_yaw_available = + object.kinematics.orientation_availability != + autoware_perception_msgs::msg::DetectedObjectKinematics::UNAVAILABLE; // fill covariance matrix + using autoware::universe_utils::xyzrpy_covariance_index::XYZRPY_COV_IDX; auto & pose_cov = updating_object.kinematics.pose_with_covariance.covariance; const double cos_yaw = std::cos(pose_yaw); const double sin_yaw = std::sin(pose_yaw); - const double sin_2yaw = std::sin(2.0f * pose_yaw); - pose_cov[utils::MSG_COV_IDX::X_X] = - r_cov_x * cos_yaw * cos_yaw + r_cov_y * sin_yaw * sin_yaw; // x - x - pose_cov[utils::MSG_COV_IDX::X_Y] = 0.5f * (r_cov_x - r_cov_y) * sin_2yaw; // x - y - pose_cov[utils::MSG_COV_IDX::Y_Y] = - r_cov_x * sin_yaw * sin_yaw + r_cov_y * cos_yaw * cos_yaw; // y - y - pose_cov[utils::MSG_COV_IDX::Y_X] = pose_cov[utils::MSG_COV_IDX::X_Y]; // y - x - pose_cov[utils::MSG_COV_IDX::X_YAW] = 0.0; // x - yaw - pose_cov[utils::MSG_COV_IDX::Y_YAW] = 0.0; // y - yaw - pose_cov[utils::MSG_COV_IDX::YAW_X] = 0.0; // yaw - x - pose_cov[utils::MSG_COV_IDX::YAW_Y] = 0.0; // yaw - y - pose_cov[utils::MSG_COV_IDX::YAW_YAW] = ekf_params_.r_cov_yaw; // yaw - yaw + const double sin_2yaw = std::sin(2.0 * pose_yaw); + pose_cov[XYZRPY_COV_IDX::X_X] = + r_cov_x * cos_yaw * cos_yaw + r_cov_y * sin_yaw * sin_yaw; // x - x + pose_cov[XYZRPY_COV_IDX::X_Y] = 0.5 * (r_cov_x - r_cov_y) * sin_2yaw; // x - y + pose_cov[XYZRPY_COV_IDX::Y_Y] = + r_cov_x * sin_yaw * sin_yaw + r_cov_y * cos_yaw * cos_yaw; // y - y + pose_cov[XYZRPY_COV_IDX::Y_X] = pose_cov[XYZRPY_COV_IDX::X_Y]; // y - x + pose_cov[XYZRPY_COV_IDX::X_YAW] = 0.0; // x - yaw + pose_cov[XYZRPY_COV_IDX::Y_YAW] = 0.0; // y - yaw + pose_cov[XYZRPY_COV_IDX::YAW_X] = 0.0; // yaw - x + pose_cov[XYZRPY_COV_IDX::YAW_Y] = 0.0; // yaw - y + pose_cov[XYZRPY_COV_IDX::YAW_YAW] = object_model_.measurement_covariance.yaw; // yaw - yaw if (!is_yaw_available) { - pose_cov[utils::MSG_COV_IDX::YAW_YAW] *= 1e3; // yaw is not available, multiply large value + pose_cov[XYZRPY_COV_IDX::YAW_YAW] *= 1e3; // yaw is not available, multiply large value } auto & twist_cov = updating_object.kinematics.twist_with_covariance.covariance; - twist_cov[utils::MSG_COV_IDX::X_X] = ekf_params_.r_cov_vel; // vel - vel + twist_cov[XYZRPY_COV_IDX::X_X] = object_model_.measurement_covariance.vel_long; // vel - vel } return updating_object; @@ -316,34 +285,35 @@ bool NormalVehicleTracker::measureWithPose( bool NormalVehicleTracker::measureWithShape( const autoware_perception_msgs::msg::DetectedObject & object) { - if (!object.shape.type == autoware_perception_msgs::msg::Shape::BOUNDING_BOX) { + if (object.shape.type != autoware_perception_msgs::msg::Shape::BOUNDING_BOX) { // do not update shape if the input is not a bounding box return false; } // check object size abnormality - constexpr double size_max = 30.0; // [m] + constexpr double size_max = 35.0; // [m] constexpr double size_min = 1.0; // [m] - if (object.shape.dimensions.x > size_max || object.shape.dimensions.y > size_max) { - return false; - } else if (object.shape.dimensions.x < size_min || object.shape.dimensions.y < size_min) { + bool is_size_valid = + (object.shape.dimensions.x <= size_max && object.shape.dimensions.y <= size_max && + object.shape.dimensions.x >= size_min && object.shape.dimensions.y >= size_min); + if (!is_size_valid) { return false; } + // update object size constexpr double gain = 0.5; constexpr double gain_inv = 1.0 - gain; - - // update object size bounding_box_.length = gain_inv * bounding_box_.length + gain * object.shape.dimensions.x; bounding_box_.width = gain_inv * bounding_box_.width + gain * object.shape.dimensions.y; bounding_box_.height = gain_inv * bounding_box_.height + gain * object.shape.dimensions.z; // set maximum and minimum size - constexpr double max_size = 20.0; - constexpr double min_size = 1.0; - bounding_box_.length = std::min(std::max(bounding_box_.length, min_size), max_size); - bounding_box_.width = std::min(std::max(bounding_box_.width, min_size), max_size); - bounding_box_.height = std::min(std::max(bounding_box_.height, min_size), max_size); + bounding_box_.length = std::clamp( + bounding_box_.length, object_model_.size_limit.length_min, object_model_.size_limit.length_max); + bounding_box_.width = std::clamp( + bounding_box_.width, object_model_.size_limit.width_min, object_model_.size_limit.width_max); + bounding_box_.height = std::clamp( + bounding_box_.height, object_model_.size_limit.height_min, object_model_.size_limit.height_max); // update motion model motion_model_.updateExtendedState(bounding_box_.length); diff --git a/perception/multi_object_tracker/src/tracker/model/pass_through_tracker.cpp b/perception/multi_object_tracker/src/tracker/model/pass_through_tracker.cpp index 3e4f23cd440bc..17be415480360 100644 --- a/perception/multi_object_tracker/src/tracker/model/pass_through_tracker.cpp +++ b/perception/multi_object_tracker/src/tracker/model/pass_through_tracker.cpp @@ -15,6 +15,15 @@ // // Author: v1.0 Yutaka Shimizu // +#define EIGEN_MPL2_ONLY +#include "multi_object_tracker/tracker/model/pass_through_tracker.hpp" + +#include "autoware/universe_utils/ros/msg_covariance.hpp" +#include "multi_object_tracker/utils/utils.hpp" +#include "object_recognition_utils/object_recognition_utils.hpp" + +#include +#include #include #include @@ -27,14 +36,6 @@ #include #endif -#define EIGEN_MPL2_ONLY -#include "multi_object_tracker/tracker/model/pass_through_tracker.hpp" -#include "multi_object_tracker/utils/utils.hpp" -#include "object_recognition_utils/object_recognition_utils.hpp" - -#include -#include - PassThroughTracker::PassThroughTracker( const rclcpp::Time & time, const autoware_perception_msgs::msg::DetectedObject & object, const geometry_msgs::msg::Transform & /*self_transform*/, const size_t channel_size, @@ -86,25 +87,26 @@ bool PassThroughTracker::measure( bool PassThroughTracker::getTrackedObject( const rclcpp::Time & time, autoware_perception_msgs::msg::TrackedObject & object) const { + using autoware::universe_utils::xyzrpy_covariance_index::XYZRPY_COV_IDX; object = object_recognition_utils::toTrackedObject(object_); object.object_id = getUUID(); object.classification = getClassification(); - object.kinematics.pose_with_covariance.covariance[utils::MSG_COV_IDX::X_X] = 0.0; - object.kinematics.pose_with_covariance.covariance[utils::MSG_COV_IDX::X_Y] = 0.0; - object.kinematics.pose_with_covariance.covariance[utils::MSG_COV_IDX::Y_X] = 0.0; - object.kinematics.pose_with_covariance.covariance[utils::MSG_COV_IDX::Y_Y] = 0.0; - object.kinematics.pose_with_covariance.covariance[utils::MSG_COV_IDX::Z_Z] = 0.0; - object.kinematics.pose_with_covariance.covariance[utils::MSG_COV_IDX::ROLL_ROLL] = 0.0; - object.kinematics.pose_with_covariance.covariance[utils::MSG_COV_IDX::PITCH_PITCH] = 0.0; - object.kinematics.pose_with_covariance.covariance[utils::MSG_COV_IDX::YAW_YAW] = 0.0; + object.kinematics.pose_with_covariance.covariance[XYZRPY_COV_IDX::X_X] = 0.0; + object.kinematics.pose_with_covariance.covariance[XYZRPY_COV_IDX::X_Y] = 0.0; + object.kinematics.pose_with_covariance.covariance[XYZRPY_COV_IDX::Y_X] = 0.0; + object.kinematics.pose_with_covariance.covariance[XYZRPY_COV_IDX::Y_Y] = 0.0; + object.kinematics.pose_with_covariance.covariance[XYZRPY_COV_IDX::Z_Z] = 0.0; + object.kinematics.pose_with_covariance.covariance[XYZRPY_COV_IDX::ROLL_ROLL] = 0.0; + object.kinematics.pose_with_covariance.covariance[XYZRPY_COV_IDX::PITCH_PITCH] = 0.0; + object.kinematics.pose_with_covariance.covariance[XYZRPY_COV_IDX::YAW_YAW] = 0.0; // twist covariance - object.kinematics.twist_with_covariance.covariance[utils::MSG_COV_IDX::X_X] = 0.0; - object.kinematics.twist_with_covariance.covariance[utils::MSG_COV_IDX::Y_Y] = 0.0; - object.kinematics.twist_with_covariance.covariance[utils::MSG_COV_IDX::Z_Z] = 0.0; - object.kinematics.twist_with_covariance.covariance[utils::MSG_COV_IDX::ROLL_ROLL] = 0.0; - object.kinematics.twist_with_covariance.covariance[utils::MSG_COV_IDX::PITCH_PITCH] = 0.0; - object.kinematics.twist_with_covariance.covariance[utils::MSG_COV_IDX::YAW_YAW] = 0.0; + object.kinematics.twist_with_covariance.covariance[XYZRPY_COV_IDX::X_X] = 0.0; + object.kinematics.twist_with_covariance.covariance[XYZRPY_COV_IDX::Y_Y] = 0.0; + object.kinematics.twist_with_covariance.covariance[XYZRPY_COV_IDX::Z_Z] = 0.0; + object.kinematics.twist_with_covariance.covariance[XYZRPY_COV_IDX::ROLL_ROLL] = 0.0; + object.kinematics.twist_with_covariance.covariance[XYZRPY_COV_IDX::PITCH_PITCH] = 0.0; + object.kinematics.twist_with_covariance.covariance[XYZRPY_COV_IDX::YAW_YAW] = 0.0; const double dt = (time - last_update_time_).seconds(); if (0.5 /*500msec*/ < dt) { diff --git a/perception/multi_object_tracker/src/tracker/model/pedestrian_tracker.cpp b/perception/multi_object_tracker/src/tracker/model/pedestrian_tracker.cpp index 887810066f038..d9412c3b5739c 100644 --- a/perception/multi_object_tracker/src/tracker/model/pedestrian_tracker.cpp +++ b/perception/multi_object_tracker/src/tracker/model/pedestrian_tracker.cpp @@ -15,14 +15,19 @@ // // Author: v1.0 Yukihiro Saito // +#define EIGEN_MPL2_ONLY #include "multi_object_tracker/tracker/model/pedestrian_tracker.hpp" +#include "autoware/universe_utils/geometry/boost_polygon_utils.hpp" +#include "autoware/universe_utils/math/normalization.hpp" +#include "autoware/universe_utils/math/unit_conversion.hpp" +#include "autoware/universe_utils/ros/msg_covariance.hpp" #include "multi_object_tracker/utils/utils.hpp" +#include "object_recognition_utils/object_recognition_utils.hpp" -#include -#include -#include +#include +#include #include #include @@ -34,11 +39,6 @@ #else #include #endif -#include "object_recognition_utils/object_recognition_utils.hpp" - -#define EIGEN_MPL2_ONLY -#include -#include using Label = autoware_perception_msgs::msg::ObjectClassification; @@ -55,18 +55,11 @@ PedestrianTracker::PedestrianTracker( // initialize existence probability initializeExistenceProbabilities(channel_index, object.existence_probability); - // Initialize parameters - // measurement noise covariance - float r_stddev_x = 0.4; // [m] - float r_stddev_y = 0.4; // [m] - float r_stddev_yaw = autoware::universe_utils::deg2rad(30); // [rad] - ekf_params_.r_cov_x = std::pow(r_stddev_x, 2.0); - ekf_params_.r_cov_y = std::pow(r_stddev_y, 2.0); - ekf_params_.r_cov_yaw = std::pow(r_stddev_yaw, 2.0); - // OBJECT SHAPE MODEL - bounding_box_ = {0.5, 0.5, 1.7}; - cylinder_ = {0.5, 1.7}; + bounding_box_ = { + object_model_.init_size.length, object_model_.init_size.width, + object_model_.init_size.height}; // default value + cylinder_ = {object_model_.init_size.length, object_model_.init_size.height}; // default value if (object.shape.type == autoware_perception_msgs::msg::Shape::BOUNDING_BOX) { bounding_box_ = { object.shape.dimensions.x, object.shape.dimensions.y, object.shape.dimensions.z}; @@ -76,78 +69,70 @@ PedestrianTracker::PedestrianTracker( // do not update polygon shape } // set maximum and minimum size - constexpr double max_size = 5.0; - constexpr double min_size = 0.3; - bounding_box_.length = std::min(std::max(bounding_box_.length, min_size), max_size); - bounding_box_.width = std::min(std::max(bounding_box_.width, min_size), max_size); - bounding_box_.height = std::min(std::max(bounding_box_.height, min_size), max_size); - cylinder_.width = std::min(std::max(cylinder_.width, min_size), max_size); - cylinder_.height = std::min(std::max(cylinder_.height, min_size), max_size); + bounding_box_.length = std::clamp( + bounding_box_.length, object_model_.size_limit.length_min, object_model_.size_limit.length_max); + bounding_box_.width = std::clamp( + bounding_box_.width, object_model_.size_limit.width_min, object_model_.size_limit.width_max); + bounding_box_.height = std::clamp( + bounding_box_.height, object_model_.size_limit.height_min, object_model_.size_limit.height_max); + cylinder_.width = std::clamp( + cylinder_.width, object_model_.size_limit.length_min, object_model_.size_limit.length_max); + cylinder_.height = std::clamp( + cylinder_.height, object_model_.size_limit.height_min, object_model_.size_limit.height_max); // Set motion model parameters { - constexpr double q_stddev_x = 0.5; // [m/s] - constexpr double q_stddev_y = 0.5; // [m/s] - constexpr double q_stddev_yaw = autoware::universe_utils::deg2rad(20); // [rad/s] - constexpr double q_stddev_vx = 9.8 * 0.3; // [m/(s*s)] - constexpr double q_stddev_wz = autoware::universe_utils::deg2rad(30); // [rad/(s*s)] + const double q_stddev_x = object_model_.process_noise.vel_long; + const double q_stddev_y = object_model_.process_noise.vel_lat; + const double q_stddev_yaw = object_model_.process_noise.yaw_rate; + const double q_stddev_vx = object_model_.process_noise.acc_long; + const double q_stddev_wz = object_model_.process_noise.acc_turn; motion_model_.setMotionParams(q_stddev_x, q_stddev_y, q_stddev_yaw, q_stddev_vx, q_stddev_wz); } // Set motion limits - motion_model_.setMotionLimits( - autoware::universe_utils::kmph2mps(100), /* [m/s] maximum velocity */ - 30.0 /* [deg/s] maximum turn rate */ - ); + { + const double max_vel = object_model_.process_limit.vel_long_max; + const double max_turn_rate = object_model_.process_limit.yaw_rate_max; + motion_model_.setMotionLimits(max_vel, max_turn_rate); // maximum velocity and slip angle + } // Set initial state { + using autoware::universe_utils::xyzrpy_covariance_index::XYZRPY_COV_IDX; const double x = object.kinematics.pose_with_covariance.pose.position.x; const double y = object.kinematics.pose_with_covariance.pose.position.y; const double yaw = tf2::getYaw(object.kinematics.pose_with_covariance.pose.orientation); - auto pose_cov = object.kinematics.pose_with_covariance.covariance; - double vel = 0.0; - double wz = 0.0; - double vel_cov; - double wz_cov; - - if (object.kinematics.has_twist) { - vel = object.kinematics.twist_with_covariance.twist.linear.x; - wz = object.kinematics.twist_with_covariance.twist.angular.z; - } + auto pose_cov = object.kinematics.pose_with_covariance.covariance; if (!object.kinematics.has_position_covariance) { // initial state covariance - constexpr double p0_stddev_x = 2.0; // in object coordinate [m] - constexpr double p0_stddev_y = 2.0; // in object coordinate [m] - constexpr double p0_stddev_yaw = - autoware::universe_utils::deg2rad(1000); // in map coordinate [rad] - constexpr double p0_cov_x = p0_stddev_x * p0_stddev_x; - constexpr double p0_cov_y = p0_stddev_y * p0_stddev_y; - constexpr double p0_cov_yaw = p0_stddev_yaw * p0_stddev_yaw; + const auto & p0_cov_x = object_model_.initial_covariance.pos_x; + const auto & p0_cov_y = object_model_.initial_covariance.pos_y; + const auto & p0_cov_yaw = object_model_.initial_covariance.yaw; const double cos_yaw = std::cos(yaw); const double sin_yaw = std::sin(yaw); const double sin_2yaw = std::sin(2.0 * yaw); - pose_cov[utils::MSG_COV_IDX::X_X] = - p0_cov_x * cos_yaw * cos_yaw + p0_cov_y * sin_yaw * sin_yaw; - pose_cov[utils::MSG_COV_IDX::X_Y] = 0.5 * (p0_cov_x - p0_cov_y) * sin_2yaw; - pose_cov[utils::MSG_COV_IDX::Y_Y] = - p0_cov_x * sin_yaw * sin_yaw + p0_cov_y * cos_yaw * cos_yaw; - pose_cov[utils::MSG_COV_IDX::Y_X] = pose_cov[utils::MSG_COV_IDX::X_Y]; - pose_cov[utils::MSG_COV_IDX::YAW_YAW] = p0_cov_yaw; + pose_cov[XYZRPY_COV_IDX::X_X] = p0_cov_x * cos_yaw * cos_yaw + p0_cov_y * sin_yaw * sin_yaw; + pose_cov[XYZRPY_COV_IDX::X_Y] = 0.5 * (p0_cov_x - p0_cov_y) * sin_2yaw; + pose_cov[XYZRPY_COV_IDX::Y_Y] = p0_cov_x * sin_yaw * sin_yaw + p0_cov_y * cos_yaw * cos_yaw; + pose_cov[XYZRPY_COV_IDX::Y_X] = pose_cov[XYZRPY_COV_IDX::X_Y]; + pose_cov[XYZRPY_COV_IDX::YAW_YAW] = p0_cov_yaw; + } + + double vel = 0.0; + double wz = 0.0; + if (object.kinematics.has_twist) { + vel = object.kinematics.twist_with_covariance.twist.linear.x; + wz = object.kinematics.twist_with_covariance.twist.angular.z; } - if (!object.kinematics.has_twist_covariance) { - constexpr double p0_stddev_vel = - autoware::universe_utils::kmph2mps(120); // in object coordinate [m/s] - constexpr double p0_stddev_wz = - autoware::universe_utils::deg2rad(360); // in object coordinate [rad/s] - vel_cov = std::pow(p0_stddev_vel, 2.0); - wz_cov = std::pow(p0_stddev_wz, 2.0); - } else { - vel_cov = object.kinematics.twist_with_covariance.covariance[utils::MSG_COV_IDX::X_X]; - wz_cov = object.kinematics.twist_with_covariance.covariance[utils::MSG_COV_IDX::YAW_YAW]; + double vel_cov = object_model_.initial_covariance.vel_long; + double wz_cov = object_model_.initial_covariance.yaw_rate; + if (object.kinematics.has_twist_covariance) { + vel_cov = object.kinematics.twist_with_covariance.covariance[XYZRPY_COV_IDX::X_X]; + wz_cov = object.kinematics.twist_with_covariance.covariance[XYZRPY_COV_IDX::YAW_YAW]; } // initialize motion model @@ -162,26 +147,46 @@ bool PedestrianTracker::predict(const rclcpp::Time & time) autoware_perception_msgs::msg::DetectedObject PedestrianTracker::getUpdatingObject( const autoware_perception_msgs::msg::DetectedObject & object, - const geometry_msgs::msg::Transform & /*self_transform*/) + const geometry_msgs::msg::Transform & /*self_transform*/) const { autoware_perception_msgs::msg::DetectedObject updating_object = object; // UNCERTAINTY MODEL if (!object.kinematics.has_position_covariance) { - const double & r_cov_x = ekf_params_.r_cov_x; - const double & r_cov_y = ekf_params_.r_cov_y; - auto & pose_cov = updating_object.kinematics.pose_with_covariance.covariance; + // measurement noise covariance + auto r_cov_x = object_model_.measurement_covariance.pos_x; + auto r_cov_y = object_model_.measurement_covariance.pos_y; + + // yaw angle fix const double pose_yaw = tf2::getYaw(object.kinematics.pose_with_covariance.pose.orientation); + const bool is_yaw_available = + object.kinematics.orientation_availability != + autoware_perception_msgs::msg::DetectedObjectKinematics::UNAVAILABLE; + + // fill covariance matrix + using autoware::universe_utils::xyzrpy_covariance_index::XYZRPY_COV_IDX; + auto & pose_cov = updating_object.kinematics.pose_with_covariance.covariance; const double cos_yaw = std::cos(pose_yaw); const double sin_yaw = std::sin(pose_yaw); - const double sin_2yaw = std::sin(2.0f * pose_yaw); - pose_cov[utils::MSG_COV_IDX::X_X] = - r_cov_x * cos_yaw * cos_yaw + r_cov_y * sin_yaw * sin_yaw; // x - x - pose_cov[utils::MSG_COV_IDX::X_Y] = 0.5f * (r_cov_x - r_cov_y) * sin_2yaw; // x - y - pose_cov[utils::MSG_COV_IDX::Y_Y] = - r_cov_x * sin_yaw * sin_yaw + r_cov_y * cos_yaw * cos_yaw; // y - y - pose_cov[utils::MSG_COV_IDX::Y_X] = pose_cov[utils::MSG_COV_IDX::X_Y]; // y - x + const double sin_2yaw = std::sin(2.0 * pose_yaw); + pose_cov[XYZRPY_COV_IDX::X_X] = + r_cov_x * cos_yaw * cos_yaw + r_cov_y * sin_yaw * sin_yaw; // x - x + pose_cov[XYZRPY_COV_IDX::X_Y] = 0.5 * (r_cov_x - r_cov_y) * sin_2yaw; // x - y + pose_cov[XYZRPY_COV_IDX::Y_Y] = + r_cov_x * sin_yaw * sin_yaw + r_cov_y * cos_yaw * cos_yaw; // y - y + pose_cov[XYZRPY_COV_IDX::Y_X] = pose_cov[XYZRPY_COV_IDX::X_Y]; // y - x + pose_cov[XYZRPY_COV_IDX::X_YAW] = 0.0; // x - yaw + pose_cov[XYZRPY_COV_IDX::Y_YAW] = 0.0; // y - yaw + pose_cov[XYZRPY_COV_IDX::YAW_X] = 0.0; // yaw - x + pose_cov[XYZRPY_COV_IDX::YAW_Y] = 0.0; // yaw - y + pose_cov[XYZRPY_COV_IDX::YAW_YAW] = object_model_.measurement_covariance.yaw; // yaw - yaw + if (!is_yaw_available) { + pose_cov[XYZRPY_COV_IDX::YAW_YAW] *= 1e3; // yaw is not available, multiply large value + } + auto & twist_cov = updating_object.kinematics.twist_with_covariance.covariance; + twist_cov[XYZRPY_COV_IDX::X_X] = object_model_.measurement_covariance.vel_long; // vel - vel } + return updating_object; } @@ -216,43 +221,48 @@ bool PedestrianTracker::measureWithShape( // check bound box size abnormality constexpr double size_max = 30.0; // [m] constexpr double size_min = 0.1; // [m] - if ( - object.shape.dimensions.x > size_max || object.shape.dimensions.y > size_max || - object.shape.dimensions.z > size_max) { - return false; - } else if ( - object.shape.dimensions.x < size_min || object.shape.dimensions.y < size_min || - object.shape.dimensions.z < size_min) { + bool is_size_valid = + (object.shape.dimensions.x <= size_max && object.shape.dimensions.y <= size_max && + object.shape.dimensions.z <= size_max && object.shape.dimensions.x >= size_min && + object.shape.dimensions.y >= size_min && object.shape.dimensions.z >= size_min); + if (!is_size_valid) { return false; } // update bounding box size bounding_box_.length = gain_inv * bounding_box_.length + gain * object.shape.dimensions.x; bounding_box_.width = gain_inv * bounding_box_.width + gain * object.shape.dimensions.y; bounding_box_.height = gain_inv * bounding_box_.height + gain * object.shape.dimensions.z; + } else if (object.shape.type == autoware_perception_msgs::msg::Shape::CYLINDER) { // check cylinder size abnormality constexpr double size_max = 30.0; // [m] constexpr double size_min = 0.1; // [m] - if (object.shape.dimensions.x > size_max || object.shape.dimensions.z > size_max) { - return false; - } else if (object.shape.dimensions.x < size_min || object.shape.dimensions.z < size_min) { + bool is_size_valid = + (object.shape.dimensions.x <= size_max && object.shape.dimensions.z <= size_max && + object.shape.dimensions.x >= size_min && object.shape.dimensions.z >= size_min); + if (!is_size_valid) { return false; } + // update cylinder size cylinder_.width = gain_inv * cylinder_.width + gain * object.shape.dimensions.x; cylinder_.height = gain_inv * cylinder_.height + gain * object.shape.dimensions.z; + } else { // do not update polygon shape return false; } // set maximum and minimum size - constexpr double max_size = 5.0; - constexpr double min_size = 0.3; - bounding_box_.length = std::min(std::max(bounding_box_.length, min_size), max_size); - bounding_box_.width = std::min(std::max(bounding_box_.width, min_size), max_size); - bounding_box_.height = std::min(std::max(bounding_box_.height, min_size), max_size); - cylinder_.width = std::min(std::max(cylinder_.width, min_size), max_size); - cylinder_.height = std::min(std::max(cylinder_.height, min_size), max_size); + bounding_box_.length = std::clamp( + bounding_box_.length, object_model_.size_limit.length_min, object_model_.size_limit.length_max); + bounding_box_.width = std::clamp( + bounding_box_.width, object_model_.size_limit.width_min, object_model_.size_limit.width_max); + bounding_box_.height = std::clamp( + bounding_box_.height, object_model_.size_limit.height_min, object_model_.size_limit.height_max); + cylinder_.width = std::clamp( + cylinder_.width, object_model_.size_limit.length_min, object_model_.size_limit.length_max); + cylinder_.height = std::clamp( + cylinder_.height, object_model_.size_limit.height_min, object_model_.size_limit.height_max); return true; } diff --git a/perception/multi_object_tracker/src/tracker/model/unknown_tracker.cpp b/perception/multi_object_tracker/src/tracker/model/unknown_tracker.cpp index 8e484623ef0aa..6905d7c3b8ced 100644 --- a/perception/multi_object_tracker/src/tracker/model/unknown_tracker.cpp +++ b/perception/multi_object_tracker/src/tracker/model/unknown_tracker.cpp @@ -11,14 +11,18 @@ // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. +#define EIGEN_MPL2_ONLY #include "multi_object_tracker/tracker/model/unknown_tracker.hpp" +#include "autoware/universe_utils/geometry/boost_polygon_utils.hpp" +#include "autoware/universe_utils/math/normalization.hpp" +#include "autoware/universe_utils/math/unit_conversion.hpp" +#include "autoware/universe_utils/ros/msg_covariance.hpp" #include "multi_object_tracker/utils/utils.hpp" -#include -#include -#include +#include +#include #include #include @@ -32,10 +36,6 @@ #endif #include "object_recognition_utils/object_recognition_utils.hpp" -#define EIGEN_MPL2_ONLY -#include -#include - UnknownTracker::UnknownTracker( const rclcpp::Time & time, const autoware_perception_msgs::msg::DetectedObject & object, const geometry_msgs::msg::Transform & /*self_transform*/, const size_t channel_size, @@ -73,6 +73,7 @@ UnknownTracker::UnknownTracker( // Set initial state { + using autoware::universe_utils::xyzrpy_covariance_index::XYZRPY_COV_IDX; const double x = object.kinematics.pose_with_covariance.pose.position.x; const double y = object.kinematics.pose_with_covariance.pose.position.y; auto pose_cov = object.kinematics.pose_with_covariance.covariance; @@ -98,12 +99,10 @@ UnknownTracker::UnknownTracker( const double cos_yaw = std::cos(yaw); const double sin_yaw = std::sin(yaw); const double sin_2yaw = std::sin(2.0 * yaw); - pose_cov[utils::MSG_COV_IDX::X_X] = - p0_cov_x * cos_yaw * cos_yaw + p0_cov_y * sin_yaw * sin_yaw; - pose_cov[utils::MSG_COV_IDX::X_Y] = 0.5 * (p0_cov_x - p0_cov_y) * sin_2yaw; - pose_cov[utils::MSG_COV_IDX::Y_Y] = - p0_cov_x * sin_yaw * sin_yaw + p0_cov_y * cos_yaw * cos_yaw; - pose_cov[utils::MSG_COV_IDX::Y_X] = pose_cov[utils::MSG_COV_IDX::X_Y]; + pose_cov[XYZRPY_COV_IDX::X_X] = p0_cov_x * cos_yaw * cos_yaw + p0_cov_y * sin_yaw * sin_yaw; + pose_cov[XYZRPY_COV_IDX::X_Y] = 0.5 * (p0_cov_x - p0_cov_y) * sin_2yaw; + pose_cov[XYZRPY_COV_IDX::Y_Y] = p0_cov_x * sin_yaw * sin_yaw + p0_cov_y * cos_yaw * cos_yaw; + pose_cov[XYZRPY_COV_IDX::Y_X] = pose_cov[XYZRPY_COV_IDX::X_Y]; } if (!object.kinematics.has_twist_covariance) { @@ -111,24 +110,24 @@ UnknownTracker::UnknownTracker( constexpr double p0_stddev_vy = autoware::universe_utils::kmph2mps(10); // [m/s] const double p0_cov_vx = std::pow(p0_stddev_vx, 2.0); const double p0_cov_vy = std::pow(p0_stddev_vy, 2.0); - twist_cov[utils::MSG_COV_IDX::X_X] = p0_cov_vx; - twist_cov[utils::MSG_COV_IDX::X_Y] = 0.0; - twist_cov[utils::MSG_COV_IDX::Y_X] = 0.0; - twist_cov[utils::MSG_COV_IDX::Y_Y] = p0_cov_vy; + twist_cov[XYZRPY_COV_IDX::X_X] = p0_cov_vx; + twist_cov[XYZRPY_COV_IDX::X_Y] = 0.0; + twist_cov[XYZRPY_COV_IDX::Y_X] = 0.0; + twist_cov[XYZRPY_COV_IDX::Y_Y] = p0_cov_vy; } // rotate twist covariance matrix, since it is in the vehicle coordinate system Eigen::MatrixXd twist_cov_rotate(2, 2); - twist_cov_rotate(0, 0) = twist_cov[utils::MSG_COV_IDX::X_X]; - twist_cov_rotate(0, 1) = twist_cov[utils::MSG_COV_IDX::X_Y]; - twist_cov_rotate(1, 0) = twist_cov[utils::MSG_COV_IDX::Y_X]; - twist_cov_rotate(1, 1) = twist_cov[utils::MSG_COV_IDX::Y_Y]; + twist_cov_rotate(0, 0) = twist_cov[XYZRPY_COV_IDX::X_X]; + twist_cov_rotate(0, 1) = twist_cov[XYZRPY_COV_IDX::X_Y]; + twist_cov_rotate(1, 0) = twist_cov[XYZRPY_COV_IDX::Y_X]; + twist_cov_rotate(1, 1) = twist_cov[XYZRPY_COV_IDX::Y_Y]; Eigen::MatrixXd R_yaw = Eigen::Rotation2Dd(-yaw).toRotationMatrix(); Eigen::MatrixXd twist_cov_rotated = R_yaw * twist_cov_rotate * R_yaw.transpose(); - twist_cov[utils::MSG_COV_IDX::X_X] = twist_cov_rotated(0, 0); - twist_cov[utils::MSG_COV_IDX::X_Y] = twist_cov_rotated(0, 1); - twist_cov[utils::MSG_COV_IDX::Y_X] = twist_cov_rotated(1, 0); - twist_cov[utils::MSG_COV_IDX::Y_Y] = twist_cov_rotated(1, 1); + twist_cov[XYZRPY_COV_IDX::X_X] = twist_cov_rotated(0, 0); + twist_cov[XYZRPY_COV_IDX::X_Y] = twist_cov_rotated(0, 1); + twist_cov[XYZRPY_COV_IDX::Y_X] = twist_cov_rotated(1, 0); + twist_cov[XYZRPY_COV_IDX::Y_Y] = twist_cov_rotated(1, 1); // initialize motion model motion_model_.initialize(time, x, y, pose_cov, vx, vy, twist_cov); @@ -148,6 +147,8 @@ autoware_perception_msgs::msg::DetectedObject UnknownTracker::getUpdatingObject( // UNCERTAINTY MODEL if (!object.kinematics.has_position_covariance) { + // fill covariance matrix + using autoware::universe_utils::xyzrpy_covariance_index::XYZRPY_COV_IDX; const double & r_cov_x = ekf_params_.r_cov_x; const double & r_cov_y = ekf_params_.r_cov_y; auto & pose_cov = updating_object.kinematics.pose_with_covariance.covariance; @@ -155,12 +156,12 @@ autoware_perception_msgs::msg::DetectedObject UnknownTracker::getUpdatingObject( const double cos_yaw = std::cos(pose_yaw); const double sin_yaw = std::sin(pose_yaw); const double sin_2yaw = std::sin(2.0f * pose_yaw); - pose_cov[utils::MSG_COV_IDX::X_X] = - r_cov_x * cos_yaw * cos_yaw + r_cov_y * sin_yaw * sin_yaw; // x - x - pose_cov[utils::MSG_COV_IDX::X_Y] = 0.5f * (r_cov_x - r_cov_y) * sin_2yaw; // x - y - pose_cov[utils::MSG_COV_IDX::Y_Y] = - r_cov_x * sin_yaw * sin_yaw + r_cov_y * cos_yaw * cos_yaw; // y - y - pose_cov[utils::MSG_COV_IDX::Y_X] = pose_cov[utils::MSG_COV_IDX::X_Y]; // y - x + pose_cov[XYZRPY_COV_IDX::X_X] = + r_cov_x * cos_yaw * cos_yaw + r_cov_y * sin_yaw * sin_yaw; // x - x + pose_cov[XYZRPY_COV_IDX::X_Y] = 0.5f * (r_cov_x - r_cov_y) * sin_2yaw; // x - y + pose_cov[XYZRPY_COV_IDX::Y_Y] = + r_cov_x * sin_yaw * sin_yaw + r_cov_y * cos_yaw * cos_yaw; // y - y + pose_cov[XYZRPY_COV_IDX::Y_X] = pose_cov[XYZRPY_COV_IDX::X_Y]; // y - x } return updating_object; } diff --git a/perception/multi_object_tracker/src/tracker/motion_model/bicycle_motion_model.cpp b/perception/multi_object_tracker/src/tracker/motion_model/bicycle_motion_model.cpp index f21855c2356ef..466108a2bef0e 100644 --- a/perception/multi_object_tracker/src/tracker/motion_model/bicycle_motion_model.cpp +++ b/perception/multi_object_tracker/src/tracker/motion_model/bicycle_motion_model.cpp @@ -15,25 +15,25 @@ // // Author: v1.0 Taekjin Lee // +#define EIGEN_MPL2_ONLY #include "multi_object_tracker/tracker/motion_model/bicycle_motion_model.hpp" +#include "autoware/universe_utils/math/normalization.hpp" +#include "autoware/universe_utils/math/unit_conversion.hpp" +#include "autoware/universe_utils/ros/msg_covariance.hpp" #include "multi_object_tracker/tracker/motion_model/motion_model_base.hpp" #include "multi_object_tracker/utils/utils.hpp" -#include -#include - -#define EIGEN_MPL2_ONLY #include #include // cspell: ignore CTRV // Bicycle CTRV motion model // CTRV : Constant Turn Rate and constant Velocity +using autoware::universe_utils::xyzrpy_covariance_index::XYZRPY_COV_IDX; -BicycleMotionModel::BicycleMotionModel() -: MotionModel(), logger_(rclcpp::get_logger("BicycleMotionModel")) +BicycleMotionModel::BicycleMotionModel() : logger_(rclcpp::get_logger("BicycleMotionModel")) { // Initialize motion parameters setDefaultParams(); @@ -44,11 +44,16 @@ void BicycleMotionModel::setDefaultParams() // set default motion parameters constexpr double q_stddev_acc_long = 9.8 * 0.35; // [m/(s*s)] uncertain longitudinal acceleration constexpr double q_stddev_acc_lat = 9.8 * 0.15; // [m/(s*s)] uncertain lateral acceleration - constexpr double q_stddev_yaw_rate_min = 1.5; // [deg/s] uncertain yaw change rate - constexpr double q_stddev_yaw_rate_max = 15.0; // [deg/s] uncertain yaw change rate - constexpr double q_stddev_slip_rate_min = 0.3; // [deg/s] uncertain slip angle change rate - constexpr double q_stddev_slip_rate_max = 10.0; // [deg/s] uncertain slip angle change rate - constexpr double q_max_slip_angle = 30.0; // [deg] max slip angle + constexpr double q_stddev_yaw_rate_min = + autoware::universe_utils::deg2rad(1.5); // [rad/s] uncertain yaw change rate + constexpr double q_stddev_yaw_rate_max = + autoware::universe_utils::deg2rad(15.0); // [rad/s] uncertain yaw change rate + constexpr double q_stddev_slip_rate_min = + autoware::universe_utils::deg2rad(0.3); // [rad/s] uncertain slip angle change rate + constexpr double q_stddev_slip_rate_max = + autoware::universe_utils::deg2rad(10.0); // [rad/s] uncertain slip angle change rate + constexpr double q_max_slip_angle = + autoware::universe_utils::deg2rad(30.0); // [rad] max slip angle // extended state parameters constexpr double lf_ratio = 0.3; // 30% front from the center constexpr double lf_min = 1.0; // minimum of 1.0m @@ -79,13 +84,13 @@ void BicycleMotionModel::setMotionParams( // set process noise covariance parameters motion_params_.q_stddev_acc_long = q_stddev_acc_long; motion_params_.q_stddev_acc_lat = q_stddev_acc_lat; - motion_params_.q_stddev_yaw_rate_min = autoware::universe_utils::deg2rad(q_stddev_yaw_rate_min); - motion_params_.q_stddev_yaw_rate_max = autoware::universe_utils::deg2rad(q_stddev_yaw_rate_max); - motion_params_.q_cov_slip_rate_min = - std::pow(autoware::universe_utils::deg2rad(q_stddev_slip_rate_min), 2.0); - motion_params_.q_cov_slip_rate_max = - std::pow(autoware::universe_utils::deg2rad(q_stddev_slip_rate_max), 2.0); - motion_params_.q_max_slip_angle = autoware::universe_utils::deg2rad(q_max_slip_angle); + motion_params_.q_cov_acc_long = q_stddev_acc_long * q_stddev_acc_long; + motion_params_.q_cov_acc_lat = q_stddev_acc_lat * q_stddev_acc_lat; + motion_params_.q_stddev_yaw_rate_min = q_stddev_yaw_rate_min; + motion_params_.q_stddev_yaw_rate_max = q_stddev_yaw_rate_max; + motion_params_.q_cov_slip_rate_min = q_stddev_slip_rate_min * q_stddev_slip_rate_min; + motion_params_.q_cov_slip_rate_max = q_stddev_slip_rate_max * q_stddev_slip_rate_max; + motion_params_.q_max_slip_angle = q_max_slip_angle; constexpr double minimum_wheel_pos = 0.01; // minimum of 0.01m if (lf_min < minimum_wheel_pos || lr_min < minimum_wheel_pos) { @@ -103,7 +108,7 @@ void BicycleMotionModel::setMotionLimits(const double & max_vel, const double & { // set motion limitations motion_params_.max_vel = max_vel; - motion_params_.max_slip = autoware::universe_utils::deg2rad(max_slip); + motion_params_.max_slip = max_slip; } bool BicycleMotionModel::initialize( @@ -117,9 +122,9 @@ bool BicycleMotionModel::initialize( // initialize covariance matrix P Eigen::MatrixXd P = Eigen::MatrixXd::Zero(DIM, DIM); - P(IDX::X, IDX::X) = pose_cov[utils::MSG_COV_IDX::X_X]; - P(IDX::Y, IDX::Y) = pose_cov[utils::MSG_COV_IDX::Y_Y]; - P(IDX::YAW, IDX::YAW) = pose_cov[utils::MSG_COV_IDX::YAW_YAW]; + P(IDX::X, IDX::X) = pose_cov[XYZRPY_COV_IDX::X_X]; + P(IDX::Y, IDX::Y) = pose_cov[XYZRPY_COV_IDX::Y_Y]; + P(IDX::YAW, IDX::YAW) = pose_cov[XYZRPY_COV_IDX::YAW_YAW]; P(IDX::VEL, IDX::VEL) = vel_cov; P(IDX::SLIP, IDX::SLIP) = slip_cov; @@ -147,10 +152,10 @@ bool BicycleMotionModel::updateStatePose( C(1, IDX::Y) = 1.0; Eigen::MatrixXd R = Eigen::MatrixXd::Zero(DIM_Y, DIM_Y); - R(0, 0) = pose_cov[utils::MSG_COV_IDX::X_X]; - R(0, 1) = pose_cov[utils::MSG_COV_IDX::X_Y]; - R(1, 0) = pose_cov[utils::MSG_COV_IDX::Y_X]; - R(1, 1) = pose_cov[utils::MSG_COV_IDX::Y_Y]; + R(0, 0) = pose_cov[XYZRPY_COV_IDX::X_X]; + R(0, 1) = pose_cov[XYZRPY_COV_IDX::X_Y]; + R(1, 0) = pose_cov[XYZRPY_COV_IDX::Y_X]; + R(1, 1) = pose_cov[XYZRPY_COV_IDX::Y_Y]; return ekf_.update(Y, C, R); } @@ -186,15 +191,15 @@ bool BicycleMotionModel::updateStatePoseHead( C(2, IDX::YAW) = 1.0; Eigen::MatrixXd R = Eigen::MatrixXd::Zero(DIM_Y, DIM_Y); - R(0, 0) = pose_cov[utils::MSG_COV_IDX::X_X]; - R(0, 1) = pose_cov[utils::MSG_COV_IDX::X_Y]; - R(1, 0) = pose_cov[utils::MSG_COV_IDX::Y_X]; - R(1, 1) = pose_cov[utils::MSG_COV_IDX::Y_Y]; - R(0, 2) = pose_cov[utils::MSG_COV_IDX::X_YAW]; - R(1, 2) = pose_cov[utils::MSG_COV_IDX::Y_YAW]; - R(2, 0) = pose_cov[utils::MSG_COV_IDX::YAW_X]; - R(2, 1) = pose_cov[utils::MSG_COV_IDX::YAW_Y]; - R(2, 2) = pose_cov[utils::MSG_COV_IDX::YAW_YAW]; + R(0, 0) = pose_cov[XYZRPY_COV_IDX::X_X]; + R(0, 1) = pose_cov[XYZRPY_COV_IDX::X_Y]; + R(1, 0) = pose_cov[XYZRPY_COV_IDX::Y_X]; + R(1, 1) = pose_cov[XYZRPY_COV_IDX::Y_Y]; + R(0, 2) = pose_cov[XYZRPY_COV_IDX::X_YAW]; + R(1, 2) = pose_cov[XYZRPY_COV_IDX::Y_YAW]; + R(2, 0) = pose_cov[XYZRPY_COV_IDX::YAW_X]; + R(2, 1) = pose_cov[XYZRPY_COV_IDX::YAW_Y]; + R(2, 2) = pose_cov[XYZRPY_COV_IDX::YAW_YAW]; return ekf_.update(Y, C, R); } @@ -232,16 +237,16 @@ bool BicycleMotionModel::updateStatePoseHeadVel( C(3, IDX::VEL) = 1.0; Eigen::MatrixXd R = Eigen::MatrixXd::Zero(DIM_Y, DIM_Y); - R(0, 0) = pose_cov[utils::MSG_COV_IDX::X_X]; - R(0, 1) = pose_cov[utils::MSG_COV_IDX::X_Y]; - R(1, 0) = pose_cov[utils::MSG_COV_IDX::Y_X]; - R(1, 1) = pose_cov[utils::MSG_COV_IDX::Y_Y]; - R(0, 2) = pose_cov[utils::MSG_COV_IDX::X_YAW]; - R(1, 2) = pose_cov[utils::MSG_COV_IDX::Y_YAW]; - R(2, 0) = pose_cov[utils::MSG_COV_IDX::YAW_X]; - R(2, 1) = pose_cov[utils::MSG_COV_IDX::YAW_Y]; - R(2, 2) = pose_cov[utils::MSG_COV_IDX::YAW_YAW]; - R(3, 3) = twist_cov[utils::MSG_COV_IDX::X_X]; + R(0, 0) = pose_cov[XYZRPY_COV_IDX::X_X]; + R(0, 1) = pose_cov[XYZRPY_COV_IDX::X_Y]; + R(1, 0) = pose_cov[XYZRPY_COV_IDX::Y_X]; + R(1, 1) = pose_cov[XYZRPY_COV_IDX::Y_Y]; + R(0, 2) = pose_cov[XYZRPY_COV_IDX::X_YAW]; + R(1, 2) = pose_cov[XYZRPY_COV_IDX::Y_YAW]; + R(2, 0) = pose_cov[XYZRPY_COV_IDX::YAW_X]; + R(2, 1) = pose_cov[XYZRPY_COV_IDX::YAW_Y]; + R(2, 2) = pose_cov[XYZRPY_COV_IDX::YAW_YAW]; + R(3, 3) = twist_cov[XYZRPY_COV_IDX::X_X]; return ekf_.update(Y, C, R); } @@ -354,10 +359,8 @@ bool BicycleMotionModel::predictStateStep(const double dt, KalmanFilter & ekf) c A(IDX::YAW, IDX::SLIP) = vel / lr_ * cos_slip * dt; // Process noise covariance Q - double q_stddev_yaw_rate{0.0}; - if (vel <= 0.01) { - q_stddev_yaw_rate = motion_params_.q_stddev_yaw_rate_min; - } else { + double q_stddev_yaw_rate = motion_params_.q_stddev_yaw_rate_min; + if (vel > 0.01) { /* uncertainty of the yaw rate is limited by the following: * - centripetal acceleration a_lat : d(yaw)/dt = w = a_lat/v * - or maximum slip angle slip_max : w = v*sin(slip_max)/l_r @@ -365,8 +368,9 @@ bool BicycleMotionModel::predictStateStep(const double dt, KalmanFilter & ekf) c q_stddev_yaw_rate = std::min( motion_params_.q_stddev_acc_lat / vel, vel * std::sin(motion_params_.q_max_slip_angle) / lr_); // [rad/s] - q_stddev_yaw_rate = std::min(q_stddev_yaw_rate, motion_params_.q_stddev_yaw_rate_max); - q_stddev_yaw_rate = std::max(q_stddev_yaw_rate, motion_params_.q_stddev_yaw_rate_min); + q_stddev_yaw_rate = std::clamp( + q_stddev_yaw_rate, motion_params_.q_stddev_yaw_rate_min, + motion_params_.q_stddev_yaw_rate_max); } double q_cov_slip_rate{0.0}; if (vel <= 0.01) { @@ -384,11 +388,13 @@ bool BicycleMotionModel::predictStateStep(const double dt, KalmanFilter & ekf) c q_cov_slip_rate = std::min(q_cov_slip_rate, motion_params_.q_cov_slip_rate_max); q_cov_slip_rate = std::max(q_cov_slip_rate, motion_params_.q_cov_slip_rate_min); } - const double q_cov_x = std::pow(0.5 * motion_params_.q_stddev_acc_long * dt * dt, 2); - const double q_cov_y = std::pow(0.5 * motion_params_.q_stddev_acc_lat * dt * dt, 2); - const double q_cov_yaw = std::pow(q_stddev_yaw_rate * dt, 2); - const double q_cov_vel = std::pow(motion_params_.q_stddev_acc_long * dt, 2); - const double q_cov_slip = q_cov_slip_rate * dt * dt; + const double dt2 = dt * dt; + const double dt4 = dt2 * dt2; + const double q_cov_x = 0.25 * motion_params_.q_cov_acc_long * dt4; + const double q_cov_y = 0.25 * motion_params_.q_cov_acc_lat * dt4; + const double q_cov_yaw = q_stddev_yaw_rate * q_stddev_yaw_rate * dt2; + const double q_cov_vel = motion_params_.q_cov_acc_long * dt2; + const double q_cov_slip = q_cov_slip_rate * dt2; Eigen::MatrixXd Q = Eigen::MatrixXd::Zero(DIM, DIM); // Rotate the covariance matrix according to the vehicle yaw @@ -445,14 +451,14 @@ bool BicycleMotionModel::getPredictedState( constexpr double zz_cov = 0.1 * 0.1; // TODO(yukkysaito) Currently tentative constexpr double rr_cov = 0.1 * 0.1; // TODO(yukkysaito) Currently tentative constexpr double pp_cov = 0.1 * 0.1; // TODO(yukkysaito) Currently tentative - pose_cov[utils::MSG_COV_IDX::X_X] = P(IDX::X, IDX::X); - pose_cov[utils::MSG_COV_IDX::X_Y] = P(IDX::X, IDX::Y); - pose_cov[utils::MSG_COV_IDX::Y_X] = P(IDX::Y, IDX::X); - pose_cov[utils::MSG_COV_IDX::Y_Y] = P(IDX::Y, IDX::Y); - pose_cov[utils::MSG_COV_IDX::YAW_YAW] = P(IDX::YAW, IDX::YAW); - pose_cov[utils::MSG_COV_IDX::Z_Z] = zz_cov; - pose_cov[utils::MSG_COV_IDX::ROLL_ROLL] = rr_cov; - pose_cov[utils::MSG_COV_IDX::PITCH_PITCH] = pp_cov; + pose_cov[XYZRPY_COV_IDX::X_X] = P(IDX::X, IDX::X); + pose_cov[XYZRPY_COV_IDX::X_Y] = P(IDX::X, IDX::Y); + pose_cov[XYZRPY_COV_IDX::Y_X] = P(IDX::Y, IDX::X); + pose_cov[XYZRPY_COV_IDX::Y_Y] = P(IDX::Y, IDX::Y); + pose_cov[XYZRPY_COV_IDX::YAW_YAW] = P(IDX::YAW, IDX::YAW); + pose_cov[XYZRPY_COV_IDX::Z_Z] = zz_cov; + pose_cov[XYZRPY_COV_IDX::ROLL_ROLL] = rr_cov; + pose_cov[XYZRPY_COV_IDX::PITCH_PITCH] = pp_cov; // set twist covariance Eigen::MatrixXd cov_jacob(3, 2); @@ -466,18 +472,18 @@ bool BicycleMotionModel::getPredictedState( constexpr double vz_cov = 0.1 * 0.1; // TODO(yukkysaito) Currently tentative constexpr double wx_cov = 0.1 * 0.1; // TODO(yukkysaito) Currently tentative constexpr double wy_cov = 0.1 * 0.1; // TODO(yukkysaito) Currently tentative - twist_cov[utils::MSG_COV_IDX::X_X] = twist_cov_mat(0, 0); - twist_cov[utils::MSG_COV_IDX::X_Y] = twist_cov_mat(0, 1); - twist_cov[utils::MSG_COV_IDX::X_YAW] = twist_cov_mat(0, 2); - twist_cov[utils::MSG_COV_IDX::Y_X] = twist_cov_mat(1, 0); - twist_cov[utils::MSG_COV_IDX::Y_Y] = twist_cov_mat(1, 1); - twist_cov[utils::MSG_COV_IDX::Y_YAW] = twist_cov_mat(1, 2); - twist_cov[utils::MSG_COV_IDX::YAW_X] = twist_cov_mat(2, 0); - twist_cov[utils::MSG_COV_IDX::YAW_Y] = twist_cov_mat(2, 1); - twist_cov[utils::MSG_COV_IDX::YAW_YAW] = twist_cov_mat(2, 2); - twist_cov[utils::MSG_COV_IDX::Z_Z] = vz_cov; - twist_cov[utils::MSG_COV_IDX::ROLL_ROLL] = wx_cov; - twist_cov[utils::MSG_COV_IDX::PITCH_PITCH] = wy_cov; + twist_cov[XYZRPY_COV_IDX::X_X] = twist_cov_mat(0, 0); + twist_cov[XYZRPY_COV_IDX::X_Y] = twist_cov_mat(0, 1); + twist_cov[XYZRPY_COV_IDX::X_YAW] = twist_cov_mat(0, 2); + twist_cov[XYZRPY_COV_IDX::Y_X] = twist_cov_mat(1, 0); + twist_cov[XYZRPY_COV_IDX::Y_Y] = twist_cov_mat(1, 1); + twist_cov[XYZRPY_COV_IDX::Y_YAW] = twist_cov_mat(1, 2); + twist_cov[XYZRPY_COV_IDX::YAW_X] = twist_cov_mat(2, 0); + twist_cov[XYZRPY_COV_IDX::YAW_Y] = twist_cov_mat(2, 1); + twist_cov[XYZRPY_COV_IDX::YAW_YAW] = twist_cov_mat(2, 2); + twist_cov[XYZRPY_COV_IDX::Z_Z] = vz_cov; + twist_cov[XYZRPY_COV_IDX::ROLL_ROLL] = wx_cov; + twist_cov[XYZRPY_COV_IDX::PITCH_PITCH] = wy_cov; return true; } diff --git a/perception/multi_object_tracker/src/tracker/motion_model/ctrv_motion_model.cpp b/perception/multi_object_tracker/src/tracker/motion_model/ctrv_motion_model.cpp index 79ce6e5dbb61c..b19af1d26d162 100644 --- a/perception/multi_object_tracker/src/tracker/motion_model/ctrv_motion_model.cpp +++ b/perception/multi_object_tracker/src/tracker/motion_model/ctrv_motion_model.cpp @@ -15,23 +15,24 @@ // // Author: v1.0 Taekjin Lee // +#define EIGEN_MPL2_ONLY #include "multi_object_tracker/tracker/motion_model/ctrv_motion_model.hpp" +#include "autoware/universe_utils/math/normalization.hpp" +#include "autoware/universe_utils/math/unit_conversion.hpp" +#include "autoware/universe_utils/ros/msg_covariance.hpp" #include "multi_object_tracker/tracker/motion_model/motion_model_base.hpp" #include "multi_object_tracker/utils/utils.hpp" -#include -#include - -#define EIGEN_MPL2_ONLY #include #include // cspell: ignore CTRV // Constant Turn Rate and constant Velocity (CTRV) motion model +using autoware::universe_utils::xyzrpy_covariance_index::XYZRPY_COV_IDX; -CTRVMotionModel::CTRVMotionModel() : MotionModel(), logger_(rclcpp::get_logger("CTRVMotionModel")) +CTRVMotionModel::CTRVMotionModel() : logger_(rclcpp::get_logger("CTRVMotionModel")) { // Initialize motion parameters setDefaultParams(); @@ -63,11 +64,11 @@ void CTRVMotionModel::setMotionParams( const double & q_stddev_vel, const double & q_stddev_wz) { // set process noise covariance parameters - motion_params_.q_cov_x = std::pow(q_stddev_x, 2.0); - motion_params_.q_cov_y = std::pow(q_stddev_y, 2.0); - motion_params_.q_cov_yaw = std::pow(q_stddev_yaw, 2.0); - motion_params_.q_cov_vel = std::pow(q_stddev_vel, 2.0); - motion_params_.q_cov_wz = std::pow(q_stddev_wz, 2.0); + motion_params_.q_cov_x = q_stddev_x * q_stddev_x; + motion_params_.q_cov_y = q_stddev_y * q_stddev_y; + motion_params_.q_cov_yaw = q_stddev_yaw * q_stddev_yaw; + motion_params_.q_cov_vel = q_stddev_vel * q_stddev_vel; + motion_params_.q_cov_wz = q_stddev_wz * q_stddev_wz; } void CTRVMotionModel::setMotionLimits(const double & max_vel, const double & max_wz) @@ -88,9 +89,9 @@ bool CTRVMotionModel::initialize( // initialize covariance matrix P Eigen::MatrixXd P = Eigen::MatrixXd::Zero(DIM, DIM); - P(IDX::X, IDX::X) = pose_cov[utils::MSG_COV_IDX::X_X]; - P(IDX::Y, IDX::Y) = pose_cov[utils::MSG_COV_IDX::Y_Y]; - P(IDX::YAW, IDX::YAW) = pose_cov[utils::MSG_COV_IDX::YAW_YAW]; + P(IDX::X, IDX::X) = pose_cov[XYZRPY_COV_IDX::X_X]; + P(IDX::Y, IDX::Y) = pose_cov[XYZRPY_COV_IDX::Y_Y]; + P(IDX::YAW, IDX::YAW) = pose_cov[XYZRPY_COV_IDX::YAW_YAW]; P(IDX::VEL, IDX::VEL) = vel_cov; P(IDX::WZ, IDX::WZ) = wz_cov; @@ -115,10 +116,10 @@ bool CTRVMotionModel::updateStatePose( C(1, IDX::Y) = 1.0; Eigen::MatrixXd R = Eigen::MatrixXd::Zero(DIM_Y, DIM_Y); - R(0, 0) = pose_cov[utils::MSG_COV_IDX::X_X]; - R(0, 1) = pose_cov[utils::MSG_COV_IDX::X_Y]; - R(1, 0) = pose_cov[utils::MSG_COV_IDX::Y_X]; - R(1, 1) = pose_cov[utils::MSG_COV_IDX::Y_Y]; + R(0, 0) = pose_cov[XYZRPY_COV_IDX::X_X]; + R(0, 1) = pose_cov[XYZRPY_COV_IDX::X_Y]; + R(1, 0) = pose_cov[XYZRPY_COV_IDX::Y_X]; + R(1, 1) = pose_cov[XYZRPY_COV_IDX::Y_Y]; return ekf_.update(Y, C, R); } @@ -154,15 +155,15 @@ bool CTRVMotionModel::updateStatePoseHead( C(2, IDX::YAW) = 1.0; Eigen::MatrixXd R = Eigen::MatrixXd::Zero(DIM_Y, DIM_Y); - R(0, 0) = pose_cov[utils::MSG_COV_IDX::X_X]; - R(0, 1) = pose_cov[utils::MSG_COV_IDX::X_Y]; - R(1, 0) = pose_cov[utils::MSG_COV_IDX::Y_X]; - R(1, 1) = pose_cov[utils::MSG_COV_IDX::Y_Y]; - R(0, 2) = pose_cov[utils::MSG_COV_IDX::X_YAW]; - R(1, 2) = pose_cov[utils::MSG_COV_IDX::Y_YAW]; - R(2, 0) = pose_cov[utils::MSG_COV_IDX::YAW_X]; - R(2, 1) = pose_cov[utils::MSG_COV_IDX::YAW_Y]; - R(2, 2) = pose_cov[utils::MSG_COV_IDX::YAW_YAW]; + R(0, 0) = pose_cov[XYZRPY_COV_IDX::X_X]; + R(0, 1) = pose_cov[XYZRPY_COV_IDX::X_Y]; + R(1, 0) = pose_cov[XYZRPY_COV_IDX::Y_X]; + R(1, 1) = pose_cov[XYZRPY_COV_IDX::Y_Y]; + R(0, 2) = pose_cov[XYZRPY_COV_IDX::X_YAW]; + R(1, 2) = pose_cov[XYZRPY_COV_IDX::Y_YAW]; + R(2, 0) = pose_cov[XYZRPY_COV_IDX::YAW_X]; + R(2, 1) = pose_cov[XYZRPY_COV_IDX::YAW_Y]; + R(2, 2) = pose_cov[XYZRPY_COV_IDX::YAW_YAW]; return ekf_.update(Y, C, R); } @@ -200,16 +201,16 @@ bool CTRVMotionModel::updateStatePoseHeadVel( C(3, IDX::VEL) = 1.0; Eigen::MatrixXd R = Eigen::MatrixXd::Zero(DIM_Y, DIM_Y); - R(0, 0) = pose_cov[utils::MSG_COV_IDX::X_X]; - R(0, 1) = pose_cov[utils::MSG_COV_IDX::X_Y]; - R(1, 0) = pose_cov[utils::MSG_COV_IDX::Y_X]; - R(1, 1) = pose_cov[utils::MSG_COV_IDX::Y_Y]; - R(0, 2) = pose_cov[utils::MSG_COV_IDX::X_YAW]; - R(1, 2) = pose_cov[utils::MSG_COV_IDX::Y_YAW]; - R(2, 0) = pose_cov[utils::MSG_COV_IDX::YAW_X]; - R(2, 1) = pose_cov[utils::MSG_COV_IDX::YAW_Y]; - R(2, 2) = pose_cov[utils::MSG_COV_IDX::YAW_YAW]; - R(3, 3) = twist_cov[utils::MSG_COV_IDX::X_X]; + R(0, 0) = pose_cov[XYZRPY_COV_IDX::X_X]; + R(0, 1) = pose_cov[XYZRPY_COV_IDX::X_Y]; + R(1, 0) = pose_cov[XYZRPY_COV_IDX::Y_X]; + R(1, 1) = pose_cov[XYZRPY_COV_IDX::Y_Y]; + R(0, 2) = pose_cov[XYZRPY_COV_IDX::X_YAW]; + R(1, 2) = pose_cov[XYZRPY_COV_IDX::Y_YAW]; + R(2, 0) = pose_cov[XYZRPY_COV_IDX::YAW_X]; + R(2, 1) = pose_cov[XYZRPY_COV_IDX::YAW_Y]; + R(2, 2) = pose_cov[XYZRPY_COV_IDX::YAW_YAW]; + R(3, 3) = twist_cov[XYZRPY_COV_IDX::X_X]; return ekf_.update(Y, C, R); } @@ -295,11 +296,12 @@ bool CTRVMotionModel::predictStateStep(const double dt, KalmanFilter & ekf) cons A(IDX::YAW, IDX::WZ) = dt; // Process noise covariance Q - const double q_cov_x = std::pow(0.5 * motion_params_.q_cov_x * dt, 2); - const double q_cov_y = std::pow(0.5 * motion_params_.q_cov_y * dt, 2); - const double q_cov_yaw = std::pow(motion_params_.q_cov_yaw * dt, 2); - const double q_cov_vel = std::pow(motion_params_.q_cov_vel * dt, 2); - const double q_cov_wz = std::pow(motion_params_.q_cov_wz * dt, 2); + const double dt2 = dt * dt; + const double q_cov_x = motion_params_.q_cov_x * dt2; + const double q_cov_y = motion_params_.q_cov_y * dt2; + const double q_cov_yaw = motion_params_.q_cov_yaw * dt2; + const double q_cov_vel = motion_params_.q_cov_vel * dt2; + const double q_cov_wz = motion_params_.q_cov_wz * dt2; Eigen::MatrixXd Q = Eigen::MatrixXd::Zero(DIM, DIM); // Rotate the covariance matrix according to the vehicle yaw // because q_cov_x and y are in the vehicle coordinate system. @@ -355,32 +357,32 @@ bool CTRVMotionModel::getPredictedState( constexpr double zz_cov = 0.1 * 0.1; // TODO(yukkysaito) Currently tentative constexpr double rr_cov = 0.1 * 0.1; // TODO(yukkysaito) Currently tentative constexpr double pp_cov = 0.1 * 0.1; // TODO(yukkysaito) Currently tentative - pose_cov[utils::MSG_COV_IDX::X_X] = P(IDX::X, IDX::X); - pose_cov[utils::MSG_COV_IDX::X_Y] = P(IDX::X, IDX::Y); - pose_cov[utils::MSG_COV_IDX::Y_X] = P(IDX::Y, IDX::X); - pose_cov[utils::MSG_COV_IDX::Y_Y] = P(IDX::Y, IDX::Y); - pose_cov[utils::MSG_COV_IDX::YAW_YAW] = P(IDX::YAW, IDX::YAW); - pose_cov[utils::MSG_COV_IDX::Z_Z] = zz_cov; - pose_cov[utils::MSG_COV_IDX::ROLL_ROLL] = rr_cov; - pose_cov[utils::MSG_COV_IDX::PITCH_PITCH] = pp_cov; + pose_cov[XYZRPY_COV_IDX::X_X] = P(IDX::X, IDX::X); + pose_cov[XYZRPY_COV_IDX::X_Y] = P(IDX::X, IDX::Y); + pose_cov[XYZRPY_COV_IDX::Y_X] = P(IDX::Y, IDX::X); + pose_cov[XYZRPY_COV_IDX::Y_Y] = P(IDX::Y, IDX::Y); + pose_cov[XYZRPY_COV_IDX::YAW_YAW] = P(IDX::YAW, IDX::YAW); + pose_cov[XYZRPY_COV_IDX::Z_Z] = zz_cov; + pose_cov[XYZRPY_COV_IDX::ROLL_ROLL] = rr_cov; + pose_cov[XYZRPY_COV_IDX::PITCH_PITCH] = pp_cov; // set twist covariance constexpr double vy_cov = 0.1 * 0.1; // TODO(yukkysaito) Currently tentative constexpr double vz_cov = 0.1 * 0.1; // TODO(yukkysaito) Currently tentative constexpr double wx_cov = 0.1 * 0.1; // TODO(yukkysaito) Currently tentative constexpr double wy_cov = 0.1 * 0.1; // TODO(yukkysaito) Currently tentative - twist_cov[utils::MSG_COV_IDX::X_X] = P(IDX::VEL, IDX::VEL); - twist_cov[utils::MSG_COV_IDX::X_Y] = 0.0; - twist_cov[utils::MSG_COV_IDX::X_YAW] = P(IDX::VEL, IDX::WZ); - twist_cov[utils::MSG_COV_IDX::Y_X] = 0.0; - twist_cov[utils::MSG_COV_IDX::Y_Y] = vy_cov; - twist_cov[utils::MSG_COV_IDX::Y_YAW] = 0.0; - twist_cov[utils::MSG_COV_IDX::YAW_X] = P(IDX::WZ, IDX::VEL); - twist_cov[utils::MSG_COV_IDX::YAW_Y] = 0.0; - twist_cov[utils::MSG_COV_IDX::YAW_YAW] = P(IDX::WZ, IDX::WZ); - twist_cov[utils::MSG_COV_IDX::Z_Z] = vz_cov; - twist_cov[utils::MSG_COV_IDX::ROLL_ROLL] = wx_cov; - twist_cov[utils::MSG_COV_IDX::PITCH_PITCH] = wy_cov; + twist_cov[XYZRPY_COV_IDX::X_X] = P(IDX::VEL, IDX::VEL); + twist_cov[XYZRPY_COV_IDX::X_Y] = 0.0; + twist_cov[XYZRPY_COV_IDX::X_YAW] = P(IDX::VEL, IDX::WZ); + twist_cov[XYZRPY_COV_IDX::Y_X] = 0.0; + twist_cov[XYZRPY_COV_IDX::Y_Y] = vy_cov; + twist_cov[XYZRPY_COV_IDX::Y_YAW] = 0.0; + twist_cov[XYZRPY_COV_IDX::YAW_X] = P(IDX::WZ, IDX::VEL); + twist_cov[XYZRPY_COV_IDX::YAW_Y] = 0.0; + twist_cov[XYZRPY_COV_IDX::YAW_YAW] = P(IDX::WZ, IDX::WZ); + twist_cov[XYZRPY_COV_IDX::Z_Z] = vz_cov; + twist_cov[XYZRPY_COV_IDX::ROLL_ROLL] = wx_cov; + twist_cov[XYZRPY_COV_IDX::PITCH_PITCH] = wy_cov; return true; } diff --git a/perception/multi_object_tracker/src/tracker/motion_model/cv_motion_model.cpp b/perception/multi_object_tracker/src/tracker/motion_model/cv_motion_model.cpp index 8b3e9014f52d7..231ba73796ed9 100644 --- a/perception/multi_object_tracker/src/tracker/motion_model/cv_motion_model.cpp +++ b/perception/multi_object_tracker/src/tracker/motion_model/cv_motion_model.cpp @@ -15,25 +15,26 @@ // // Author: v1.0 Taekjin Lee // +#define EIGEN_MPL2_ONLY #include "multi_object_tracker/tracker/motion_model/cv_motion_model.hpp" +#include "autoware/universe_utils/math/normalization.hpp" +#include "autoware/universe_utils/math/unit_conversion.hpp" +#include "autoware/universe_utils/ros/msg_covariance.hpp" #include "multi_object_tracker/tracker/motion_model/motion_model_base.hpp" #include "multi_object_tracker/utils/utils.hpp" -#include -#include - -#include - -#define EIGEN_MPL2_ONLY #include #include +#include + // cspell: ignore CV // Constant Velocity (CV) motion model +using autoware::universe_utils::xyzrpy_covariance_index::XYZRPY_COV_IDX; -CVMotionModel::CVMotionModel() : MotionModel(), logger_(rclcpp::get_logger("CVMotionModel")) +CVMotionModel::CVMotionModel() : logger_(rclcpp::get_logger("CVMotionModel")) { // Initialize motion parameters setDefaultParams(); @@ -63,10 +64,10 @@ void CVMotionModel::setMotionParams( const double & q_stddev_vy) { // set process noise covariance parameters - motion_params_.q_cov_x = std::pow(q_stddev_x, 2.0); - motion_params_.q_cov_y = std::pow(q_stddev_y, 2.0); - motion_params_.q_cov_vx = std::pow(q_stddev_vx, 2.0); - motion_params_.q_cov_vy = std::pow(q_stddev_vy, 2.0); + motion_params_.q_cov_x = q_stddev_x * q_stddev_x; + motion_params_.q_cov_y = q_stddev_y * q_stddev_y; + motion_params_.q_cov_vx = q_stddev_vx * q_stddev_vx; + motion_params_.q_cov_vy = q_stddev_vy * q_stddev_vy; } void CVMotionModel::setMotionLimits(const double & max_vx, const double & max_vy) @@ -87,10 +88,10 @@ bool CVMotionModel::initialize( // initialize covariance matrix P Eigen::MatrixXd P = Eigen::MatrixXd::Zero(DIM, DIM); - P(IDX::X, IDX::X) = pose_cov[utils::MSG_COV_IDX::X_X]; - P(IDX::Y, IDX::Y) = pose_cov[utils::MSG_COV_IDX::Y_Y]; - P(IDX::VX, IDX::VX) = twist_cov[utils::MSG_COV_IDX::X_X]; - P(IDX::VY, IDX::VY) = twist_cov[utils::MSG_COV_IDX::Y_Y]; + P(IDX::X, IDX::X) = pose_cov[XYZRPY_COV_IDX::X_X]; + P(IDX::Y, IDX::Y) = pose_cov[XYZRPY_COV_IDX::Y_Y]; + P(IDX::VX, IDX::VX) = twist_cov[XYZRPY_COV_IDX::X_X]; + P(IDX::VY, IDX::VY) = twist_cov[XYZRPY_COV_IDX::Y_Y]; return MotionModel::initialize(time, X, P); } @@ -113,10 +114,10 @@ bool CVMotionModel::updateStatePose( C(1, IDX::Y) = 1.0; Eigen::MatrixXd R = Eigen::MatrixXd::Zero(DIM_Y, DIM_Y); - R(0, 0) = pose_cov[utils::MSG_COV_IDX::X_X]; - R(0, 1) = pose_cov[utils::MSG_COV_IDX::X_Y]; - R(1, 0) = pose_cov[utils::MSG_COV_IDX::Y_X]; - R(1, 1) = pose_cov[utils::MSG_COV_IDX::Y_Y]; + R(0, 0) = pose_cov[XYZRPY_COV_IDX::X_X]; + R(0, 1) = pose_cov[XYZRPY_COV_IDX::X_Y]; + R(1, 0) = pose_cov[XYZRPY_COV_IDX::Y_X]; + R(1, 1) = pose_cov[XYZRPY_COV_IDX::Y_Y]; return ekf_.update(Y, C, R); } @@ -142,14 +143,14 @@ bool CVMotionModel::updateStatePoseVel( C(3, IDX::VY) = 1.0; Eigen::MatrixXd R = Eigen::MatrixXd::Zero(DIM_Y, DIM_Y); - R(0, 0) = pose_cov[utils::MSG_COV_IDX::X_X]; - R(0, 1) = pose_cov[utils::MSG_COV_IDX::X_Y]; - R(1, 0) = pose_cov[utils::MSG_COV_IDX::Y_X]; - R(1, 1) = pose_cov[utils::MSG_COV_IDX::Y_Y]; - R(2, 2) = twist_cov[utils::MSG_COV_IDX::X_X]; - R(2, 3) = twist_cov[utils::MSG_COV_IDX::X_Y]; - R(3, 2) = twist_cov[utils::MSG_COV_IDX::Y_X]; - R(3, 3) = twist_cov[utils::MSG_COV_IDX::Y_Y]; + R(0, 0) = pose_cov[XYZRPY_COV_IDX::X_X]; + R(0, 1) = pose_cov[XYZRPY_COV_IDX::X_Y]; + R(1, 0) = pose_cov[XYZRPY_COV_IDX::Y_X]; + R(1, 1) = pose_cov[XYZRPY_COV_IDX::Y_Y]; + R(2, 2) = twist_cov[XYZRPY_COV_IDX::X_X]; + R(2, 3) = twist_cov[XYZRPY_COV_IDX::X_Y]; + R(3, 2) = twist_cov[XYZRPY_COV_IDX::Y_X]; + R(3, 3) = twist_cov[XYZRPY_COV_IDX::Y_Y]; return ekf_.update(Y, C, R); } @@ -272,14 +273,14 @@ bool CVMotionModel::getPredictedState( constexpr double rr_cov = 0.1 * 0.1; // TODO(yukkysaito) Currently tentative constexpr double pp_cov = 0.1 * 0.1; // TODO(yukkysaito) Currently tentative constexpr double yaw_cov = 0.1 * 0.1; // TODO(yukkysaito) Currently tentative - pose_cov[utils::MSG_COV_IDX::X_X] = P(IDX::X, IDX::X); - pose_cov[utils::MSG_COV_IDX::X_Y] = P(IDX::X, IDX::Y); - pose_cov[utils::MSG_COV_IDX::Y_X] = P(IDX::Y, IDX::X); - pose_cov[utils::MSG_COV_IDX::Y_Y] = P(IDX::Y, IDX::Y); - pose_cov[utils::MSG_COV_IDX::Z_Z] = zz_cov; - pose_cov[utils::MSG_COV_IDX::ROLL_ROLL] = rr_cov; - pose_cov[utils::MSG_COV_IDX::PITCH_PITCH] = pp_cov; - pose_cov[utils::MSG_COV_IDX::YAW_YAW] = yaw_cov; + pose_cov[XYZRPY_COV_IDX::X_X] = P(IDX::X, IDX::X); + pose_cov[XYZRPY_COV_IDX::X_Y] = P(IDX::X, IDX::Y); + pose_cov[XYZRPY_COV_IDX::Y_X] = P(IDX::Y, IDX::X); + pose_cov[XYZRPY_COV_IDX::Y_Y] = P(IDX::Y, IDX::Y); + pose_cov[XYZRPY_COV_IDX::Z_Z] = zz_cov; + pose_cov[XYZRPY_COV_IDX::ROLL_ROLL] = rr_cov; + pose_cov[XYZRPY_COV_IDX::PITCH_PITCH] = pp_cov; + pose_cov[XYZRPY_COV_IDX::YAW_YAW] = yaw_cov; // set twist covariance constexpr double vz_cov = 0.1 * 0.1; // TODO(yukkysaito) Currently tentative @@ -294,14 +295,14 @@ bool CVMotionModel::getPredictedState( twist_cov_rotate(1, 1) = P(IDX::VY, IDX::VY); Eigen::MatrixXd R_yaw = Eigen::Rotation2Dd(-yaw).toRotationMatrix(); Eigen::MatrixXd twist_cov_rotated = R_yaw * twist_cov_rotate * R_yaw.transpose(); - twist_cov[utils::MSG_COV_IDX::X_X] = twist_cov_rotated(0, 0); - twist_cov[utils::MSG_COV_IDX::X_Y] = twist_cov_rotated(0, 1); - twist_cov[utils::MSG_COV_IDX::Y_X] = twist_cov_rotated(1, 0); - twist_cov[utils::MSG_COV_IDX::Y_Y] = twist_cov_rotated(1, 1); - twist_cov[utils::MSG_COV_IDX::Z_Z] = vz_cov; - twist_cov[utils::MSG_COV_IDX::ROLL_ROLL] = wx_cov; - twist_cov[utils::MSG_COV_IDX::PITCH_PITCH] = wy_cov; - twist_cov[utils::MSG_COV_IDX::YAW_YAW] = wz_cov; + twist_cov[XYZRPY_COV_IDX::X_X] = twist_cov_rotated(0, 0); + twist_cov[XYZRPY_COV_IDX::X_Y] = twist_cov_rotated(0, 1); + twist_cov[XYZRPY_COV_IDX::Y_X] = twist_cov_rotated(1, 0); + twist_cov[XYZRPY_COV_IDX::Y_Y] = twist_cov_rotated(1, 1); + twist_cov[XYZRPY_COV_IDX::Z_Z] = vz_cov; + twist_cov[XYZRPY_COV_IDX::ROLL_ROLL] = wx_cov; + twist_cov[XYZRPY_COV_IDX::PITCH_PITCH] = wy_cov; + twist_cov[XYZRPY_COV_IDX::YAW_YAW] = wz_cov; return true; } diff --git a/perception/object_merger/CMakeLists.txt b/perception/object_merger/CMakeLists.txt index b02983db11af3..2d9b490945caf 100644 --- a/perception/object_merger/CMakeLists.txt +++ b/perception/object_merger/CMakeLists.txt @@ -18,18 +18,18 @@ include_directories( ${EIGEN3_INCLUDE_DIR} ) -ament_auto_add_library(object_association_merger SHARED - src/object_association_merger/data_association/data_association.cpp - src/object_association_merger/data_association/mu_successive_shortest_path/mu_successive_shortest_path_wrapper.cpp - src/object_association_merger/node.cpp +ament_auto_add_library(${PROJECT_NAME} SHARED + src/association/data_association.cpp + src/association/solver/mu_successive_shortest_path_wrapper.cpp + src/object_association_merger_node.cpp ) -target_link_libraries(object_association_merger +target_link_libraries(${PROJECT_NAME} Eigen3::Eigen ) -rclcpp_components_register_node(object_association_merger - PLUGIN "object_association::ObjectAssociationMergerNode" +rclcpp_components_register_node(${PROJECT_NAME} + PLUGIN "autoware::object_merger::ObjectAssociationMergerNode" EXECUTABLE object_association_merger_node ) diff --git a/perception/object_merger/README.md b/perception/object_merger/README.md index ca7f533a2c1f1..f9d8c91b2a4c1 100644 --- a/perception/object_merger/README.md +++ b/perception/object_merger/README.md @@ -25,16 +25,9 @@ The successive shortest path algorithm is used to solve the data association pro ## Parameters -| Name | Type | Description | -| --------------------------- | --------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `can_assign_matrix` | double | Assignment table for data association | -| `max_dist_matrix` | double | Maximum distance table for data association | -| `max_area_matrix` | double | Maximum area table for data association | -| `min_area_matrix` | double | Minimum area table for data association | -| `max_rad_matrix` | double | Maximum angle table for data association | -| `base_link_frame_id` | double | association frame | -| `distance_threshold_list` | `std::vector` | Distance threshold for each class used in judging overlap. The class order depends on [ObjectClassification](https://github.com/autowarefoundation/autoware_msgs/blob/main/autoware_perception_msgs/msg/ObjectClassification.msg). | -| `generalized_iou_threshold` | `std::vector` | Generalized IoU threshold for each class | +{{ json_to_markdown("perception/object_merger/schema/object_association_merger.schema.json") }} +{{ json_to_markdown("perception/object_merger/schema/data_association_matrix.schema.json") }} +{{ json_to_markdown("perception/object_merger/schema/overlapped_judge.schema.json") }} ## Tips diff --git a/perception/object_merger/config/object_association_merger.param.yaml b/perception/object_merger/config/object_association_merger.param.yaml index 5e5dafad5d573..e9b86cd64be29 100644 --- a/perception/object_merger/config/object_association_merger.param.yaml +++ b/perception/object_merger/config/object_association_merger.param.yaml @@ -2,4 +2,7 @@ ros__parameters: sync_queue_size: 20 precision_threshold_to_judge_overlapped: 0.4 + recall_threshold_to_judge_overlapped: 0.5 remove_overlapped_unknown_objects: true + base_link_frame_id: base_link + priority_mode: 3 # PriorityMode::Confidence diff --git a/perception/object_merger/include/object_merger/data_association/data_association.hpp b/perception/object_merger/include/autoware_object_merger/association/data_association.hpp similarity index 75% rename from perception/object_merger/include/object_merger/data_association/data_association.hpp rename to perception/object_merger/include/autoware_object_merger/association/data_association.hpp index f8eec33f4a71e..8433e8f7af537 100644 --- a/perception/object_merger/include/object_merger/data_association/data_association.hpp +++ b/perception/object_merger/include/autoware_object_merger/association/data_association.hpp @@ -16,21 +16,25 @@ // Author: v1.0 Yukihiro Saito // -#ifndef OBJECT_MERGER__DATA_ASSOCIATION__DATA_ASSOCIATION_HPP_ -#define OBJECT_MERGER__DATA_ASSOCIATION__DATA_ASSOCIATION_HPP_ - -#include -#include -#include -#include +#ifndef AUTOWARE_OBJECT_MERGER__ASSOCIATION__DATA_ASSOCIATION_HPP_ +#define AUTOWARE_OBJECT_MERGER__ASSOCIATION__DATA_ASSOCIATION_HPP_ #define EIGEN_MPL2_ONLY -#include "object_merger/data_association/solver/gnn_solver.hpp" + +#include "autoware_object_merger/association/solver/gnn_solver.hpp" #include #include -#include +#include "autoware_perception_msgs/msg/detected_objects.hpp" + +#include +#include +#include +#include + +namespace autoware::object_merger +{ class DataAssociation { @@ -40,7 +44,7 @@ class DataAssociation Eigen::MatrixXd max_rad_matrix_; Eigen::MatrixXd min_iou_matrix_; const double score_threshold_; - std::unique_ptr gnn_solver_ptr_; + std::unique_ptr gnn_solver_ptr_; public: EIGEN_MAKE_ALIGNED_OPERATOR_NEW @@ -56,4 +60,6 @@ class DataAssociation virtual ~DataAssociation() {} }; -#endif // OBJECT_MERGER__DATA_ASSOCIATION__DATA_ASSOCIATION_HPP_ +} // namespace autoware::object_merger + +#endif // AUTOWARE_OBJECT_MERGER__ASSOCIATION__DATA_ASSOCIATION_HPP_ diff --git a/perception/object_merger/include/object_merger/data_association/solver/gnn_solver.hpp b/perception/object_merger/include/autoware_object_merger/association/solver/gnn_solver.hpp similarity index 57% rename from perception/object_merger/include/object_merger/data_association/solver/gnn_solver.hpp rename to perception/object_merger/include/autoware_object_merger/association/solver/gnn_solver.hpp index cead11d516421..6421dd6108a4a 100644 --- a/perception/object_merger/include/object_merger/data_association/solver/gnn_solver.hpp +++ b/perception/object_merger/include/autoware_object_merger/association/solver/gnn_solver.hpp @@ -12,11 +12,11 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef OBJECT_MERGER__DATA_ASSOCIATION__SOLVER__GNN_SOLVER_HPP_ -#define OBJECT_MERGER__DATA_ASSOCIATION__SOLVER__GNN_SOLVER_HPP_ +#ifndef AUTOWARE_OBJECT_MERGER__ASSOCIATION__SOLVER__GNN_SOLVER_HPP_ +#define AUTOWARE_OBJECT_MERGER__ASSOCIATION__SOLVER__GNN_SOLVER_HPP_ -#include "object_merger/data_association/solver/gnn_solver_interface.hpp" -#include "object_merger/data_association/solver/mu_successive_shortest_path.hpp" -#include "object_merger/data_association/solver/successive_shortest_path.hpp" +#include "autoware_object_merger/association/solver/gnn_solver_interface.hpp" +#include "autoware_object_merger/association/solver/mu_ssp.hpp" +#include "autoware_object_merger/association/solver/ssp.hpp" -#endif // OBJECT_MERGER__DATA_ASSOCIATION__SOLVER__GNN_SOLVER_HPP_ +#endif // AUTOWARE_OBJECT_MERGER__ASSOCIATION__SOLVER__GNN_SOLVER_HPP_ diff --git a/perception/radar_object_tracker/include/radar_object_tracker/data_association/solver/gnn_solver_interface.hpp b/perception/object_merger/include/autoware_object_merger/association/solver/gnn_solver_interface.hpp similarity index 75% rename from perception/radar_object_tracker/include/radar_object_tracker/data_association/solver/gnn_solver_interface.hpp rename to perception/object_merger/include/autoware_object_merger/association/solver/gnn_solver_interface.hpp index 9b633f04ca190..6a0702f056ba7 100644 --- a/perception/radar_object_tracker/include/radar_object_tracker/data_association/solver/gnn_solver_interface.hpp +++ b/perception/object_merger/include/autoware_object_merger/association/solver/gnn_solver_interface.hpp @@ -12,13 +12,13 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef RADAR_OBJECT_TRACKER__DATA_ASSOCIATION__SOLVER__GNN_SOLVER_INTERFACE_HPP_ -#define RADAR_OBJECT_TRACKER__DATA_ASSOCIATION__SOLVER__GNN_SOLVER_INTERFACE_HPP_ +#ifndef AUTOWARE_OBJECT_MERGER__ASSOCIATION__SOLVER__GNN_SOLVER_INTERFACE_HPP_ +#define AUTOWARE_OBJECT_MERGER__ASSOCIATION__SOLVER__GNN_SOLVER_INTERFACE_HPP_ #include #include -namespace gnn_solver +namespace autoware::object_merger::gnn_solver { class GnnSolverInterface { @@ -30,6 +30,6 @@ class GnnSolverInterface const std::vector> & cost, std::unordered_map * direct_assignment, std::unordered_map * reverse_assignment) = 0; }; -} // namespace gnn_solver +} // namespace autoware::object_merger::gnn_solver -#endif // RADAR_OBJECT_TRACKER__DATA_ASSOCIATION__SOLVER__GNN_SOLVER_INTERFACE_HPP_ +#endif // AUTOWARE_OBJECT_MERGER__ASSOCIATION__SOLVER__GNN_SOLVER_INTERFACE_HPP_ diff --git a/perception/object_merger/include/object_merger/data_association/solver/mu_successive_shortest_path.hpp b/perception/object_merger/include/autoware_object_merger/association/solver/mu_ssp.hpp similarity index 71% rename from perception/object_merger/include/object_merger/data_association/solver/mu_successive_shortest_path.hpp rename to perception/object_merger/include/autoware_object_merger/association/solver/mu_ssp.hpp index 0464a29e6b2e5..364e9306112a7 100644 --- a/perception/object_merger/include/object_merger/data_association/solver/mu_successive_shortest_path.hpp +++ b/perception/object_merger/include/autoware_object_merger/association/solver/mu_ssp.hpp @@ -12,15 +12,15 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef OBJECT_MERGER__DATA_ASSOCIATION__SOLVER__MU_SUCCESSIVE_SHORTEST_PATH_HPP_ -#define OBJECT_MERGER__DATA_ASSOCIATION__SOLVER__MU_SUCCESSIVE_SHORTEST_PATH_HPP_ +#ifndef AUTOWARE_OBJECT_MERGER__ASSOCIATION__SOLVER__MU_SSP_HPP_ +#define AUTOWARE_OBJECT_MERGER__ASSOCIATION__SOLVER__MU_SSP_HPP_ -#include "object_merger/data_association/solver/gnn_solver_interface.hpp" +#include "autoware_object_merger/association/solver/gnn_solver_interface.hpp" #include #include -namespace gnn_solver +namespace autoware::object_merger::gnn_solver { class MuSSP : public GnnSolverInterface { @@ -32,6 +32,6 @@ class MuSSP : public GnnSolverInterface const std::vector> & cost, std::unordered_map * direct_assignment, std::unordered_map * reverse_assignment) override; }; -} // namespace gnn_solver +} // namespace autoware::object_merger::gnn_solver -#endif // OBJECT_MERGER__DATA_ASSOCIATION__SOLVER__MU_SUCCESSIVE_SHORTEST_PATH_HPP_ +#endif // AUTOWARE_OBJECT_MERGER__ASSOCIATION__SOLVER__MU_SSP_HPP_ diff --git a/perception/object_merger/include/object_merger/data_association/solver/successive_shortest_path.hpp b/perception/object_merger/include/autoware_object_merger/association/solver/ssp.hpp similarity index 71% rename from perception/object_merger/include/object_merger/data_association/solver/successive_shortest_path.hpp rename to perception/object_merger/include/autoware_object_merger/association/solver/ssp.hpp index 618879a01866f..41bf390829ade 100644 --- a/perception/object_merger/include/object_merger/data_association/solver/successive_shortest_path.hpp +++ b/perception/object_merger/include/autoware_object_merger/association/solver/ssp.hpp @@ -12,15 +12,15 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef OBJECT_MERGER__DATA_ASSOCIATION__SOLVER__SUCCESSIVE_SHORTEST_PATH_HPP_ -#define OBJECT_MERGER__DATA_ASSOCIATION__SOLVER__SUCCESSIVE_SHORTEST_PATH_HPP_ +#ifndef AUTOWARE_OBJECT_MERGER__ASSOCIATION__SOLVER__SSP_HPP_ +#define AUTOWARE_OBJECT_MERGER__ASSOCIATION__SOLVER__SSP_HPP_ -#include "object_merger/data_association/solver/gnn_solver_interface.hpp" +#include "autoware_object_merger/association/solver/gnn_solver_interface.hpp" #include #include -namespace gnn_solver +namespace autoware::object_merger::gnn_solver { class SSP : public GnnSolverInterface { @@ -32,6 +32,6 @@ class SSP : public GnnSolverInterface const std::vector> & cost, std::unordered_map * direct_assignment, std::unordered_map * reverse_assignment) override; }; -} // namespace gnn_solver +} // namespace autoware::object_merger::gnn_solver -#endif // OBJECT_MERGER__DATA_ASSOCIATION__SOLVER__SUCCESSIVE_SHORTEST_PATH_HPP_ +#endif // AUTOWARE_OBJECT_MERGER__ASSOCIATION__SOLVER__SSP_HPP_ diff --git a/perception/object_merger/include/object_merger/utils/utils.hpp b/perception/object_merger/include/object_merger/utils/utils.hpp deleted file mode 100644 index bb167cbe20bd6..0000000000000 --- a/perception/object_merger/include/object_merger/utils/utils.hpp +++ /dev/null @@ -1,73 +0,0 @@ -// Copyright 2020 Tier IV, Inc. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. -// -// -// Author: v1.0 Yukihiro Saito -// - -#ifndef OBJECT_MERGER__UTILS__UTILS_HPP_ -#define OBJECT_MERGER__UTILS__UTILS_HPP_ - -#include -#include -#include -#include - -#include -#include -#include - -namespace utils -{ -enum MSG_COV_IDX { - X_X = 0, - X_Y = 1, - X_Z = 2, - X_ROLL = 3, - X_PITCH = 4, - X_YAW = 5, - Y_X = 6, - Y_Y = 7, - Y_Z = 8, - Y_ROLL = 9, - Y_PITCH = 10, - Y_YAW = 11, - Z_X = 12, - Z_Y = 13, - Z_Z = 14, - Z_ROLL = 15, - Z_PITCH = 16, - Z_YAW = 17, - ROLL_X = 18, - ROLL_Y = 19, - ROLL_Z = 20, - ROLL_ROLL = 21, - ROLL_PITCH = 22, - ROLL_YAW = 23, - PITCH_X = 24, - PITCH_Y = 25, - PITCH_Z = 26, - PITCH_ROLL = 27, - PITCH_PITCH = 28, - PITCH_YAW = 29, - YAW_X = 30, - YAW_Y = 31, - YAW_Z = 32, - YAW_ROLL = 33, - YAW_PITCH = 34, - YAW_YAW = 35 -}; -} // namespace utils - -#endif // OBJECT_MERGER__UTILS__UTILS_HPP_ diff --git a/perception/object_merger/package.xml b/perception/object_merger/package.xml index 0340a477936b8..5ca261b3ddf0e 100644 --- a/perception/object_merger/package.xml +++ b/perception/object_merger/package.xml @@ -22,7 +22,6 @@ rclcpp_components tf2 tf2_ros - tier4_perception_msgs ament_lint_auto autoware_lint_common diff --git a/perception/object_merger/schema/data_association_matrix.schema.json b/perception/object_merger/schema/data_association_matrix.schema.json new file mode 100644 index 0000000000000..68dc977224ba5 --- /dev/null +++ b/perception/object_merger/schema/data_association_matrix.schema.json @@ -0,0 +1,41 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "title": "Data Association Matrix Parameters", + "type": "object", + "properties": { + "ros__parameters": { + "type": "object", + "properties": { + "can_assign_matrix": { + "type": "array", + "items": { + "type": "number" + }, + "description": "Assignment table for data association" + }, + "max_dist_matrix": { + "type": "array", + "items": { + "type": "number" + }, + "description": "Maximum distance table for data association" + }, + "max_rad_matrix": { + "type": "array", + "items": { + "type": "number" + }, + "description": "Maximum angle table for data association. If value is greater than pi, it will be ignored." + }, + "min_iou_matrix": { + "type": "array", + "items": { + "type": "number" + }, + "description": "Minimum IoU threshold matrix for data association. If value is negative, it will be ignored." + } + }, + "required": ["can_assign_matrix", "max_dist_matrix", "max_rad_matrix", "min_iou_matrix"] + } + } +} diff --git a/perception/object_merger/schema/object_association_merger.schema.json b/perception/object_merger/schema/object_association_merger.schema.json new file mode 100644 index 0000000000000..11090fab9c7b3 --- /dev/null +++ b/perception/object_merger/schema/object_association_merger.schema.json @@ -0,0 +1,63 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "title": "Parameters for Object Association Merger Node", + "type": "object", + "definitions": { + "object_association_merger": { + "type": "object", + "properties": { + "sync_queue_size": { + "type": "integer", + "description": "The size of the synchronization queue.", + "default": 20 + }, + "precision_threshold_to_judge_overlapped": { + "type": "number", + "description": "The precision threshold to judge if objects are overlapped.", + "default": 0.4 + }, + "recall_threshold_to_judge_overlapped": { + "type": "number", + "description": "The recall threshold to judge if objects are overlapped.", + "default": 0.5 + }, + "remove_overlapped_unknown_objects": { + "type": "boolean", + "description": "Flag to remove overlapped unknown objects.", + "default": true + }, + "base_link_frame_id": { + "type": "string", + "description": "The frame ID of the association frame.", + "default": "base_link" + }, + "priority_mode": { + "type": "integer", + "description": "Index for the priority_mode.", + "default": 3, + "enum": [0, 1, 2, 3] + } + }, + "required": [ + "sync_queue_size", + "precision_threshold_to_judge_overlapped", + "recall_threshold_to_judge_overlapped", + "remove_overlapped_unknown_objects", + "base_link_frame_id", + "priority_mode" + ] + } + }, + "properties": { + "/**": { + "type": "object", + "properties": { + "ros__parameters": { + "$ref": "#/definitions/object_association_merger" + } + }, + "required": ["ros__parameters"] + } + }, + "required": ["/**"] +} diff --git a/perception/object_merger/schema/overlapped_judge.schema.json b/perception/object_merger/schema/overlapped_judge.schema.json new file mode 100644 index 0000000000000..b65464c6201d1 --- /dev/null +++ b/perception/object_merger/schema/overlapped_judge.schema.json @@ -0,0 +1,27 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "title": "Overlapped Judge Parameters", + "type": "object", + "properties": { + "ros__parameters": { + "type": "object", + "properties": { + "distance_threshold_list": { + "type": "array", + "items": { + "type": "number" + }, + "description": "Distance threshold for each class used in judging overlap." + }, + "generalized_iou_threshold": { + "type": "array", + "items": { + "type": "number" + }, + "description": "Generalized IoU threshold for each class." + } + }, + "required": ["distance_threshold_list", "generalized_iou_threshold"] + } + } +} diff --git a/perception/object_merger/src/object_association_merger/data_association/data_association.cpp b/perception/object_merger/src/association/data_association.cpp similarity index 95% rename from perception/object_merger/src/object_association_merger/data_association/data_association.cpp rename to perception/object_merger/src/association/data_association.cpp index c32fe480ff4af..e317ac63d9831 100644 --- a/perception/object_merger/src/object_association_merger/data_association/data_association.cpp +++ b/perception/object_merger/src/association/data_association.cpp @@ -12,11 +12,10 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "object_merger/data_association/data_association.hpp" +#include "autoware_object_merger/association/data_association.hpp" #include "autoware/universe_utils/geometry/geometry.hpp" -#include "object_merger/data_association/solver/gnn_solver.hpp" -#include "object_merger/utils/utils.hpp" +#include "autoware_object_merger/association/solver/gnn_solver.hpp" #include "object_recognition_utils/object_recognition_utils.hpp" #include @@ -47,6 +46,9 @@ double getFormedYawAngle( } } // namespace +namespace autoware::object_merger +{ + DataAssociation::DataAssociation( std::vector can_assign_vector, std::vector max_dist_vector, std::vector max_rad_vector, std::vector min_iou_vector) @@ -77,7 +79,7 @@ DataAssociation::DataAssociation( min_iou_matrix_ = min_iou_matrix_tmp.transpose(); } - gnn_solver_ptr_ = std::make_unique(); + gnn_solver_ptr_ = std::make_unique(); } void DataAssociation::assign( @@ -139,7 +141,7 @@ Eigen::MatrixXd DataAssociation::calcScoreMatrix( bool passed_gate = true; // dist gate - if (passed_gate) { + { // passed_gate is always true if (max_dist < dist) passed_gate = false; } // angle gate @@ -172,3 +174,5 @@ Eigen::MatrixXd DataAssociation::calcScoreMatrix( return score_matrix; } + +} // namespace autoware::object_merger diff --git a/perception/radar_object_tracker/src/data_association/mu_successive_shortest_path/mu_successive_shortest_path_wrapper.cpp b/perception/object_merger/src/association/solver/mu_successive_shortest_path_wrapper.cpp similarity index 87% rename from perception/radar_object_tracker/src/data_association/mu_successive_shortest_path/mu_successive_shortest_path_wrapper.cpp rename to perception/object_merger/src/association/solver/mu_successive_shortest_path_wrapper.cpp index 43f974c2621e1..6a5dd6473da1f 100644 --- a/perception/radar_object_tracker/src/data_association/mu_successive_shortest_path/mu_successive_shortest_path_wrapper.cpp +++ b/perception/object_merger/src/association/solver/mu_successive_shortest_path_wrapper.cpp @@ -12,7 +12,7 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "radar_object_tracker/data_association/solver/mu_successive_shortest_path.hpp" +#include "autoware_object_merger/association/solver/mu_ssp.hpp" #include @@ -24,7 +24,7 @@ #include #include -namespace gnn_solver +namespace autoware::object_merger::gnn_solver { void MuSSP::maximizeLinearAssignment( const std::vector> & cost, std::unordered_map * direct_assignment, @@ -38,4 +38,4 @@ void MuSSP::maximizeLinearAssignment( // Solve DA by muSSP solve_muSSP(cost, direct_assignment, reverse_assignment); } -} // namespace gnn_solver +} // namespace autoware::object_merger::gnn_solver diff --git a/perception/radar_object_tracker/src/data_association/successive_shortest_path/successive_shortest_path.cpp b/perception/object_merger/src/association/solver/successive_shortest_path.cpp similarity index 98% rename from perception/radar_object_tracker/src/data_association/successive_shortest_path/successive_shortest_path.cpp rename to perception/object_merger/src/association/solver/successive_shortest_path.cpp index c904a9005b27e..782aa1ef4ac31 100644 --- a/perception/radar_object_tracker/src/data_association/successive_shortest_path/successive_shortest_path.cpp +++ b/perception/object_merger/src/association/solver/successive_shortest_path.cpp @@ -12,7 +12,7 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "radar_object_tracker/data_association/solver/successive_shortest_path.hpp" +#include "autoware_object_merger/association/solver/ssp.hpp" #include #include @@ -22,7 +22,7 @@ #include #include -namespace gnn_solver +namespace autoware::object_merger::gnn_solver { struct ResidualEdge { @@ -367,4 +367,4 @@ void SSP::maximizeLinearAssignment( } #endif } -} // namespace gnn_solver +} // namespace autoware::object_merger::gnn_solver diff --git a/perception/object_merger/src/object_association_merger/node.cpp b/perception/object_merger/src/object_association_merger_node.cpp similarity index 93% rename from perception/object_merger/src/object_association_merger/node.cpp rename to perception/object_merger/src/object_association_merger_node.cpp index 5077268731955..321f1a8da8d9e 100644 --- a/perception/object_merger/src/object_association_merger/node.cpp +++ b/perception/object_merger/src/object_association_merger_node.cpp @@ -12,20 +12,21 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "object_merger/node.hpp" +#define EIGEN_MPL2_ONLY + +#include "object_association_merger_node.hpp" #include "autoware/universe_utils/geometry/geometry.hpp" -#include "object_merger/utils/utils.hpp" #include "object_recognition_utils/object_recognition_utils.hpp" +#include +#include + #include #include #include - -#define EIGEN_MPL2_ONLY -#include -#include +#include using Label = autoware_perception_msgs::msg::ObjectClassification; @@ -70,7 +71,7 @@ std::map convertListToClassMap(const std::vector & distance } } // namespace -namespace object_association +namespace autoware::object_merger { ObjectAssociationMergerNode::ObjectAssociationMergerNode(const rclcpp::NodeOptions & node_options) : rclcpp::Node("object_association_merger_node", node_options), @@ -80,16 +81,14 @@ ObjectAssociationMergerNode::ObjectAssociationMergerNode(const rclcpp::NodeOptio object1_sub_(this, "input/object1", rclcpp::QoS{1}.get_rmw_qos_profile()) { // Parameters - base_link_frame_id_ = declare_parameter("base_link_frame_id", "base_link"); - priority_mode_ = static_cast( - declare_parameter("priority_mode", static_cast(PriorityMode::Confidence))); - sync_queue_size_ = declare_parameter("sync_queue_size", 20); - remove_overlapped_unknown_objects_ = - declare_parameter("remove_overlapped_unknown_objects", true); + base_link_frame_id_ = declare_parameter("base_link_frame_id"); + priority_mode_ = static_cast(declare_parameter("priority_mode")); + sync_queue_size_ = declare_parameter("sync_queue_size"); + remove_overlapped_unknown_objects_ = declare_parameter("remove_overlapped_unknown_objects"); overlapped_judge_param_.precision_threshold = declare_parameter("precision_threshold_to_judge_overlapped"); overlapped_judge_param_.recall_threshold = - declare_parameter("recall_threshold_to_judge_overlapped", 0.5); + declare_parameter("recall_threshold_to_judge_overlapped"); overlapped_judge_param_.generalized_iou_threshold = convertListToClassMap(declare_parameter>("generalized_iou_threshold")); @@ -106,7 +105,7 @@ ObjectAssociationMergerNode::ObjectAssociationMergerNode(const rclcpp::NodeOptio const auto max_dist_matrix = this->declare_parameter>("max_dist_matrix"); const auto max_rad_matrix = this->declare_parameter>("max_rad_matrix"); const auto min_iou_matrix = this->declare_parameter>("min_iou_matrix"); - data_association_ = std::make_unique( + data_association_ = std::make_unique( can_assign_matrix, max_dist_matrix, max_rad_matrix, min_iou_matrix); // Create publishers and subscribers @@ -233,7 +232,7 @@ void ObjectAssociationMergerNode::objectsCallback( processing_time_publisher_->publish( "debug/processing_time_ms", stop_watch_ptr_->toc("processing_time", true)); } -} // namespace object_association +} // namespace autoware::object_merger #include -RCLCPP_COMPONENTS_REGISTER_NODE(object_association::ObjectAssociationMergerNode) +RCLCPP_COMPONENTS_REGISTER_NODE(autoware::object_merger::ObjectAssociationMergerNode) diff --git a/perception/object_merger/include/object_merger/node.hpp b/perception/object_merger/src/object_association_merger_node.hpp similarity index 85% rename from perception/object_merger/include/object_merger/node.hpp rename to perception/object_merger/src/object_association_merger_node.hpp index 2f5918a9c7f17..d5b5a1ae018a6 100644 --- a/perception/object_merger/include/object_merger/node.hpp +++ b/perception/object_merger/src/object_association_merger_node.hpp @@ -12,17 +12,17 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef OBJECT_MERGER__NODE_HPP_ -#define OBJECT_MERGER__NODE_HPP_ +#ifndef OBJECT_ASSOCIATION_MERGER_NODE_HPP_ +#define OBJECT_ASSOCIATION_MERGER_NODE_HPP_ -#include "object_merger/data_association/data_association.hpp" +#include "autoware/universe_utils/ros/debug_publisher.hpp" +#include "autoware/universe_utils/ros/published_time_publisher.hpp" +#include "autoware/universe_utils/system/stop_watch.hpp" +#include "autoware_object_merger/association/data_association.hpp" -#include -#include -#include #include -#include +#include "autoware_perception_msgs/msg/detected_objects.hpp" #include #include @@ -45,7 +45,7 @@ #include #include -namespace object_association +namespace autoware::object_merger { class ObjectAssociationMergerNode : public rclcpp::Node { @@ -89,6 +89,6 @@ class ObjectAssociationMergerNode : public rclcpp::Node std::unique_ptr published_time_publisher_; }; -} // namespace object_association +} // namespace autoware::object_merger -#endif // OBJECT_MERGER__NODE_HPP_ +#endif // OBJECT_ASSOCIATION_MERGER_NODE_HPP_ diff --git a/perception/object_range_splitter/CMakeLists.txt b/perception/object_range_splitter/CMakeLists.txt index 92c6c0668a085..1da71a31b26d0 100644 --- a/perception/object_range_splitter/CMakeLists.txt +++ b/perception/object_range_splitter/CMakeLists.txt @@ -4,12 +4,12 @@ project(object_range_splitter) find_package(autoware_cmake REQUIRED) autoware_package() -ament_auto_add_library(object_range_splitter SHARED - src/node.cpp +ament_auto_add_library(${PROJECT_NAME} SHARED + src/object_range_splitter_node.cpp ) -rclcpp_components_register_node(object_range_splitter - PLUGIN "object_range_splitter::ObjectRangeSplitterNode" +rclcpp_components_register_node(${PROJECT_NAME} + PLUGIN "autoware::object_range_splitter::ObjectRangeSplitterNode" EXECUTABLE object_range_splitter_node ) diff --git a/perception/object_range_splitter/package.xml b/perception/object_range_splitter/package.xml index bef3ea76354d1..7fc9245ee894f 100644 --- a/perception/object_range_splitter/package.xml +++ b/perception/object_range_splitter/package.xml @@ -14,7 +14,6 @@ autoware_perception_msgs rclcpp rclcpp_components - sensor_msgs ament_lint_auto autoware_lint_common diff --git a/perception/object_range_splitter/src/node.cpp b/perception/object_range_splitter/src/object_range_splitter_node.cpp similarity index 91% rename from perception/object_range_splitter/src/node.cpp rename to perception/object_range_splitter/src/object_range_splitter_node.cpp index 978c40275fb9b..5d5a4aad70011 100644 --- a/perception/object_range_splitter/src/node.cpp +++ b/perception/object_range_splitter/src/object_range_splitter_node.cpp @@ -12,9 +12,9 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "object_range_splitter/node.hpp" +#include "object_range_splitter_node.hpp" -namespace object_range_splitter +namespace autoware::object_range_splitter { ObjectRangeSplitterNode::ObjectRangeSplitterNode(const rclcpp::NodeOptions & node_options) : Node("object_range_splitter_node", node_options) @@ -59,7 +59,7 @@ void ObjectRangeSplitterNode::objectCallback( long_range_object_pub_->publish(output_long_range_object_msg); short_range_object_pub_->publish(output_short_range_object_msg); } -} // namespace object_range_splitter +} // namespace autoware::object_range_splitter #include -RCLCPP_COMPONENTS_REGISTER_NODE(object_range_splitter::ObjectRangeSplitterNode) +RCLCPP_COMPONENTS_REGISTER_NODE(autoware::object_range_splitter::ObjectRangeSplitterNode) diff --git a/perception/object_range_splitter/include/object_range_splitter/node.hpp b/perception/object_range_splitter/src/object_range_splitter_node.hpp similarity index 81% rename from perception/object_range_splitter/include/object_range_splitter/node.hpp rename to perception/object_range_splitter/src/object_range_splitter_node.hpp index a4dbb2fb634aa..19e5924272eb9 100644 --- a/perception/object_range_splitter/include/object_range_splitter/node.hpp +++ b/perception/object_range_splitter/src/object_range_splitter_node.hpp @@ -12,16 +12,16 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef OBJECT_RANGE_SPLITTER__NODE_HPP_ -#define OBJECT_RANGE_SPLITTER__NODE_HPP_ +#ifndef OBJECT_RANGE_SPLITTER_NODE_HPP_ +#define OBJECT_RANGE_SPLITTER_NODE_HPP_ #include -#include +#include "autoware_perception_msgs/msg/detected_objects.hpp" #include -namespace object_range_splitter +namespace autoware::object_range_splitter { class ObjectRangeSplitterNode : public rclcpp::Node { @@ -42,6 +42,6 @@ class ObjectRangeSplitterNode : public rclcpp::Node float spilt_range_; }; -} // namespace object_range_splitter +} // namespace autoware::object_range_splitter -#endif // OBJECT_RANGE_SPLITTER__NODE_HPP_ +#endif // OBJECT_RANGE_SPLITTER_NODE_HPP_ diff --git a/perception/object_velocity_splitter/CMakeLists.txt b/perception/object_velocity_splitter/CMakeLists.txt index 68e87a3355f86..afad278e383d8 100644 --- a/perception/object_velocity_splitter/CMakeLists.txt +++ b/perception/object_velocity_splitter/CMakeLists.txt @@ -6,12 +6,12 @@ find_package(autoware_cmake REQUIRED) autoware_package() # Targets -ament_auto_add_library(object_velocity_splitter_node_component SHARED - src/object_velocity_splitter_node/object_velocity_splitter_node.cpp +ament_auto_add_library(${PROJECT_NAME} SHARED + src/object_velocity_splitter_node.cpp ) -rclcpp_components_register_node(object_velocity_splitter_node_component - PLUGIN "object_velocity_splitter::ObjectVelocitySplitterNode" +rclcpp_components_register_node(${PROJECT_NAME} + PLUGIN "autoware::object_velocity_splitter::ObjectVelocitySplitterNode" EXECUTABLE object_velocity_splitter_node ) diff --git a/perception/object_velocity_splitter/src/object_velocity_splitter_node/object_velocity_splitter_node.cpp b/perception/object_velocity_splitter/src/object_velocity_splitter_node.cpp similarity index 93% rename from perception/object_velocity_splitter/src/object_velocity_splitter_node/object_velocity_splitter_node.cpp rename to perception/object_velocity_splitter/src/object_velocity_splitter_node.cpp index e15871d18840f..a10692faeff1e 100644 --- a/perception/object_velocity_splitter/src/object_velocity_splitter_node/object_velocity_splitter_node.cpp +++ b/perception/object_velocity_splitter/src/object_velocity_splitter_node.cpp @@ -12,7 +12,7 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "object_velocity_splitter/object_velocity_splitter_node.hpp" +#include "object_velocity_splitter_node.hpp" #include "autoware/universe_utils/geometry/geometry.hpp" @@ -40,7 +40,7 @@ bool update_param( } } // namespace -namespace object_velocity_splitter +namespace autoware::object_velocity_splitter { using autoware_perception_msgs::msg::DetectedObject; using autoware_perception_msgs::msg::DetectedObjects; @@ -110,7 +110,7 @@ rcl_interfaces::msg::SetParametersResult ObjectVelocitySplitterNode::onSetParam( return result; } -} // namespace object_velocity_splitter +} // namespace autoware::object_velocity_splitter #include "rclcpp_components/register_node_macro.hpp" -RCLCPP_COMPONENTS_REGISTER_NODE(object_velocity_splitter::ObjectVelocitySplitterNode) +RCLCPP_COMPONENTS_REGISTER_NODE(autoware::object_velocity_splitter::ObjectVelocitySplitterNode) diff --git a/perception/object_velocity_splitter/include/object_velocity_splitter/object_velocity_splitter_node.hpp b/perception/object_velocity_splitter/src/object_velocity_splitter_node.hpp similarity index 85% rename from perception/object_velocity_splitter/include/object_velocity_splitter/object_velocity_splitter_node.hpp rename to perception/object_velocity_splitter/src/object_velocity_splitter_node.hpp index 2180b467dbbcf..ca0a9a9e65f3c 100644 --- a/perception/object_velocity_splitter/include/object_velocity_splitter/object_velocity_splitter_node.hpp +++ b/perception/object_velocity_splitter/src/object_velocity_splitter_node.hpp @@ -12,8 +12,8 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef OBJECT_VELOCITY_SPLITTER__OBJECT_VELOCITY_SPLITTER_NODE_HPP_ -#define OBJECT_VELOCITY_SPLITTER__OBJECT_VELOCITY_SPLITTER_NODE_HPP_ +#ifndef OBJECT_VELOCITY_SPLITTER_NODE_HPP_ +#define OBJECT_VELOCITY_SPLITTER_NODE_HPP_ #include "rclcpp/rclcpp.hpp" @@ -24,7 +24,7 @@ #include #include -namespace object_velocity_splitter +namespace autoware::object_velocity_splitter { using autoware_perception_msgs::msg::DetectedObject; using autoware_perception_msgs::msg::DetectedObjects; @@ -62,6 +62,6 @@ class ObjectVelocitySplitterNode : public rclcpp::Node NodeParam node_param_{}; }; -} // namespace object_velocity_splitter +} // namespace autoware::object_velocity_splitter -#endif // OBJECT_VELOCITY_SPLITTER__OBJECT_VELOCITY_SPLITTER_NODE_HPP_ +#endif // OBJECT_VELOCITY_SPLITTER_NODE_HPP_ diff --git a/perception/occupancy_grid_map_outlier_filter/package.xml b/perception/occupancy_grid_map_outlier_filter/package.xml index 0e920549eb728..23d08db09553a 100644 --- a/perception/occupancy_grid_map_outlier_filter/package.xml +++ b/perception/occupancy_grid_map_outlier_filter/package.xml @@ -17,11 +17,11 @@ ament_cmake_auto autoware_cmake + autoware_lanelet2_extension autoware_universe_utils autoware_vehicle_msgs diagnostic_updater image_transport - lanelet2_extension libopencv-dev libpcl-all-dev message_filters diff --git a/perception/probabilistic_occupancy_grid_map/src/pointcloud_based_occupancy_grid_map/occupancy_grid_map_projective.cpp b/perception/probabilistic_occupancy_grid_map/src/pointcloud_based_occupancy_grid_map/occupancy_grid_map_projective.cpp index 79fc6e8c40b5e..3846b3a7d16e7 100644 --- a/perception/probabilistic_occupancy_grid_map/src/pointcloud_based_occupancy_grid_map/occupancy_grid_map_projective.cpp +++ b/perception/probabilistic_occupancy_grid_map/src/pointcloud_based_occupancy_grid_map/occupancy_grid_map_projective.cpp @@ -225,11 +225,9 @@ void OccupancyGridMapProjectiveBlindSpot::updateWithPointCloud( if (dist_index + 1 == obstacle_pointcloud_angle_bin.size()) { const auto & source = obstacle_pointcloud_angle_bin.at(dist_index); - if (!no_visible_point_beyond) { - raytrace( - source.wx, source.wy, source.projected_wx, source.projected_wy, - occupancy_cost_value::NO_INFORMATION); - } + raytrace( + source.wx, source.wy, source.projected_wx, source.projected_wy, + occupancy_cost_value::NO_INFORMATION); continue; } @@ -238,11 +236,6 @@ void OccupancyGridMapProjectiveBlindSpot::updateWithPointCloud( obstacle_pointcloud_angle_bin.at(dist_index).range); if (next_obstacle_point_distance <= obstacle_separation_threshold_) { continue; - } else if (no_visible_point_beyond) { - const auto & source = obstacle_pointcloud_angle_bin.at(dist_index); - const auto & target = obstacle_pointcloud_angle_bin.at(dist_index + 1); - raytrace(source.wx, source.wy, target.wx, target.wy, occupancy_cost_value::NO_INFORMATION); - continue; } auto next_raw_distance = diff --git a/perception/radar_crossing_objects_noise_filter/CMakeLists.txt b/perception/radar_crossing_objects_noise_filter/CMakeLists.txt index bf4a46cda7ae6..6414739413d5c 100644 --- a/perception/radar_crossing_objects_noise_filter/CMakeLists.txt +++ b/perception/radar_crossing_objects_noise_filter/CMakeLists.txt @@ -6,12 +6,12 @@ find_package(autoware_cmake REQUIRED) autoware_package() # Targets -ament_auto_add_library(radar_crossing_objects_noise_filter_node_component SHARED - src/radar_crossing_objects_noise_filter_node/radar_crossing_objects_noise_filter_node.cpp +ament_auto_add_library(${PROJECT_NAME}_node_component SHARED + src/radar_crossing_objects_noise_filter_node.cpp ) -rclcpp_components_register_node(radar_crossing_objects_noise_filter_node_component - PLUGIN "radar_crossing_objects_noise_filter::RadarCrossingObjectsNoiseFilterNode" +rclcpp_components_register_node(${PROJECT_NAME}_node_component + PLUGIN "autoware::radar_crossing_objects_noise_filter::RadarCrossingObjectsNoiseFilterNode" EXECUTABLE radar_crossing_objects_noise_filter_node ) @@ -23,10 +23,10 @@ if(BUILD_TESTING) ament_lint_auto_find_test_dependencies() file(GLOB_RECURSE test_files test/**/*.cpp) - ament_add_ros_isolated_gtest(radar_crossing_objects_noise_filter ${test_files}) + ament_add_ros_isolated_gtest(${PROJECT_NAME} ${test_files}) - target_link_libraries(radar_crossing_objects_noise_filter - radar_crossing_objects_noise_filter_node_component + target_link_libraries(${PROJECT_NAME} + ${PROJECT_NAME}_node_component ) endif() diff --git a/perception/radar_crossing_objects_noise_filter/package.xml b/perception/radar_crossing_objects_noise_filter/package.xml index 0db40b24e809c..d0245dfe62a40 100644 --- a/perception/radar_crossing_objects_noise_filter/package.xml +++ b/perception/radar_crossing_objects_noise_filter/package.xml @@ -16,7 +16,6 @@ autoware_perception_msgs autoware_universe_utils - geometry_msgs rclcpp rclcpp_components tf2 diff --git a/perception/radar_crossing_objects_noise_filter/src/radar_crossing_objects_noise_filter_node/radar_crossing_objects_noise_filter_node.cpp b/perception/radar_crossing_objects_noise_filter/src/radar_crossing_objects_noise_filter_node.cpp similarity index 94% rename from perception/radar_crossing_objects_noise_filter/src/radar_crossing_objects_noise_filter_node/radar_crossing_objects_noise_filter_node.cpp rename to perception/radar_crossing_objects_noise_filter/src/radar_crossing_objects_noise_filter_node.cpp index 2f5884c04ce37..13231b83bf27b 100644 --- a/perception/radar_crossing_objects_noise_filter/src/radar_crossing_objects_noise_filter_node/radar_crossing_objects_noise_filter_node.cpp +++ b/perception/radar_crossing_objects_noise_filter/src/radar_crossing_objects_noise_filter_node.cpp @@ -12,7 +12,7 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "radar_crossing_objects_noise_filter/radar_crossing_objects_noise_filter_node.hpp" +#include "radar_crossing_objects_noise_filter_node.hpp" #include "autoware/universe_utils/geometry/geometry.hpp" #include "autoware/universe_utils/math/normalization.hpp" @@ -50,7 +50,7 @@ bool update_param( } } // namespace -namespace radar_crossing_objects_noise_filter +namespace autoware::radar_crossing_objects_noise_filter { using autoware_perception_msgs::msg::DetectedObject; using autoware_perception_msgs::msg::DetectedObjects; @@ -142,8 +142,8 @@ bool RadarCrossingObjectsNoiseFilterNode::isNoise(const DetectedObject & object) } } -} // namespace radar_crossing_objects_noise_filter +} // namespace autoware::radar_crossing_objects_noise_filter #include "rclcpp_components/register_node_macro.hpp" RCLCPP_COMPONENTS_REGISTER_NODE( - radar_crossing_objects_noise_filter::RadarCrossingObjectsNoiseFilterNode) + autoware::radar_crossing_objects_noise_filter::RadarCrossingObjectsNoiseFilterNode) diff --git a/perception/radar_crossing_objects_noise_filter/include/radar_crossing_objects_noise_filter/radar_crossing_objects_noise_filter_node.hpp b/perception/radar_crossing_objects_noise_filter/src/radar_crossing_objects_noise_filter_node.hpp similarity index 82% rename from perception/radar_crossing_objects_noise_filter/include/radar_crossing_objects_noise_filter/radar_crossing_objects_noise_filter_node.hpp rename to perception/radar_crossing_objects_noise_filter/src/radar_crossing_objects_noise_filter_node.hpp index fbd64c884d396..6e389f563a138 100644 --- a/perception/radar_crossing_objects_noise_filter/include/radar_crossing_objects_noise_filter/radar_crossing_objects_noise_filter_node.hpp +++ b/perception/radar_crossing_objects_noise_filter/src/radar_crossing_objects_noise_filter_node.hpp @@ -12,8 +12,8 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef RADAR_CROSSING_OBJECTS_NOISE_FILTER__RADAR_CROSSING_OBJECTS_NOISE_FILTER_NODE_HPP_ -#define RADAR_CROSSING_OBJECTS_NOISE_FILTER__RADAR_CROSSING_OBJECTS_NOISE_FILTER_NODE_HPP_ +#ifndef RADAR_CROSSING_OBJECTS_NOISE_FILTER_NODE_HPP_ +#define RADAR_CROSSING_OBJECTS_NOISE_FILTER_NODE_HPP_ #include "rclcpp/rclcpp.hpp" @@ -23,7 +23,7 @@ #include #include -namespace radar_crossing_objects_noise_filter +namespace autoware::radar_crossing_objects_noise_filter { using autoware_perception_msgs::msg::DetectedObject; using autoware_perception_msgs::msg::DetectedObjects; @@ -63,6 +63,6 @@ class RadarCrossingObjectsNoiseFilterNode : public rclcpp::Node bool isNoise(const DetectedObject & object); }; -} // namespace radar_crossing_objects_noise_filter +} // namespace autoware::radar_crossing_objects_noise_filter -#endif // RADAR_CROSSING_OBJECTS_NOISE_FILTER__RADAR_CROSSING_OBJECTS_NOISE_FILTER_NODE_HPP_ +#endif // RADAR_CROSSING_OBJECTS_NOISE_FILTER_NODE_HPP_ diff --git a/perception/radar_crossing_objects_noise_filter/test/test_radar_crossing_objects_noise_filter/test_radar_crossing_objects_filter_is_noise.cpp b/perception/radar_crossing_objects_noise_filter/test/test_radar_crossing_objects_noise_filter/test_radar_crossing_objects_filter_is_noise.cpp index 44ccaae8ee7fc..93475af7b3628 100644 --- a/perception/radar_crossing_objects_noise_filter/test/test_radar_crossing_objects_noise_filter/test_radar_crossing_objects_filter_is_noise.cpp +++ b/perception/radar_crossing_objects_noise_filter/test/test_radar_crossing_objects_noise_filter/test_radar_crossing_objects_filter_is_noise.cpp @@ -12,24 +12,24 @@ // See the License for the specific language governing permissions and // limitations under the License. +#include "../../src/radar_crossing_objects_noise_filter_node.hpp" #include "autoware/universe_utils/geometry/geometry.hpp" #include "autoware/universe_utils/math/unit_conversion.hpp" -#include "radar_crossing_objects_noise_filter/radar_crossing_objects_noise_filter_node.hpp" #include #include -std::shared_ptr get_node( - double angle_threshold, double velocity_threshold) +std::shared_ptr +get_node(double angle_threshold, double velocity_threshold) { rclcpp::NodeOptions node_options; node_options.parameter_overrides( {{"angle_threshold", angle_threshold}, {"velocity_threshold", velocity_threshold}}); - auto node = - std::make_shared( - node_options); + auto node = std::make_shared< + autoware::radar_crossing_objects_noise_filter::RadarCrossingObjectsNoiseFilterNode>( + node_options); return node; } diff --git a/perception/radar_fusion_to_detected_object/CMakeLists.txt b/perception/radar_fusion_to_detected_object/CMakeLists.txt index c8297567511dd..76c27d3958841 100644 --- a/perception/radar_fusion_to_detected_object/CMakeLists.txt +++ b/perception/radar_fusion_to_detected_object/CMakeLists.txt @@ -6,14 +6,14 @@ find_package(autoware_cmake REQUIRED) autoware_package() # Targets -ament_auto_add_library(radar_object_fusion_to_detected_object_node_component SHARED - src/radar_object_fusion_to_detected_object_node/radar_object_fusion_to_detected_object_node.cpp +ament_auto_add_library(${PROJECT_NAME} SHARED + src/node.cpp src/radar_fusion_to_detected_object.cpp ) -rclcpp_components_register_node(radar_object_fusion_to_detected_object_node_component - PLUGIN "radar_fusion_to_detected_object::RadarObjectFusionToDetectedObjectNode" - EXECUTABLE radar_object_fusion_to_detected_object_node +rclcpp_components_register_node(${PROJECT_NAME} + PLUGIN "autoware::radar_fusion_to_detected_object::RadarObjectFusionToDetectedObjectNode" + EXECUTABLE radar_fusion_to_detected_object_node ) # Tests diff --git a/perception/radar_fusion_to_detected_object/launch/radar_object_fusion_to_detected_object.launch.xml b/perception/radar_fusion_to_detected_object/launch/radar_object_fusion_to_detected_object.launch.xml index d43f6b9655ae5..e5505ad87d9ec 100644 --- a/perception/radar_fusion_to_detected_object/launch/radar_object_fusion_to_detected_object.launch.xml +++ b/perception/radar_fusion_to_detected_object/launch/radar_object_fusion_to_detected_object.launch.xml @@ -8,7 +8,7 @@ - + diff --git a/perception/radar_fusion_to_detected_object/package.xml b/perception/radar_fusion_to_detected_object/package.xml index aa0711f1cc6ce..2094ff7770555 100644 --- a/perception/radar_fusion_to_detected_object/package.xml +++ b/perception/radar_fusion_to_detected_object/package.xml @@ -22,7 +22,6 @@ message_filters rclcpp rclcpp_components - std_msgs ament_lint_common autoware_lint_common diff --git a/perception/radar_fusion_to_detected_object/include/radar_fusion_to_detected_object/radar_fusion_to_detected_object_node.hpp b/perception/radar_fusion_to_detected_object/src/include/node.hpp similarity index 86% rename from perception/radar_fusion_to_detected_object/include/radar_fusion_to_detected_object/radar_fusion_to_detected_object_node.hpp rename to perception/radar_fusion_to_detected_object/src/include/node.hpp index 96794509bdfc3..3f181da0041cb 100644 --- a/perception/radar_fusion_to_detected_object/include/radar_fusion_to_detected_object/radar_fusion_to_detected_object_node.hpp +++ b/perception/radar_fusion_to_detected_object/src/include/node.hpp @@ -13,13 +13,13 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef RADAR_FUSION_TO_DETECTED_OBJECT__RADAR_FUSION_TO_DETECTED_OBJECT_NODE_HPP_ -#define RADAR_FUSION_TO_DETECTED_OBJECT__RADAR_FUSION_TO_DETECTED_OBJECT_NODE_HPP_ +#ifndef NODE_HPP_ +#define NODE_HPP_ #include "message_filters/subscriber.h" #include "message_filters/sync_policies/approximate_time.h" #include "message_filters/synchronizer.h" -#include "radar_fusion_to_detected_object/radar_fusion_to_detected_object.hpp" +#include "radar_fusion_to_detected_object.hpp" #include "rclcpp/rclcpp.hpp" #include "autoware_perception_msgs/msg/detected_objects.hpp" @@ -29,7 +29,7 @@ #include #include -namespace radar_fusion_to_detected_object +namespace autoware::radar_fusion_to_detected_object { using autoware_perception_msgs::msg::DetectedObject; using autoware_perception_msgs::msg::DetectedObjects; @@ -87,6 +87,6 @@ class RadarObjectFusionToDetectedObjectNode : public rclcpp::Node const DetectedObject & radar_object, const std_msgs::msg::Header & header_); }; -} // namespace radar_fusion_to_detected_object +} // namespace autoware::radar_fusion_to_detected_object -#endif // RADAR_FUSION_TO_DETECTED_OBJECT__RADAR_FUSION_TO_DETECTED_OBJECT_NODE_HPP_ +#endif // NODE_HPP_ diff --git a/perception/radar_fusion_to_detected_object/include/radar_fusion_to_detected_object/radar_fusion_to_detected_object.hpp b/perception/radar_fusion_to_detected_object/src/include/radar_fusion_to_detected_object.hpp similarity index 91% rename from perception/radar_fusion_to_detected_object/include/radar_fusion_to_detected_object/radar_fusion_to_detected_object.hpp rename to perception/radar_fusion_to_detected_object/src/include/radar_fusion_to_detected_object.hpp index cd7e7de6b2ff4..545b7d7e41b2a 100644 --- a/perception/radar_fusion_to_detected_object/include/radar_fusion_to_detected_object/radar_fusion_to_detected_object.hpp +++ b/perception/radar_fusion_to_detected_object/src/include/radar_fusion_to_detected_object.hpp @@ -12,25 +12,26 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef RADAR_FUSION_TO_DETECTED_OBJECT__RADAR_FUSION_TO_DETECTED_OBJECT_HPP_ -#define RADAR_FUSION_TO_DETECTED_OBJECT__RADAR_FUSION_TO_DETECTED_OBJECT_HPP_ +#ifndef RADAR_FUSION_TO_DETECTED_OBJECT_HPP_ +#define RADAR_FUSION_TO_DETECTED_OBJECT_HPP_ + +#define EIGEN_MPL2_ONLY #include "autoware/universe_utils/geometry/boost_geometry.hpp" #include "rclcpp/logger.hpp" -#define EIGEN_MPL2_ONLY + #include #include #include "autoware_perception_msgs/msg/detected_objects.hpp" #include "geometry_msgs/msg/pose_with_covariance.hpp" #include "geometry_msgs/msg/twist_with_covariance.hpp" -// #include "std_msgs/msg/header.hpp" #include #include #include -namespace radar_fusion_to_detected_object +namespace autoware::radar_fusion_to_detected_object { using autoware::universe_utils::LinearRing2d; using autoware::universe_utils::Point2d; @@ -113,6 +114,6 @@ class RadarFusionToDetectedObject double getTwistNorm(const Twist & twist); LinearRing2d createObject2dWithMargin(const Point2d object_size, const double margin); }; -} // namespace radar_fusion_to_detected_object +} // namespace autoware::radar_fusion_to_detected_object -#endif // RADAR_FUSION_TO_DETECTED_OBJECT__RADAR_FUSION_TO_DETECTED_OBJECT_HPP_ +#endif // RADAR_FUSION_TO_DETECTED_OBJECT_HPP_ diff --git a/perception/radar_fusion_to_detected_object/src/radar_object_fusion_to_detected_object_node/radar_object_fusion_to_detected_object_node.cpp b/perception/radar_fusion_to_detected_object/src/node.cpp similarity index 96% rename from perception/radar_fusion_to_detected_object/src/radar_object_fusion_to_detected_object_node/radar_object_fusion_to_detected_object_node.cpp rename to perception/radar_fusion_to_detected_object/src/node.cpp index 90851674ef9eb..58e893032340c 100644 --- a/perception/radar_fusion_to_detected_object/src/radar_object_fusion_to_detected_object_node/radar_object_fusion_to_detected_object_node.cpp +++ b/perception/radar_fusion_to_detected_object/src/node.cpp @@ -13,7 +13,8 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "radar_fusion_to_detected_object/radar_fusion_to_detected_object_node.hpp" +#include "include/node.hpp" + #include "rclcpp/rclcpp.hpp" #include @@ -46,7 +47,7 @@ bool update_param( } } // namespace -namespace radar_fusion_to_detected_object +namespace autoware::radar_fusion_to_detected_object { using autoware_perception_msgs::msg::DetectedObject; using autoware_perception_msgs::msg::DetectedObjects; @@ -217,8 +218,8 @@ RadarFusionToDetectedObject::RadarInput RadarObjectFusionToDetectedObjectNode::s return output; } -} // namespace radar_fusion_to_detected_object +} // namespace autoware::radar_fusion_to_detected_object #include "rclcpp_components/register_node_macro.hpp" RCLCPP_COMPONENTS_REGISTER_NODE( - radar_fusion_to_detected_object::RadarObjectFusionToDetectedObjectNode) + autoware::radar_fusion_to_detected_object::RadarObjectFusionToDetectedObjectNode) diff --git a/perception/radar_fusion_to_detected_object/src/radar_fusion_to_detected_object.cpp b/perception/radar_fusion_to_detected_object/src/radar_fusion_to_detected_object.cpp index 5a0c1b5412312..0c90a468f2ebd 100644 --- a/perception/radar_fusion_to_detected_object/src/radar_fusion_to_detected_object.cpp +++ b/perception/radar_fusion_to_detected_object/src/radar_fusion_to_detected_object.cpp @@ -13,10 +13,10 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "radar_fusion_to_detected_object/radar_fusion_to_detected_object.hpp" +#include "include/radar_fusion_to_detected_object.hpp" -#include -#include +#include "autoware/universe_utils/geometry/geometry.hpp" +#include "autoware/universe_utils/math/normalization.hpp" #include @@ -27,7 +27,7 @@ #include #include -namespace radar_fusion_to_detected_object +namespace autoware::radar_fusion_to_detected_object { using autoware::universe_utils::LinearRing2d; using autoware::universe_utils::Point2d; @@ -358,4 +358,4 @@ LinearRing2d RadarFusionToDetectedObject::createObject2dWithMargin( return box; } -} // namespace radar_fusion_to_detected_object +} // namespace autoware::radar_fusion_to_detected_object diff --git a/perception/radar_object_clustering/CMakeLists.txt b/perception/radar_object_clustering/CMakeLists.txt index ea25353d3e1d7..9a54bbb0dae43 100644 --- a/perception/radar_object_clustering/CMakeLists.txt +++ b/perception/radar_object_clustering/CMakeLists.txt @@ -6,12 +6,12 @@ find_package(autoware_cmake REQUIRED) autoware_package() # Targets -ament_auto_add_library(radar_object_clustering_node_component SHARED - src/radar_object_clustering_node/radar_object_clustering_node.cpp +ament_auto_add_library(${PROJECT_NAME} SHARED + src/radar_object_clustering_node.cpp ) -rclcpp_components_register_node(radar_object_clustering_node_component - PLUGIN "radar_object_clustering::RadarObjectClusteringNode" +rclcpp_components_register_node(${PROJECT_NAME} + PLUGIN "autoware::radar_object_clustering::RadarObjectClusteringNode" EXECUTABLE radar_object_clustering_node ) diff --git a/perception/radar_object_clustering/src/radar_object_clustering_node/radar_object_clustering_node.cpp b/perception/radar_object_clustering/src/radar_object_clustering_node.cpp similarity index 97% rename from perception/radar_object_clustering/src/radar_object_clustering_node/radar_object_clustering_node.cpp rename to perception/radar_object_clustering/src/radar_object_clustering_node.cpp index 339ab21741d29..b867d6a193ed7 100644 --- a/perception/radar_object_clustering/src/radar_object_clustering_node/radar_object_clustering_node.cpp +++ b/perception/radar_object_clustering/src/radar_object_clustering_node.cpp @@ -12,7 +12,7 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "radar_object_clustering/radar_object_clustering_node.hpp" +#include "radar_object_clustering_node.hpp" #include "autoware/universe_utils/geometry/geometry.hpp" #include "autoware/universe_utils/math/unit_conversion.hpp" @@ -20,17 +20,18 @@ #include -#include -#include -#include -#include - #ifdef ROS_DISTRO_GALACTIC #include #else #include #endif +#include +#include +#include +#include +#include + namespace { template @@ -58,7 +59,7 @@ double get_distance(const autoware_perception_msgs::msg::DetectedObject & object } // namespace -namespace radar_object_clustering +namespace autoware::radar_object_clustering { using autoware_perception_msgs::msg::DetectedObject; using autoware_perception_msgs::msg::DetectedObjects; @@ -229,7 +230,7 @@ rcl_interfaces::msg::SetParametersResult RadarObjectClusteringNode::onSetParam( result.reason = "success"; return result; } -} // namespace radar_object_clustering +} // namespace autoware::radar_object_clustering #include "rclcpp_components/register_node_macro.hpp" -RCLCPP_COMPONENTS_REGISTER_NODE(radar_object_clustering::RadarObjectClusteringNode) +RCLCPP_COMPONENTS_REGISTER_NODE(autoware::radar_object_clustering::RadarObjectClusteringNode) diff --git a/perception/radar_object_clustering/include/radar_object_clustering/radar_object_clustering_node.hpp b/perception/radar_object_clustering/src/radar_object_clustering_node.hpp similarity index 87% rename from perception/radar_object_clustering/include/radar_object_clustering/radar_object_clustering_node.hpp rename to perception/radar_object_clustering/src/radar_object_clustering_node.hpp index 38a2235a117eb..13284a4151f49 100644 --- a/perception/radar_object_clustering/include/radar_object_clustering/radar_object_clustering_node.hpp +++ b/perception/radar_object_clustering/src/radar_object_clustering_node.hpp @@ -12,8 +12,8 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef RADAR_OBJECT_CLUSTERING__RADAR_OBJECT_CLUSTERING_NODE_HPP_ -#define RADAR_OBJECT_CLUSTERING__RADAR_OBJECT_CLUSTERING_NODE_HPP_ +#ifndef RADAR_OBJECT_CLUSTERING_NODE_HPP_ +#define RADAR_OBJECT_CLUSTERING_NODE_HPP_ #include "rclcpp/rclcpp.hpp" @@ -24,7 +24,7 @@ #include #include -namespace radar_object_clustering +namespace autoware::radar_object_clustering { using autoware_perception_msgs::msg::DetectedObject; using autoware_perception_msgs::msg::DetectedObjects; @@ -72,6 +72,6 @@ class RadarObjectClusteringNode : public rclcpp::Node bool isSameObject(const DetectedObject & object_1, const DetectedObject & object_2); }; -} // namespace radar_object_clustering +} // namespace autoware::radar_object_clustering -#endif // RADAR_OBJECT_CLUSTERING__RADAR_OBJECT_CLUSTERING_NODE_HPP_ +#endif // RADAR_OBJECT_CLUSTERING_NODE_HPP_ diff --git a/perception/radar_object_tracker/CMakeLists.txt b/perception/radar_object_tracker/CMakeLists.txt index ade45847f8a9f..b732ef6cc863c 100644 --- a/perception/radar_object_tracker/CMakeLists.txt +++ b/perception/radar_object_tracker/CMakeLists.txt @@ -20,31 +20,31 @@ include_directories( ${EIGEN3_INCLUDE_DIR} ) -ament_auto_add_library(radar_object_tracker_utils SHARED +ament_auto_add_library(${PROJECT_NAME}_utils SHARED src/utils/utils.cpp src/utils/radar_object_tracker_utils.cpp ) -ament_auto_add_library(radar_object_tracker_node SHARED - src/radar_object_tracker_node/radar_object_tracker_node.cpp +ament_auto_add_library(${PROJECT_NAME} SHARED + src/radar_object_tracker_node.cpp src/tracker/model/tracker_base.cpp src/tracker/model/linear_motion_tracker.cpp src/tracker/model/constant_turn_rate_motion_tracker.cpp - src/data_association/data_association.cpp - src/data_association/mu_successive_shortest_path/mu_successive_shortest_path_wrapper.cpp + src/association/data_association.cpp + src/association/mu_ssp/mu_successive_shortest_path_wrapper.cpp ) -target_link_libraries(radar_object_tracker_node +target_link_libraries(${PROJECT_NAME} Eigen3::Eigen yaml-cpp nlohmann_json::nlohmann_json # for debug glog::glog - radar_object_tracker_utils + ${PROJECT_NAME}_utils ) -rclcpp_components_register_node(radar_object_tracker_node - PLUGIN "RadarObjectTrackerNode" - EXECUTABLE radar_object_tracker +rclcpp_components_register_node(${PROJECT_NAME} + PLUGIN "autoware::radar_object_tracker::RadarObjectTrackerNode" + EXECUTABLE radar_object_tracker_node ) # testing @@ -52,15 +52,15 @@ if(BUILD_TESTING) find_package(ament_lint_auto REQUIRED) ament_lint_auto_find_test_dependencies() - ament_add_gtest(test_radar_object_tracker_utils + ament_add_gtest(test_${PROJECT_NAME}_utils test/test_radar_object_tracker_utils.cpp src/utils/radar_object_tracker_utils.cpp ) - target_include_directories(test_radar_object_tracker_utils PRIVATE + target_include_directories(test_${PROJECT_NAME}_utils PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include ) - target_link_libraries(test_radar_object_tracker_utils - radar_object_tracker_utils + target_link_libraries(test_${PROJECT_NAME}_utils + ${PROJECT_NAME}_utils ) endif() diff --git a/perception/radar_object_tracker/include/radar_object_tracker/data_association/data_association.hpp b/perception/radar_object_tracker/include/autoware_radar_object_tracker/association/data_association.hpp similarity index 78% rename from perception/radar_object_tracker/include/radar_object_tracker/data_association/data_association.hpp rename to perception/radar_object_tracker/include/autoware_radar_object_tracker/association/data_association.hpp index 434fe66f13790..efde1e6763cdd 100644 --- a/perception/radar_object_tracker/include/radar_object_tracker/data_association/data_association.hpp +++ b/perception/radar_object_tracker/include/autoware_radar_object_tracker/association/data_association.hpp @@ -16,25 +16,27 @@ // Author: v1.0 Yukihiro Saito // -#ifndef RADAR_OBJECT_TRACKER__DATA_ASSOCIATION__DATA_ASSOCIATION_HPP_ -#define RADAR_OBJECT_TRACKER__DATA_ASSOCIATION__DATA_ASSOCIATION_HPP_ - -#include -#include -#include -#include +#ifndef AUTOWARE_RADAR_OBJECT_TRACKER__ASSOCIATION__DATA_ASSOCIATION_HPP_ +#define AUTOWARE_RADAR_OBJECT_TRACKER__ASSOCIATION__DATA_ASSOCIATION_HPP_ #define EIGEN_MPL2_ONLY + +#include "autoware_radar_object_tracker/association/solver/gnn_solver.hpp" +#include "autoware_radar_object_tracker/tracker/tracker.hpp" #include "object_recognition_utils/object_recognition_utils.hpp" -#include "radar_object_tracker/data_association/solver/gnn_solver.hpp" -#include "radar_object_tracker/tracker/tracker.hpp" #include #include -#include +#include "autoware_perception_msgs/msg/detected_objects.hpp" +#include +#include #include +#include +#include +namespace autoware::radar_object_tracker +{ class DataAssociation { private: @@ -62,5 +64,5 @@ class DataAssociation const std::string & file_name); virtual ~DataAssociation() {} }; - -#endif // RADAR_OBJECT_TRACKER__DATA_ASSOCIATION__DATA_ASSOCIATION_HPP_ +} // namespace autoware::radar_object_tracker +#endif // AUTOWARE_RADAR_OBJECT_TRACKER__ASSOCIATION__DATA_ASSOCIATION_HPP_ diff --git a/perception/radar_object_tracker/include/radar_object_tracker/data_association/solver/gnn_solver.hpp b/perception/radar_object_tracker/include/autoware_radar_object_tracker/association/solver/gnn_solver.hpp similarity index 55% rename from perception/radar_object_tracker/include/radar_object_tracker/data_association/solver/gnn_solver.hpp rename to perception/radar_object_tracker/include/autoware_radar_object_tracker/association/solver/gnn_solver.hpp index 6d531ac2ee8c2..2aa4a577ef91c 100644 --- a/perception/radar_object_tracker/include/radar_object_tracker/data_association/solver/gnn_solver.hpp +++ b/perception/radar_object_tracker/include/autoware_radar_object_tracker/association/solver/gnn_solver.hpp @@ -12,11 +12,11 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef RADAR_OBJECT_TRACKER__DATA_ASSOCIATION__SOLVER__GNN_SOLVER_HPP_ -#define RADAR_OBJECT_TRACKER__DATA_ASSOCIATION__SOLVER__GNN_SOLVER_HPP_ +#ifndef AUTOWARE_RADAR_OBJECT_TRACKER__ASSOCIATION__SOLVER__GNN_SOLVER_HPP_ +#define AUTOWARE_RADAR_OBJECT_TRACKER__ASSOCIATION__SOLVER__GNN_SOLVER_HPP_ -#include "radar_object_tracker/data_association/solver/gnn_solver_interface.hpp" -#include "radar_object_tracker/data_association/solver/mu_successive_shortest_path.hpp" -#include "radar_object_tracker/data_association/solver/successive_shortest_path.hpp" +#include "autoware_radar_object_tracker/association/solver/gnn_solver_interface.hpp" +#include "autoware_radar_object_tracker/association/solver/mu_ssp.hpp" +#include "autoware_radar_object_tracker/association/solver/ssp.hpp" -#endif // RADAR_OBJECT_TRACKER__DATA_ASSOCIATION__SOLVER__GNN_SOLVER_HPP_ +#endif // AUTOWARE_RADAR_OBJECT_TRACKER__ASSOCIATION__SOLVER__GNN_SOLVER_HPP_ diff --git a/perception/object_merger/include/object_merger/data_association/solver/gnn_solver_interface.hpp b/perception/radar_object_tracker/include/autoware_radar_object_tracker/association/solver/gnn_solver_interface.hpp similarity index 71% rename from perception/object_merger/include/object_merger/data_association/solver/gnn_solver_interface.hpp rename to perception/radar_object_tracker/include/autoware_radar_object_tracker/association/solver/gnn_solver_interface.hpp index 447f4fa2b7ce3..ee2e98ba47278 100644 --- a/perception/object_merger/include/object_merger/data_association/solver/gnn_solver_interface.hpp +++ b/perception/radar_object_tracker/include/autoware_radar_object_tracker/association/solver/gnn_solver_interface.hpp @@ -12,13 +12,13 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef OBJECT_MERGER__DATA_ASSOCIATION__SOLVER__GNN_SOLVER_INTERFACE_HPP_ -#define OBJECT_MERGER__DATA_ASSOCIATION__SOLVER__GNN_SOLVER_INTERFACE_HPP_ +#ifndef AUTOWARE_RADAR_OBJECT_TRACKER__ASSOCIATION__SOLVER__GNN_SOLVER_INTERFACE_HPP_ +#define AUTOWARE_RADAR_OBJECT_TRACKER__ASSOCIATION__SOLVER__GNN_SOLVER_INTERFACE_HPP_ #include #include -namespace gnn_solver +namespace autoware::radar_object_tracker::gnn_solver { class GnnSolverInterface { @@ -30,6 +30,6 @@ class GnnSolverInterface const std::vector> & cost, std::unordered_map * direct_assignment, std::unordered_map * reverse_assignment) = 0; }; -} // namespace gnn_solver +} // namespace autoware::radar_object_tracker::gnn_solver -#endif // OBJECT_MERGER__DATA_ASSOCIATION__SOLVER__GNN_SOLVER_INTERFACE_HPP_ +#endif // AUTOWARE_RADAR_OBJECT_TRACKER__ASSOCIATION__SOLVER__GNN_SOLVER_INTERFACE_HPP_ diff --git a/perception/radar_object_tracker/include/radar_object_tracker/data_association/solver/mu_successive_shortest_path.hpp b/perception/radar_object_tracker/include/autoware_radar_object_tracker/association/solver/mu_ssp.hpp similarity index 69% rename from perception/radar_object_tracker/include/radar_object_tracker/data_association/solver/mu_successive_shortest_path.hpp rename to perception/radar_object_tracker/include/autoware_radar_object_tracker/association/solver/mu_ssp.hpp index d4bdd72248670..2a5a336a3eb87 100644 --- a/perception/radar_object_tracker/include/radar_object_tracker/data_association/solver/mu_successive_shortest_path.hpp +++ b/perception/radar_object_tracker/include/autoware_radar_object_tracker/association/solver/mu_ssp.hpp @@ -12,15 +12,15 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef RADAR_OBJECT_TRACKER__DATA_ASSOCIATION__SOLVER__MU_SUCCESSIVE_SHORTEST_PATH_HPP_ -#define RADAR_OBJECT_TRACKER__DATA_ASSOCIATION__SOLVER__MU_SUCCESSIVE_SHORTEST_PATH_HPP_ +#ifndef AUTOWARE_RADAR_OBJECT_TRACKER__ASSOCIATION__SOLVER__MU_SSP_HPP_ +#define AUTOWARE_RADAR_OBJECT_TRACKER__ASSOCIATION__SOLVER__MU_SSP_HPP_ -#include "radar_object_tracker/data_association/solver/gnn_solver_interface.hpp" +#include "autoware_radar_object_tracker/association/solver/gnn_solver_interface.hpp" #include #include -namespace gnn_solver +namespace autoware::radar_object_tracker::gnn_solver { class MuSSP : public GnnSolverInterface { @@ -32,6 +32,6 @@ class MuSSP : public GnnSolverInterface const std::vector> & cost, std::unordered_map * direct_assignment, std::unordered_map * reverse_assignment) override; }; -} // namespace gnn_solver +} // namespace autoware::radar_object_tracker::gnn_solver -#endif // RADAR_OBJECT_TRACKER__DATA_ASSOCIATION__SOLVER__MU_SUCCESSIVE_SHORTEST_PATH_HPP_ +#endif // AUTOWARE_RADAR_OBJECT_TRACKER__ASSOCIATION__SOLVER__MU_SSP_HPP_ diff --git a/perception/radar_object_tracker/include/radar_object_tracker/data_association/solver/successive_shortest_path.hpp b/perception/radar_object_tracker/include/autoware_radar_object_tracker/association/solver/ssp.hpp similarity index 69% rename from perception/radar_object_tracker/include/radar_object_tracker/data_association/solver/successive_shortest_path.hpp rename to perception/radar_object_tracker/include/autoware_radar_object_tracker/association/solver/ssp.hpp index b08e3c0d302a7..c956e1ebd2f6d 100644 --- a/perception/radar_object_tracker/include/radar_object_tracker/data_association/solver/successive_shortest_path.hpp +++ b/perception/radar_object_tracker/include/autoware_radar_object_tracker/association/solver/ssp.hpp @@ -12,15 +12,15 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef RADAR_OBJECT_TRACKER__DATA_ASSOCIATION__SOLVER__SUCCESSIVE_SHORTEST_PATH_HPP_ -#define RADAR_OBJECT_TRACKER__DATA_ASSOCIATION__SOLVER__SUCCESSIVE_SHORTEST_PATH_HPP_ +#ifndef AUTOWARE_RADAR_OBJECT_TRACKER__ASSOCIATION__SOLVER__SSP_HPP_ +#define AUTOWARE_RADAR_OBJECT_TRACKER__ASSOCIATION__SOLVER__SSP_HPP_ -#include "radar_object_tracker/data_association/solver/gnn_solver_interface.hpp" +#include "autoware_radar_object_tracker/association/solver/gnn_solver_interface.hpp" #include #include -namespace gnn_solver +namespace autoware::radar_object_tracker::gnn_solver { class SSP : public GnnSolverInterface { @@ -32,6 +32,6 @@ class SSP : public GnnSolverInterface const std::vector> & cost, std::unordered_map * direct_assignment, std::unordered_map * reverse_assignment) override; }; -} // namespace gnn_solver +} // namespace autoware::radar_object_tracker::gnn_solver -#endif // RADAR_OBJECT_TRACKER__DATA_ASSOCIATION__SOLVER__SUCCESSIVE_SHORTEST_PATH_HPP_ +#endif // AUTOWARE_RADAR_OBJECT_TRACKER__ASSOCIATION__SOLVER__SSP_HPP_ diff --git a/perception/radar_object_tracker/include/radar_object_tracker/tracker/model/constant_turn_rate_motion_tracker.hpp b/perception/radar_object_tracker/include/autoware_radar_object_tracker/tracker/model/constant_turn_rate_motion_tracker.hpp similarity index 86% rename from perception/radar_object_tracker/include/radar_object_tracker/tracker/model/constant_turn_rate_motion_tracker.hpp rename to perception/radar_object_tracker/include/autoware_radar_object_tracker/tracker/model/constant_turn_rate_motion_tracker.hpp index 7294f8745bec4..dab4d3f8efa24 100644 --- a/perception/radar_object_tracker/include/radar_object_tracker/tracker/model/constant_turn_rate_motion_tracker.hpp +++ b/perception/radar_object_tracker/include/autoware_radar_object_tracker/tracker/model/constant_turn_rate_motion_tracker.hpp @@ -12,15 +12,15 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef RADAR_OBJECT_TRACKER__TRACKER__MODEL__CONSTANT_TURN_RATE_MOTION_TRACKER_HPP_ -#define RADAR_OBJECT_TRACKER__TRACKER__MODEL__CONSTANT_TURN_RATE_MOTION_TRACKER_HPP_ +#ifndef AUTOWARE_RADAR_OBJECT_TRACKER__TRACKER__MODEL__CONSTANT_TURN_RATE_MOTION_TRACKER_HPP_ +#define AUTOWARE_RADAR_OBJECT_TRACKER__TRACKER__MODEL__CONSTANT_TURN_RATE_MOTION_TRACKER_HPP_ -#include "radar_object_tracker/tracker/model/tracker_base.hpp" - -#include +#include "autoware_radar_object_tracker/tracker/model/tracker_base.hpp" +#include "kalman_filter/kalman_filter.hpp" #include - +namespace autoware::radar_object_tracker +{ using Label = autoware_perception_msgs::msg::ObjectClassification; class ConstantTurnRateMotionTracker : public Tracker // means constant turn rate motion tracker { @@ -108,5 +108,5 @@ class ConstantTurnRateMotionTracker : public Tracker // means constant turn rat autoware_perception_msgs::msg::TrackedObject & object) const override; virtual ~ConstantTurnRateMotionTracker() {} }; - -#endif // RADAR_OBJECT_TRACKER__TRACKER__MODEL__CONSTANT_TURN_RATE_MOTION_TRACKER_HPP_ +} // namespace autoware::radar_object_tracker +#endif // AUTOWARE_RADAR_OBJECT_TRACKER__TRACKER__MODEL__CONSTANT_TURN_RATE_MOTION_TRACKER_HPP_ diff --git a/perception/radar_object_tracker/include/radar_object_tracker/tracker/model/linear_motion_tracker.hpp b/perception/radar_object_tracker/include/autoware_radar_object_tracker/tracker/model/linear_motion_tracker.hpp similarity index 87% rename from perception/radar_object_tracker/include/radar_object_tracker/tracker/model/linear_motion_tracker.hpp rename to perception/radar_object_tracker/include/autoware_radar_object_tracker/tracker/model/linear_motion_tracker.hpp index 39b0eaa417c88..7c96aa8fbaa36 100644 --- a/perception/radar_object_tracker/include/radar_object_tracker/tracker/model/linear_motion_tracker.hpp +++ b/perception/radar_object_tracker/include/autoware_radar_object_tracker/tracker/model/linear_motion_tracker.hpp @@ -12,15 +12,17 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef RADAR_OBJECT_TRACKER__TRACKER__MODEL__LINEAR_MOTION_TRACKER_HPP_ -#define RADAR_OBJECT_TRACKER__TRACKER__MODEL__LINEAR_MOTION_TRACKER_HPP_ +#ifndef AUTOWARE_RADAR_OBJECT_TRACKER__TRACKER__MODEL__LINEAR_MOTION_TRACKER_HPP_ +#define AUTOWARE_RADAR_OBJECT_TRACKER__TRACKER__MODEL__LINEAR_MOTION_TRACKER_HPP_ -#include "radar_object_tracker/tracker/model/tracker_base.hpp" - -#include +#include "autoware_radar_object_tracker/tracker/model/tracker_base.hpp" +#include "kalman_filter/kalman_filter.hpp" #include +namespace autoware::radar_object_tracker +{ + using Label = autoware_perception_msgs::msg::ObjectClassification; class LinearMotionTracker : public Tracker { @@ -111,5 +113,5 @@ class LinearMotionTracker : public Tracker autoware_perception_msgs::msg::TrackedObject & object) const override; virtual ~LinearMotionTracker() {} }; - -#endif // RADAR_OBJECT_TRACKER__TRACKER__MODEL__LINEAR_MOTION_TRACKER_HPP_ +} // namespace autoware::radar_object_tracker +#endif // AUTOWARE_RADAR_OBJECT_TRACKER__TRACKER__MODEL__LINEAR_MOTION_TRACKER_HPP_ diff --git a/perception/radar_object_tracker/include/radar_object_tracker/tracker/model/tracker_base.hpp b/perception/radar_object_tracker/include/autoware_radar_object_tracker/tracker/model/tracker_base.hpp similarity index 89% rename from perception/radar_object_tracker/include/radar_object_tracker/tracker/model/tracker_base.hpp rename to perception/radar_object_tracker/include/autoware_radar_object_tracker/tracker/model/tracker_base.hpp index 3135aa45b87b4..caea725ef8f81 100644 --- a/perception/radar_object_tracker/include/radar_object_tracker/tracker/model/tracker_base.hpp +++ b/perception/radar_object_tracker/include/autoware_radar_object_tracker/tracker/model/tracker_base.hpp @@ -16,12 +16,12 @@ // Author: v1.0 Yukihiro Saito // -#ifndef RADAR_OBJECT_TRACKER__TRACKER__MODEL__TRACKER_BASE_HPP_ -#define RADAR_OBJECT_TRACKER__TRACKER__MODEL__TRACKER_BASE_HPP_ +#ifndef AUTOWARE_RADAR_OBJECT_TRACKER__TRACKER__MODEL__TRACKER_BASE_HPP_ +#define AUTOWARE_RADAR_OBJECT_TRACKER__TRACKER__MODEL__TRACKER_BASE_HPP_ #define EIGEN_MPL2_ONLY +#include "autoware_radar_object_tracker/utils/utils.hpp" #include "object_recognition_utils/object_recognition_utils.hpp" -#include "radar_object_tracker/utils/utils.hpp" #include #include @@ -33,6 +33,8 @@ #include +namespace autoware::radar_object_tracker +{ class Tracker { protected: @@ -92,5 +94,5 @@ class Tracker const rclcpp::Time & time, autoware_perception_msgs::msg::TrackedObject & object) const = 0; virtual bool predict(const rclcpp::Time & time) = 0; }; - -#endif // RADAR_OBJECT_TRACKER__TRACKER__MODEL__TRACKER_BASE_HPP_ +} // namespace autoware::radar_object_tracker +#endif // AUTOWARE_RADAR_OBJECT_TRACKER__TRACKER__MODEL__TRACKER_BASE_HPP_ diff --git a/perception/radar_object_tracker/include/radar_object_tracker/tracker/tracker.hpp b/perception/radar_object_tracker/include/autoware_radar_object_tracker/tracker/tracker.hpp similarity index 79% rename from perception/radar_object_tracker/include/radar_object_tracker/tracker/tracker.hpp rename to perception/radar_object_tracker/include/autoware_radar_object_tracker/tracker/tracker.hpp index 70045e6b16a07..26222f4c65679 100644 --- a/perception/radar_object_tracker/include/radar_object_tracker/tracker/tracker.hpp +++ b/perception/radar_object_tracker/include/autoware_radar_object_tracker/tracker/tracker.hpp @@ -12,11 +12,11 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef RADAR_OBJECT_TRACKER__TRACKER__TRACKER_HPP_ -#define RADAR_OBJECT_TRACKER__TRACKER__TRACKER_HPP_ +#ifndef AUTOWARE_RADAR_OBJECT_TRACKER__TRACKER__TRACKER_HPP_ +#define AUTOWARE_RADAR_OBJECT_TRACKER__TRACKER__TRACKER_HPP_ #include "model/constant_turn_rate_motion_tracker.hpp" #include "model/linear_motion_tracker.hpp" #include "model/tracker_base.hpp" -#endif // RADAR_OBJECT_TRACKER__TRACKER__TRACKER_HPP_ +#endif // AUTOWARE_RADAR_OBJECT_TRACKER__TRACKER__TRACKER_HPP_ diff --git a/perception/radar_object_tracker/include/radar_object_tracker/utils/radar_object_tracker_utils.hpp b/perception/radar_object_tracker/include/autoware_radar_object_tracker/utils/radar_object_tracker_utils.hpp similarity index 77% rename from perception/radar_object_tracker/include/radar_object_tracker/utils/radar_object_tracker_utils.hpp rename to perception/radar_object_tracker/include/autoware_radar_object_tracker/utils/radar_object_tracker_utils.hpp index e1c33c022e427..f4fb977d65de2 100644 --- a/perception/radar_object_tracker/include/radar_object_tracker/utils/radar_object_tracker_utils.hpp +++ b/perception/radar_object_tracker/include/autoware_radar_object_tracker/utils/radar_object_tracker_utils.hpp @@ -12,12 +12,17 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef RADAR_OBJECT_TRACKER__UTILS__RADAR_OBJECT_TRACKER_UTILS_HPP_ -#define RADAR_OBJECT_TRACKER__UTILS__RADAR_OBJECT_TRACKER_UTILS_HPP_ +#ifndef AUTOWARE_RADAR_OBJECT_TRACKER__UTILS__RADAR_OBJECT_TRACKER_UTILS_HPP_ +#define AUTOWARE_RADAR_OBJECT_TRACKER__UTILS__RADAR_OBJECT_TRACKER_UTILS_HPP_ -#include -#include +#include "autoware/universe_utils/geometry/geometry.hpp" +#include "autoware/universe_utils/math/unit_conversion.hpp" +#include +#include +#include + +#include "autoware_perception_msgs/msg/tracked_object.hpp" #include #include @@ -33,13 +38,6 @@ #else #include #endif - -#include -#include -#include - -#include - #include #include #include @@ -47,7 +45,8 @@ #include #include -namespace radar_object_tracker_utils + +namespace autoware::radar_object_tracker::utils { boost::optional getTransformAnonymous( @@ -72,6 +71,6 @@ bool hasValidVelocityDirectionToLanelet( const autoware_perception_msgs::msg::TrackedObject & object, const lanelet::ConstLanelets & lanelets, const double max_lateral_velocity); -} // namespace radar_object_tracker_utils +} // namespace autoware::radar_object_tracker::utils -#endif // RADAR_OBJECT_TRACKER__UTILS__RADAR_OBJECT_TRACKER_UTILS_HPP_ +#endif // AUTOWARE_RADAR_OBJECT_TRACKER__UTILS__RADAR_OBJECT_TRACKER_UTILS_HPP_ diff --git a/perception/radar_object_tracker/include/radar_object_tracker/utils/utils.hpp b/perception/radar_object_tracker/include/autoware_radar_object_tracker/utils/utils.hpp similarity index 53% rename from perception/radar_object_tracker/include/radar_object_tracker/utils/utils.hpp rename to perception/radar_object_tracker/include/autoware_radar_object_tracker/utils/utils.hpp index 019d684e8bd46..dd011ac274b64 100644 --- a/perception/radar_object_tracker/include/radar_object_tracker/utils/utils.hpp +++ b/perception/radar_object_tracker/include/autoware_radar_object_tracker/utils/utils.hpp @@ -16,16 +16,16 @@ // Author: v1.0 Yukihiro Saito // -#ifndef RADAR_OBJECT_TRACKER__UTILS__UTILS_HPP_ -#define RADAR_OBJECT_TRACKER__UTILS__UTILS_HPP_ +#ifndef AUTOWARE_RADAR_OBJECT_TRACKER__UTILS__UTILS_HPP_ +#define AUTOWARE_RADAR_OBJECT_TRACKER__UTILS__UTILS_HPP_ #include #include #include -#include -#include -#include +#include "autoware_perception_msgs/msg/detected_object.hpp" +#include "autoware_perception_msgs/msg/shape.hpp" +#include "autoware_perception_msgs/msg/tracked_object.hpp" #include #include @@ -33,52 +33,12 @@ #include #include #include - -namespace utils +namespace autoware::radar_object_tracker::utils { -enum MSG_COV_IDX { - X_X = 0, - X_Y = 1, - X_Z = 2, - X_ROLL = 3, - X_PITCH = 4, - X_YAW = 5, - Y_X = 6, - Y_Y = 7, - Y_Z = 8, - Y_ROLL = 9, - Y_PITCH = 10, - Y_YAW = 11, - Z_X = 12, - Z_Y = 13, - Z_Z = 14, - Z_ROLL = 15, - Z_PITCH = 16, - Z_YAW = 17, - ROLL_X = 18, - ROLL_Y = 19, - ROLL_Z = 20, - ROLL_ROLL = 21, - ROLL_PITCH = 22, - ROLL_YAW = 23, - PITCH_X = 24, - PITCH_Y = 25, - PITCH_Z = 26, - PITCH_ROLL = 27, - PITCH_PITCH = 28, - PITCH_YAW = 29, - YAW_X = 30, - YAW_Y = 31, - YAW_Z = 32, - YAW_ROLL = 33, - YAW_PITCH = 34, - YAW_YAW = 35 -}; - // matrix concatenate Eigen::MatrixXd stackMatricesVertically(const std::vector & matrices); Eigen::MatrixXd stackMatricesDiagonally(const std::vector & matrices); -} // namespace utils +} // namespace autoware::radar_object_tracker::utils -#endif // RADAR_OBJECT_TRACKER__UTILS__UTILS_HPP_ +#endif // AUTOWARE_RADAR_OBJECT_TRACKER__UTILS__UTILS_HPP_ diff --git a/perception/radar_object_tracker/launch/radar_object_tracker.launch.xml b/perception/radar_object_tracker/launch/radar_object_tracker.launch.xml index dfebf334456ea..313ef4b0f9fcd 100644 --- a/perception/radar_object_tracker/launch/radar_object_tracker.launch.xml +++ b/perception/radar_object_tracker/launch/radar_object_tracker.launch.xml @@ -7,7 +7,7 @@ - + diff --git a/perception/radar_object_tracker/package.xml b/perception/radar_object_tracker/package.xml index c3c4b33946b56..79ae0385d4b5e 100644 --- a/perception/radar_object_tracker/package.xml +++ b/perception/radar_object_tracker/package.xml @@ -14,12 +14,12 @@ autoware_cmake eigen3_cmake_module + autoware_lanelet2_extension autoware_perception_msgs autoware_universe_utils eigen glog kalman_filter - lanelet2_extension mussp nlohmann-json-dev object_recognition_utils @@ -27,7 +27,6 @@ rclcpp_components tf2 tf2_ros - tier4_perception_msgs unique_identifier_msgs yaml-cpp diff --git a/perception/radar_object_tracker/src/data_association/data_association.cpp b/perception/radar_object_tracker/src/association/data_association.cpp similarity index 97% rename from perception/radar_object_tracker/src/data_association/data_association.cpp rename to perception/radar_object_tracker/src/association/data_association.cpp index b308f62a24dfa..2d906ce55bcec 100644 --- a/perception/radar_object_tracker/src/data_association/data_association.cpp +++ b/perception/radar_object_tracker/src/association/data_association.cpp @@ -12,14 +12,12 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "radar_object_tracker/data_association/data_association.hpp" +#include "autoware_radar_object_tracker/association/data_association.hpp" -#include "radar_object_tracker/data_association/solver/gnn_solver.hpp" +#include "autoware_radar_object_tracker/association/solver/gnn_solver.hpp" #include -// #include "multi_object_tracker/utils/utils.hpp" - #include #include #include @@ -71,6 +69,8 @@ double getFormedYawAngle( } } // namespace +namespace autoware::radar_object_tracker +{ DataAssociation::DataAssociation( std::vector can_assign_vector, std::vector max_dist_vector, std::vector max_area_vector, std::vector min_area_vector, @@ -209,7 +209,7 @@ Eigen::MatrixXd DataAssociation::calcScoreMatrix( bool passed_gate = true; // dist gate - if (passed_gate) { + { // passed_gate is always true if (max_dist < dist) { passed_gate = false; } @@ -294,3 +294,5 @@ Eigen::MatrixXd DataAssociation::calcScoreMatrix( return score_matrix; } + +} // namespace autoware::radar_object_tracker diff --git a/perception/object_merger/src/object_association_merger/data_association/mu_successive_shortest_path/mu_successive_shortest_path_wrapper.cpp b/perception/radar_object_tracker/src/association/mu_ssp/mu_successive_shortest_path_wrapper.cpp similarity index 86% rename from perception/object_merger/src/object_association_merger/data_association/mu_successive_shortest_path/mu_successive_shortest_path_wrapper.cpp rename to perception/radar_object_tracker/src/association/mu_ssp/mu_successive_shortest_path_wrapper.cpp index e7fa56fee88c5..2f47e3b8147d5 100644 --- a/perception/object_merger/src/object_association_merger/data_association/mu_successive_shortest_path/mu_successive_shortest_path_wrapper.cpp +++ b/perception/radar_object_tracker/src/association/mu_ssp/mu_successive_shortest_path_wrapper.cpp @@ -12,7 +12,7 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "object_merger/data_association/solver/mu_successive_shortest_path.hpp" +#include "autoware_radar_object_tracker/association/solver/mu_ssp.hpp" #include @@ -24,7 +24,7 @@ #include #include -namespace gnn_solver +namespace autoware::radar_object_tracker::gnn_solver { void MuSSP::maximizeLinearAssignment( const std::vector> & cost, std::unordered_map * direct_assignment, @@ -38,4 +38,4 @@ void MuSSP::maximizeLinearAssignment( // Solve DA by muSSP solve_muSSP(cost, direct_assignment, reverse_assignment); } -} // namespace gnn_solver +} // namespace autoware::radar_object_tracker::gnn_solver diff --git a/perception/object_merger/src/object_association_merger/data_association/successive_shortest_path/successive_shortest_path.cpp b/perception/radar_object_tracker/src/association/ssp/successive_shortest_path.cpp similarity index 98% rename from perception/object_merger/src/object_association_merger/data_association/successive_shortest_path/successive_shortest_path.cpp rename to perception/radar_object_tracker/src/association/ssp/successive_shortest_path.cpp index b38257d0188e0..7877fe5451d8d 100644 --- a/perception/object_merger/src/object_association_merger/data_association/successive_shortest_path/successive_shortest_path.cpp +++ b/perception/radar_object_tracker/src/association/ssp/successive_shortest_path.cpp @@ -12,7 +12,7 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "object_merger/data_association/solver/successive_shortest_path.hpp" +#include "autoware_radar_object_tracker/association/solver/ssp.hpp" #include #include @@ -22,7 +22,7 @@ #include #include -namespace gnn_solver +namespace autoware::radar_object_tracker::gnn_solver { struct ResidualEdge { @@ -367,4 +367,4 @@ void SSP::maximizeLinearAssignment( } #endif } -} // namespace gnn_solver +} // namespace autoware::radar_object_tracker::gnn_solver diff --git a/perception/radar_object_tracker/src/radar_object_tracker_node/radar_object_tracker_node.cpp b/perception/radar_object_tracker/src/radar_object_tracker_node.cpp similarity index 95% rename from perception/radar_object_tracker/src/radar_object_tracker_node/radar_object_tracker_node.cpp rename to perception/radar_object_tracker/src/radar_object_tracker_node.cpp index 7a868b8b11118..10bf11e46abb9 100644 --- a/perception/radar_object_tracker/src/radar_object_tracker_node/radar_object_tracker_node.cpp +++ b/perception/radar_object_tracker/src/radar_object_tracker_node.cpp @@ -12,15 +12,16 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "radar_object_tracker/radar_object_tracker_node/radar_object_tracker_node.hpp" +#define EIGEN_MPL2_ONLY + +#include "radar_object_tracker_node.hpp" -#include "radar_object_tracker/utils/radar_object_tracker_utils.hpp" -#include "radar_object_tracker/utils/utils.hpp" +#include "autoware_radar_object_tracker/utils/radar_object_tracker_utils.hpp" +#include "autoware_radar_object_tracker/utils/utils.hpp" #include #include #include -#include #include @@ -35,8 +36,9 @@ #include #include #include -#define EIGEN_MPL2_ONLY +namespace autoware::radar_object_tracker +{ using Label = autoware_perception_msgs::msg::ObjectClassification; RadarObjectTrackerNode::RadarObjectTrackerNode(const rclcpp::NodeOptions & node_options) @@ -140,7 +142,7 @@ void RadarObjectTrackerNode::onMap(const LaneletMapBin::ConstSharedPtr msg) void RadarObjectTrackerNode::onMeasurement( const autoware_perception_msgs::msg::DetectedObjects::ConstSharedPtr input_objects_msg) { - const auto self_transform = radar_object_tracker_utils::getTransformAnonymous( + const auto self_transform = autoware::radar_object_tracker::utils::getTransformAnonymous( tf_buffer_, "base_link", world_frame_id_, input_objects_msg->header.stamp); if (!self_transform) { return; @@ -236,7 +238,7 @@ std::shared_ptr RadarObjectTrackerNode::createNewTracker( void RadarObjectTrackerNode::onTimer() { rclcpp::Time current_time = this->now(); - const auto self_transform = radar_object_tracker_utils::getTransformAnonymous( + const auto self_transform = autoware::radar_object_tracker::utils::getTransformAnonymous( tf_buffer_, world_frame_id_, "base_link", current_time); if (!self_transform) { return; @@ -283,14 +285,14 @@ void RadarObjectTrackerNode::mapBasedNoiseFilter( for (auto itr = list_tracker.begin(); itr != list_tracker.end(); ++itr) { autoware_perception_msgs::msg::TrackedObject object; (*itr)->getTrackedObject(time, object); - const auto closest_lanelets = radar_object_tracker_utils::getClosestValidLanelets( + const auto closest_lanelets = autoware::radar_object_tracker::utils::getClosestValidLanelets( object, lanelet_map_ptr_, max_distance_from_lane_, max_angle_diff_from_lane_); // 1. If the object is not close to any lanelet, delete the tracker const bool no_closest_lanelet = closest_lanelets.empty(); // 2. If the object velocity direction is not close to the lanelet direction, delete the tracker const bool is_velocity_direction_close_to_lanelet = - radar_object_tracker_utils::hasValidVelocityDirectionToLanelet( + autoware::radar_object_tracker::utils::hasValidVelocityDirectionToLanelet( object, closest_lanelets, max_lateral_velocity_); if (no_closest_lanelet || !is_velocity_direction_close_to_lanelet) { // std::cout << "object removed due to map based noise filter" << " no close lanelet: " << @@ -421,5 +423,8 @@ void RadarObjectTrackerNode::publish(const rclcpp::Time & time) const // Publish tracked_objects_pub_->publish(output_msg); } +} // namespace autoware::radar_object_tracker + +#include -RCLCPP_COMPONENTS_REGISTER_NODE(RadarObjectTrackerNode) +RCLCPP_COMPONENTS_REGISTER_NODE(autoware::radar_object_tracker::RadarObjectTrackerNode) diff --git a/perception/radar_object_tracker/include/radar_object_tracker/radar_object_tracker_node/radar_object_tracker_node.hpp b/perception/radar_object_tracker/src/radar_object_tracker_node.hpp similarity index 87% rename from perception/radar_object_tracker/include/radar_object_tracker/radar_object_tracker_node/radar_object_tracker_node.hpp rename to perception/radar_object_tracker/src/radar_object_tracker_node.hpp index fe6d472eddd08..aaba15bbad05d 100644 --- a/perception/radar_object_tracker/include/radar_object_tracker/radar_object_tracker_node/radar_object_tracker_node.hpp +++ b/perception/radar_object_tracker/src/radar_object_tracker_node.hpp @@ -12,17 +12,26 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef RADAR_OBJECT_TRACKER__RADAR_OBJECT_TRACKER_NODE__RADAR_OBJECT_TRACKER_NODE_HPP_ -#define RADAR_OBJECT_TRACKER__RADAR_OBJECT_TRACKER_NODE__RADAR_OBJECT_TRACKER_NODE_HPP_ +#ifndef RADAR_OBJECT_TRACKER_NODE_HPP_ +#define RADAR_OBJECT_TRACKER_NODE_HPP_ -#include "radar_object_tracker/data_association/data_association.hpp" +#include "autoware_radar_object_tracker/association/data_association.hpp" +#include +#include +#include #include -#include -#include +#include "autoware_perception_msgs/msg/detected_objects.hpp" +#include "autoware_perception_msgs/msg/tracked_objects.hpp" #include +#include +#include +#include +#include +#include +#include #include #include #include @@ -32,17 +41,6 @@ #else #include #endif - -#include -#include -#include - -#include -#include -#include -#include -#include -#include #include #include @@ -51,6 +49,8 @@ #include #include +namespace autoware::radar_object_tracker +{ using autoware_map_msgs::msg::LaneletMapBin; using autoware_perception_msgs::msg::DetectedObject; using autoware_perception_msgs::msg::DetectedObjects; @@ -132,4 +132,6 @@ class RadarObjectTrackerNode : public rclcpp::Node inline bool shouldTrackerPublish(const std::shared_ptr tracker) const; }; -#endif // RADAR_OBJECT_TRACKER__RADAR_OBJECT_TRACKER_NODE__RADAR_OBJECT_TRACKER_NODE_HPP_ +} // namespace autoware::radar_object_tracker + +#endif // RADAR_OBJECT_TRACKER_NODE_HPP_ diff --git a/perception/radar_object_tracker/src/tracker/model/constant_turn_rate_motion_tracker.cpp b/perception/radar_object_tracker/src/tracker/model/constant_turn_rate_motion_tracker.cpp index e1603fbfab702..057a8653faea0 100644 --- a/perception/radar_object_tracker/src/tracker/model/constant_turn_rate_motion_tracker.cpp +++ b/perception/radar_object_tracker/src/tracker/model/constant_turn_rate_motion_tracker.cpp @@ -16,12 +16,14 @@ // Author: v1.0 Yukihiro Saito // -#include "radar_object_tracker/tracker/model/constant_turn_rate_motion_tracker.hpp" +#define EIGEN_MPL2_ONLY -#include "radar_object_tracker/utils/utils.hpp" +#include "autoware_radar_object_tracker/tracker/model/constant_turn_rate_motion_tracker.hpp" -#include -#include +#include "autoware/universe_utils/geometry/boost_polygon_utils.hpp" +#include "autoware/universe_utils/math/unit_conversion.hpp" +#include "autoware/universe_utils/ros/msg_covariance.hpp" +#include "autoware_radar_object_tracker/utils/utils.hpp" #include #include @@ -34,7 +36,6 @@ #include #endif -#define EIGEN_MPL2_ONLY #include #include #include @@ -42,7 +43,10 @@ #include +namespace autoware::radar_object_tracker +{ using Label = autoware_perception_msgs::msg::ObjectClassification; +using autoware::universe_utils::xyzrpy_covariance_index::XYZRPY_COV_IDX; // init static member variables bool ConstantTurnRateMotionTracker::is_initialized_ = false; @@ -114,10 +118,10 @@ ConstantTurnRateMotionTracker::ConstantTurnRateMotionTracker( // Rotate the covariance matrix according to the vehicle yaw // because p0_cov_x and y are in the vehicle coordinate system. if (object.kinematics.has_position_covariance) { - P_xy_local << object.kinematics.pose_with_covariance.covariance[utils::MSG_COV_IDX::X_X], - object.kinematics.pose_with_covariance.covariance[utils::MSG_COV_IDX::X_Y], - object.kinematics.pose_with_covariance.covariance[utils::MSG_COV_IDX::Y_X], - object.kinematics.pose_with_covariance.covariance[utils::MSG_COV_IDX::Y_Y]; + P_xy_local << object.kinematics.pose_with_covariance.covariance[XYZRPY_COV_IDX::X_X], + object.kinematics.pose_with_covariance.covariance[XYZRPY_COV_IDX::X_Y], + object.kinematics.pose_with_covariance.covariance[XYZRPY_COV_IDX::Y_X], + object.kinematics.pose_with_covariance.covariance[XYZRPY_COV_IDX::Y_Y]; P_xy = R * P_xy_local * R.transpose(); } else { // rotate @@ -128,9 +132,9 @@ ConstantTurnRateMotionTracker::ConstantTurnRateMotionTracker( // covariance often written in object frame if (object.kinematics.has_twist_covariance) { - const auto vx_cov = object.kinematics.twist_with_covariance.covariance[utils::MSG_COV_IDX::X_X]; + const auto vx_cov = object.kinematics.twist_with_covariance.covariance[XYZRPY_COV_IDX::X_X]; // const auto vy_cov = - // object.kinematics.twist_with_covariance.covariance[utils::MSG_COV_IDX::Y_Y]; + // object.kinematics.twist_with_covariance.covariance[XYZRPY_COV_IDX::Y_Y]; P(IDX::VX, IDX::VX) = vx_cov; } else { P(IDX::VX, IDX::VX) = ekf_params_.p0_cov_vx; @@ -371,11 +375,11 @@ bool ConstantTurnRateMotionTracker::measureWithPose( Rxy_local << ekf_params_.r_cov_x, 0, 0, r_cov_y; // xy in base_link coordinate Rxy = RotationBaseLink * Rxy_local * RotationBaseLink.transpose(); } else { - Rxy_local << object.kinematics.pose_with_covariance.covariance[utils::MSG_COV_IDX::X_X], - object.kinematics.pose_with_covariance.covariance[utils::MSG_COV_IDX::X_Y], - object.kinematics.pose_with_covariance.covariance[utils::MSG_COV_IDX::Y_X], + Rxy_local << object.kinematics.pose_with_covariance.covariance[XYZRPY_COV_IDX::X_X], + object.kinematics.pose_with_covariance.covariance[XYZRPY_COV_IDX::X_Y], + object.kinematics.pose_with_covariance.covariance[XYZRPY_COV_IDX::Y_X], object.kinematics.pose_with_covariance - .covariance[utils::MSG_COV_IDX::Y_Y]; // xy in vehicle coordinate + .covariance[XYZRPY_COV_IDX::Y_Y]; // xy in vehicle coordinate Rxy = RotationYaw * Rxy_local * RotationYaw.transpose(); } R_block_list.push_back(Rxy); @@ -427,7 +431,7 @@ bool ConstantTurnRateMotionTracker::measureWithPose( if (!object.kinematics.has_twist_covariance) { R_vx << ekf_params_.r_cov_vx; } else { - R_vx << object.kinematics.twist_with_covariance.covariance[utils::MSG_COV_IDX::X_X]; + R_vx << object.kinematics.twist_with_covariance.covariance[XYZRPY_COV_IDX::X_X]; } R_block_list.push_back(R_vx); } @@ -442,9 +446,9 @@ bool ConstantTurnRateMotionTracker::measureWithPose( } // stacking matrices vertically or diagonally - const auto C = utils::stackMatricesVertically(C_list); - const auto Y = utils::stackMatricesVertically(Y_list); - const auto R = utils::stackMatricesDiagonally(R_block_list); + const auto C = autoware::radar_object_tracker::utils::stackMatricesVertically(C_list); + const auto Y = autoware::radar_object_tracker::utils::stackMatricesVertically(Y_list); + const auto R = autoware::radar_object_tracker::utils::stackMatricesDiagonally(R_block_list); // 4. EKF update if (!ekf_.update(Y, C, R)) { @@ -582,27 +586,27 @@ bool ConstantTurnRateMotionTracker::getTrackedObject( constexpr double z_cov = 1. * 1.; // TODO(yukkysaito) Currently tentative constexpr double yaw_cov = 0.1 * 0.1; // TODO(yukkysaito) Currently tentative - pose_with_cov.covariance[utils::MSG_COV_IDX::X_X] = P_xy(IDX::X, IDX::X); - pose_with_cov.covariance[utils::MSG_COV_IDX::X_Y] = P_xy(IDX::X, IDX::Y); - pose_with_cov.covariance[utils::MSG_COV_IDX::Y_X] = P_xy(IDX::Y, IDX::X); - pose_with_cov.covariance[utils::MSG_COV_IDX::Y_Y] = P_xy(IDX::Y, IDX::Y); - pose_with_cov.covariance[utils::MSG_COV_IDX::Z_Z] = z_cov; - pose_with_cov.covariance[utils::MSG_COV_IDX::ROLL_ROLL] = no_info_cov; - pose_with_cov.covariance[utils::MSG_COV_IDX::PITCH_PITCH] = no_info_cov; - pose_with_cov.covariance[utils::MSG_COV_IDX::YAW_YAW] = yaw_cov; + pose_with_cov.covariance[XYZRPY_COV_IDX::X_X] = P_xy(IDX::X, IDX::X); + pose_with_cov.covariance[XYZRPY_COV_IDX::X_Y] = P_xy(IDX::X, IDX::Y); + pose_with_cov.covariance[XYZRPY_COV_IDX::Y_X] = P_xy(IDX::Y, IDX::X); + pose_with_cov.covariance[XYZRPY_COV_IDX::Y_Y] = P_xy(IDX::Y, IDX::Y); + pose_with_cov.covariance[XYZRPY_COV_IDX::Z_Z] = z_cov; + pose_with_cov.covariance[XYZRPY_COV_IDX::ROLL_ROLL] = no_info_cov; + pose_with_cov.covariance[XYZRPY_COV_IDX::PITCH_PITCH] = no_info_cov; + pose_with_cov.covariance[XYZRPY_COV_IDX::YAW_YAW] = yaw_cov; // twist covariance constexpr double vz_cov = 0.2 * 0.2; // TODO(Yoshi Ri) Currently tentative constexpr double wz_cov = 0.2 * 0.2; // TODO(yukkysaito) Currently tentative - twist_with_cov.covariance[utils::MSG_COV_IDX::X_X] = P(IDX::VX, IDX::VX); - twist_with_cov.covariance[utils::MSG_COV_IDX::X_Y] = 0.0; - twist_with_cov.covariance[utils::MSG_COV_IDX::Y_X] = 0.0; - twist_with_cov.covariance[utils::MSG_COV_IDX::Y_Y] = no_info_cov; - twist_with_cov.covariance[utils::MSG_COV_IDX::Z_Z] = vz_cov; - twist_with_cov.covariance[utils::MSG_COV_IDX::ROLL_ROLL] = no_info_cov; - twist_with_cov.covariance[utils::MSG_COV_IDX::PITCH_PITCH] = no_info_cov; - twist_with_cov.covariance[utils::MSG_COV_IDX::YAW_YAW] = wz_cov; + twist_with_cov.covariance[XYZRPY_COV_IDX::X_X] = P(IDX::VX, IDX::VX); + twist_with_cov.covariance[XYZRPY_COV_IDX::X_Y] = 0.0; + twist_with_cov.covariance[XYZRPY_COV_IDX::Y_X] = 0.0; + twist_with_cov.covariance[XYZRPY_COV_IDX::Y_Y] = no_info_cov; + twist_with_cov.covariance[XYZRPY_COV_IDX::Z_Z] = vz_cov; + twist_with_cov.covariance[XYZRPY_COV_IDX::ROLL_ROLL] = no_info_cov; + twist_with_cov.covariance[XYZRPY_COV_IDX::PITCH_PITCH] = no_info_cov; + twist_with_cov.covariance[XYZRPY_COV_IDX::YAW_YAW] = wz_cov; // set shape if (object.shape.type == autoware_perception_msgs::msg::Shape::BOUNDING_BOX) { @@ -622,3 +626,5 @@ bool ConstantTurnRateMotionTracker::getTrackedObject( return true; } + +} // namespace autoware::radar_object_tracker diff --git a/perception/radar_object_tracker/src/tracker/model/linear_motion_tracker.cpp b/perception/radar_object_tracker/src/tracker/model/linear_motion_tracker.cpp index 1bc548fa7a951..a9f7fffff1156 100644 --- a/perception/radar_object_tracker/src/tracker/model/linear_motion_tracker.cpp +++ b/perception/radar_object_tracker/src/tracker/model/linear_motion_tracker.cpp @@ -16,12 +16,14 @@ // Author: v1.0 Yukihiro Saito // -#include "radar_object_tracker/tracker/model/linear_motion_tracker.hpp" +#define EIGEN_MPL2_ONLY -#include "radar_object_tracker/utils/utils.hpp" +#include "autoware_radar_object_tracker/tracker/model/linear_motion_tracker.hpp" -#include -#include +#include "autoware/universe_utils/geometry/boost_polygon_utils.hpp" +#include "autoware/universe_utils/math/unit_conversion.hpp" +#include "autoware/universe_utils/ros/msg_covariance.hpp" +#include "autoware_radar_object_tracker/utils/utils.hpp" #include #include @@ -34,7 +36,6 @@ #include #endif -#define EIGEN_MPL2_ONLY #include #include #include @@ -42,7 +43,10 @@ #include +namespace autoware::radar_object_tracker +{ using Label = autoware_perception_msgs::msg::ObjectClassification; +using autoware::universe_utils::xyzrpy_covariance_index::XYZRPY_COV_IDX; // initialize static parameter bool LinearMotionTracker::is_initialized_ = false; @@ -119,10 +123,10 @@ LinearMotionTracker::LinearMotionTracker( // Rotate the covariance matrix according to the vehicle yaw // because p0_cov_x and y are in the vehicle coordinate system. if (object.kinematics.has_position_covariance) { - P_xy_local << object.kinematics.pose_with_covariance.covariance[utils::MSG_COV_IDX::X_X], - object.kinematics.pose_with_covariance.covariance[utils::MSG_COV_IDX::X_Y], - object.kinematics.pose_with_covariance.covariance[utils::MSG_COV_IDX::Y_X], - object.kinematics.pose_with_covariance.covariance[utils::MSG_COV_IDX::Y_Y]; + P_xy_local << object.kinematics.pose_with_covariance.covariance[XYZRPY_COV_IDX::X_X], + object.kinematics.pose_with_covariance.covariance[XYZRPY_COV_IDX::X_Y], + object.kinematics.pose_with_covariance.covariance[XYZRPY_COV_IDX::Y_X], + object.kinematics.pose_with_covariance.covariance[XYZRPY_COV_IDX::Y_Y]; P_xy = R * P_xy_local * R.transpose(); } else { // rotate @@ -130,29 +134,15 @@ LinearMotionTracker::LinearMotionTracker( } // covariance often written in object frame if (object.kinematics.has_twist_covariance) { - const auto vx_cov = object.kinematics.twist_with_covariance.covariance[utils::MSG_COV_IDX::X_X]; - const auto vy_cov = object.kinematics.twist_with_covariance.covariance[utils::MSG_COV_IDX::Y_Y]; + const auto vx_cov = object.kinematics.twist_with_covariance.covariance[XYZRPY_COV_IDX::X_X]; + const auto vy_cov = object.kinematics.twist_with_covariance.covariance[XYZRPY_COV_IDX::Y_Y]; P_v_xy_local << vx_cov, 0.0, 0.0, vy_cov; P_v_xy = R * P_v_xy_local * R.transpose(); } else { P_v_xy = R * P_v_xy_local * R.transpose(); } - // acceleration covariance often written in object frame - const bool has_acceleration_covariance = - false; // currently message does not have acceleration covariance - if (has_acceleration_covariance) { - // const auto ax_cov = - // object.kinematics.acceleration_with_covariance.covariance[utils::MSG_COV_IDX::X_X]; // This - // is future update - // const auto ay_cov = - // object.kinematics.acceleration_with_covariance.covariance[utils::MSG_COV_IDX::Y_Y]; // This - // is future update - // Eigen::Matrix2d P_a_xy_local; - // P_a_xy_local << ax_cov, 0.0, 0.0, ay_cov; - P_a_xy = R * P_a_xy_local * R.transpose(); - } else { - P_a_xy = R * P_a_xy_local * R.transpose(); - } + + P_a_xy = R * P_a_xy_local * R.transpose(); // put value in P matrix // use block description. This assume x,y,vx,vy,ax,ay in this order @@ -415,11 +405,11 @@ bool LinearMotionTracker::measureWithPose( Rxy_local << ekf_params_.r_cov_x, 0, 0, r_cov_y; // xy in base_link coordinate Rxy = RotationBaseLink * Rxy_local * RotationBaseLink.transpose(); } else { - Rxy_local << object.kinematics.pose_with_covariance.covariance[utils::MSG_COV_IDX::X_X], - object.kinematics.pose_with_covariance.covariance[utils::MSG_COV_IDX::X_Y], - object.kinematics.pose_with_covariance.covariance[utils::MSG_COV_IDX::Y_X], + Rxy_local << object.kinematics.pose_with_covariance.covariance[XYZRPY_COV_IDX::X_X], + object.kinematics.pose_with_covariance.covariance[XYZRPY_COV_IDX::X_Y], + object.kinematics.pose_with_covariance.covariance[XYZRPY_COV_IDX::Y_X], object.kinematics.pose_with_covariance - .covariance[utils::MSG_COV_IDX::Y_Y]; // xy in vehicle coordinate + .covariance[XYZRPY_COV_IDX::Y_Y]; // xy in vehicle coordinate Rxy = RotationYaw * Rxy_local * RotationYaw.transpose(); } R_block_list.push_back(Rxy); @@ -447,8 +437,8 @@ bool LinearMotionTracker::measureWithPose( R_v_xy_local << ekf_params_.r_cov_vx, 0, 0, ekf_params_.r_cov_vy; R_v_xy = RotationBaseLink * R_v_xy_local * RotationBaseLink.transpose(); } else { - R_v_xy_local << object.kinematics.twist_with_covariance.covariance[utils::MSG_COV_IDX::X_X], - 0, 0, object.kinematics.twist_with_covariance.covariance[utils::MSG_COV_IDX::Y_Y]; + R_v_xy_local << object.kinematics.twist_with_covariance.covariance[XYZRPY_COV_IDX::X_X], 0, 0, + object.kinematics.twist_with_covariance.covariance[XYZRPY_COV_IDX::Y_Y]; R_v_xy = RotationYaw * R_v_xy_local * RotationYaw.transpose(); } R_block_list.push_back(R_v_xy); @@ -467,9 +457,9 @@ bool LinearMotionTracker::measureWithPose( // Eigen::MatrixXd R = Eigen::MatrixXd::Zero(row_number, row_number); // stacking matrices vertically or diagonally - const auto C = utils::stackMatricesVertically(C_list); - const auto Y = utils::stackMatricesVertically(Y_list); - const auto R = utils::stackMatricesDiagonally(R_block_list); + const auto C = autoware::radar_object_tracker::utils::stackMatricesVertically(C_list); + const auto Y = autoware::radar_object_tracker::utils::stackMatricesVertically(Y_list); + const auto R = autoware::radar_object_tracker::utils::stackMatricesDiagonally(R_block_list); // 4. EKF update if (!ekf_.update(Y, C, R)) { @@ -637,37 +627,37 @@ bool LinearMotionTracker::getTrackedObject( constexpr double z_cov = 1. * 1.; // TODO(yukkysaito) Currently tentative constexpr double yaw_cov = 0.1 * 0.1; // TODO(yukkysaito) Currently tentative - pose_with_cov.covariance[utils::MSG_COV_IDX::X_X] = P_xy(IDX::X, IDX::X); - pose_with_cov.covariance[utils::MSG_COV_IDX::X_Y] = P_xy(IDX::X, IDX::Y); - pose_with_cov.covariance[utils::MSG_COV_IDX::Y_X] = P_xy(IDX::Y, IDX::X); - pose_with_cov.covariance[utils::MSG_COV_IDX::Y_Y] = P_xy(IDX::Y, IDX::Y); - pose_with_cov.covariance[utils::MSG_COV_IDX::Z_Z] = z_cov; - pose_with_cov.covariance[utils::MSG_COV_IDX::ROLL_ROLL] = no_info_cov; - pose_with_cov.covariance[utils::MSG_COV_IDX::PITCH_PITCH] = no_info_cov; - pose_with_cov.covariance[utils::MSG_COV_IDX::YAW_YAW] = yaw_cov; + pose_with_cov.covariance[XYZRPY_COV_IDX::X_X] = P_xy(IDX::X, IDX::X); + pose_with_cov.covariance[XYZRPY_COV_IDX::X_Y] = P_xy(IDX::X, IDX::Y); + pose_with_cov.covariance[XYZRPY_COV_IDX::Y_X] = P_xy(IDX::Y, IDX::X); + pose_with_cov.covariance[XYZRPY_COV_IDX::Y_Y] = P_xy(IDX::Y, IDX::Y); + pose_with_cov.covariance[XYZRPY_COV_IDX::Z_Z] = z_cov; + pose_with_cov.covariance[XYZRPY_COV_IDX::ROLL_ROLL] = no_info_cov; + pose_with_cov.covariance[XYZRPY_COV_IDX::PITCH_PITCH] = no_info_cov; + pose_with_cov.covariance[XYZRPY_COV_IDX::YAW_YAW] = yaw_cov; // twist covariance constexpr double vz_cov = 0.2 * 0.2; // TODO(Yoshi Ri) Currently tentative constexpr double wz_cov = 0.2 * 0.2; // TODO(yukkysaito) Currently tentative - twist_with_cov.covariance[utils::MSG_COV_IDX::X_X] = P_v_xy(IDX::X, IDX::X); - twist_with_cov.covariance[utils::MSG_COV_IDX::X_Y] = P_v_xy(IDX::X, IDX::Y); - twist_with_cov.covariance[utils::MSG_COV_IDX::Y_X] = P_v_xy(IDX::Y, IDX::X); - twist_with_cov.covariance[utils::MSG_COV_IDX::Y_Y] = P_v_xy(IDX::Y, IDX::Y); - twist_with_cov.covariance[utils::MSG_COV_IDX::Z_Z] = vz_cov; - twist_with_cov.covariance[utils::MSG_COV_IDX::ROLL_ROLL] = no_info_cov; - twist_with_cov.covariance[utils::MSG_COV_IDX::PITCH_PITCH] = no_info_cov; - twist_with_cov.covariance[utils::MSG_COV_IDX::YAW_YAW] = wz_cov; + twist_with_cov.covariance[XYZRPY_COV_IDX::X_X] = P_v_xy(IDX::X, IDX::X); + twist_with_cov.covariance[XYZRPY_COV_IDX::X_Y] = P_v_xy(IDX::X, IDX::Y); + twist_with_cov.covariance[XYZRPY_COV_IDX::Y_X] = P_v_xy(IDX::Y, IDX::X); + twist_with_cov.covariance[XYZRPY_COV_IDX::Y_Y] = P_v_xy(IDX::Y, IDX::Y); + twist_with_cov.covariance[XYZRPY_COV_IDX::Z_Z] = vz_cov; + twist_with_cov.covariance[XYZRPY_COV_IDX::ROLL_ROLL] = no_info_cov; + twist_with_cov.covariance[XYZRPY_COV_IDX::PITCH_PITCH] = no_info_cov; + twist_with_cov.covariance[XYZRPY_COV_IDX::YAW_YAW] = wz_cov; // acceleration covariance - acceleration_with_cov.covariance[utils::MSG_COV_IDX::X_X] = P_a_xy(IDX::X, IDX::X); - acceleration_with_cov.covariance[utils::MSG_COV_IDX::X_Y] = P_a_xy(IDX::X, IDX::Y); - acceleration_with_cov.covariance[utils::MSG_COV_IDX::Y_X] = P_a_xy(IDX::Y, IDX::X); - acceleration_with_cov.covariance[utils::MSG_COV_IDX::Y_Y] = P_a_xy(IDX::Y, IDX::Y); - acceleration_with_cov.covariance[utils::MSG_COV_IDX::Z_Z] = no_info_cov; - acceleration_with_cov.covariance[utils::MSG_COV_IDX::ROLL_ROLL] = no_info_cov; - acceleration_with_cov.covariance[utils::MSG_COV_IDX::PITCH_PITCH] = no_info_cov; - acceleration_with_cov.covariance[utils::MSG_COV_IDX::YAW_YAW] = no_info_cov; + acceleration_with_cov.covariance[XYZRPY_COV_IDX::X_X] = P_a_xy(IDX::X, IDX::X); + acceleration_with_cov.covariance[XYZRPY_COV_IDX::X_Y] = P_a_xy(IDX::X, IDX::Y); + acceleration_with_cov.covariance[XYZRPY_COV_IDX::Y_X] = P_a_xy(IDX::Y, IDX::X); + acceleration_with_cov.covariance[XYZRPY_COV_IDX::Y_Y] = P_a_xy(IDX::Y, IDX::Y); + acceleration_with_cov.covariance[XYZRPY_COV_IDX::Z_Z] = no_info_cov; + acceleration_with_cov.covariance[XYZRPY_COV_IDX::ROLL_ROLL] = no_info_cov; + acceleration_with_cov.covariance[XYZRPY_COV_IDX::PITCH_PITCH] = no_info_cov; + acceleration_with_cov.covariance[XYZRPY_COV_IDX::YAW_YAW] = no_info_cov; // set shape if (object.shape.type == autoware_perception_msgs::msg::Shape::BOUNDING_BOX) { @@ -687,3 +677,4 @@ bool LinearMotionTracker::getTrackedObject( return true; } +} // namespace autoware::radar_object_tracker diff --git a/perception/radar_object_tracker/src/tracker/model/tracker_base.cpp b/perception/radar_object_tracker/src/tracker/model/tracker_base.cpp index d38b80067cf5a..ce63d96dfb83a 100644 --- a/perception/radar_object_tracker/src/tracker/model/tracker_base.cpp +++ b/perception/radar_object_tracker/src/tracker/model/tracker_base.cpp @@ -14,13 +14,15 @@ // // -#include "radar_object_tracker/tracker/model/tracker_base.hpp" +#include "autoware_radar_object_tracker/tracker/model/tracker_base.hpp" -#include "radar_object_tracker/utils/utils.hpp" +#include "autoware_radar_object_tracker/utils/utils.hpp" #include #include +namespace autoware::radar_object_tracker +{ Tracker::Tracker( const rclcpp::Time & time, const std::vector & classification) @@ -61,3 +63,4 @@ geometry_msgs::msg::PoseWithCovariance Tracker::getPoseWithCovariance( getTrackedObject(time, object); return object.kinematics.pose_with_covariance; } +} // namespace autoware::radar_object_tracker diff --git a/perception/radar_object_tracker/src/utils/radar_object_tracker_utils.cpp b/perception/radar_object_tracker/src/utils/radar_object_tracker_utils.cpp index ba2500a60beff..f0a6a5dfd384d 100644 --- a/perception/radar_object_tracker/src/utils/radar_object_tracker_utils.cpp +++ b/perception/radar_object_tracker/src/utils/radar_object_tracker_utils.cpp @@ -12,9 +12,9 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "radar_object_tracker/utils/radar_object_tracker_utils.hpp" +#include "autoware_radar_object_tracker/utils/radar_object_tracker_utils.hpp" -namespace radar_object_tracker_utils +namespace autoware::radar_object_tracker::utils { boost::optional getTransformAnonymous( @@ -151,4 +151,4 @@ bool hasValidVelocityDirectionToLanelet( return false; } -} // namespace radar_object_tracker_utils +} // namespace autoware::radar_object_tracker::utils diff --git a/perception/radar_object_tracker/src/utils/utils.cpp b/perception/radar_object_tracker/src/utils/utils.cpp index cb9d3813e0ecc..351c4cfd21159 100644 --- a/perception/radar_object_tracker/src/utils/utils.cpp +++ b/perception/radar_object_tracker/src/utils/utils.cpp @@ -12,9 +12,9 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "radar_object_tracker/utils/utils.hpp" +#include "autoware_radar_object_tracker/utils/utils.hpp" -namespace utils +namespace autoware::radar_object_tracker::utils { // concatenate matrices vertically Eigen::MatrixXd stackMatricesVertically(const std::vector & matrices) @@ -68,4 +68,4 @@ Eigen::MatrixXd stackMatricesDiagonally(const std::vector & mat return result; } -} // namespace utils +} // namespace autoware::radar_object_tracker::utils diff --git a/perception/radar_object_tracker/test/test_radar_object_tracker_utils.cpp b/perception/radar_object_tracker/test/test_radar_object_tracker_utils.cpp index 681ae9f1854f7..e9b2a9800d628 100644 --- a/perception/radar_object_tracker/test/test_radar_object_tracker_utils.cpp +++ b/perception/radar_object_tracker/test/test_radar_object_tracker_utils.cpp @@ -12,12 +12,12 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "radar_object_tracker/utils/radar_object_tracker_utils.hpp" +#include "autoware_radar_object_tracker/utils/radar_object_tracker_utils.hpp" #include +using autoware::radar_object_tracker::utils::checkCloseLaneletCondition; using autoware_perception_msgs::msg::TrackedObject; -using radar_object_tracker_utils::checkCloseLaneletCondition; // helper function to create a dummy straight lanelet lanelet::Lanelet createDummyStraightLanelet(double length, double width) diff --git a/perception/radar_tracks_msgs_converter/CMakeLists.txt b/perception/radar_tracks_msgs_converter/CMakeLists.txt index 766fb4b939904..4ff3be1fa45a4 100644 --- a/perception/radar_tracks_msgs_converter/CMakeLists.txt +++ b/perception/radar_tracks_msgs_converter/CMakeLists.txt @@ -5,12 +5,12 @@ find_package(autoware_cmake REQUIRED) autoware_package() ## Targets -ament_auto_add_library(radar_tracks_msgs_converter_node_component SHARED - src/radar_tracks_msgs_converter_node/radar_tracks_msgs_converter_node.cpp +ament_auto_add_library(${PROJECT_NAME} SHARED + src/radar_tracks_msgs_converter_node.cpp ) -rclcpp_components_register_node(radar_tracks_msgs_converter_node_component - PLUGIN "radar_tracks_msgs_converter::RadarTracksMsgsConverterNode" +rclcpp_components_register_node(${PROJECT_NAME} + PLUGIN "autoware::radar_tracks_msgs_converter::RadarTracksMsgsConverterNode" EXECUTABLE radar_tracks_msgs_converter_node ) diff --git a/perception/radar_tracks_msgs_converter/package.xml b/perception/radar_tracks_msgs_converter/package.xml index ad0257dbbd726..534c9a20fa0df 100644 --- a/perception/radar_tracks_msgs_converter/package.xml +++ b/perception/radar_tracks_msgs_converter/package.xml @@ -17,14 +17,12 @@ autoware_perception_msgs autoware_universe_utils - geometry_msgs nav_msgs radar_msgs rclcpp rclcpp_components tf2 tf2_geometry_msgs - tf2_ros ament_lint_auto autoware_lint_common diff --git a/perception/radar_tracks_msgs_converter/src/radar_tracks_msgs_converter_node/radar_tracks_msgs_converter_node.cpp b/perception/radar_tracks_msgs_converter/src/radar_tracks_msgs_converter_node.cpp similarity index 98% rename from perception/radar_tracks_msgs_converter/src/radar_tracks_msgs_converter_node/radar_tracks_msgs_converter_node.cpp rename to perception/radar_tracks_msgs_converter/src/radar_tracks_msgs_converter_node.cpp index 36f4d3cd3da90..5d45595733c4a 100644 --- a/perception/radar_tracks_msgs_converter/src/radar_tracks_msgs_converter_node/radar_tracks_msgs_converter_node.cpp +++ b/perception/radar_tracks_msgs_converter/src/radar_tracks_msgs_converter_node.cpp @@ -12,7 +12,7 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "radar_tracks_msgs_converter/radar_tracks_msgs_converter_node.hpp" +#include "radar_tracks_msgs_converter_node.hpp" #include "autoware/universe_utils/geometry/geometry.hpp" #include "autoware/universe_utils/math/unit_conversion.hpp" @@ -56,7 +56,7 @@ bool update_param( } } // namespace -namespace radar_tracks_msgs_converter +namespace autoware::radar_tracks_msgs_converter { enum class RadarTrackObjectID { @@ -407,7 +407,7 @@ uint8_t RadarTracksMsgsConverterNode::convertClassification(const uint16_t class } } -} // namespace radar_tracks_msgs_converter +} // namespace autoware::radar_tracks_msgs_converter #include "rclcpp_components/register_node_macro.hpp" -RCLCPP_COMPONENTS_REGISTER_NODE(radar_tracks_msgs_converter::RadarTracksMsgsConverterNode) +RCLCPP_COMPONENTS_REGISTER_NODE(autoware::radar_tracks_msgs_converter::RadarTracksMsgsConverterNode) diff --git a/perception/radar_tracks_msgs_converter/include/radar_tracks_msgs_converter/radar_tracks_msgs_converter_node.hpp b/perception/radar_tracks_msgs_converter/src/radar_tracks_msgs_converter_node.hpp similarity index 93% rename from perception/radar_tracks_msgs_converter/include/radar_tracks_msgs_converter/radar_tracks_msgs_converter_node.hpp rename to perception/radar_tracks_msgs_converter/src/radar_tracks_msgs_converter_node.hpp index 30d02c90b0594..36528711cc403 100644 --- a/perception/radar_tracks_msgs_converter/include/radar_tracks_msgs_converter/radar_tracks_msgs_converter_node.hpp +++ b/perception/radar_tracks_msgs_converter/src/radar_tracks_msgs_converter_node.hpp @@ -12,8 +12,8 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef RADAR_TRACKS_MSGS_CONVERTER__RADAR_TRACKS_MSGS_CONVERTER_NODE_HPP_ -#define RADAR_TRACKS_MSGS_CONVERTER__RADAR_TRACKS_MSGS_CONVERTER_NODE_HPP_ +#ifndef RADAR_TRACKS_MSGS_CONVERTER_NODE_HPP_ +#define RADAR_TRACKS_MSGS_CONVERTER_NODE_HPP_ #include "autoware/universe_utils/ros/transform_listener.hpp" #include "rclcpp/rclcpp.hpp" @@ -32,7 +32,7 @@ #include #include -namespace radar_tracks_msgs_converter +namespace autoware::radar_tracks_msgs_converter { using autoware_perception_msgs::msg::DetectedObject; using autoware_perception_msgs::msg::DetectedObjectKinematics; @@ -114,6 +114,6 @@ class RadarTracksMsgsConverterNode : public rclcpp::Node uint8_t convertClassification(const uint16_t classification); }; -} // namespace radar_tracks_msgs_converter +} // namespace autoware::radar_tracks_msgs_converter -#endif // RADAR_TRACKS_MSGS_CONVERTER__RADAR_TRACKS_MSGS_CONVERTER_NODE_HPP_ +#endif // RADAR_TRACKS_MSGS_CONVERTER_NODE_HPP_ diff --git a/perception/raindrop_cluster_filter/CMakeLists.txt b/perception/raindrop_cluster_filter/CMakeLists.txt index a867bc740317b..17eef97cc2f52 100644 --- a/perception/raindrop_cluster_filter/CMakeLists.txt +++ b/perception/raindrop_cluster_filter/CMakeLists.txt @@ -10,11 +10,11 @@ autoware_package() # Targets ament_auto_add_library(low_intensity_cluster_filter SHARED - src/low_intensity_cluster_filter.cpp + src/low_intensity_cluster_filter_node.cpp ) rclcpp_components_register_node(low_intensity_cluster_filter - PLUGIN "low_intensity_cluster_filter::LowIntensityClusterFilter" + PLUGIN "autoware::low_intensity_cluster_filter::LowIntensityClusterFilter" EXECUTABLE low_intensity_cluster_filter_node) diff --git a/perception/raindrop_cluster_filter/package.xml b/perception/raindrop_cluster_filter/package.xml index bff3f71db6f04..a660e42dd91ce 100644 --- a/perception/raindrop_cluster_filter/package.xml +++ b/perception/raindrop_cluster_filter/package.xml @@ -12,9 +12,9 @@ ament_cmake autoware_cmake + autoware_lanelet2_extension autoware_universe_utils detected_object_validation - lanelet2_extension message_filters nav_msgs pcl_conversions diff --git a/perception/raindrop_cluster_filter/src/low_intensity_cluster_filter.cpp b/perception/raindrop_cluster_filter/src/low_intensity_cluster_filter_node.cpp similarity index 95% rename from perception/raindrop_cluster_filter/src/low_intensity_cluster_filter.cpp rename to perception/raindrop_cluster_filter/src/low_intensity_cluster_filter_node.cpp index 4772c355ec0b6..105fc98722a26 100644 --- a/perception/raindrop_cluster_filter/src/low_intensity_cluster_filter.cpp +++ b/perception/raindrop_cluster_filter/src/low_intensity_cluster_filter_node.cpp @@ -12,15 +12,17 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "raindrop_cluster_filter/low_intensity_cluster_filter/low_intensity_cluster_filter.hpp" +#include "low_intensity_cluster_filter_node.hpp" + +#include "autoware/universe_utils/geometry/geometry.hpp" -#include #include #include #include -namespace low_intensity_cluster_filter + +namespace autoware::low_intensity_cluster_filter { LowIntensityClusterFilter::LowIntensityClusterFilter(const rclcpp::NodeOptions & node_options) : Node("low_intensity_cluster_filter_node", node_options), @@ -135,7 +137,7 @@ bool LowIntensityClusterFilter::isValidatedCluster(const sensor_msgs::msg::Point return false; } -} // namespace low_intensity_cluster_filter +} // namespace autoware::low_intensity_cluster_filter #include -RCLCPP_COMPONENTS_REGISTER_NODE(low_intensity_cluster_filter::LowIntensityClusterFilter) +RCLCPP_COMPONENTS_REGISTER_NODE(autoware::low_intensity_cluster_filter::LowIntensityClusterFilter) diff --git a/perception/raindrop_cluster_filter/include/raindrop_cluster_filter/low_intensity_cluster_filter/low_intensity_cluster_filter.hpp b/perception/raindrop_cluster_filter/src/low_intensity_cluster_filter_node.hpp similarity index 78% rename from perception/raindrop_cluster_filter/include/raindrop_cluster_filter/low_intensity_cluster_filter/low_intensity_cluster_filter.hpp rename to perception/raindrop_cluster_filter/src/low_intensity_cluster_filter_node.hpp index b3ebc4d916947..8e21306218ab2 100644 --- a/perception/raindrop_cluster_filter/include/raindrop_cluster_filter/low_intensity_cluster_filter/low_intensity_cluster_filter.hpp +++ b/perception/raindrop_cluster_filter/src/low_intensity_cluster_filter_node.hpp @@ -12,26 +12,25 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef RAINDROP_CLUSTER_FILTER__LOW_INTENSITY_CLUSTER_FILTER__LOW_INTENSITY_CLUSTER_FILTER_HPP_ -#define RAINDROP_CLUSTER_FILTER__LOW_INTENSITY_CLUSTER_FILTER__LOW_INTENSITY_CLUSTER_FILTER_HPP_ +#ifndef LOW_INTENSITY_CLUSTER_FILTER_NODE_HPP_ +#define LOW_INTENSITY_CLUSTER_FILTER_NODE_HPP_ +#include "autoware/universe_utils/ros/debug_publisher.hpp" +#include "autoware/universe_utils/system/stop_watch.hpp" #include "detected_object_validation/utils/utils.hpp" -#include -#include +#include #include -#include +#include "tier4_perception_msgs/msg/detected_objects_with_feature.hpp" #include #include -// #include -#include #include #include -namespace low_intensity_cluster_filter +namespace autoware::low_intensity_cluster_filter { class LowIntensityClusterFilter : public rclcpp::Node @@ -73,6 +72,6 @@ class LowIntensityClusterFilter : public rclcpp::Node std::unique_ptr debug_publisher_ptr_{nullptr}; }; -} // namespace low_intensity_cluster_filter +} // namespace autoware::low_intensity_cluster_filter -#endif // RAINDROP_CLUSTER_FILTER__LOW_INTENSITY_CLUSTER_FILTER__LOW_INTENSITY_CLUSTER_FILTER_HPP_ +#endif // LOW_INTENSITY_CLUSTER_FILTER_NODE_HPP_ diff --git a/perception/simple_object_merger/CMakeLists.txt b/perception/simple_object_merger/CMakeLists.txt index 10bd0efa7b8fc..1a65bdf96050e 100644 --- a/perception/simple_object_merger/CMakeLists.txt +++ b/perception/simple_object_merger/CMakeLists.txt @@ -6,12 +6,12 @@ find_package(autoware_cmake REQUIRED) autoware_package() # Targets -ament_auto_add_library(simple_object_merger_node_component SHARED - src/simple_object_merger_node/simple_object_merger_node.cpp +ament_auto_add_library(${PROJECT_NAME} SHARED + src/simple_object_merger_node.cpp ) -rclcpp_components_register_node(simple_object_merger_node_component - PLUGIN "simple_object_merger::SimpleObjectMergerNode" +rclcpp_components_register_node(${PROJECT_NAME} + PLUGIN "autoware::simple_object_merger::SimpleObjectMergerNode" EXECUTABLE simple_object_merger_node ) diff --git a/perception/simple_object_merger/src/simple_object_merger_node/simple_object_merger_node.cpp b/perception/simple_object_merger/src/simple_object_merger_node.cpp similarity index 96% rename from perception/simple_object_merger/src/simple_object_merger_node/simple_object_merger_node.cpp rename to perception/simple_object_merger/src/simple_object_merger_node.cpp index faa531560ba09..98c7fd03c1e99 100644 --- a/perception/simple_object_merger/src/simple_object_merger_node/simple_object_merger_node.cpp +++ b/perception/simple_object_merger/src/simple_object_merger_node.cpp @@ -12,7 +12,7 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "simple_object_merger/simple_object_merger_node.hpp" +#include "simple_object_merger_node.hpp" #include @@ -65,7 +65,7 @@ autoware_perception_msgs::msg::DetectedObjects::SharedPtr getTransformedObjects( } // namespace -namespace simple_object_merger +namespace autoware::simple_object_merger { using namespace std::literals; using std::chrono::duration; @@ -193,7 +193,7 @@ void SimpleObjectMergerNode::onTimer() pub_objects_->publish(output_objects); } -} // namespace simple_object_merger +} // namespace autoware::simple_object_merger #include "rclcpp_components/register_node_macro.hpp" -RCLCPP_COMPONENTS_REGISTER_NODE(simple_object_merger::SimpleObjectMergerNode) +RCLCPP_COMPONENTS_REGISTER_NODE(autoware::simple_object_merger::SimpleObjectMergerNode) diff --git a/perception/simple_object_merger/include/simple_object_merger/simple_object_merger_node.hpp b/perception/simple_object_merger/src/simple_object_merger_node.hpp similarity index 89% rename from perception/simple_object_merger/include/simple_object_merger/simple_object_merger_node.hpp rename to perception/simple_object_merger/src/simple_object_merger_node.hpp index d25eef20cc676..d5f907ff09ef2 100644 --- a/perception/simple_object_merger/include/simple_object_merger/simple_object_merger_node.hpp +++ b/perception/simple_object_merger/src/simple_object_merger_node.hpp @@ -12,8 +12,8 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef SIMPLE_OBJECT_MERGER__SIMPLE_OBJECT_MERGER_NODE_HPP_ -#define SIMPLE_OBJECT_MERGER__SIMPLE_OBJECT_MERGER_NODE_HPP_ +#ifndef SIMPLE_OBJECT_MERGER_NODE_HPP_ +#define SIMPLE_OBJECT_MERGER_NODE_HPP_ #include "autoware/universe_utils/ros/transform_listener.hpp" #include "rclcpp/rclcpp.hpp" @@ -25,7 +25,7 @@ #include #include -namespace simple_object_merger +namespace autoware::simple_object_merger { using autoware_perception_msgs::msg::DetectedObject; using autoware_perception_msgs::msg::DetectedObjects; @@ -76,6 +76,6 @@ class SimpleObjectMergerNode : public rclcpp::Node size_t input_topic_size; }; -} // namespace simple_object_merger +} // namespace autoware::simple_object_merger -#endif // SIMPLE_OBJECT_MERGER__SIMPLE_OBJECT_MERGER_NODE_HPP_ +#endif // SIMPLE_OBJECT_MERGER_NODE_HPP_ diff --git a/perception/tensorrt_yolox/README.md b/perception/tensorrt_yolox/README.md index ca407b1ff6811..af88e73cc04a6 100644 --- a/perception/tensorrt_yolox/README.md +++ b/perception/tensorrt_yolox/README.md @@ -2,13 +2,13 @@ ## Purpose -This package detects target objects e.g., cars, trucks, bicycles, and pedestrians on a image based on [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) model. +This package detects target objects e.g., cars, trucks, bicycles, and pedestrians and segment target objects such as cars, trucks, buses and pedestrian, building, vegetation, road, sidewalk on a image based on [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) model with multi-header structure. ## Inner-workings / Algorithms ### Cite - + Zheng Ge, Songtao Liu, Feng Wang, Zeming Li, Jian Sun, "YOLOX: Exceeding YOLO Series in 2021", arXiv preprint arXiv:2107.08430, 2021 [[ref](https://arxiv.org/abs/2107.08430)] @@ -22,10 +22,12 @@ Zheng Ge, Songtao Liu, Feng Wang, Zeming Li, Jian Sun, "YOLOX: Exceeding YOLO Se ### Output -| Name | Type | Description | -| ------------- | -------------------------------------------------- | -------------------------------------------------- | -| `out/objects` | `tier4_perception_msgs/DetectedObjectsWithFeature` | The detected objects with 2D bounding boxes | -| `out/image` | `sensor_msgs/Image` | The image with 2D bounding boxes for visualization | +| Name | Type | Description | +| ---------------- | -------------------------------------------------- | ------------------------------------------------------------------- | +| `out/objects` | `tier4_perception_msgs/DetectedObjectsWithFeature` | The detected objects with 2D bounding boxes | +| `out/image` | `sensor_msgs/Image` | The image with 2D bounding boxes for visualization | +| `out/mask` | `sensor_msgs/Image` | The semantic segmentation mask | +| `out/color_mask` | `sensor_msgs/Image` | The colorized image of semantic segmentation mask for visualization | ## Parameters @@ -40,20 +42,33 @@ Zheng Ge, Songtao Liu, Feng Wang, Zeming Li, Jian Sun, "YOLOX: Exceeding YOLO Se ### Node Parameters -| Name | Type | Default Value | Description | -| ----------------------------- | ------ | ------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `model_path` | string | "" | The onnx file name for yolox model | -| `label_path` | string | "" | The label file with label names for detected objects written on it | -| `precision` | string | "fp16" | The inference mode: "fp32", "fp16", "int8" | -| `build_only` | bool | false | shutdown node after TensorRT engine file is built | -| `calibration_algorithm` | string | "MinMax" | Calibration algorithm to be used for quantization when precision==int8. Valid value is one of: Entropy",("Legacy" \| "Percentile"), "MinMax"] | -| `dla_core_id` | int | -1 | If positive ID value is specified, the node assign inference task to the DLA core | -| `quantize_first_layer` | bool | false | If true, set the operating precision for the first (input) layer to be fp16. This option is valid only when precision==int8 | -| `quantize_last_layer` | bool | false | If true, set the operating precision for the last (output) layer to be fp16. This option is valid only when precision==int8 | -| `profile_per_layer` | bool | false | If true, profiler function will be enabled. Since the profile function may affect execution speed, it is recommended to set this flag true only for development purpose. | -| `clip_value` | double | 0.0 | If positive value is specified, the value of each layer output will be clipped between [0.0, clip_value]. This option is valid only when precision==int8 and used to manually specify the dynamic range instead of using any calibration | -| `preprocess_on_gpu` | bool | true | If true, pre-processing is performed on GPU | -| `calibration_image_list_path` | string | "" | Path to a file which contains path to images. Those images will be used for int8 quantization. | +| Name | Type | Default Value | Description | +| -------------------------------------- | ------ | ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `model_path` | string | "" | The onnx file name for yolox model | +| `model_name` | string | "" | The yolox model name:
"yolox-sPlus-T4-960x960-pseudo-finetune" for detection only, could reduce resource and processing_time
"yolox-sPlus-opt-pseudoV2-T4-960x960-T4-seg16cls" for multi-task including semantic segmentation | +| `label_path` | string | "" | The label file with label names for detected objects written on it | +| `precision` | string | "fp16" | The inference mode: "fp32", "fp16", "int8" | +| `build_only` | bool | false | shutdown node after TensorRT engine file is built | +| `calibration_algorithm` | string | "MinMax" | Calibration algorithm to be used for quantization when precision==int8. Valid value is one of: Entropy",("Legacy" \| "Percentile"), "MinMax"] | +| `dla_core_id` | int | -1 | If positive ID value is specified, the node assign inference task to the DLA core | +| `quantize_first_layer` | bool | false | If true, set the operating precision for the first (input) layer to be fp16. This option is valid only when precision==int8 | +| `quantize_last_layer` | bool | false | If true, set the operating precision for the last (output) layer to be fp16. This option is valid only when precision==int8 | +| `profile_per_layer` | bool | false | If true, profiler function will be enabled. Since the profile function may affect execution speed, it is recommended to set this flag true only for development purpose. | +| `clip_value` | double | 0.0 | If positive value is specified, the value of each layer output will be clipped between [0.0, clip_value]. This option is valid only when precision==int8 and used to manually specify the dynamic range instead of using any calibration | +| `preprocess_on_gpu` | bool | true | If true, pre-processing is performed on GPU | +| `calibration_image_list_path` | string | "" | Path to a file which contains path to images. Those images will be used for int8 quantization. | +| `yolox_s_plus_opt_param_path` | string | "" | Path to parameter file | +| `is_publish_color_mask` | bool | false | If true, publish color mask for result visualization | +| `is_roi_overlap_segment` | bool | true | If true, overlay detected object roi onto semantic segmentation to avoid over-filtering pointcloud especially small size objects | +| `overlap_roi_score_threshold` | float | 0.3 | minimum existence_probability of detected roi considered to replace segmentation | +| `roi_overlay_segment_label.UNKNOWN` | bool | true | If true, unknown objects roi will be overlaid onto sematic segmentation mask. | +| `roi_overlay_segment_label.CAR` | bool | false | If true, car objects roi will be overlaid onto sematic segmentation mask. | +| `roi_overlay_segment_label.TRUCK` | bool | false | If true, truck objects roi will be overlaid onto sematic segmentation mask. | +| `roi_overlay_segment_label.BUS` | bool | false | If true, bus objects roi will be overlaid onto sematic segmentation mask. | +| `roi_overlay_segment_label.MOTORCYCLE` | bool | true | If true, motorcycle objects roi will be overlaid onto sematic segmentation mask. | +| `roi_overlay_segment_label.BICYCLE` | bool | true | If true, bicycle objects roi will be overlaid onto sematic segmentation mask. | +| `roi_overlay_segment_label.PEDESTRIAN` | bool | true | If true, pedestrian objects roi will be overlaid onto sematic segmentation mask. | +| `roi_overlay_segment_label.ANIMAL` | bool | true | If true, animal objects roi will be overlaid onto sematic segmentation mask. | ## Assumptions / Known limits @@ -69,6 +84,27 @@ The label contained in detected 2D bounding boxes (i.e., `out/objects`) will be If other labels (case insensitive) are contained in the file specified via the `label_file` parameter, those are labeled as `UNKNOWN`, while detected rectangles are drawn in the visualization result (`out/image`). +The semantic segmentation mask is a gray image whose each pixel is index of one following class: + +| index | semantic name | +| ----- | ---------------- | +| 0 | road | +| 1 | building | +| 2 | wall | +| 3 | obstacle | +| 4 | traffic_light | +| 5 | traffic_sign | +| 6 | person | +| 7 | vehicle | +| 8 | bike | +| 9 | road | +| 10 | sidewalk | +| 11 | roadPaint | +| 12 | curbstone | +| 13 | crosswalk_others | +| 14 | vegetation | +| 15 | sky | + ## Onnx model A sample model (named `yolox-tiny.onnx`) is downloaded by ansible script on env preparation stage, if not, please, follow [Manual downloading of artifacts](https://github.com/autowarefoundation/autoware/tree/main/ansible/roles/artifacts). @@ -79,11 +115,12 @@ hence these parameters are ignored when users specify ONNX models including this This package accepts both `EfficientNMS_TRT` attached ONNXs and [models published from the official YOLOX repository](https://github.com/Megvii-BaseDetection/YOLOX/tree/main/demo/ONNXRuntime#download-onnx-models) (we referred to them as "plain" models). -In addition to `yolox-tiny.onnx`, a custom model named `yolox-sPlus-opt.onnx` is either available. -This model is based on YOLOX-s and tuned to perform more accurate detection with almost comparable execution speed with `yolox-tiny`. +In addition to `yolox-tiny.onnx`, a custom model named `yolox-sPlus-opt-pseudoV2-T4-960x960-T4-seg16cls` is either available. +This model is multi-header structure model which is based on YOLOX-s and tuned to perform more accurate detection with almost comparable execution speed with `yolox-tiny`. To get better results with this model, users are recommended to use some specific running arguments such as `precision:=int8`, `calibration_algorithm:=Entropy`, `clip_value:=6.0`. Users can refer `launch/yolox_sPlus_opt.launch.xml` to see how this model can be used. +Beside detection result, this model also output image semantic segmentation result for pointcloud filtering purpose. All models are automatically converted to TensorRT format. These converted files will be saved in the same directory as specified ONNX files @@ -146,7 +183,7 @@ Please refer [the official document](https://github.com/Megvii-BaseDetection/YOL ## Label file -A sample label file (named `label.txt`)is also downloaded automatically during env preparation process +A sample label file (named `label.txt`) and semantic segmentation color map file (name `semseg_color_map.csv`) are also downloaded automatically during env preparation process (**NOTE:** This file is incompatible with models that output labels for the COCO dataset (e.g., models from the official YOLOX repository)). This file represents the correspondence between class index (integer outputted from YOLOX network) and @@ -157,3 +194,4 @@ with labels according to the order in this file. - - +- diff --git a/perception/tensorrt_yolox/config/yolox_s_plus_opt.param.yaml b/perception/tensorrt_yolox/config/yolox_s_plus_opt.param.yaml index bc67173442094..57c1b40c44a4d 100644 --- a/perception/tensorrt_yolox/config/yolox_s_plus_opt.param.yaml +++ b/perception/tensorrt_yolox/config/yolox_s_plus_opt.param.yaml @@ -1,7 +1,29 @@ +# cspell: ignore semseg /**: ros__parameters: + # refine segmentation mask by overlay roi class + # disable when sematic segmentation accuracy is good enough + is_roi_overlap_segment: true + + # minimum existence_probability of detected roi considered to replace segmentation + overlap_roi_score_threshold: 0.3 + + # publish color mask for result visualization + is_publish_color_mask: false + + roi_overlay_segment_label: + UNKNOWN : true + CAR : false + TRUCK : false + BUS : false + MOTORCYCLE : true + BICYCLE : true + PEDESTRIAN : true + ANIMAL: true + model_path: "$(var data_path)/tensorrt_yolox/$(var model_name).onnx" label_path: "$(var data_path)/tensorrt_yolox/label.txt" + color_map_path: "$(var data_path)/tensorrt_yolox/semseg_color_map.csv" score_threshold: 0.35 nms_threshold: 0.7 precision: "int8" # Operation precision to be used on inference. Valid value is one of: [fp32, fp16, int8]. diff --git a/perception/tensorrt_yolox/include/tensorrt_yolox/preprocess.hpp b/perception/tensorrt_yolox/include/tensorrt_yolox/preprocess.hpp index 3549ae35e70ea..faac6de4e3284 100644 --- a/perception/tensorrt_yolox/include/tensorrt_yolox/preprocess.hpp +++ b/perception/tensorrt_yolox/include/tensorrt_yolox/preprocess.hpp @@ -179,6 +179,21 @@ extern void crop_resize_bilinear_letterbox_nhwc_to_nchw32_batch_gpu( extern void multi_scale_resize_bilinear_letterbox_nhwc_to_nchw32_batch_gpu( float * dst, unsigned char * src, int d_w, int d_h, int d_c, Roi * d_roi, int s_w, int s_h, int s_c, int batch, float norm, cudaStream_t stream); -} // namespace tensorrt_yolox +/** + * @brief Argmax on GPU + * @param[out] dst processed image + * @param[in] src probability map + * @param[in] d_w width for output + * @param[in] d_h height for output + * @param[in] s_w width for input + * @param[in] s_h height for input + * @param[in] s_c channel for input + * @param[in] batch batch size + * @param[in] stream cuda stream + */ +extern void argmax_gpu( + unsigned char * dst, float * src, int d_w, int d_h, int s_w, int s_h, int s_c, int batch, + cudaStream_t stream); +} // namespace tensorrt_yolox #endif // TENSORRT_YOLOX__PREPROCESS_HPP_ diff --git a/perception/tensorrt_yolox/include/tensorrt_yolox/tensorrt_yolox.hpp b/perception/tensorrt_yolox/include/tensorrt_yolox/tensorrt_yolox.hpp index c42222a70c96b..d287c8a44d4cf 100644 --- a/perception/tensorrt_yolox/include/tensorrt_yolox/tensorrt_yolox.hpp +++ b/perception/tensorrt_yolox/include/tensorrt_yolox/tensorrt_yolox.hpp @@ -52,6 +52,13 @@ struct GridAndStride int stride; }; +typedef struct Colormap_ +{ + int id; + std::string name; + std::vector color; +} Colormap; + /** * @class TrtYoloX * @brief TensorRT YOLOX for faster inference @@ -85,7 +92,7 @@ class TrtYoloX const bool use_gpu_preprocess = false, std::string calibration_image_list_file = std::string(), const double norm_factor = 1.0, [[maybe_unused]] const std::string & cache_dir = "", const tensorrt_common::BatchConfig & batch_config = {1, 1, 1}, - const size_t max_workspace_size = (1 << 30)); + const size_t max_workspace_size = (1 << 30), const std::string & color_map_path = ""); /** * @brief Deconstruct TrtYoloX */ @@ -96,7 +103,9 @@ class TrtYoloX * @param[out] objects results for object detection * @param[in] images batched images */ - bool doInference(const std::vector & images, ObjectArrays & objects); + bool doInference( + const std::vector & images, ObjectArrays & objects, std::vector & masks, + std::vector & color_masks); /** * @brief run inference including pre-process and post-process @@ -130,6 +139,22 @@ class TrtYoloX */ void printProfiling(void); + /** + * @brief get num for multitask heads + */ + int getMultitaskNum(void); + + /** + * @brief get colorized masks from index using specific colormap + * @param[out] cmask colorized mask + * @param[in] index multitask index + * @param[in] colormap colormap for masks + */ + void getColorizedMask( + const std::vector & colormap, const cv::Mat & mask, + cv::Mat & colorized_mask); + inline std::vector getColorMap() { return sematic_color_map_; } + private: /** * @brief run preprocess including resizing, letterbox, NHWC2NCHW and toFloat on CPU @@ -177,7 +202,9 @@ class TrtYoloX const cv::Mat & images, int batch_size, ObjectArrays & objects); bool feedforward(const std::vector & images, ObjectArrays & objects); - bool feedforwardAndDecode(const std::vector & images, ObjectArrays & objects); + bool feedforwardAndDecode( + const std::vector & images, ObjectArrays & objects, std::vector & masks, + std::vector & color_masks); void decodeOutputs(float * prob, ObjectArray & objects, float scale, cv::Size & img_size) const; void generateGridsAndStride( const int target_w, const int target_h, const std::vector & strides, @@ -206,6 +233,26 @@ class TrtYoloX void nmsSortedBboxes( const ObjectArray & face_objects, std::vector & picked, float nms_threshold) const; + /** + * @brief get a mask image for a segmentation head + * @param[out] argmax argmax results + * @param[in] prob probability map + * @param[in] dims dimension for probability map + * @param[in] out_w mask width excluding letterbox + * @param[in] out_h mask height excluding letterbox + */ + cv::Mat getMaskImage(float * prob, nvinfer1::Dims dims, int out_w, int out_h); + + /** + * @brief get a mask image on GPUs for a segmentation head + * @param[out] mask image + * @param[in] prob probability map on device + * @param[in] out_w mask width excluding letterbox + * @param[in] out_h mask height excluding letterbox + * @param[in] b current batch + */ + cv::Mat getMaskImageGpu(float * d_prob, nvinfer1::Dims dims, int out_w, int out_h, int b); + std::unique_ptr trt_common_; std::vector input_h_; @@ -249,6 +296,20 @@ class TrtYoloX CudaUniquePtrHost roi_h_; // device pointer for ROI CudaUniquePtr roi_d_; + + // flag whether model has multitasks + int multitask_; + // buff size for segmentation heads + CudaUniquePtr segmentation_out_prob_d_; + CudaUniquePtrHost segmentation_out_prob_h_; + size_t segmentation_out_elem_num_; + size_t segmentation_out_elem_num_per_batch_; + std::vector segmentation_masks_; + // host buffer for argmax postprocessing on GPU + CudaUniquePtrHost argmax_buf_h_; + // device buffer for argmax postprocessing on GPU + CudaUniquePtr argmax_buf_d_; + std::vector sematic_color_map_; }; } // namespace tensorrt_yolox diff --git a/perception/tensorrt_yolox/include/tensorrt_yolox/tensorrt_yolox_node.hpp b/perception/tensorrt_yolox/include/tensorrt_yolox/tensorrt_yolox_node.hpp index 6044148a932ae..48ffedb98441d 100644 --- a/perception/tensorrt_yolox/include/tensorrt_yolox/tensorrt_yolox_node.hpp +++ b/perception/tensorrt_yolox/include/tensorrt_yolox/tensorrt_yolox_node.hpp @@ -15,6 +15,8 @@ #ifndef TENSORRT_YOLOX__TENSORRT_YOLOX_NODE_HPP_ #define TENSORRT_YOLOX__TENSORRT_YOLOX_NODE_HPP_ +#include "object_recognition_utils/object_recognition_utils.hpp" + #include #include #include @@ -25,6 +27,7 @@ #include #include #include +#include #if __has_include() #include @@ -41,10 +44,30 @@ namespace tensorrt_yolox { +// cspell: ignore Semseg using LabelMap = std::map; - +using Label = tier4_perception_msgs::msg::Semantic; class TrtYoloXNode : public rclcpp::Node { + struct RoiOverlaySemsegLabel + { + bool UNKNOWN; + bool CAR; + bool TRUCK; + bool BUS; + bool MOTORCYCLE; + bool BICYCLE; + bool PEDESTRIAN; + bool ANIMAL; + bool isOverlay(const uint8_t label) const + { + return (label == Label::UNKNOWN && UNKNOWN) || (label == Label::CAR && CAR) || + (label == Label::TRUCK && TRUCK) || (label == Label::BUS && BUS) || + (label == Label::ANIMAL && ANIMAL) || (label == Label::MOTORBIKE && MOTORCYCLE) || + (label == Label::BICYCLE && BICYCLE) || (label == Label::PEDESTRIAN && PEDESTRIAN); + }; + }; // struct RoiOverlaySemsegLabel + public: explicit TrtYoloXNode(const rclcpp::NodeOptions & node_options); @@ -53,8 +76,13 @@ class TrtYoloXNode : public rclcpp::Node void onImage(const sensor_msgs::msg::Image::ConstSharedPtr msg); bool readLabelFile(const std::string & label_path); void replaceLabelMap(); - + void overlapSegmentByRoi( + const tensorrt_yolox::Object & object, cv::Mat & mask, const int width, const int height); + int mapRoiLabel2SegLabel(const int32_t roi_label_index); image_transport::Publisher image_pub_; + image_transport::Publisher mask_pub_; + image_transport::Publisher color_mask_pub_; + rclcpp::Publisher::SharedPtr objects_pub_; image_transport::Subscriber image_sub_; @@ -63,6 +91,21 @@ class TrtYoloXNode : public rclcpp::Node LabelMap label_map_; std::unique_ptr trt_yolox_; + bool is_roi_overlap_segment_; + bool is_publish_color_mask_; + float overlap_roi_score_threshold_; + // TODO(badai-nguyen): change to function + std::map remap_roi_to_semantic_ = { + {"UNKNOWN", 3}, // other + {"ANIMAL", 0}, // other + {"PEDESTRIAN", 6}, // person + {"CAR", 7}, // car + {"TRUCK", 7}, // truck + {"BUS", 7}, // bus + {"BICYCLE", 8}, // bicycle + {"MOTORBIKE", 8}, // motorcycle + }; + RoiOverlaySemsegLabel roi_overlay_segment_labels_; std::unique_ptr> stop_watch_ptr_; std::unique_ptr debug_publisher_; }; diff --git a/perception/tensorrt_yolox/launch/yolox_s_plus_opt.launch.xml b/perception/tensorrt_yolox/launch/yolox_s_plus_opt.launch.xml index dd15eda2913ce..e4436a0424be5 100644 --- a/perception/tensorrt_yolox/launch/yolox_s_plus_opt.launch.xml +++ b/perception/tensorrt_yolox/launch/yolox_s_plus_opt.launch.xml @@ -1,22 +1,30 @@ - + - + + + + + diff --git a/perception/tensorrt_yolox/src/preprocess.cu b/perception/tensorrt_yolox/src/preprocess.cu index 3c3087c536f10..f384de2975aa4 100644 --- a/perception/tensorrt_yolox/src/preprocess.cu +++ b/perception/tensorrt_yolox/src/preprocess.cu @@ -594,4 +594,39 @@ void multi_scale_resize_bilinear_letterbox_nhwc_to_nchw32_batch_gpu( cuda_gridsize(N), block, 0, stream>>>(N, dst, src, d_h, d_w, s_h, s_w, d_roi, norm, batch); } +__global__ void argmax_gpu_kernel( + int N, unsigned char * dst, float * src, int dst_h, int dst_w, int src_c, int src_h, int src_w, + int batch) +{ + // NHWC + int index = (blockIdx.x + blockIdx.y * gridDim.x) * blockDim.x + threadIdx.x; + + if (index >= N) return; + int c = 0; + int w = index % dst_w; + int h = index / (dst_w); + + int b; + for (b = 0; b < batch; b++) { + float max_prob = 0.0; + int max_index = 0; + int dst_index = w + dst_w * h + b * dst_h * dst_w; + for (c = 0; c < src_c; c++) { + int src_index = w + src_w * h + c * src_h * src_w + b * src_c * src_h * src_w; + max_index = max_prob < src[src_index] ? c : max_index; + max_prob = max_prob < src[src_index] ? src[src_index] : max_prob; + } + dst[dst_index] = max_index; + } +} + +void argmax_gpu( + unsigned char * dst, float * src, int d_w, int d_h, int s_w, int s_h, int s_c, int batch, + cudaStream_t stream) +{ + int N = d_w * d_h; + argmax_gpu_kernel<<>>( + N, dst, src, d_h, d_w, s_c, s_h, s_w, batch); +} + } // namespace tensorrt_yolox diff --git a/perception/tensorrt_yolox/src/tensorrt_yolox.cpp b/perception/tensorrt_yolox/src/tensorrt_yolox.cpp index 11f68f580acc2..34155c3477003 100644 --- a/perception/tensorrt_yolox/src/tensorrt_yolox.cpp +++ b/perception/tensorrt_yolox/src/tensorrt_yolox.cpp @@ -15,12 +15,18 @@ #include "cuda_utils/cuda_check_error.hpp" #include "cuda_utils/cuda_unique_ptr.hpp" +#include #include #include #include +#include + #include +#include #include +#include +#include #include #include #include @@ -97,6 +103,52 @@ std::vector loadImageList(const std::string & filename, const std:: } return fileList; } + +std::vector get_seg_colormap(const std::string & filename) +{ + std::vector seg_cmap; + if (filename != "not-specified") { + std::vector color_list = loadListFromTextFile(filename); + for (int i = 0; i < static_cast(color_list.size()); i++) { + if (i == 0) { + // Skip header + continue; + } + std::string colormapString = color_list[i]; + tensorrt_yolox::Colormap cmap; + size_t npos = colormapString.find_first_of(','); + assert(npos != std::string::npos); + std::string substr = colormapString.substr(0, npos); + int id = static_cast(std::stoi(trim(substr))); + colormapString.erase(0, npos + 1); + + npos = colormapString.find_first_of(','); + assert(npos != std::string::npos); + substr = colormapString.substr(0, npos); + std::string name = (trim(substr)); + cmap.id = id; + cmap.name = name; + colormapString.erase(0, npos + 1); + while (!colormapString.empty()) { + size_t npos = colormapString.find_first_of(','); + if (npos != std::string::npos) { + substr = colormapString.substr(0, npos); + unsigned char c = (unsigned char)std::stoi(trim(substr)); + cmap.color.push_back(c); + colormapString.erase(0, npos + 1); + } else { + unsigned char c = (unsigned char)std::stoi(trim(colormapString)); + cmap.color.push_back(c); + break; + } + } + + seg_cmap.push_back(cmap); + } + } + return seg_cmap; +} + } // anonymous namespace namespace tensorrt_yolox @@ -106,12 +158,14 @@ TrtYoloX::TrtYoloX( const float score_threshold, const float nms_threshold, tensorrt_common::BuildConfig build_config, const bool use_gpu_preprocess, std::string calibration_image_list_path, const double norm_factor, [[maybe_unused]] const std::string & cache_dir, const tensorrt_common::BatchConfig & batch_config, - const size_t max_workspace_size) + const size_t max_workspace_size, const std::string & color_map_path) { src_width_ = -1; src_height_ = -1; norm_factor_ = norm_factor; batch_size_ = batch_config[2]; + multitask_ = 0; + sematic_color_map_ = get_seg_colormap(color_map_path); if (precision == "int8") { if (build_config.clip_value <= 0.0) { if (calibration_image_list_path.empty()) { @@ -196,9 +250,18 @@ TrtYoloX::TrtYoloX( needs_output_decode_ = false; break; default: + needs_output_decode_ = true; + // The following three values are considered only if the specified model is plain one + num_class_ = num_class; + score_threshold_ = score_threshold; + nms_threshold_ = nms_threshold; + // Todo : Support multiple segmentation heads + multitask_++; + /* std::stringstream s; s << "\"" << model_path << "\" is unsupported format"; throw std::runtime_error{s.str()}; + */ } // GPU memory allocation @@ -234,10 +297,31 @@ TrtYoloX::TrtYoloX( out_scores_d_ = cuda_utils::make_unique(batch_config[2] * max_detections_); out_classes_d_ = cuda_utils::make_unique(batch_config[2] * max_detections_); } + if (multitask_) { + // Allocate buffer for segmentation + segmentation_out_elem_num_ = 0; + for (int m = 0; m < multitask_; m++) { + const auto output_dims = + trt_common_->getBindingDimensions(m + 2); // 0 : input, 1 : output for detections + size_t out_elem_num = std::accumulate( + output_dims.d + 1, output_dims.d + output_dims.nbDims, 1, std::multiplies()); + out_elem_num = out_elem_num * batch_config[2]; + segmentation_out_elem_num_ += out_elem_num; + } + segmentation_out_elem_num_per_batch_ = + static_cast(segmentation_out_elem_num_ / batch_config[2]); + segmentation_out_prob_d_ = cuda_utils::make_unique(segmentation_out_elem_num_); + segmentation_out_prob_h_ = + cuda_utils::make_unique_host(segmentation_out_elem_num_, cudaHostAllocPortable); + } if (use_gpu_preprocess) { use_gpu_preprocess_ = true; image_buf_h_ = nullptr; image_buf_d_ = nullptr; + if (multitask_) { + argmax_buf_h_ = nullptr; + argmax_buf_d_ = nullptr; + } } else { use_gpu_preprocess_ = false; } @@ -252,6 +336,9 @@ TrtYoloX::~TrtYoloX() if (image_buf_d_) { image_buf_d_.reset(); } + if (argmax_buf_d_) { + argmax_buf_d_.reset(); + } } } @@ -294,6 +381,26 @@ void TrtYoloX::initPreprocessBuffer(int width, int height) width * height * 3 * batch_size_, cudaHostAllocWriteCombined); image_buf_d_ = cuda_utils::make_unique(width * height * 3 * batch_size_); } + if (multitask_) { + size_t argmax_out_elem_num = 0; + for (int m = 0; m < multitask_; m++) { + const auto output_dims = + trt_common_->getBindingDimensions(m + 2); // 0 : input, 1 : output for detections + const float scale = std::min( + output_dims.d[3] / static_cast(width), + output_dims.d[2] / static_cast(height)); + int out_w = static_cast(width * scale); + int out_h = static_cast(height * scale); + // size_t out_elem_num = std::accumulate( + // output_dims.d + 1, output_dims.d + output_dims.nbDims, 1, std::multiplies()); + // out_elem_num = out_elem_num * batch_size_; + size_t out_elem_num = out_w * out_h * batch_size_; + argmax_out_elem_num += out_elem_num; + } + argmax_buf_h_ = + cuda_utils::make_unique_host(argmax_out_elem_num, cudaHostAllocPortable); + argmax_buf_d_ = cuda_utils::make_unique(argmax_out_elem_num); + } } } @@ -321,6 +428,12 @@ void TrtYoloX::preprocessGpu(const std::vector & images) if (image_buf_d_) { image_buf_d_.reset(); } + if (argmax_buf_h_) { + argmax_buf_h_.reset(); + } + if (argmax_buf_d_) { + argmax_buf_d_.reset(); + } } } src_width_ = width; @@ -333,6 +446,7 @@ void TrtYoloX::preprocessGpu(const std::vector & images) const float input_height = static_cast(input_dims.d[2]); const float input_width = static_cast(input_dims.d[3]); int b = 0; + size_t argmax_out_elem_num = 0; for (const auto & image : images) { if (!image_buf_h_) { const float scale = std::min(input_width / image.cols, input_height / image.rows); @@ -348,7 +462,31 @@ void TrtYoloX::preprocessGpu(const std::vector & images) image_buf_h_.get() + index, &image.data[0], image.cols * image.rows * 3 * sizeof(unsigned char)); b++; + + if (multitask_) { + for (int m = 0; m < multitask_; m++) { + const auto output_dims = + trt_common_->getBindingDimensions(m + 2); // 0: input, 1: output for detections + const float scale = std::min( + output_dims.d[3] / static_cast(image.cols), + output_dims.d[2] / static_cast(image.rows)); + int out_w = static_cast(image.cols * scale); + int out_h = static_cast(image.rows * scale); + argmax_out_elem_num += out_w * out_h * batch_size; + } + } + } + + if (multitask_) { + if (!argmax_buf_h_) { + argmax_buf_h_ = + cuda_utils::make_unique_host(argmax_out_elem_num, cudaHostAllocPortable); + } + if (!argmax_buf_d_) { + argmax_buf_d_ = cuda_utils::make_unique(argmax_out_elem_num); + } } + // Copy into device memory CHECK_CUDA_ERROR(cudaMemcpyAsync( image_buf_d_.get(), image_buf_h_.get(), @@ -406,7 +544,9 @@ void TrtYoloX::preprocess(const std::vector & images) // No Need for Sync } -bool TrtYoloX::doInference(const std::vector & images, ObjectArrays & objects) +bool TrtYoloX::doInference( + const std::vector & images, ObjectArrays & objects, std::vector & masks, + [[maybe_unused]] std::vector & color_masks) { if (!trt_common_->isInitialized()) { return false; @@ -419,7 +559,7 @@ bool TrtYoloX::doInference(const std::vector & images, ObjectArrays & o } if (needs_output_decode_) { - return feedforwardAndDecode(images, objects); + return feedforwardAndDecode(images, objects, masks, color_masks); } else { return feedforward(images, objects); } @@ -659,6 +799,8 @@ void TrtYoloX::multiScalePreprocess(const cv::Mat & image, const std::vector & images, ObjectArrays & objects, const std::vector & rois) { + std::vector masks; + std::vector color_masks; if (!trt_common_->isInitialized()) { return false; } @@ -669,7 +811,7 @@ bool TrtYoloX::doInferenceWithRoi( } if (needs_output_decode_) { - return feedforwardAndDecode(images, objects); + return feedforwardAndDecode(images, objects, masks, color_masks); } else { return feedforward(images, objects); } @@ -678,7 +820,6 @@ bool TrtYoloX::doInferenceWithRoi( bool TrtYoloX::doMultiScaleInference( const cv::Mat & image, ObjectArrays & objects, const std::vector & rois) { - std::vector images; if (!trt_common_->isInitialized()) { return false; } @@ -747,10 +888,14 @@ bool TrtYoloX::feedforward(const std::vector & images, ObjectArrays & o return true; } -bool TrtYoloX::feedforwardAndDecode(const std::vector & images, ObjectArrays & objects) +bool TrtYoloX::feedforwardAndDecode( + const std::vector & images, ObjectArrays & objects, std::vector & out_masks, + [[maybe_unused]] std::vector & color_masks) { std::vector buffers = {input_d_.get(), out_prob_d_.get()}; - + if (multitask_) { + buffers = {input_d_.get(), out_prob_d_.get(), segmentation_out_prob_d_.get()}; + } trt_common_->enqueueV2(buffers.data(), *stream_, nullptr); const auto batch_size = images.size(); @@ -758,6 +903,11 @@ bool TrtYoloX::feedforwardAndDecode(const std::vector & images, ObjectA CHECK_CUDA_ERROR(cudaMemcpyAsync( out_prob_h_.get(), out_prob_d_.get(), sizeof(float) * out_elem_num_, cudaMemcpyDeviceToHost, *stream_)); + if (multitask_ && !use_gpu_preprocess_) { + CHECK_CUDA_ERROR(cudaMemcpyAsync( + segmentation_out_prob_h_.get(), segmentation_out_prob_d_.get(), + sizeof(float) * segmentation_out_elem_num_, cudaMemcpyDeviceToHost, *stream_)); + } cudaStreamSynchronize(*stream_); objects.clear(); @@ -766,7 +916,43 @@ bool TrtYoloX::feedforwardAndDecode(const std::vector & images, ObjectA float * batch_prob = out_prob_h_.get() + (i * out_elem_num_per_batch_); ObjectArray object_array; decodeOutputs(batch_prob, object_array, scales_[i], image_size); + // add refine mask using object objects.emplace_back(object_array); + if (multitask_) { + segmentation_masks_.clear(); + + size_t counter = 0; + int batch = + static_cast(segmentation_out_elem_num_ / segmentation_out_elem_num_per_batch_); + for (int m = 0; m < multitask_; m++) { + const auto output_dims = + trt_common_->getBindingDimensions(m + 2); // 0 : input, 1 : output for detections + size_t out_elem_num = std::accumulate( + output_dims.d + 1, output_dims.d + output_dims.nbDims, 1, std::multiplies()); + out_elem_num = out_elem_num * batch; + const float scale = std::min( + output_dims.d[3] / static_cast(image_size.width), + output_dims.d[2] / static_cast(image_size.height)); + int out_w = static_cast(image_size.width * scale); + int out_h = static_cast(image_size.height * scale); + cv::Mat mask; + if (use_gpu_preprocess_) { + float * d_segmentation_results = + segmentation_out_prob_d_.get() + (i * segmentation_out_elem_num_per_batch_); + mask = getMaskImageGpu(&(d_segmentation_results[counter]), output_dims, out_w, out_h, i); + } else { + float * segmentation_results = + segmentation_out_prob_h_.get() + (i * segmentation_out_elem_num_per_batch_); + mask = getMaskImage(&(segmentation_results[counter]), output_dims, out_w, out_h); + } + segmentation_masks_.emplace_back(std::move(mask)); + counter += out_elem_num; + } + // semantic segmentation was fixed as first task + out_masks.at(i) = segmentation_masks_.at(0); + } else { + continue; + } } return true; } @@ -1036,4 +1222,72 @@ void TrtYoloX::nmsSortedBboxes( } } +cv::Mat TrtYoloX::getMaskImageGpu(float * d_prob, nvinfer1::Dims dims, int out_w, int out_h, int b) +{ + // NCHW + int classes = dims.d[1]; + int height = dims.d[2]; + int width = dims.d[3]; + cv::Mat mask = cv::Mat::zeros(out_h, out_w, CV_8UC1); + int index = b * out_w * out_h; + argmax_gpu( + (unsigned char *)argmax_buf_d_.get() + index, d_prob, out_w, out_h, width, height, classes, 1, + *stream_); + CHECK_CUDA_ERROR(cudaMemcpyAsync( + argmax_buf_h_.get(), argmax_buf_d_.get(), sizeof(unsigned char) * 1 * out_w * out_h, + cudaMemcpyDeviceToHost, *stream_)); + cudaStreamSynchronize(*stream_); + std::memcpy(mask.data, argmax_buf_h_.get() + index, sizeof(unsigned char) * 1 * out_w * out_h); + return mask; +} + +cv::Mat TrtYoloX::getMaskImage(float * prob, nvinfer1::Dims dims, int out_w, int out_h) +{ + // NCHW + int classes = dims.d[1]; + int height = dims.d[2]; + int width = dims.d[3]; + cv::Mat mask = cv::Mat::zeros(out_h, out_w, CV_8UC1); + // argmax + // #pragma omp parallel for + for (int y = 0; y < out_h; y++) { + for (int x = 0; x < out_w; x++) { + float max = 0.0; + int index = 0; + for (int c = 0; c < classes; c++) { + float value = prob[c * height * width + y * width + x]; + if (max < value) { + max = value; + index = c; + } + } + mask.at(y, x) = index; + } + } + return mask; +} + +int TrtYoloX::getMultitaskNum(void) +{ + return multitask_; +} + +void TrtYoloX::getColorizedMask( + const std::vector & colormap, const cv::Mat & mask, cv::Mat & cmask) +{ + int width = mask.cols; + int height = mask.rows; + if ((cmask.cols != mask.cols) || (cmask.rows != mask.rows)) { + throw std::runtime_error("input and output image have difference size."); + } + for (int y = 0; y < height; y++) { + for (int x = 0; x < width; x++) { + unsigned char id = mask.at(y, x); + cmask.at(y, x)[0] = colormap[id].color[2]; + cmask.at(y, x)[1] = colormap[id].color[1]; + cmask.at(y, x)[2] = colormap[id].color[0]; + } + } +} + } // namespace tensorrt_yolox diff --git a/perception/tensorrt_yolox/src/tensorrt_yolox_node.cpp b/perception/tensorrt_yolox/src/tensorrt_yolox_node.cpp index 4ee18b99e4bc6..c13384918bd9c 100644 --- a/perception/tensorrt_yolox/src/tensorrt_yolox_node.cpp +++ b/perception/tensorrt_yolox/src/tensorrt_yolox_node.cpp @@ -33,7 +33,7 @@ TrtYoloXNode::TrtYoloXNode(const rclcpp::NodeOptions & node_options) stop_watch_ptr_ = std::make_unique>(); debug_publisher_ = - std::make_unique(this, "tensorrt_yolox"); + std::make_unique(this, this->get_name()); stop_watch_ptr_->tic("cyclic_time"); stop_watch_ptr_->tic("processing_time"); } @@ -96,25 +96,51 @@ TrtYoloXNode::TrtYoloXNode(const rclcpp::NodeOptions & node_options) ("Path to a file which contains path to images." "Those images will be used for int8 quantization.")); + std::string color_map_path = declare_parameter_with_description( + "color_map_path", "", ("Path to a file which contains path to color map.")); if (!readLabelFile(label_path)) { RCLCPP_ERROR(this->get_logger(), "Could not find label file"); rclcpp::shutdown(); } + + is_roi_overlap_segment_ = declare_parameter("is_roi_overlap_segment"); + is_publish_color_mask_ = declare_parameter("is_publish_color_mask"); + overlap_roi_score_threshold_ = declare_parameter("overlap_roi_score_threshold"); + roi_overlay_segment_labels_.UNKNOWN = + declare_parameter("roi_overlay_segment_label.UNKNOWN"); + roi_overlay_segment_labels_.CAR = declare_parameter("roi_overlay_segment_label.CAR"); + roi_overlay_segment_labels_.TRUCK = declare_parameter("roi_overlay_segment_label.TRUCK"); + roi_overlay_segment_labels_.BUS = declare_parameter("roi_overlay_segment_label.BUS"); + roi_overlay_segment_labels_.MOTORCYCLE = + declare_parameter("roi_overlay_segment_label.MOTORCYCLE"); + roi_overlay_segment_labels_.BICYCLE = + declare_parameter("roi_overlay_segment_label.BICYCLE"); + roi_overlay_segment_labels_.PEDESTRIAN = + declare_parameter("roi_overlay_segment_label.PEDESTRIAN"); + roi_overlay_segment_labels_.ANIMAL = declare_parameter("roi_overlay_segment_label.ANIMAL"); replaceLabelMap(); tensorrt_common::BuildConfig build_config( calibration_algorithm, dla_core_id, quantize_first_layer, quantize_last_layer, profile_per_layer, clip_value); + const double norm_factor = 1.0; + const std::string cache_dir = ""; + const tensorrt_common::BatchConfig batch_config{1, 1, 1}; + const size_t max_workspace_size = (1 << 30); + trt_yolox_ = std::make_unique( model_path, precision, label_map_.size(), score_threshold, nms_threshold, build_config, - preprocess_on_gpu, calibration_image_list_path); + preprocess_on_gpu, calibration_image_list_path, norm_factor, cache_dir, batch_config, + max_workspace_size, color_map_path); timer_ = rclcpp::create_timer(this, get_clock(), 100ms, std::bind(&TrtYoloXNode::onConnect, this)); objects_pub_ = this->create_publisher( "~/out/objects", 1); + mask_pub_ = image_transport::create_publisher(this, "~/out/mask"); + color_mask_pub_ = image_transport::create_publisher(this, "~/out/color_mask"); image_pub_ = image_transport::create_publisher(this, "~/out/image"); if (declare_parameter("build_only", false)) { @@ -129,7 +155,8 @@ void TrtYoloXNode::onConnect() if ( objects_pub_->get_subscription_count() == 0 && objects_pub_->get_intra_process_subscription_count() == 0 && - image_pub_.getNumSubscribers() == 0) { + image_pub_.getNumSubscribers() == 0 && mask_pub_.getNumSubscribers() == 0 && + color_mask_pub_.getNumSubscribers() == 0) { image_sub_.shutdown(); } else if (!image_sub_) { image_sub_ = image_transport::create_subscription( @@ -154,10 +181,16 @@ void TrtYoloXNode::onImage(const sensor_msgs::msg::Image::ConstSharedPtr msg) const auto height = in_image_ptr->image.rows; tensorrt_yolox::ObjectArrays objects; - if (!trt_yolox_->doInference({in_image_ptr->image}, objects)) { + std::vector masks = {cv::Mat(cv::Size(height, width), CV_8UC1, cv::Scalar(0))}; + std::vector color_masks = { + cv::Mat(cv::Size(height, width), CV_8UC3, cv::Scalar(0, 0, 0))}; + + if (!trt_yolox_->doInference({in_image_ptr->image}, objects, masks, color_masks)) { RCLCPP_WARN(this->get_logger(), "Fail to inference"); return; } + auto & mask = masks.at(0); + for (const auto & yolox_object : objects.at(0)) { tier4_perception_msgs::msg::DetectedObjectWithFeature object; object.feature.roi.x_offset = yolox_object.x_offset; @@ -177,9 +210,21 @@ void TrtYoloXNode::onImage(const sensor_msgs::msg::Image::ConstSharedPtr msg) cv::rectangle( in_image_ptr->image, cv::Point(left, top), cv::Point(right, bottom), cv::Scalar(0, 0, 255), 3, 8, 0); + // Refine mask: replacing segmentation mask by roi class + // This should remove when the segmentation accuracy is high + if (is_roi_overlap_segment_ && trt_yolox_->getMultitaskNum() > 0) { + overlapSegmentByRoi(yolox_object, mask, width, height); + } + } + // TODO(badai-nguyen): consider to change to 4bits data transfer + if (trt_yolox_->getMultitaskNum() > 0) { + sensor_msgs::msg::Image::SharedPtr out_mask_msg = + cv_bridge::CvImage(std_msgs::msg::Header(), sensor_msgs::image_encodings::MONO8, mask) + .toImageMsg(); + out_mask_msg->header = msg->header; + mask_pub_.publish(out_mask_msg); } image_pub_.publish(in_image_ptr->toImageMsg()); - out_objects.header = msg->header; objects_pub_->publish(out_objects); @@ -198,6 +243,16 @@ void TrtYoloXNode::onImage(const sensor_msgs::msg::Image::ConstSharedPtr msg) debug_publisher_->publish( "debug/pipeline_latency_ms", pipeline_latency_ms); } + + if (is_publish_color_mask_ && trt_yolox_->getMultitaskNum() > 0) { + cv::Mat color_mask = cv::Mat::zeros(mask.rows, mask.cols, CV_8UC3); + trt_yolox_->getColorizedMask(trt_yolox_->getColorMap(), mask, color_mask); + sensor_msgs::msg::Image::SharedPtr output_color_mask_msg = + cv_bridge::CvImage(std_msgs::msg::Header(), sensor_msgs::image_encodings::BGR8, color_mask) + .toImageMsg(); + output_color_mask_msg->header = msg->header; + color_mask_pub_.publish(output_color_mask_msg); + } } bool TrtYoloXNode::readLabelFile(const std::string & label_path) @@ -234,6 +289,37 @@ void TrtYoloXNode::replaceLabelMap() } } +int TrtYoloXNode::mapRoiLabel2SegLabel(const int32_t roi_label_index) +{ + if (roi_overlay_segment_labels_.isOverlay(static_cast(roi_label_index))) { + std::string label = label_map_[roi_label_index]; + + return remap_roi_to_semantic_[label]; + } + return -1; +} + +void TrtYoloXNode::overlapSegmentByRoi( + const tensorrt_yolox::Object & roi_object, cv::Mat & mask, const int orig_width, + const int orig_height) +{ + if (roi_object.score < overlap_roi_score_threshold_) return; + int seg_class_index = mapRoiLabel2SegLabel(roi_object.type); + if (seg_class_index < 0) return; + + const float scale_x = static_cast(mask.cols) / static_cast(orig_width); + const float scale_y = static_cast(mask.rows) / static_cast(orig_height); + const int roi_width = static_cast(roi_object.width * scale_x); + const int roi_height = static_cast(roi_object.height * scale_y); + const int roi_x_offset = static_cast(roi_object.x_offset * scale_x); + const int roi_y_offset = static_cast(roi_object.y_offset * scale_y); + + cv::Mat replace_roi( + cv::Size(roi_width, roi_height), mask.type(), static_cast(seg_class_index)); + replace_roi.copyTo(mask.colRange(roi_x_offset, roi_x_offset + roi_width) + .rowRange(roi_y_offset, roi_y_offset + roi_height)); +} + } // namespace tensorrt_yolox #include "rclcpp_components/register_node_macro.hpp" diff --git a/perception/tensorrt_yolox/src/yolox_single_image_inference_node.cpp b/perception/tensorrt_yolox/src/yolox_single_image_inference_node.cpp index 0657f0096b07e..360f41e470e38 100644 --- a/perception/tensorrt_yolox/src/yolox_single_image_inference_node.cpp +++ b/perception/tensorrt_yolox/src/yolox_single_image_inference_node.cpp @@ -47,7 +47,9 @@ class YoloXSingleImageInferenceNode : public rclcpp::Node auto trt_yolox = std::make_unique(model_path, precision); auto image = cv::imread(image_path); tensorrt_yolox::ObjectArrays objects; - trt_yolox->doInference({image}, objects); + std::vector masks; + std::vector color_masks; + trt_yolox->doInference({image}, objects, masks, color_masks); for (const auto & object : objects[0]) { const auto left = object.x_offset; const auto top = object.y_offset; diff --git a/perception/tracking_object_merger/CMakeLists.txt b/perception/tracking_object_merger/CMakeLists.txt index 9c8e5a321d9be..7e7c698365922 100644 --- a/perception/tracking_object_merger/CMakeLists.txt +++ b/perception/tracking_object_merger/CMakeLists.txt @@ -22,22 +22,22 @@ include_directories( ${EIGEN3_INCLUDE_DIR} ) -ament_auto_add_library(decorative_tracker_merger_node SHARED - src/data_association/data_association.cpp - src/data_association/mu_successive_shortest_path/mu_successive_shortest_path_wrapper.cpp - src/decorative_tracker_merger.cpp +ament_auto_add_library(${PROJECT_NAME} SHARED + src/association/data_association.cpp + src/association/solver/mu_successive_shortest_path_wrapper.cpp + src/decorative_tracker_merger_node.cpp src/utils/utils.cpp src/utils/tracker_state.cpp ) -target_link_libraries(decorative_tracker_merger_node +target_link_libraries(${PROJECT_NAME} Eigen3::Eigen glog::glog ) -rclcpp_components_register_node(decorative_tracker_merger_node - PLUGIN "tracking_object_merger::DecorativeTrackerMergerNode" - EXECUTABLE decorative_tracker_merger +rclcpp_components_register_node(${PROJECT_NAME} + PLUGIN "autoware::tracking_object_merger::DecorativeTrackerMergerNode" + EXECUTABLE decorative_tracker_merger_node ) ament_auto_package(INSTALL_TO_SHARE diff --git a/perception/tracking_object_merger/include/tracking_object_merger/data_association/data_association.hpp b/perception/tracking_object_merger/include/autoware_tracking_object_merger/association/data_association.hpp similarity index 77% rename from perception/tracking_object_merger/include/tracking_object_merger/data_association/data_association.hpp rename to perception/tracking_object_merger/include/autoware_tracking_object_merger/association/data_association.hpp index 96a474f9dac95..a272e42a74e80 100644 --- a/perception/tracking_object_merger/include/tracking_object_merger/data_association/data_association.hpp +++ b/perception/tracking_object_merger/include/autoware_tracking_object_merger/association/data_association.hpp @@ -16,26 +16,28 @@ // Author: v1.0 Yukihiro Saito // -#ifndef TRACKING_OBJECT_MERGER__DATA_ASSOCIATION__DATA_ASSOCIATION_HPP_ -#define TRACKING_OBJECT_MERGER__DATA_ASSOCIATION__DATA_ASSOCIATION_HPP_ - -#include -#include -#include -#include +#ifndef AUTOWARE_TRACKING_OBJECT_MERGER__ASSOCIATION__DATA_ASSOCIATION_HPP_ +#define AUTOWARE_TRACKING_OBJECT_MERGER__ASSOCIATION__DATA_ASSOCIATION_HPP_ #define EIGEN_MPL2_ONLY -#include "tracking_object_merger/data_association/solver/gnn_solver.hpp" -#include "tracking_object_merger/utils/tracker_state.hpp" + +#include "autoware_tracking_object_merger/association/solver/gnn_solver.hpp" +#include "autoware_tracking_object_merger/utils/tracker_state.hpp" #include #include -#include -#include +#include "autoware_perception_msgs/msg/detected_objects.hpp" +#include "autoware_perception_msgs/msg/tracked_objects.hpp" +#include +#include #include +#include +#include +namespace autoware::tracking_object_merger +{ class DataAssociation { private: @@ -68,4 +70,6 @@ class DataAssociation virtual ~DataAssociation() {} }; -#endif // TRACKING_OBJECT_MERGER__DATA_ASSOCIATION__DATA_ASSOCIATION_HPP_ +} // namespace autoware::tracking_object_merger + +#endif // AUTOWARE_TRACKING_OBJECT_MERGER__ASSOCIATION__DATA_ASSOCIATION_HPP_ diff --git a/localization/yabloc/yabloc_pose_initializer/src/camera/camera_pose_initializer_node.cpp b/perception/tracking_object_merger/include/autoware_tracking_object_merger/association/solver/gnn_solver.hpp similarity index 56% rename from localization/yabloc/yabloc_pose_initializer/src/camera/camera_pose_initializer_node.cpp rename to perception/tracking_object_merger/include/autoware_tracking_object_merger/association/solver/gnn_solver.hpp index 2bcfe073351f8..4cea0e3cbe96f 100644 --- a/localization/yabloc/yabloc_pose_initializer/src/camera/camera_pose_initializer_node.cpp +++ b/perception/tracking_object_merger/include/autoware_tracking_object_merger/association/solver/gnn_solver.hpp @@ -12,15 +12,11 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "yabloc_pose_initializer/camera/camera_pose_initializer.hpp" +#ifndef AUTOWARE_TRACKING_OBJECT_MERGER__ASSOCIATION__SOLVER__GNN_SOLVER_HPP_ +#define AUTOWARE_TRACKING_OBJECT_MERGER__ASSOCIATION__SOLVER__GNN_SOLVER_HPP_ -int main(int argc, char * argv[]) -{ - rclcpp::init(argc, argv); - auto node = std::make_shared(); - rclcpp::executors::MultiThreadedExecutor executor; - executor.add_node(node); - executor.spin(); - rclcpp::shutdown(); - return 0; -} +#include "autoware_tracking_object_merger/association/solver/gnn_solver_interface.hpp" +#include "autoware_tracking_object_merger/association/solver/mu_ssp.hpp" +#include "autoware_tracking_object_merger/association/solver/ssp.hpp" + +#endif // AUTOWARE_TRACKING_OBJECT_MERGER__ASSOCIATION__SOLVER__GNN_SOLVER_HPP_ diff --git a/perception/tracking_object_merger/include/tracking_object_merger/data_association/solver/gnn_solver_interface.hpp b/perception/tracking_object_merger/include/autoware_tracking_object_merger/association/solver/gnn_solver_interface.hpp similarity index 71% rename from perception/tracking_object_merger/include/tracking_object_merger/data_association/solver/gnn_solver_interface.hpp rename to perception/tracking_object_merger/include/autoware_tracking_object_merger/association/solver/gnn_solver_interface.hpp index e915b5d2a9e7b..d751075773a09 100644 --- a/perception/tracking_object_merger/include/tracking_object_merger/data_association/solver/gnn_solver_interface.hpp +++ b/perception/tracking_object_merger/include/autoware_tracking_object_merger/association/solver/gnn_solver_interface.hpp @@ -12,13 +12,13 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef TRACKING_OBJECT_MERGER__DATA_ASSOCIATION__SOLVER__GNN_SOLVER_INTERFACE_HPP_ -#define TRACKING_OBJECT_MERGER__DATA_ASSOCIATION__SOLVER__GNN_SOLVER_INTERFACE_HPP_ +#ifndef AUTOWARE_TRACKING_OBJECT_MERGER__ASSOCIATION__SOLVER__GNN_SOLVER_INTERFACE_HPP_ +#define AUTOWARE_TRACKING_OBJECT_MERGER__ASSOCIATION__SOLVER__GNN_SOLVER_INTERFACE_HPP_ #include #include -namespace gnn_solver +namespace autoware::tracking_object_merger::gnn_solver { class GnnSolverInterface { @@ -30,6 +30,6 @@ class GnnSolverInterface const std::vector> & cost, std::unordered_map * direct_assignment, std::unordered_map * reverse_assignment) = 0; }; -} // namespace gnn_solver +} // namespace autoware::tracking_object_merger::gnn_solver -#endif // TRACKING_OBJECT_MERGER__DATA_ASSOCIATION__SOLVER__GNN_SOLVER_INTERFACE_HPP_ +#endif // AUTOWARE_TRACKING_OBJECT_MERGER__ASSOCIATION__SOLVER__GNN_SOLVER_INTERFACE_HPP_ diff --git a/perception/tracking_object_merger/include/tracking_object_merger/data_association/solver/mu_successive_shortest_path.hpp b/perception/tracking_object_merger/include/autoware_tracking_object_merger/association/solver/mu_ssp.hpp similarity index 68% rename from perception/tracking_object_merger/include/tracking_object_merger/data_association/solver/mu_successive_shortest_path.hpp rename to perception/tracking_object_merger/include/autoware_tracking_object_merger/association/solver/mu_ssp.hpp index 5c0ebc04fdde3..47c23bc8eb1b1 100644 --- a/perception/tracking_object_merger/include/tracking_object_merger/data_association/solver/mu_successive_shortest_path.hpp +++ b/perception/tracking_object_merger/include/autoware_tracking_object_merger/association/solver/mu_ssp.hpp @@ -12,15 +12,15 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef TRACKING_OBJECT_MERGER__DATA_ASSOCIATION__SOLVER__MU_SUCCESSIVE_SHORTEST_PATH_HPP_ -#define TRACKING_OBJECT_MERGER__DATA_ASSOCIATION__SOLVER__MU_SUCCESSIVE_SHORTEST_PATH_HPP_ +#ifndef AUTOWARE_TRACKING_OBJECT_MERGER__ASSOCIATION__SOLVER__MU_SSP_HPP_ +#define AUTOWARE_TRACKING_OBJECT_MERGER__ASSOCIATION__SOLVER__MU_SSP_HPP_ -#include "tracking_object_merger/data_association/solver/gnn_solver_interface.hpp" +#include "autoware_tracking_object_merger/association/solver/gnn_solver_interface.hpp" #include #include -namespace gnn_solver +namespace autoware::tracking_object_merger::gnn_solver { class MuSSP : public GnnSolverInterface { @@ -32,6 +32,6 @@ class MuSSP : public GnnSolverInterface const std::vector> & cost, std::unordered_map * direct_assignment, std::unordered_map * reverse_assignment) override; }; -} // namespace gnn_solver +} // namespace autoware::tracking_object_merger::gnn_solver -#endif // TRACKING_OBJECT_MERGER__DATA_ASSOCIATION__SOLVER__MU_SUCCESSIVE_SHORTEST_PATH_HPP_ +#endif // AUTOWARE_TRACKING_OBJECT_MERGER__ASSOCIATION__SOLVER__MU_SSP_HPP_ diff --git a/perception/tracking_object_merger/include/tracking_object_merger/data_association/solver/successive_shortest_path.hpp b/perception/tracking_object_merger/include/autoware_tracking_object_merger/association/solver/ssp.hpp similarity index 68% rename from perception/tracking_object_merger/include/tracking_object_merger/data_association/solver/successive_shortest_path.hpp rename to perception/tracking_object_merger/include/autoware_tracking_object_merger/association/solver/ssp.hpp index 47a737cf58298..bcfac73f3e43b 100644 --- a/perception/tracking_object_merger/include/tracking_object_merger/data_association/solver/successive_shortest_path.hpp +++ b/perception/tracking_object_merger/include/autoware_tracking_object_merger/association/solver/ssp.hpp @@ -12,15 +12,15 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef TRACKING_OBJECT_MERGER__DATA_ASSOCIATION__SOLVER__SUCCESSIVE_SHORTEST_PATH_HPP_ -#define TRACKING_OBJECT_MERGER__DATA_ASSOCIATION__SOLVER__SUCCESSIVE_SHORTEST_PATH_HPP_ +#ifndef AUTOWARE_TRACKING_OBJECT_MERGER__ASSOCIATION__SOLVER__SSP_HPP_ +#define AUTOWARE_TRACKING_OBJECT_MERGER__ASSOCIATION__SOLVER__SSP_HPP_ -#include "tracking_object_merger/data_association/solver/gnn_solver_interface.hpp" +#include "autoware_tracking_object_merger/association/solver/gnn_solver_interface.hpp" #include #include -namespace gnn_solver +namespace autoware::tracking_object_merger::gnn_solver { class SSP : public GnnSolverInterface { @@ -32,6 +32,6 @@ class SSP : public GnnSolverInterface const std::vector> & cost, std::unordered_map * direct_assignment, std::unordered_map * reverse_assignment) override; }; -} // namespace gnn_solver +} // namespace autoware::tracking_object_merger::gnn_solver -#endif // TRACKING_OBJECT_MERGER__DATA_ASSOCIATION__SOLVER__SUCCESSIVE_SHORTEST_PATH_HPP_ +#endif // AUTOWARE_TRACKING_OBJECT_MERGER__ASSOCIATION__SOLVER__SSP_HPP_ diff --git a/perception/tracking_object_merger/include/tracking_object_merger/utils/tracker_state.hpp b/perception/tracking_object_merger/include/autoware_tracking_object_merger/utils/tracker_state.hpp similarity index 90% rename from perception/tracking_object_merger/include/tracking_object_merger/utils/tracker_state.hpp rename to perception/tracking_object_merger/include/autoware_tracking_object_merger/utils/tracker_state.hpp index a92cd9194b26a..ed1f69191b652 100644 --- a/perception/tracking_object_merger/include/tracking_object_merger/utils/tracker_state.hpp +++ b/perception/tracking_object_merger/include/autoware_tracking_object_merger/utils/tracker_state.hpp @@ -12,17 +12,17 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef TRACKING_OBJECT_MERGER__UTILS__TRACKER_STATE_HPP_ -#define TRACKING_OBJECT_MERGER__UTILS__TRACKER_STATE_HPP_ +#ifndef AUTOWARE_TRACKING_OBJECT_MERGER__UTILS__TRACKER_STATE_HPP_ +#define AUTOWARE_TRACKING_OBJECT_MERGER__UTILS__TRACKER_STATE_HPP_ #include #include #include -#include -#include -#include -#include +#include "autoware_perception_msgs/msg/object_classification.hpp" +#include "autoware_perception_msgs/msg/tracked_object.hpp" +#include "autoware_perception_msgs/msg/tracked_object_kinematics.hpp" +#include "autoware_perception_msgs/msg/tracked_objects.hpp" #include #include @@ -32,6 +32,9 @@ #include #include #include + +namespace autoware::tracking_object_merger +{ using autoware_perception_msgs::msg::TrackedObject; using autoware_perception_msgs::msg::TrackedObjects; @@ -144,5 +147,6 @@ class TrackerState TrackedObjects getTrackedObjectsFromTrackerStates( std::vector & tracker_states, const rclcpp::Time & time); +} // namespace autoware::tracking_object_merger -#endif // TRACKING_OBJECT_MERGER__UTILS__TRACKER_STATE_HPP_ +#endif // AUTOWARE_TRACKING_OBJECT_MERGER__UTILS__TRACKER_STATE_HPP_ diff --git a/perception/tracking_object_merger/include/tracking_object_merger/utils/utils.hpp b/perception/tracking_object_merger/include/autoware_tracking_object_merger/utils/utils.hpp similarity index 71% rename from perception/tracking_object_merger/include/tracking_object_merger/utils/utils.hpp rename to perception/tracking_object_merger/include/autoware_tracking_object_merger/utils/utils.hpp index 1778e09da5cd9..eb288c4e853ad 100644 --- a/perception/tracking_object_merger/include/tracking_object_merger/utils/utils.hpp +++ b/perception/tracking_object_merger/include/autoware_tracking_object_merger/utils/utils.hpp @@ -14,19 +14,18 @@ // // -#ifndef TRACKING_OBJECT_MERGER__UTILS__UTILS_HPP_ -#define TRACKING_OBJECT_MERGER__UTILS__UTILS_HPP_ +#ifndef AUTOWARE_TRACKING_OBJECT_MERGER__UTILS__UTILS_HPP_ +#define AUTOWARE_TRACKING_OBJECT_MERGER__UTILS__UTILS_HPP_ -// #include #include "autoware/universe_utils/geometry/geometry.hpp" #include -#include -#include -#include -#include -#include +#include "autoware_perception_msgs/msg/object_classification.hpp" +#include "autoware_perception_msgs/msg/shape.hpp" +#include "autoware_perception_msgs/msg/tracked_object.hpp" +#include "autoware_perception_msgs/msg/tracked_object_kinematics.hpp" +#include "autoware_perception_msgs/msg/tracked_objects.hpp" #include #include #include @@ -42,49 +41,13 @@ #include #include +namespace autoware::tracking_object_merger +{ using autoware_perception_msgs::msg::TrackedObject; using autoware_perception_msgs::msg::TrackedObjects; + namespace utils { -enum MSG_COV_IDX { - X_X = 0, - X_Y = 1, - X_Z = 2, - X_ROLL = 3, - X_PITCH = 4, - X_YAW = 5, - Y_X = 6, - Y_Y = 7, - Y_Z = 8, - Y_ROLL = 9, - Y_PITCH = 10, - Y_YAW = 11, - Z_X = 12, - Z_Y = 13, - Z_Z = 14, - Z_ROLL = 15, - Z_PITCH = 16, - Z_YAW = 17, - ROLL_X = 18, - ROLL_Y = 19, - ROLL_Z = 20, - ROLL_ROLL = 21, - ROLL_PITCH = 22, - ROLL_YAW = 23, - PITCH_X = 24, - PITCH_Y = 25, - PITCH_Z = 26, - PITCH_ROLL = 27, - PITCH_PITCH = 28, - PITCH_YAW = 29, - YAW_X = 30, - YAW_Y = 31, - YAW_Z = 32, - YAW_ROLL = 33, - YAW_PITCH = 34, - YAW_YAW = 35 -}; - // linear interpolation for tracked objects TrackedObject linearInterpolationForTrackedObject( const TrackedObject & obj1, const TrackedObject & obj2); @@ -93,7 +56,7 @@ TrackedObject linearInterpolationForTrackedObject( TrackedObject predictPastOrFutureTrackedObject(const TrackedObject & obj, const double dt); TrackedObjects predictPastOrFutureTrackedObjects( - const TrackedObjects & obj, const std_msgs::msg::Header & header); + const TrackedObjects & input_objects, const std_msgs::msg::Header & header); // predict tracked objects TrackedObjects interpolateTrackedObjects( @@ -103,6 +66,9 @@ TrackedObjects interpolateTrackedObjects( namespace merger_utils { +using autoware_perception_msgs::msg::TrackedObject; +using autoware_perception_msgs::msg::TrackedObjects; + // merge policy enum MergePolicy : int { SKIP = 0, OVERWRITE = 1, FUSION = 2 }; @@ -132,4 +98,6 @@ void updateWholeTrackedObject(TrackedObject & main_obj, const TrackedObject & su } // namespace merger_utils -#endif // TRACKING_OBJECT_MERGER__UTILS__UTILS_HPP_ +} // namespace autoware::tracking_object_merger + +#endif // AUTOWARE_TRACKING_OBJECT_MERGER__UTILS__UTILS_HPP_ diff --git a/perception/tracking_object_merger/include/tracking_object_merger/data_association/solver/gnn_solver.hpp b/perception/tracking_object_merger/include/tracking_object_merger/data_association/solver/gnn_solver.hpp deleted file mode 100644 index 31240848f1977..0000000000000 --- a/perception/tracking_object_merger/include/tracking_object_merger/data_association/solver/gnn_solver.hpp +++ /dev/null @@ -1,22 +0,0 @@ -// Copyright 2023 TIER IV, Inc. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef TRACKING_OBJECT_MERGER__DATA_ASSOCIATION__SOLVER__GNN_SOLVER_HPP_ -#define TRACKING_OBJECT_MERGER__DATA_ASSOCIATION__SOLVER__GNN_SOLVER_HPP_ - -#include "tracking_object_merger/data_association/solver/gnn_solver_interface.hpp" -#include "tracking_object_merger/data_association/solver/mu_successive_shortest_path.hpp" -#include "tracking_object_merger/data_association/solver/successive_shortest_path.hpp" - -#endif // TRACKING_OBJECT_MERGER__DATA_ASSOCIATION__SOLVER__GNN_SOLVER_HPP_ diff --git a/perception/tracking_object_merger/launch/decorative_tracker_merger.launch.xml b/perception/tracking_object_merger/launch/decorative_tracker_merger.launch.xml index 52b0841f97e97..2cc2a69e295e6 100644 --- a/perception/tracking_object_merger/launch/decorative_tracker_merger.launch.xml +++ b/perception/tracking_object_merger/launch/decorative_tracker_merger.launch.xml @@ -7,7 +7,7 @@ - + diff --git a/perception/tracking_object_merger/package.xml b/perception/tracking_object_merger/package.xml index 370f5beb3e3b3..cc23578773fe2 100644 --- a/perception/tracking_object_merger/package.xml +++ b/perception/tracking_object_merger/package.xml @@ -23,7 +23,6 @@ rclcpp_components tf2 tf2_ros - tier4_perception_msgs ament_lint_auto autoware_lint_common diff --git a/perception/tracking_object_merger/src/data_association/data_association.cpp b/perception/tracking_object_merger/src/association/data_association.cpp similarity index 96% rename from perception/tracking_object_merger/src/data_association/data_association.cpp rename to perception/tracking_object_merger/src/association/data_association.cpp index 4d10a415df7da..046fcf5986469 100644 --- a/perception/tracking_object_merger/src/data_association/data_association.cpp +++ b/perception/tracking_object_merger/src/association/data_association.cpp @@ -12,12 +12,12 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "tracking_object_merger/data_association/data_association.hpp" +#include "autoware_tracking_object_merger/association/data_association.hpp" #include "autoware/universe_utils/geometry/geometry.hpp" +#include "autoware_tracking_object_merger/association/solver/gnn_solver.hpp" +#include "autoware_tracking_object_merger/utils/utils.hpp" #include "object_recognition_utils/object_recognition_utils.hpp" -#include "tracking_object_merger/data_association/solver/gnn_solver.hpp" -#include "tracking_object_merger/utils/utils.hpp" #include #include @@ -47,6 +47,8 @@ double getFormedYawAngle( } } // namespace +namespace autoware::tracking_object_merger +{ DataAssociation::DataAssociation( std::vector can_assign_vector, std::vector max_dist_vector, std::vector max_rad_vector, std::vector min_iou_vector, @@ -189,7 +191,7 @@ double DataAssociation::calcScoreBetweenObjects( bool passed_gate = true; // dist gate - if (passed_gate) { + { // passed_gate is always true if (max_dist < dist) passed_gate = false; } // angle gate @@ -229,3 +231,5 @@ double DataAssociation::calcScoreBetweenObjects( } return score; } + +} // namespace autoware::tracking_object_merger diff --git a/perception/tracking_object_merger/src/data_association/mu_successive_shortest_path/mu_successive_shortest_path_wrapper.cpp b/perception/tracking_object_merger/src/association/solver/mu_successive_shortest_path_wrapper.cpp similarity index 85% rename from perception/tracking_object_merger/src/data_association/mu_successive_shortest_path/mu_successive_shortest_path_wrapper.cpp rename to perception/tracking_object_merger/src/association/solver/mu_successive_shortest_path_wrapper.cpp index fcc79c0a3cbd7..d2db6eba068ec 100644 --- a/perception/tracking_object_merger/src/data_association/mu_successive_shortest_path/mu_successive_shortest_path_wrapper.cpp +++ b/perception/tracking_object_merger/src/association/solver/mu_successive_shortest_path_wrapper.cpp @@ -12,7 +12,7 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "tracking_object_merger/data_association/solver/mu_successive_shortest_path.hpp" +#include "autoware_tracking_object_merger/association/solver/mu_ssp.hpp" #include @@ -24,7 +24,7 @@ #include #include -namespace gnn_solver +namespace autoware::tracking_object_merger::gnn_solver { void MuSSP::maximizeLinearAssignment( const std::vector> & cost, std::unordered_map * direct_assignment, @@ -38,4 +38,4 @@ void MuSSP::maximizeLinearAssignment( // Solve DA by muSSP solve_muSSP(cost, direct_assignment, reverse_assignment); } -} // namespace gnn_solver +} // namespace autoware::tracking_object_merger::gnn_solver diff --git a/perception/tracking_object_merger/src/data_association/successive_shortest_path/successive_shortest_path.cpp b/perception/tracking_object_merger/src/association/solver/successive_shortest_path.cpp similarity index 98% rename from perception/tracking_object_merger/src/data_association/successive_shortest_path/successive_shortest_path.cpp rename to perception/tracking_object_merger/src/association/solver/successive_shortest_path.cpp index 133f0d377373f..7be715b24bd5f 100644 --- a/perception/tracking_object_merger/src/data_association/successive_shortest_path/successive_shortest_path.cpp +++ b/perception/tracking_object_merger/src/association/solver/successive_shortest_path.cpp @@ -12,7 +12,7 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "tracking_object_merger/data_association/solver/successive_shortest_path.hpp" +#include "autoware_tracking_object_merger/association/solver/ssp.hpp" #include #include @@ -22,7 +22,7 @@ #include #include -namespace gnn_solver +namespace autoware::tracking_object_merger::gnn_solver { struct ResidualEdge { @@ -367,4 +367,4 @@ void SSP::maximizeLinearAssignment( } #endif } -} // namespace gnn_solver +} // namespace autoware::tracking_object_merger::gnn_solver diff --git a/perception/tracking_object_merger/src/decorative_tracker_merger.cpp b/perception/tracking_object_merger/src/decorative_tracker_merger_node.cpp similarity index 97% rename from perception/tracking_object_merger/src/decorative_tracker_merger.cpp rename to perception/tracking_object_merger/src/decorative_tracker_merger_node.cpp index 99dccab3ff72c..6f65941c0a747 100644 --- a/perception/tracking_object_merger/src/decorative_tracker_merger.cpp +++ b/perception/tracking_object_merger/src/decorative_tracker_merger_node.cpp @@ -12,11 +12,16 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "tracking_object_merger/decorative_tracker_merger.hpp" +#define EIGEN_MPL2_ONLY + +#include "decorative_tracker_merger_node.hpp" +#include "autoware_tracking_object_merger/association/solver/ssp.hpp" +#include "autoware_tracking_object_merger/utils/utils.hpp" #include "object_recognition_utils/object_recognition_utils.hpp" -#include "tracking_object_merger/data_association/solver/successive_shortest_path.hpp" -#include "tracking_object_merger/utils/utils.hpp" + +#include +#include #include @@ -25,13 +30,9 @@ #include #include -#define EIGEN_MPL2_ONLY -#include -#include - using Label = autoware_perception_msgs::msg::ObjectClassification; -namespace tracking_object_merger +namespace autoware::tracking_object_merger { using autoware_perception_msgs::msg::TrackedObject; @@ -419,7 +420,7 @@ TrackerState DecorativeTrackerMergerNode::createNewTracker( return new_tracker; } -} // namespace tracking_object_merger +} // namespace autoware::tracking_object_merger #include -RCLCPP_COMPONENTS_REGISTER_NODE(tracking_object_merger::DecorativeTrackerMergerNode) +RCLCPP_COMPONENTS_REGISTER_NODE(autoware::tracking_object_merger::DecorativeTrackerMergerNode) diff --git a/perception/tracking_object_merger/include/tracking_object_merger/decorative_tracker_merger.hpp b/perception/tracking_object_merger/src/decorative_tracker_merger_node.hpp similarity index 87% rename from perception/tracking_object_merger/include/tracking_object_merger/decorative_tracker_merger.hpp rename to perception/tracking_object_merger/src/decorative_tracker_merger_node.hpp index 3683ba02b38cd..ae19f51876490 100644 --- a/perception/tracking_object_merger/include/tracking_object_merger/decorative_tracker_merger.hpp +++ b/perception/tracking_object_merger/src/decorative_tracker_merger_node.hpp @@ -12,19 +12,19 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef TRACKING_OBJECT_MERGER__DECORATIVE_TRACKER_MERGER_HPP_ -#define TRACKING_OBJECT_MERGER__DECORATIVE_TRACKER_MERGER_HPP_ +#ifndef DECORATIVE_TRACKER_MERGER_NODE_HPP_ +#define DECORATIVE_TRACKER_MERGER_NODE_HPP_ -#include "tracking_object_merger/data_association/data_association.hpp" -#include "tracking_object_merger/utils/tracker_state.hpp" -#include "tracking_object_merger/utils/utils.hpp" +#include "autoware/universe_utils/ros/debug_publisher.hpp" +#include "autoware/universe_utils/ros/published_time_publisher.hpp" +#include "autoware/universe_utils/system/stop_watch.hpp" +#include "autoware_tracking_object_merger/association/data_association.hpp" +#include "autoware_tracking_object_merger/utils/tracker_state.hpp" +#include "autoware_tracking_object_merger/utils/utils.hpp" -#include -#include -#include #include -#include +#include "autoware_perception_msgs/msg/tracked_objects.hpp" #include #ifdef ROS_DISTRO_GALACTIC @@ -43,7 +43,7 @@ #include #include -namespace tracking_object_merger +namespace autoware::tracking_object_merger { class DecorativeTrackerMergerNode : public rclcpp::Node @@ -131,6 +131,6 @@ class DecorativeTrackerMergerNode : public rclcpp::Node } logging_; }; -} // namespace tracking_object_merger +} // namespace autoware::tracking_object_merger -#endif // TRACKING_OBJECT_MERGER__DECORATIVE_TRACKER_MERGER_HPP_ +#endif // DECORATIVE_TRACKER_MERGER_NODE_HPP_ diff --git a/perception/tracking_object_merger/src/utils/tracker_state.cpp b/perception/tracking_object_merger/src/utils/tracker_state.cpp index 36da060a962c3..54b1d73c375a5 100644 --- a/perception/tracking_object_merger/src/utils/tracker_state.cpp +++ b/perception/tracking_object_merger/src/utils/tracker_state.cpp @@ -12,9 +12,12 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "tracking_object_merger/utils/tracker_state.hpp" +#include "autoware_tracking_object_merger/utils/tracker_state.hpp" -#include "tracking_object_merger/utils/utils.hpp" +#include "autoware_tracking_object_merger/utils/utils.hpp" + +namespace autoware::tracking_object_merger +{ using autoware_perception_msgs::msg::TrackedObject; using autoware_perception_msgs::msg::TrackedObjects; @@ -325,3 +328,5 @@ TrackedObjects getTrackedObjectsFromTrackerStates( tracked_objects.header.frame_id = "map"; // TODO(yoshiri): get frame_id from input return tracked_objects; } + +} // namespace autoware::tracking_object_merger diff --git a/perception/tracking_object_merger/src/utils/utils.cpp b/perception/tracking_object_merger/src/utils/utils.cpp index e981ef09a21b0..1ae705aa38f15 100644 --- a/perception/tracking_object_merger/src/utils/utils.cpp +++ b/perception/tracking_object_merger/src/utils/utils.cpp @@ -12,11 +12,11 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "tracking_object_merger/utils/utils.hpp" +#include "autoware_tracking_object_merger/utils/utils.hpp" -#include -#include -#include +#include "autoware_perception_msgs/msg/shape.hpp" +#include "autoware_perception_msgs/msg/tracked_object.hpp" +#include "autoware_perception_msgs/msg/tracked_objects.hpp" #include #include @@ -25,7 +25,7 @@ using autoware_perception_msgs::msg::TrackedObject; using autoware_perception_msgs::msg::TrackedObjects; -namespace utils +namespace autoware::tracking_object_merger::utils { /** @@ -153,20 +153,20 @@ TrackedObject predictPastOrFutureTrackedObject(const TrackedObject & obj, const /** * @brief predict past or future tracked objects * - * @param obj + * @param input_objects * @param header * @return TrackedObjects */ TrackedObjects predictPastOrFutureTrackedObjects( - const TrackedObjects & obj, const std_msgs::msg::Header & header) + const TrackedObjects & input_objects, const std_msgs::msg::Header & header) { // for each object, predict past or future TrackedObjects output_objects; - output_objects.header = obj.header; + output_objects.header = input_objects.header; output_objects.header.stamp = header.stamp; - const auto dt = (rclcpp::Time(header.stamp) - rclcpp::Time(obj.header.stamp)).seconds(); - for (const auto & obj : obj.objects) { + const auto dt = (rclcpp::Time(header.stamp) - rclcpp::Time(input_objects.header.stamp)).seconds(); + for (const auto & obj : input_objects.objects) { output_objects.objects.push_back(predictPastOrFutureTrackedObject(obj, dt)); } return output_objects; @@ -243,9 +243,9 @@ TrackedObjects interpolateTrackedObjects( return output_objects; } -} // namespace utils +} // namespace autoware::tracking_object_merger::utils -namespace merger_utils +namespace autoware::tracking_object_merger::merger_utils { double mean(const double a, const double b) @@ -513,4 +513,4 @@ void updateWholeTrackedObject(TrackedObject & main_obj, const TrackedObject & su main_obj = sub_obj; } -} // namespace merger_utils +} // namespace autoware::tracking_object_merger::merger_utils diff --git a/perception/traffic_light_arbiter/include/traffic_light_arbiter/signal_match_validator.hpp b/perception/traffic_light_arbiter/include/traffic_light_arbiter/signal_match_validator.hpp index acec687e0243d..721341bed915d 100644 --- a/perception/traffic_light_arbiter/include/traffic_light_arbiter/signal_match_validator.hpp +++ b/perception/traffic_light_arbiter/include/traffic_light_arbiter/signal_match_validator.hpp @@ -15,7 +15,7 @@ #ifndef TRAFFIC_LIGHT_ARBITER__SIGNAL_MATCH_VALIDATOR_HPP_ #define TRAFFIC_LIGHT_ARBITER__SIGNAL_MATCH_VALIDATOR_HPP_ -#include +#include #include #include diff --git a/perception/traffic_light_arbiter/package.xml b/perception/traffic_light_arbiter/package.xml index f5e396ba4c0fb..6e8fc61bc5306 100644 --- a/perception/traffic_light_arbiter/package.xml +++ b/perception/traffic_light_arbiter/package.xml @@ -11,10 +11,10 @@ ament_cmake_auto autoware_cmake + autoware_lanelet2_extension autoware_map_msgs autoware_perception_msgs lanelet2_core - lanelet2_extension rclcpp rclcpp_components diff --git a/perception/traffic_light_arbiter/src/traffic_light_arbiter.cpp b/perception/traffic_light_arbiter/src/traffic_light_arbiter.cpp index 498148d568cc9..033c7fbfb6a35 100644 --- a/perception/traffic_light_arbiter/src/traffic_light_arbiter.cpp +++ b/perception/traffic_light_arbiter/src/traffic_light_arbiter.cpp @@ -12,8 +12,8 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include -#include +#include +#include #include #include diff --git a/perception/traffic_light_map_based_detector/include/traffic_light_map_based_detector/node.hpp b/perception/traffic_light_map_based_detector/include/traffic_light_map_based_detector/node.hpp index e6e60cd97272b..3aeba4c4a913a 100644 --- a/perception/traffic_light_map_based_detector/include/traffic_light_map_based_detector/node.hpp +++ b/perception/traffic_light_map_based_detector/include/traffic_light_map_based_detector/node.hpp @@ -15,7 +15,7 @@ #ifndef TRAFFIC_LIGHT_MAP_BASED_DETECTOR__NODE_HPP_ #define TRAFFIC_LIGHT_MAP_BASED_DETECTOR__NODE_HPP_ -#include +#include #include #include @@ -25,7 +25,12 @@ #include #include -#include +#if __has_include() +#include // for ROS 2 Jazzy or newer +#else +#include // for ROS 2 Humble or older +#endif + #include #include #include diff --git a/perception/traffic_light_map_based_detector/package.xml b/perception/traffic_light_map_based_detector/package.xml index e72cac75cd809..7553b136d791a 100644 --- a/perception/traffic_light_map_based_detector/package.xml +++ b/perception/traffic_light_map_based_detector/package.xml @@ -13,12 +13,12 @@ autoware_cmake + autoware_lanelet2_extension autoware_map_msgs autoware_planning_msgs autoware_universe_utils geometry_msgs image_geometry - lanelet2_extension rclcpp rclcpp_components sensor_msgs diff --git a/perception/traffic_light_map_based_detector/src/node.cpp b/perception/traffic_light_map_based_detector/src/node.cpp index 7750bf642516a..7d241d71ce185 100644 --- a/perception/traffic_light_map_based_detector/src/node.cpp +++ b/perception/traffic_light_map_based_detector/src/node.cpp @@ -16,9 +16,9 @@ #include #include -#include -#include -#include +#include +#include +#include #include #include diff --git a/perception/traffic_light_multi_camera_fusion/package.xml b/perception/traffic_light_multi_camera_fusion/package.xml index 36dce830a2595..db942d4bb60b9 100644 --- a/perception/traffic_light_multi_camera_fusion/package.xml +++ b/perception/traffic_light_multi_camera_fusion/package.xml @@ -12,9 +12,9 @@ autoware_cmake + autoware_lanelet2_extension autoware_map_msgs autoware_perception_msgs - lanelet2_extension rclcpp rclcpp_components sensor_msgs diff --git a/perception/traffic_light_multi_camera_fusion/src/node.cpp b/perception/traffic_light_multi_camera_fusion/src/node.cpp index a0367459276da..607aabdefa3cc 100644 --- a/perception/traffic_light_multi_camera_fusion/src/node.cpp +++ b/perception/traffic_light_multi_camera_fusion/src/node.cpp @@ -14,8 +14,8 @@ #include "traffic_light_multi_camera_fusion/node.hpp" -#include -#include +#include +#include #include #include diff --git a/perception/traffic_light_occlusion_predictor/include/traffic_light_occlusion_predictor/nodelet.hpp b/perception/traffic_light_occlusion_predictor/include/traffic_light_occlusion_predictor/nodelet.hpp index 638e2797f0a6e..3cd270adf9383 100644 --- a/perception/traffic_light_occlusion_predictor/include/traffic_light_occlusion_predictor/nodelet.hpp +++ b/perception/traffic_light_occlusion_predictor/include/traffic_light_occlusion_predictor/nodelet.hpp @@ -27,7 +27,12 @@ #include #include -#include +#if __has_include() +#include // for ROS 2 Jazzy or newer +#else +#include // for ROS 2 Humble or older +#endif + #include #include #include diff --git a/perception/traffic_light_occlusion_predictor/include/traffic_light_occlusion_predictor/occlusion_predictor.hpp b/perception/traffic_light_occlusion_predictor/include/traffic_light_occlusion_predictor/occlusion_predictor.hpp index c14a5d56dc5d4..066f438a9544f 100644 --- a/perception/traffic_light_occlusion_predictor/include/traffic_light_occlusion_predictor/occlusion_predictor.hpp +++ b/perception/traffic_light_occlusion_predictor/include/traffic_light_occlusion_predictor/occlusion_predictor.hpp @@ -24,7 +24,12 @@ #include #include -#include +#if __has_include() +#include // for ROS 2 Jazzy or newer +#else +#include // for ROS 2 Humble or older +#endif + #include #include #include diff --git a/perception/traffic_light_occlusion_predictor/package.xml b/perception/traffic_light_occlusion_predictor/package.xml index 9a701548092d9..313c6e1536e85 100644 --- a/perception/traffic_light_occlusion_predictor/package.xml +++ b/perception/traffic_light_occlusion_predictor/package.xml @@ -12,11 +12,11 @@ autoware_cmake + autoware_lanelet2_extension autoware_map_msgs autoware_universe_utils geometry_msgs image_geometry - lanelet2_extension pcl_msgs perception_utils rclcpp diff --git a/perception/traffic_light_occlusion_predictor/src/nodelet.cpp b/perception/traffic_light_occlusion_predictor/src/nodelet.cpp index 1a2eaa6c4d941..0202a26e46ef6 100644 --- a/perception/traffic_light_occlusion_predictor/src/nodelet.cpp +++ b/perception/traffic_light_occlusion_predictor/src/nodelet.cpp @@ -15,9 +15,9 @@ #include "traffic_light_occlusion_predictor/nodelet.hpp" #include -#include -#include -#include +#include +#include +#include #include #include diff --git a/perception/traffic_light_visualization/CMakeLists.txt b/perception/traffic_light_visualization/CMakeLists.txt index 8b5f54c034c7b..08f6b48cabbd9 100644 --- a/perception/traffic_light_visualization/CMakeLists.txt +++ b/perception/traffic_light_visualization/CMakeLists.txt @@ -8,6 +8,7 @@ find_package(OpenCV REQUIRED) ament_auto_add_library(traffic_light_roi_visualizer_nodelet SHARED src/traffic_light_roi_visualizer/nodelet.cpp + src/traffic_light_roi_visualizer/shape_draw.cpp ) target_link_libraries(traffic_light_roi_visualizer_nodelet @@ -24,6 +25,12 @@ ament_auto_add_executable(traffic_light_map_visualizer_node src/traffic_light_map_visualizer/main.cpp ) +# Copy the assets directory to the installation directory +install( + DIRECTORY images/ + DESTINATION share/${PROJECT_NAME}/images +) + ament_auto_package( INSTALL_TO_SHARE launch diff --git a/perception/traffic_light_visualization/images/circle.png b/perception/traffic_light_visualization/images/circle.png new file mode 100644 index 0000000000000..1f44f7bff39a2 Binary files /dev/null and b/perception/traffic_light_visualization/images/circle.png differ diff --git a/perception/traffic_light_visualization/images/cross.png b/perception/traffic_light_visualization/images/cross.png new file mode 100644 index 0000000000000..9848d3eeab14e Binary files /dev/null and b/perception/traffic_light_visualization/images/cross.png differ diff --git a/perception/traffic_light_visualization/images/down_left_arrow.png b/perception/traffic_light_visualization/images/down_left_arrow.png new file mode 100644 index 0000000000000..0444591a65121 Binary files /dev/null and b/perception/traffic_light_visualization/images/down_left_arrow.png differ diff --git a/perception/traffic_light_visualization/images/left_arrow.png b/perception/traffic_light_visualization/images/left_arrow.png new file mode 100644 index 0000000000000..e48efaa8092e7 Binary files /dev/null and b/perception/traffic_light_visualization/images/left_arrow.png differ diff --git a/perception/traffic_light_visualization/images/straight_arrow.png b/perception/traffic_light_visualization/images/straight_arrow.png new file mode 100644 index 0000000000000..d941d7acbc8f5 Binary files /dev/null and b/perception/traffic_light_visualization/images/straight_arrow.png differ diff --git a/perception/traffic_light_visualization/images/unknown.png b/perception/traffic_light_visualization/images/unknown.png new file mode 100644 index 0000000000000..52393aeeea1fe Binary files /dev/null and b/perception/traffic_light_visualization/images/unknown.png differ diff --git a/perception/traffic_light_visualization/include/traffic_light_visualization/traffic_light_map_visualizer/node.hpp b/perception/traffic_light_visualization/include/traffic_light_visualization/traffic_light_map_visualizer/node.hpp index 725c32360c34c..2cf9a98aea443 100644 --- a/perception/traffic_light_visualization/include/traffic_light_visualization/traffic_light_map_visualizer/node.hpp +++ b/perception/traffic_light_visualization/include/traffic_light_visualization/traffic_light_map_visualizer/node.hpp @@ -15,7 +15,7 @@ #ifndef TRAFFIC_LIGHT_VISUALIZATION__TRAFFIC_LIGHT_MAP_VISUALIZER__NODE_HPP_ #define TRAFFIC_LIGHT_VISUALIZATION__TRAFFIC_LIGHT_MAP_VISUALIZER__NODE_HPP_ -#include +#include #include #include diff --git a/perception/traffic_light_visualization/include/traffic_light_visualization/traffic_light_roi_visualizer/nodelet.hpp b/perception/traffic_light_visualization/include/traffic_light_visualization/traffic_light_roi_visualizer/nodelet.hpp index a4d916d968071..f7ac7471e21ef 100644 --- a/perception/traffic_light_visualization/include/traffic_light_visualization/traffic_light_roi_visualizer/nodelet.hpp +++ b/perception/traffic_light_visualization/include/traffic_light_visualization/traffic_light_roi_visualizer/nodelet.hpp @@ -23,7 +23,11 @@ #include #include -#include +#if __has_include() +#include // for ROS 2 Jazzy or newer +#else +#include // for ROS 2 Humble or older +#endif #include #include #include @@ -81,6 +85,20 @@ class TrafficLightRoiVisualizerNodelet : public rclcpp::Node {tier4_perception_msgs::msg::TrafficLightElement::UNKNOWN, "unknown"}, }; + std::string extractShapeName(const std::string & label) + { + size_t start_pos = label.find('-'); + if (start_pos != std::string::npos) { + start_pos++; // Start after the hyphen + size_t end_pos = label.find(',', start_pos); // Find the next comma after the hyphen + if (end_pos == std::string::npos) { // If no comma is found, take the rest of the string + end_pos = label.length(); + } + return label.substr(start_pos, end_pos - start_pos); + } + return "unknown"; // Return "unknown" if no hyphen is found + } + bool createRect( cv::Mat & image, const tier4_perception_msgs::msg::TrafficLightRoi & tl_roi, const cv::Scalar & color); diff --git a/perception/traffic_light_visualization/include/traffic_light_visualization/traffic_light_roi_visualizer/shape_draw.hpp b/perception/traffic_light_visualization/include/traffic_light_visualization/traffic_light_roi_visualizer/shape_draw.hpp new file mode 100644 index 0000000000000..7f83104edf48e --- /dev/null +++ b/perception/traffic_light_visualization/include/traffic_light_visualization/traffic_light_roi_visualizer/shape_draw.hpp @@ -0,0 +1,51 @@ +// Copyright 2024 The Autoware Contributors +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +#pragma once +#include +#include +#include + +#include +#include + +#include +#include +#include + +struct DrawFunctionParams +{ + cv::Mat & image; + cv::Point position; + cv::Scalar color; + int size; + float probability; +}; + +using DrawFunction = std::function; + +void drawShape( + const DrawFunctionParams & params, const std::string & filename, bool flipHorizontally, + bool flipVertically, int x_offset, int y_offset, double scale_factor = 0.3); +void drawCircle(const DrawFunctionParams & params); +void drawLeftArrow(const DrawFunctionParams & params); +void drawRightArrow(const DrawFunctionParams & params); +void drawStraightArrow(const DrawFunctionParams & params); +void drawDownArrow(const DrawFunctionParams & params); +void drawDownLeftArrow(const DrawFunctionParams & params); +void drawDownRightArrow(const DrawFunctionParams & params); +void drawCross(const DrawFunctionParams & params); +void drawUnknown(const DrawFunctionParams & params); +void drawTrafficLightShape( + cv::Mat & image, const std::string & shape, const cv::Point & position, const cv::Scalar & color, + int size, float probability); diff --git a/perception/traffic_light_visualization/package.xml b/perception/traffic_light_visualization/package.xml index 3d3683a8be6cc..ee113a34f10e4 100644 --- a/perception/traffic_light_visualization/package.xml +++ b/perception/traffic_light_visualization/package.xml @@ -11,10 +11,10 @@ ament_cmake_auto autoware_cmake + autoware_lanelet2_extension autoware_perception_msgs cv_bridge image_transport - lanelet2_extension message_filters rclcpp rclcpp_components diff --git a/perception/traffic_light_visualization/src/traffic_light_map_visualizer/node.cpp b/perception/traffic_light_visualization/src/traffic_light_map_visualizer/node.cpp index 028e36157650a..95944a29d6092 100644 --- a/perception/traffic_light_visualization/src/traffic_light_map_visualizer/node.cpp +++ b/perception/traffic_light_visualization/src/traffic_light_map_visualizer/node.cpp @@ -14,9 +14,9 @@ #include "traffic_light_visualization/traffic_light_map_visualizer/node.hpp" -#include -#include -#include +#include +#include +#include #include diff --git a/perception/traffic_light_visualization/src/traffic_light_roi_visualizer/nodelet.cpp b/perception/traffic_light_visualization/src/traffic_light_roi_visualizer/nodelet.cpp index 45d6759e6cb7d..df097e64ecd36 100644 --- a/perception/traffic_light_visualization/src/traffic_light_roi_visualizer/nodelet.cpp +++ b/perception/traffic_light_visualization/src/traffic_light_roi_visualizer/nodelet.cpp @@ -14,6 +14,8 @@ #include "traffic_light_visualization/traffic_light_roi_visualizer/nodelet.hpp" // NOLINT(whitespace/line_length) +#include "traffic_light_visualization/traffic_light_roi_visualizer/shape_draw.hpp" // NOLINT(whitespace/line_length) + #include #include @@ -93,29 +95,24 @@ bool TrafficLightRoiVisualizerNodelet::createRect( { cv::Scalar color; if (result.label.find("red") != std::string::npos) { - color = cv::Scalar{255, 0, 0}; + color = cv::Scalar{254, 149, 149}; } else if (result.label.find("yellow") != std::string::npos) { - color = cv::Scalar{0, 255, 0}; + color = cv::Scalar{254, 250, 149}; } else if (result.label.find("green") != std::string::npos) { - color = cv::Scalar{0, 0, 255}; + color = cv::Scalar{149, 254, 161}; } else { - color = cv::Scalar{255, 255, 255}; + color = cv::Scalar{250, 250, 250}; } cv::rectangle( image, cv::Point(tl_roi.roi.x_offset, tl_roi.roi.y_offset), cv::Point(tl_roi.roi.x_offset + tl_roi.roi.width, tl_roi.roi.y_offset + tl_roi.roi.height), - color, 3); + color, 2); - int offset = 40; - cv::putText( - image, std::to_string(result.prob), - cv::Point(tl_roi.roi.x_offset, tl_roi.roi.y_offset - (offset * 0)), cv::FONT_HERSHEY_COMPLEX, - 1.1, color, 3); + std::string shape_name = extractShapeName(result.label); - cv::putText( - image, result.label, cv::Point(tl_roi.roi.x_offset, tl_roi.roi.y_offset - (offset * 1)), - cv::FONT_HERSHEY_COMPLEX, 1.1, color, 2); + drawTrafficLightShape( + image, shape_name, cv::Point(tl_roi.roi.x_offset, tl_roi.roi.y_offset), color, 16, result.prob); return true; } diff --git a/perception/traffic_light_visualization/src/traffic_light_roi_visualizer/shape_draw.cpp b/perception/traffic_light_visualization/src/traffic_light_roi_visualizer/shape_draw.cpp new file mode 100644 index 0000000000000..91a00a940885c --- /dev/null +++ b/perception/traffic_light_visualization/src/traffic_light_roi_visualizer/shape_draw.cpp @@ -0,0 +1,173 @@ +// Copyright 2024 The Autoware Contributors +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +#include "traffic_light_visualization/traffic_light_roi_visualizer/shape_draw.hpp" + +#include "opencv2/core/types.hpp" +#include "opencv2/highgui.hpp" +#include "opencv2/imgproc.hpp" + +void drawShape( + const DrawFunctionParams & params, const std::string & filename, bool flipHorizontally, + bool flipVertically, int x_offset, int y_offset, double scale_factor) +{ + std::string filepath = + ament_index_cpp::get_package_share_directory("traffic_light_visualization") + "/images/" + + filename; + cv::Mat shapeImg = cv::imread(filepath, cv::IMREAD_UNCHANGED); + if (shapeImg.empty()) { + std::cerr << "Failed to load image: " << filepath << std::endl; + return; + } + + if (flipHorizontally) { + cv::flip(shapeImg, shapeImg, 1); // Flip horizontally + } + + if (flipVertically) { + cv::flip(shapeImg, shapeImg, 0); // Flip vertically + } + + cv::resize( + shapeImg, shapeImg, cv::Size(params.size, params.size), scale_factor, scale_factor, + cv::INTER_AREA); + + // Calculate the center position including offsets + cv::Point position( + params.position.x + x_offset, params.position.y - shapeImg.rows / 2 + y_offset); + + // Check for image boundaries + if ( + position.x < 0 || position.y < 0 || position.x + shapeImg.cols > params.image.cols || + position.y + shapeImg.rows > params.image.rows) { + std::cerr << "Adjusted position is out of image bounds." << std::endl; + return; + } + + // Calculate the width of the text + std::string probabilityText = + std::to_string(static_cast(round(params.probability * 100))) + "%"; + int baseline = 0; + cv::Size textSize = + cv::getTextSize(probabilityText, cv::FONT_HERSHEY_SIMPLEX, 0.75, 2, &baseline); + + // Adjust the filled rectangle to be at the top edge and the correct width + int filledRectWidth = + shapeImg.cols + (filename != "unknown.png" ? textSize.width + 10 : 5); // Add some padding + int filledRectHeight = shapeImg.rows + 10; // Add some padding + + cv::rectangle( + params.image, cv::Rect(position.x - 2, position.y - 5, filledRectWidth, filledRectHeight), + params.color, + -1); // Filled rectangle + + // Create ROI on the destination image + cv::Mat destinationROI = params.image(cv::Rect(position, cv::Size(shapeImg.cols, shapeImg.rows))); + + // Overlay the image onto the main image + for (int y = 0; y < shapeImg.rows; ++y) { + for (int x = 0; x < shapeImg.cols; ++x) { + cv::Vec4b & pixel = shapeImg.at(y, x); + if (pixel[3] != 0) { // Only non-transparent pixels + destinationROI.at(y, x) = cv::Vec3b(pixel[0], pixel[1], pixel[2]); + } + } + } + + // Position the probability text right next to the shape + if (filename != "unknown.png") { + cv::putText( + params.image, probabilityText, + cv::Point( + position.x + shapeImg.cols + 5, position.y + shapeImg.rows / 2 + textSize.height / 2), + cv::FONT_HERSHEY_SIMPLEX, 0.75, cv::Scalar(0, 0, 0), 2, cv::LINE_AA); + } +} + +void drawCircle(const DrawFunctionParams & params) +{ + int y_offset = params.size / 2 + 5; + drawShape(params, "circle.png", false, false, 0, -y_offset); +} + +void drawLeftArrow(const DrawFunctionParams & params) +{ + int y_offset = params.size / 2 + 5; + drawShape(params, "left_arrow.png", false, false, 0, -y_offset); +} + +void drawRightArrow(const DrawFunctionParams & params) +{ + int y_offset = params.size / 2 + 5; + drawShape(params, "left_arrow.png", true, false, 0, -y_offset); +} + +void drawStraightArrow(const DrawFunctionParams & params) +{ + int y_offset = params.size / 2 + 5; // This adjusts the base position upwards + + drawShape(params, "straight_arrow.png", false, false, 0, -y_offset); +} +void drawDownArrow(const DrawFunctionParams & params) +{ + int y_offset = params.size / 2 + 5; // This adjusts the base position upwards + drawShape(params, "straight_arrow.png", false, true, 0, -y_offset); +} + +void drawDownLeftArrow(const DrawFunctionParams & params) +{ + int y_offset = params.size / 2 + 5; + drawShape(params, "down_left_arrow.png", false, false, 0, -y_offset); +} + +void drawDownRightArrow(const DrawFunctionParams & params) +{ + int y_offset = params.size / 2 + 5; + drawShape(params, "down_left_arrow.png", true, false, 0, -y_offset); +} + +void drawCross(const DrawFunctionParams & params) +{ + int y_offset = params.size / 2 + 5; + + drawShape(params, "cross.png", false, false, 0, -y_offset); +} + +void drawUnknown(const DrawFunctionParams & params) +{ + int y_offset = params.size / 2 + 5; + drawShape(params, "unknown.png", false, false, 0, -y_offset); +} + +void drawTrafficLightShape( + cv::Mat & image, const std::string & shape, const cv::Point & position, const cv::Scalar & color, + int size, float probability) +{ + static std::map shapeToFunction = { + {"circle", drawCircle}, + {"left", drawLeftArrow}, + {"right", drawRightArrow}, + {"straight", drawStraightArrow}, + {"down", drawDownArrow}, + {"down_left", drawDownLeftArrow}, + {"down_right", drawDownRightArrow}, + {"cross", drawCross}, + {"unknown", drawUnknown}}; + auto it = shapeToFunction.find(shape); + if (it != shapeToFunction.end()) { + DrawFunctionParams params{image, position, color, size, probability}; + it->second(params); + } else { + std::cerr << "Unknown shape: " << shape << std::endl; + } +} diff --git a/planning/.pages b/planning/.pages index 67c70b928bdf8..424f83cf47611 100644 --- a/planning/.pages +++ b/planning/.pages @@ -1,35 +1,35 @@ nav: - 'Introduction': planning - 'Behavior Path Planner': - - 'About Behavior Path': planning/autoware_behavior_path_planner + - 'About Behavior Path': planning/behavior_path_planner/autoware_behavior_path_planner - 'Concept': - - 'Planner Manager': planning/autoware_behavior_path_planner/docs/behavior_path_planner_manager_design - - 'Scene Module Interface': planning/autoware_behavior_path_planner/docs/behavior_path_planner_interface_design - - 'Path Generation': planning/autoware_behavior_path_planner_common/docs/behavior_path_planner_path_generation_design - - 'Collision Assessment/Safety Check': planning/autoware_behavior_path_planner_common/docs/behavior_path_planner_safety_check - - 'Dynamic Drivable Area': planning/autoware_behavior_path_planner_common/docs/behavior_path_planner_drivable_area_design - - 'Turn Signal': planning/autoware_behavior_path_planner_common/docs/behavior_path_planner_turn_signal_design + - 'Planner Manager': planning/behavior_path_planner/autoware_behavior_path_planner/docs/behavior_path_planner_manager_design + - 'Scene Module Interface': planning/behavior_path_planner/autoware_behavior_path_planner/docs/behavior_path_planner_interface_design + - 'Path Generation': planning/behavior_path_planner/autoware_behavior_path_planner_common/docs/behavior_path_planner_path_generation_design + - 'Collision Assessment/Safety Check': planning/behavior_path_planner/autoware_behavior_path_planner_common/docs/behavior_path_planner_safety_check + - 'Dynamic Drivable Area': planning/behavior_path_planner/autoware_behavior_path_planner_common/docs/behavior_path_planner_drivable_area_design + - 'Turn Signal': planning/behavior_path_planner/autoware_behavior_path_planner_common/docs/behavior_path_planner_turn_signal_design - 'Scene Module': - - 'Avoidance by Lane Change': planning/autoware_behavior_path_avoidance_by_lane_change_module - - 'Dynamic Obstacle Avoidance': planning/autoware_behavior_path_dynamic_obstacle_avoidance_module - - 'Goal Planner': planning/autoware_behavior_path_goal_planner_module - - 'Lane Change': planning/autoware_behavior_path_lane_change_module - - 'Side Shift': planning/autoware_behavior_path_side_shift_module - - 'Start Planner': planning/autoware_behavior_path_start_planner_module - - 'Static Obstacle Avoidance': planning/autoware_behavior_path_static_obstacle_avoidance_module + - 'Avoidance by Lane Change': planning/behavior_path_planner/autoware_behavior_path_avoidance_by_lane_change_module + - 'Dynamic Obstacle Avoidance': planning/behavior_path_planner/autoware_behavior_path_dynamic_obstacle_avoidance_module + - 'Goal Planner': planning/behavior_path_planner/autoware_behavior_path_goal_planner_module + - 'Lane Change': planning/behavior_path_planner/autoware_behavior_path_lane_change_module + - 'Side Shift': planning/behavior_path_planner/autoware_behavior_path_side_shift_module + - 'Start Planner': planning/behavior_path_planner/autoware_behavior_path_start_planner_module + - 'Static Obstacle Avoidance': planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module - 'Behavior Velocity Planner': - 'About Behavior Velocity': planning/behavior_velocity_planner/autoware_behavior_velocity_planner - 'Template for Custom Module': planning/behavior_velocity_planner/autoware_behavior_velocity_template_module - 'Available Module': - 'Blind Spot': planning/behavior_velocity_planner/autoware_behavior_velocity_blind_spot_module - 'Crosswalk': planning/behavior_velocity_planner/autoware_behavior_velocity_crosswalk_module - - 'Detection Area': planning/behavior_velocity_detection_area_module + - 'Detection Area': planning/behavior_velocity_planner/autoware_behavior_velocity_detection_area_module - 'Intersection': planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module - - 'No Drivable Lane': planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module + - 'No Drivable Lane': planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module - 'No Stopping Area': planning/behavior_velocity_planner/autoware_behavior_velocity_no_stopping_area_module - 'Occlusion Spot': planning/behavior_velocity_planner/autoware_behavior_velocity_occlusion_spot_module - 'Run Out': planning/behavior_velocity_planner/autoware_behavior_velocity_run_out_module - - 'Speed Bump': planning/behavior_velocity_planner/behavior_velocity_speed_bump_module + - 'Speed Bump': planning/behavior_velocity_planner/autoware_behavior_velocity_speed_bump_module - 'Stop Line': planning/behavior_velocity_planner/autoware_behavior_velocity_stop_line_module - 'Traffic Light': planning/behavior_velocity_planner/autoware_behavior_velocity_traffic_light_module - 'Virtual Traffic Light': planning/behavior_velocity_planner/autoware_behavior_velocity_virtual_traffic_light_module @@ -48,7 +48,7 @@ nav: - 'Obstacle Cruise Planner': - 'About Obstacle Cruise': planning/autoware_obstacle_cruise_planner - 'How to Debug': planning/autoware_obstacle_cruise_planner/docs/debug - - 'Obstacle Stop Planner': planning/obstacle_stop_planner + - 'Obstacle Stop Planner': planning/autoware_obstacle_stop_planner - 'Path Smoother': - 'About Path Smoother': planning/autoware_path_smoother - 'Elastic Band': planning/autoware_path_smoother/docs/eb @@ -64,7 +64,7 @@ nav: - 'About Motion Velocity Planner': planning/motion_velocity_planner/autoware_motion_velocity_planner_node/ - 'Available Modules': - 'Dynamic Obstacle Stop': planning/motion_velocity_planner/autoware_motion_velocity_dynamic_obstacle_stop_module/ - - 'Out of Lane': planning/motion_velocity_planner/autoware_motion_velocity_planner_out_of_lane_module/ + - 'Out of Lane': planning/motion_velocity_planner/autoware_motion_velocity_out_of_lane_module/ - 'Obstacle Velocity Limiter': planning/motion_velocity_planner/autoware_motion_velocity_obstacle_velocity_limiter_module/ - 'Velocity Smoother': - 'About Velocity Smoother': planning/autoware_velocity_smoother @@ -72,14 +72,13 @@ nav: - 'Scenario Selector': planning/autoware_scenario_selector - 'Static Centerline Generator': planning/autoware_static_centerline_generator - 'API and Library': - - 'Costmap Generator': planning/costmap_generator + - 'Costmap Generator': planning/autoware_costmap_generator - 'External Velocity Limit Selector': planning/autoware_external_velocity_limit_selector - 'Objects of Interest Marker Interface': planning/autoware_objects_of_interest_marker_interface - 'Route Handler': planning/autoware_route_handler - 'RTC Interface': planning/autoware_rtc_interface - 'Additional Tools': - 'Remaining Distance Time Calculator': planning/autoware_remaining_distance_time_calculator - - 'RTC Replayer': planning/rtc_replayer - 'Planning Debug Tools': - 'About Planning Debug Tools': https://github.com/autowarefoundation/autoware_tools/tree/main/planning/planning_debug_tools - 'Stop Reason Visualizer': https://github.com/autowarefoundation/autoware_tools/blob/main/planning/planning_debug_tools/doc-stop-reason-visualizer.md diff --git a/planning/autoware_costmap_generator/include/autoware_costmap_generator/costmap_generator.hpp b/planning/autoware_costmap_generator/include/autoware_costmap_generator/costmap_generator.hpp index 093c88541e9c8..40ed862647095 100644 --- a/planning/autoware_costmap_generator/include/autoware_costmap_generator/costmap_generator.hpp +++ b/planning/autoware_costmap_generator/include/autoware_costmap_generator/costmap_generator.hpp @@ -48,9 +48,9 @@ #include "autoware_costmap_generator/objects_to_costmap.hpp" #include "autoware_costmap_generator/points_to_costmap.hpp" +#include #include #include -#include #include #include diff --git a/planning/autoware_costmap_generator/nodes/autoware_costmap_generator/costmap_generator_node.cpp b/planning/autoware_costmap_generator/nodes/autoware_costmap_generator/costmap_generator_node.cpp index 4302ac6ada645..3cccd9fab0b48 100644 --- a/planning/autoware_costmap_generator/nodes/autoware_costmap_generator/costmap_generator_node.cpp +++ b/planning/autoware_costmap_generator/nodes/autoware_costmap_generator/costmap_generator_node.cpp @@ -45,10 +45,10 @@ #include "autoware_costmap_generator/costmap_generator.hpp" #include "autoware_costmap_generator/object_map_utils.hpp" -#include -#include -#include -#include +#include +#include +#include +#include #include #include diff --git a/planning/autoware_costmap_generator/package.xml b/planning/autoware_costmap_generator/package.xml index aa1ae80eaf953..682e06a310bcb 100644 --- a/planning/autoware_costmap_generator/package.xml +++ b/planning/autoware_costmap_generator/package.xml @@ -15,10 +15,10 @@ ament_cmake_auto autoware_cmake + autoware_lanelet2_extension autoware_map_msgs autoware_perception_msgs grid_map_ros - lanelet2_extension libpcl-all-dev pcl_conversions pcl_ros diff --git a/planning/autoware_freespace_planning_algorithms/autoware_freespace_planning_algorithms/astar_search.py b/planning/autoware_freespace_planning_algorithms/autoware_freespace_planning_algorithms/astar_search.py index 2ffa20150fb0c..7b03a4024dc1b 100644 --- a/planning/autoware_freespace_planning_algorithms/autoware_freespace_planning_algorithms/astar_search.py +++ b/planning/autoware_freespace_planning_algorithms/autoware_freespace_planning_algorithms/astar_search.py @@ -24,6 +24,12 @@ AstarParam = _fp.AstarParam +class PlannerWaypoint: + def __init__(self, pose, is_back): + self.pose = pose + self.is_back = is_back + + class PlannerWaypoints: def __init__(self): self.waypoints = [] @@ -59,6 +65,9 @@ def getWaypoints(self): for waypoint in waypoints_vector.waypoints: pos = Point(x=waypoint[0], y=waypoint[1], z=waypoint[2]) quat = Quaternion(x=waypoint[3], y=waypoint[4], z=waypoint[5], w=waypoint[6]) - waypoints.waypoints.append(Pose(position=pos, orientation=quat)) + is_bask = bool(waypoint[7]) + waypoints.waypoints.append( + PlannerWaypoint(Pose(position=pos, orientation=quat), is_bask) + ) return waypoints diff --git a/planning/autoware_freespace_planning_algorithms/scripts/bind/astar_search_pybind.cpp b/planning/autoware_freespace_planning_algorithms/scripts/bind/astar_search_pybind.cpp index 0a76067bc77f8..8932fb277abe8 100644 --- a/planning/autoware_freespace_planning_algorithms/scripts/bind/astar_search_pybind.cpp +++ b/planning/autoware_freespace_planning_algorithms/scripts/bind/astar_search_pybind.cpp @@ -89,8 +89,9 @@ class AstarSearchPython : public freespace_planning_algorithms::AstarSearch // python-side. So this function returns [*position, *quaternion] as double array const auto & xyz = waypoint.pose.pose.position; const auto & quat = waypoint.pose.pose.orientation; + const double & is_back = waypoint.is_back; waypoints_vector.waypoints.push_back( - std::vector({xyz.x, xyz.y, xyz.z, quat.x, quat.y, quat.z, quat.w})); + std::vector({xyz.x, xyz.y, xyz.z, quat.x, quat.y, quat.z, quat.w, is_back})); } waypoints_vector.length = waypoints.compute_length(); return waypoints_vector; @@ -99,6 +100,7 @@ class AstarSearchPython : public freespace_planning_algorithms::AstarSearch namespace py = pybind11; +// cppcheck-suppress syntaxError PYBIND11_MODULE(autoware_freespace_planning_algorithms_pybind, p) { auto pyPlannerWaypointsVector = diff --git a/planning/autoware_freespace_planning_algorithms/src/abstract_algorithm.cpp b/planning/autoware_freespace_planning_algorithms/src/abstract_algorithm.cpp index 2d47c7acb9801..c434635ff1401 100644 --- a/planning/autoware_freespace_planning_algorithms/src/abstract_algorithm.cpp +++ b/planning/autoware_freespace_planning_algorithms/src/abstract_algorithm.cpp @@ -91,7 +91,7 @@ geometry_msgs::msg::Pose local2global( double PlannerWaypoints::compute_length() const { if (waypoints.empty()) { - std::runtime_error("cannot compute cost because waypoint has size 0"); + throw std::runtime_error("cannot compute cost because waypoint has size 0"); } double total_cost = 0.0; for (size_t i = 0; i < waypoints.size() - 1; ++i) { diff --git a/planning/autoware_freespace_planning_algorithms/src/astar_search.cpp b/planning/autoware_freespace_planning_algorithms/src/astar_search.cpp index 6d093b3c7c0cd..1902b7aa2d091 100644 --- a/planning/autoware_freespace_planning_algorithms/src/astar_search.cpp +++ b/planning/autoware_freespace_planning_algorithms/src/astar_search.cpp @@ -86,8 +86,9 @@ AstarSearch::TransitionTable createTransitionTable( for (int i = 0; i < turning_radius_size; ++i) { double R = R_min + i * dR; double step = R * dtheta; - const NodeUpdate forward_left{ - R * sin(dtheta), R * (1 - cos(dtheta)), dtheta, step, true, false}; + const double shift_x = R * sin(dtheta); + const double shift_y = R * (1 - cos(dtheta)); + const NodeUpdate forward_left{shift_x, shift_y, dtheta, step, true, false}; const NodeUpdate forward_right = forward_left.flipped(); forward_node_candidates.push_back(forward_left); forward_node_candidates.push_back(forward_right); @@ -130,10 +131,7 @@ void AstarSearch::setMap(const nav_msgs::msg::OccupancyGrid & costmap) { AbstractPlanningAlgorithm::setMap(costmap); - clearNodes(); - x_scale_ = costmap_.info.height; - graph_.reserve(100000); } bool AstarSearch::makePlan( @@ -142,6 +140,9 @@ bool AstarSearch::makePlan( start_pose_ = global2local(costmap_, start_pose); goal_pose_ = global2local(costmap_, goal_pose); + clearNodes(); + graph_.reserve(100000); + if (!setStartNode()) { return false; } diff --git a/planning/autoware_mission_planner/package.xml b/planning/autoware_mission_planner/package.xml index deaf9b8e942a2..a7c146f150e7f 100644 --- a/planning/autoware_mission_planner/package.xml +++ b/planning/autoware_mission_planner/package.xml @@ -18,6 +18,7 @@ autoware_cmake autoware_adapi_v1_msgs + autoware_lanelet2_extension autoware_map_msgs autoware_motion_utils autoware_planning_msgs @@ -25,7 +26,6 @@ autoware_universe_utils autoware_vehicle_info_utils geometry_msgs - lanelet2_extension pluginlib rclcpp rclcpp_components diff --git a/planning/autoware_mission_planner/src/lanelet2_plugins/default_planner.cpp b/planning/autoware_mission_planner/src/lanelet2_plugins/default_planner.cpp index 69c52b9c2eafb..3a9bfc1357798 100644 --- a/planning/autoware_mission_planner/src/lanelet2_plugins/default_planner.cpp +++ b/planning/autoware_mission_planner/src/lanelet2_plugins/default_planner.cpp @@ -21,11 +21,11 @@ #include #include #include +#include +#include +#include +#include #include -#include -#include -#include -#include #include #include diff --git a/planning/autoware_mission_planner/src/mission_planner/mission_planner.cpp b/planning/autoware_mission_planner/src/mission_planner/mission_planner.cpp index e484b9915aa87..0df01257f049e 100644 --- a/planning/autoware_mission_planner/src/mission_planner/mission_planner.cpp +++ b/planning/autoware_mission_planner/src/mission_planner/mission_planner.cpp @@ -16,10 +16,10 @@ #include "service_utils.hpp" -#include -#include -#include -#include +#include +#include +#include +#include #include #include diff --git a/planning/autoware_obstacle_cruise_planner/config/obstacle_cruise_planner.param.yaml b/planning/autoware_obstacle_cruise_planner/config/obstacle_cruise_planner.param.yaml index 8b338e9d0cd9a..e797faaff9aa9 100644 --- a/planning/autoware_obstacle_cruise_planner/config/obstacle_cruise_planner.param.yaml +++ b/planning/autoware_obstacle_cruise_planner/config/obstacle_cruise_planner.param.yaml @@ -37,6 +37,7 @@ motorcycle: true bicycle: true pedestrian: true + pointcloud: false cruise_obstacle_type: inside: @@ -68,8 +69,17 @@ motorcycle: true bicycle: true pedestrian: true + pointcloud: false behavior_determination: + pointcloud_search_radius: 5.0 + pointcloud_voxel_grid_x: 0.05 + pointcloud_voxel_grid_y: 0.05 + pointcloud_voxel_grid_z: 100000.0 + pointcloud_cluster_tolerance: 1.0 + pointcloud_min_cluster_size: 1 + pointcloud_max_cluster_size: 100000 + decimate_trajectory_step_length : 2.0 # longitudinal step length to calculate trajectory polygon for collision checking prediction_resampling_time_interval: 0.1 diff --git a/planning/autoware_obstacle_cruise_planner/include/autoware/obstacle_cruise_planner/common_structs.hpp b/planning/autoware_obstacle_cruise_planner/include/autoware/obstacle_cruise_planner/common_structs.hpp index e4ae670ef6b97..fc8a0a96ade42 100644 --- a/planning/autoware_obstacle_cruise_planner/include/autoware/obstacle_cruise_planner/common_structs.hpp +++ b/planning/autoware_obstacle_cruise_planner/include/autoware/obstacle_cruise_planner/common_structs.hpp @@ -58,15 +58,15 @@ struct Obstacle const geometry_msgs::msg::Pose & arg_pose, const double ego_to_obstacle_distance, const double lat_dist_from_obstacle_to_traj) : stamp(arg_stamp), + ego_to_obstacle_distance(ego_to_obstacle_distance), + lat_dist_from_obstacle_to_traj(lat_dist_from_obstacle_to_traj), pose(arg_pose), orientation_reliable(true), twist(object.kinematics.initial_twist_with_covariance.twist), twist_reliable(true), classification(object.classification.at(0)), uuid(autoware::universe_utils::toHexString(object.object_id)), - shape(object.shape), - ego_to_obstacle_distance(ego_to_obstacle_distance), - lat_dist_from_obstacle_to_traj(lat_dist_from_obstacle_to_traj) + shape(object.shape) { predicted_paths.clear(); for (const auto & path : object.kinematics.predicted_paths) { @@ -74,9 +74,26 @@ struct Obstacle } } - Polygon2d toPolygon() const { return autoware::universe_utils::toPolygon2d(pose, shape); } + Obstacle( + const rclcpp::Time & arg_stamp, + const std::optional & stop_collision_point, + const std::optional & slow_down_front_collision_point, + const std::optional & slow_down_back_collision_point, + const double ego_to_obstacle_distance, const double lat_dist_from_obstacle_to_traj) + : stamp(arg_stamp), + ego_to_obstacle_distance(ego_to_obstacle_distance), + lat_dist_from_obstacle_to_traj(lat_dist_from_obstacle_to_traj), + stop_collision_point(stop_collision_point), + slow_down_front_collision_point(slow_down_front_collision_point), + slow_down_back_collision_point(slow_down_back_collision_point) + { + } rclcpp::Time stamp; // This is not the current stamp, but when the object was observed. + double ego_to_obstacle_distance; + double lat_dist_from_obstacle_to_traj; + + // for PredictedObject geometry_msgs::msg::Pose pose; // interpolated with the current stamp bool orientation_reliable; Twist twist; @@ -85,8 +102,11 @@ struct Obstacle std::string uuid; Shape shape; std::vector predicted_paths; - double ego_to_obstacle_distance; - double lat_dist_from_obstacle_to_traj; + + // for PointCloud + std::optional stop_collision_point; + std::optional slow_down_front_collision_point; + std::optional slow_down_back_collision_point; }; struct TargetObstacleInterface diff --git a/planning/autoware_obstacle_cruise_planner/include/autoware/obstacle_cruise_planner/node.hpp b/planning/autoware_obstacle_cruise_planner/include/autoware/obstacle_cruise_planner/node.hpp index dd9a6c3880672..b96605f57b943 100644 --- a/planning/autoware_obstacle_cruise_planner/include/autoware/obstacle_cruise_planner/node.hpp +++ b/planning/autoware_obstacle_cruise_planner/include/autoware/obstacle_cruise_planner/node.hpp @@ -27,6 +27,9 @@ #include #include +#include +#include + #include #include #include @@ -58,16 +61,27 @@ class ObstacleCruisePlannerNode : public rclcpp::Node std::vector convertToObstacles( const Odometry & odometry, const PredictedObjects & objects, const std::vector & traj_points) const; + std::vector convertToObstacles( + const Odometry & odometry, const PointCloud2 & pointcloud, + const std::vector & traj_points, + const std_msgs::msg::Header & traj_header) const; std::tuple, std::vector, std::vector> - determineEgoBehaviorAgainstObstacles( + determineEgoBehaviorAgainstPredictedObjectObstacles( const Odometry & odometry, const PredictedObjects & objects, const std::vector & traj_points, const std::vector & obstacles); + std::tuple, std::vector, std::vector> + determineEgoBehaviorAgainstPointCloudObstacles( + const Odometry & odometry, const std::vector & traj_points, + const std::vector & obstacles); std::vector decimateTrajectoryPoints( const Odometry & odometry, const std::vector & traj_points) const; - std::optional createStopObstacle( + std::optional createStopObstacleForPredictedObject( const Odometry & odometry, const std::vector & traj_points, const std::vector & traj_polys, const Obstacle & obstacle, const double precise_lateral_dist) const; + std::optional createStopObstacleForPointCloud( + const std::vector & traj_points, const Obstacle & obstacle, + const double precise_lateral_dist) const; bool isStopObstacle(const uint8_t label) const; bool isInsideCruiseObstacle(const uint8_t label) const; bool isOutsideCruiseObstacle(const uint8_t label) const; @@ -94,9 +108,11 @@ class ObstacleCruisePlannerNode : public rclcpp::Node double calcCollisionTimeMargin( const Odometry & odometry, const std::vector & collision_points, const std::vector & traj_points, const bool is_driving_forward) const; - std::optional createSlowDownObstacle( + std::optional createSlowDownObstacleForPredictedObject( const Odometry & odometry, const std::vector & traj_points, const Obstacle & obstacle, const double precise_lat_dist); + std::optional createSlowDownObstacleForPointCloud( + const Obstacle & obstacle, const double precise_lat_dist); PlannerData createPlannerData( const Odometry & odometry, const AccelWithCovarianceStamped & acc, const std::vector & traj_points) const; @@ -116,6 +132,8 @@ class ObstacleCruisePlannerNode : public rclcpp::Node bool enable_debug_info_; bool enable_calculation_time_info_; + bool use_pointcloud_for_stop_; + bool use_pointcloud_for_slow_down_; double min_behavior_stop_margin_; bool enable_approaching_on_curve_; double additional_safe_distance_margin_on_curve_; @@ -149,9 +167,14 @@ class ObstacleCruisePlannerNode : public rclcpp::Node this, "~/input/odometry"}; autoware::universe_utils::InterProcessPollingSubscriber objects_sub_{ this, "~/input/objects"}; + autoware::universe_utils::InterProcessPollingSubscriber pointcloud_sub_{ + this, "~/input/pointcloud"}; autoware::universe_utils::InterProcessPollingSubscriber acc_sub_{ this, "~/input/acceleration"}; + std::unique_ptr tf_buffer_; + std::shared_ptr tf_listener_{nullptr}; + // Vehicle Parameters VehicleInfo vehicle_info_; @@ -182,6 +205,13 @@ class ObstacleCruisePlannerNode : public rclcpp::Node void onParam(const std::vector & parameters); double decimate_trajectory_step_length; + double pointcloud_search_radius; + double pointcloud_voxel_grid_x; + double pointcloud_voxel_grid_y; + double pointcloud_voxel_grid_z; + double pointcloud_cluster_tolerance; + int pointcloud_min_cluster_size; + int pointcloud_max_cluster_size; // hysteresis for stop and cruise double obstacle_velocity_threshold_from_cruise_to_stop; double obstacle_velocity_threshold_from_stop_to_cruise; @@ -271,6 +301,7 @@ class ObstacleCruisePlannerNode : public rclcpp::Node bool is_driving_forward_{true}; bool enable_slow_down_planning_{false}; + bool use_pointcloud_{false}; std::vector prev_closest_stop_obstacles_{}; diff --git a/planning/autoware_obstacle_cruise_planner/include/autoware/obstacle_cruise_planner/optimization_based_planner/optimization_based_planner.hpp b/planning/autoware_obstacle_cruise_planner/include/autoware/obstacle_cruise_planner/optimization_based_planner/optimization_based_planner.hpp index f08ff8fef369c..e173a19ea19de 100644 --- a/planning/autoware_obstacle_cruise_planner/include/autoware/obstacle_cruise_planner/optimization_based_planner/optimization_based_planner.hpp +++ b/planning/autoware_obstacle_cruise_planner/include/autoware/obstacle_cruise_planner/optimization_based_planner/optimization_based_planner.hpp @@ -21,8 +21,8 @@ #include "autoware/obstacle_cruise_planner/type_alias.hpp" #include "autoware_vehicle_info_utils/vehicle_info_utils.hpp" -#include -#include +#include +#include #include #include diff --git a/planning/autoware_obstacle_cruise_planner/include/autoware/obstacle_cruise_planner/planner_interface.hpp b/planning/autoware_obstacle_cruise_planner/include/autoware/obstacle_cruise_planner/planner_interface.hpp index 0641d076bb3ff..3679c253e2bbb 100644 --- a/planning/autoware_obstacle_cruise_planner/include/autoware/obstacle_cruise_planner/planner_interface.hpp +++ b/planning/autoware_obstacle_cruise_planner/include/autoware/obstacle_cruise_planner/planner_interface.hpp @@ -63,12 +63,13 @@ class PlannerInterface void setParam( const bool enable_debug_info, const bool enable_calculation_time_info, - const double min_behavior_stop_margin, const double enable_approaching_on_curve, - const double additional_safe_distance_margin_on_curve, + const bool use_pointcloud, const double min_behavior_stop_margin, + const double enable_approaching_on_curve, const double additional_safe_distance_margin_on_curve, const double min_safe_distance_margin_on_curve, const bool suppress_sudden_obstacle_stop) { enable_debug_info_ = enable_debug_info; enable_calculation_time_info_ = enable_calculation_time_info; + use_pointcloud_ = use_pointcloud; min_behavior_stop_margin_ = min_behavior_stop_margin; enable_approaching_on_curve_ = enable_approaching_on_curve; additional_safe_distance_margin_on_curve_ = additional_safe_distance_margin_on_curve; @@ -117,6 +118,7 @@ class PlannerInterface // Parameters bool enable_debug_info_{false}; bool enable_calculation_time_info_{false}; + bool use_pointcloud_{false}; LongitudinalInfo longitudinal_info_; double min_behavior_stop_margin_; bool enable_approaching_on_curve_; diff --git a/planning/autoware_obstacle_cruise_planner/include/autoware/obstacle_cruise_planner/type_alias.hpp b/planning/autoware_obstacle_cruise_planner/include/autoware/obstacle_cruise_planner/type_alias.hpp index 5721efd051f63..03e536ce238cd 100644 --- a/planning/autoware_obstacle_cruise_planner/include/autoware/obstacle_cruise_planner/type_alias.hpp +++ b/planning/autoware_obstacle_cruise_planner/include/autoware/obstacle_cruise_planner/type_alias.hpp @@ -27,6 +27,7 @@ #include "geometry_msgs/msg/transform_stamped.hpp" #include "geometry_msgs/msg/twist_stamped.hpp" #include "nav_msgs/msg/odometry.hpp" +#include "sensor_msgs/msg/point_cloud2.hpp" #include "tier4_debug_msgs/msg/float32_stamped.hpp" #include "tier4_planning_msgs/msg/stop_factor.hpp" #include "tier4_planning_msgs/msg/stop_reason_array.hpp" @@ -35,6 +36,9 @@ #include "tier4_planning_msgs/msg/velocity_limit_clear_command.hpp" #include "visualization_msgs/msg/marker_array.hpp" +#include +#include + using autoware::vehicle_info_utils::VehicleInfo; using autoware_adapi_v1_msgs::msg::PlanningBehavior; using autoware_adapi_v1_msgs::msg::VelocityFactor; @@ -50,6 +54,7 @@ using geometry_msgs::msg::AccelStamped; using geometry_msgs::msg::AccelWithCovarianceStamped; using geometry_msgs::msg::Twist; using nav_msgs::msg::Odometry; +using sensor_msgs::msg::PointCloud2; using tier4_debug_msgs::msg::Float32Stamped; using tier4_planning_msgs::msg::StopFactor; using tier4_planning_msgs::msg::StopReason; @@ -63,4 +68,6 @@ namespace bg = boost::geometry; using autoware::universe_utils::Point2d; using autoware::universe_utils::Polygon2d; +using PointCloud = pcl::PointCloud; + #endif // AUTOWARE__OBSTACLE_CRUISE_PLANNER__TYPE_ALIAS_HPP_ diff --git a/planning/autoware_obstacle_cruise_planner/package.xml b/planning/autoware_obstacle_cruise_planner/package.xml index 6055327a906e1..79335dc23221b 100644 --- a/planning/autoware_obstacle_cruise_planner/package.xml +++ b/planning/autoware_obstacle_cruise_planner/package.xml @@ -18,6 +18,7 @@ autoware_cmake autoware_adapi_v1_msgs + autoware_lanelet2_extension autoware_motion_utils autoware_perception_msgs autoware_planning_msgs @@ -26,7 +27,6 @@ autoware_vehicle_info_utils geometry_msgs interpolation - lanelet2_extension nav_msgs object_recognition_utils osqp_interface diff --git a/planning/autoware_obstacle_cruise_planner/src/node.cpp b/planning/autoware_obstacle_cruise_planner/src/node.cpp index 9682ac63104c8..c8f962c3ad711 100644 --- a/planning/autoware_obstacle_cruise_planner/src/node.cpp +++ b/planning/autoware_obstacle_cruise_planner/src/node.cpp @@ -23,8 +23,15 @@ #include "autoware/universe_utils/ros/update_param.hpp" #include "object_recognition_utils/predicted_path_utils.hpp" +#include +#include + #include +#include +#include +#include + #include #include @@ -194,6 +201,15 @@ std::vector resampleTrajectoryPoints( return autoware::motion_utils::convertToTrajectoryPointArray(resampled_traj); } +geometry_msgs::msg::Point toGeomPoint(const pcl::PointXYZ & point) +{ + geometry_msgs::msg::Point geom_point; + geom_point.x = point.x; + geom_point.y = point.y; + geom_point.z = point.z; + return geom_point; +} + geometry_msgs::msg::Point toGeomPoint(const autoware::universe_utils::Point2d & point) { geometry_msgs::msg::Point geom_point; @@ -216,6 +232,12 @@ bool isLowerConsideringHysteresis( } return false; } + +template +void concatenate(std::vector & first, const std::vector & last) +{ + first.insert(first.end(), last.begin(), last.end()); +} } // namespace namespace autoware::motion_planning @@ -225,6 +247,20 @@ ObstacleCruisePlannerNode::BehaviorDeterminationParam::BehaviorDeterminationPara { // behavior determination decimate_trajectory_step_length = node.declare_parameter("behavior_determination.decimate_trajectory_step_length"); + pointcloud_search_radius = + node.declare_parameter("behavior_determination.pointcloud_search_radius"); + pointcloud_voxel_grid_x = + node.declare_parameter("behavior_determination.pointcloud_voxel_grid_x"); + pointcloud_voxel_grid_y = + node.declare_parameter("behavior_determination.pointcloud_voxel_grid_y"); + pointcloud_voxel_grid_z = + node.declare_parameter("behavior_determination.pointcloud_voxel_grid_z"); + pointcloud_cluster_tolerance = + node.declare_parameter("behavior_determination.pointcloud_cluster_tolerance"); + pointcloud_min_cluster_size = + node.declare_parameter("behavior_determination.pointcloud_min_cluster_size"); + pointcloud_max_cluster_size = + node.declare_parameter("behavior_determination.pointcloud_max_cluster_size"); obstacle_velocity_threshold_from_cruise_to_stop = node.declare_parameter( "behavior_determination.obstacle_velocity_threshold_from_cruise_to_stop"); obstacle_velocity_threshold_from_stop_to_cruise = node.declare_parameter( @@ -284,6 +320,21 @@ void ObstacleCruisePlannerNode::BehaviorDeterminationParam::onParam( autoware::universe_utils::updateParam( parameters, "behavior_determination.decimate_trajectory_step_length", decimate_trajectory_step_length); + autoware::universe_utils::updateParam( + parameters, "behavior_determination.pointcloud_search_radius", pointcloud_search_radius); + autoware::universe_utils::updateParam( + parameters, "behavior_determination.pointcloud_voxel_grid_x", pointcloud_voxel_grid_x); + autoware::universe_utils::updateParam( + parameters, "behavior_determination.pointcloud_voxel_grid_y", pointcloud_voxel_grid_y); + autoware::universe_utils::updateParam( + parameters, "behavior_determination.pointcloud_voxel_grid_z", pointcloud_voxel_grid_z); + autoware::universe_utils::updateParam( + parameters, "behavior_determination.pointcloud_cluster_tolerance", + pointcloud_cluster_tolerance); + autoware::universe_utils::updateParam( + parameters, "behavior_determination.pointcloud_min_cluster_size", pointcloud_min_cluster_size); + autoware::universe_utils::updateParam( + parameters, "behavior_determination.pointcloud_max_cluster_size", pointcloud_max_cluster_size); autoware::universe_utils::updateParam( parameters, "behavior_determination.crossing_obstacle.obstacle_velocity_threshold", crossing_obstacle_velocity_threshold); @@ -387,6 +438,10 @@ ObstacleCruisePlannerNode::ObstacleCruisePlannerNode(const rclcpp::NodeOptions & debug_slow_down_planning_info_pub_ = create_publisher("~/debug/slow_down_planning_info", 1); + // tf listener + tf_buffer_ = std::make_unique(get_clock()); + tf_listener_ = std::make_shared(*tf_buffer_); + const auto longitudinal_info = LongitudinalInfo(*this); ego_nearest_param_ = EgoNearestParam(*this); @@ -395,6 +450,11 @@ ObstacleCruisePlannerNode::ObstacleCruisePlannerNode(const rclcpp::NodeOptions & enable_calculation_time_info_ = declare_parameter("common.enable_calculation_time_info"); enable_slow_down_planning_ = declare_parameter("common.enable_slow_down_planning"); + use_pointcloud_for_stop_ = declare_parameter("common.stop_obstacle_type.pointcloud"); + use_pointcloud_for_slow_down_ = + declare_parameter("common.slow_down_obstacle_type.pointcloud"); + use_pointcloud_ = use_pointcloud_for_stop_ || use_pointcloud_for_slow_down_; + behavior_determination_param_ = BehaviorDeterminationParam(*this); { // planning algorithm @@ -409,7 +469,7 @@ ObstacleCruisePlannerNode::ObstacleCruisePlannerNode(const rclcpp::NodeOptions & planner_ptr_ = std::make_unique( *this, longitudinal_info, vehicle_info_, ego_nearest_param_, debug_data_ptr_); } else { - std::logic_error("Designated algorithm is not supported."); + throw std::logic_error("Designated algorithm is not supported."); } min_behavior_stop_margin_ = declare_parameter("common.min_behavior_stop_margin"); @@ -422,7 +482,7 @@ ObstacleCruisePlannerNode::ObstacleCruisePlannerNode(const rclcpp::NodeOptions & suppress_sudden_obstacle_stop_ = declare_parameter("common.suppress_sudden_obstacle_stop"); planner_ptr_->setParam( - enable_debug_info_, enable_calculation_time_info_, min_behavior_stop_margin_, + enable_debug_info_, enable_calculation_time_info_, use_pointcloud_, min_behavior_stop_margin_, enable_approaching_on_curve_, additional_safe_distance_margin_on_curve_, min_safe_distance_margin_on_curve_, suppress_sudden_obstacle_stop_); } @@ -476,7 +536,7 @@ rcl_interfaces::msg::SetParametersResult ObstacleCruisePlannerNode::onParam( min_safe_distance_margin_on_curve_); planner_ptr_->setParam( - enable_debug_info_, enable_calculation_time_info_, min_behavior_stop_margin_, + enable_debug_info_, enable_calculation_time_info_, use_pointcloud_, min_behavior_stop_margin_, enable_approaching_on_curve_, additional_safe_distance_margin_on_curve_, min_safe_distance_margin_on_curve_, suppress_sudden_obstacle_stop_); @@ -495,13 +555,13 @@ void ObstacleCruisePlannerNode::onTrajectory(const Trajectory::ConstSharedPtr ms { const auto ego_odom_ptr = ego_odom_sub_.takeData(); const auto objects_ptr = objects_sub_.takeData(); + const auto pointcloud_ptr = use_pointcloud_ ? pointcloud_sub_.takeData() : nullptr; const auto acc_ptr = acc_sub_.takeData(); - if (!ego_odom_ptr || !objects_ptr || !acc_ptr) { + if (!ego_odom_ptr || (!objects_ptr && (!use_pointcloud_ || !pointcloud_ptr)) || !acc_ptr) { return; } const auto & ego_odom = *ego_odom_ptr; - const auto & objects = *objects_ptr; const auto & acc = *acc_ptr; const auto traj_points = autoware::motion_utils::convertToTrajectoryPointArray(*msg); @@ -516,15 +576,39 @@ void ObstacleCruisePlannerNode::onTrajectory(const Trajectory::ConstSharedPtr ms const auto is_driving_forward = autoware::motion_utils::isDrivingForwardWithTwist(traj_points); is_driving_forward_ = is_driving_forward ? is_driving_forward.value() : is_driving_forward_; - // 1. Convert predicted objects to obstacles which are - // (1) with a proper label - // (2) in front of ego - // (3) not too far from trajectory - const auto target_obstacles = convertToObstacles(ego_odom, objects, traj_points); + const auto & [stop_obstacles, cruise_obstacles, slow_down_obstacles] = [&]() { + std::vector stop_obstacles; + std::vector cruise_obstacles; + std::vector slow_down_obstacles; + if (objects_ptr) { + // 1. Convert predicted objects to obstacles which are + // (1) with a proper label + // (2) in front of ego + // (3) not too far from trajectory + const auto target_obstacles = convertToObstacles(ego_odom, *objects_ptr, traj_points); + + // 2. Determine ego's behavior against each obstacle from stop, cruise and slow down. + const auto & [stop_object_obstacles, cruise_object_obstacles, slow_down_object_obstacles] = + determineEgoBehaviorAgainstPredictedObjectObstacles( + ego_odom, *objects_ptr, traj_points, target_obstacles); + + concatenate(stop_obstacles, stop_object_obstacles); + concatenate(cruise_obstacles, cruise_object_obstacles); + concatenate(slow_down_obstacles, slow_down_object_obstacles); + } + if (pointcloud_ptr && !pointcloud_ptr->data.empty()) { + const auto target_obstacles = + convertToObstacles(ego_odom, *pointcloud_ptr, traj_points, msg->header); + + const auto & [stop_pc_obstacles, cruise_pc_obstacles, slow_down_pc_obstacles] = + determineEgoBehaviorAgainstPointCloudObstacles(ego_odom, traj_points, target_obstacles); - // 2. Determine ego's behavior against each obstacle from stop, cruise and slow down. - const auto & [stop_obstacles, cruise_obstacles, slow_down_obstacles] = - determineEgoBehaviorAgainstObstacles(ego_odom, objects, traj_points, target_obstacles); + concatenate(stop_obstacles, stop_pc_obstacles); + concatenate(cruise_obstacles, cruise_pc_obstacles); + concatenate(slow_down_obstacles, slow_down_pc_obstacles); + } + return std::make_tuple(stop_obstacles, cruise_obstacles, slow_down_obstacles); + }(); // 3. Create data for planning const auto planner_data = createPlannerData(ego_odom, acc, traj_points); @@ -715,6 +799,124 @@ std::vector ObstacleCruisePlannerNode::convertToObstacles( return target_obstacles; } +std::vector ObstacleCruisePlannerNode::convertToObstacles( + const Odometry & odometry, const PointCloud2 & pointcloud, + const std::vector & traj_points, const std_msgs::msg::Header & traj_header) const +{ + stop_watch_.tic(__func__); + + const auto & p = behavior_determination_param_; + + std::vector target_obstacles; + + std::optional transform_stamped{}; + try { + transform_stamped = tf_buffer_->lookupTransform( + traj_header.frame_id, pointcloud.header.frame_id, pointcloud.header.stamp, + rclcpp::Duration::from_seconds(0.5)); + } catch (tf2::TransformException & ex) { + RCLCPP_ERROR_STREAM( + get_logger(), "Failed to look up transform from " << traj_header.frame_id << " to " + << pointcloud.header.frame_id); + transform_stamped = std::nullopt; + } + + if (transform_stamped) { + // 1. transform pointcloud + PointCloud::Ptr pointcloud_ptr(new PointCloud); + pcl::fromROSMsg(pointcloud, *pointcloud_ptr); + const Eigen::Matrix4f transform = + tf2::transformToEigen(transform_stamped.value().transform).matrix().cast(); + pcl::transformPointCloud(*pointcloud_ptr, *pointcloud_ptr, transform); + + // 2. downsample & cluster pointcloud + PointCloud::Ptr filtered_points_ptr(new PointCloud); + pcl::VoxelGrid filter; + filter.setInputCloud(pointcloud_ptr); + filter.setLeafSize( + p.pointcloud_voxel_grid_x, p.pointcloud_voxel_grid_y, p.pointcloud_voxel_grid_z); + filter.filter(*filtered_points_ptr); + + pcl::search::KdTree::Ptr tree(new pcl::search::KdTree); + tree->setInputCloud(filtered_points_ptr); + std::vector clusters; + pcl::EuclideanClusterExtraction ec; + ec.setClusterTolerance(p.pointcloud_cluster_tolerance); + ec.setMinClusterSize(p.pointcloud_min_cluster_size); + ec.setMaxClusterSize(p.pointcloud_max_cluster_size); + ec.setSearchMethod(tree); + ec.setInputCloud(filtered_points_ptr); + ec.extract(clusters); + + const auto max_lat_margin = + std::max(p.max_lat_margin_for_stop_against_unknown, p.max_lat_margin_for_slow_down); + const size_t ego_idx = ego_nearest_param_.findIndex(traj_points, odometry.pose.pose); + + // 3. convert clusters to obstacles + for (const auto & cluster_indices : clusters) { + double ego_to_stop_collision_distance = std::numeric_limits::max(); + double ego_to_slow_down_front_collision_distance = std::numeric_limits::max(); + double ego_to_slow_down_back_collision_distance = std::numeric_limits::min(); + double lat_dist_from_obstacle_to_traj = std::numeric_limits::max(); + double ego_to_obstacle_distance = std::numeric_limits::max(); + std::optional stop_collision_point = std::nullopt; + std::optional slow_down_front_collision_point = std::nullopt; + std::optional slow_down_back_collision_point = std::nullopt; + + for (const auto & index : cluster_indices.indices) { + const auto obstacle_point = toGeomPoint(filtered_points_ptr->points[index]); + const auto current_lat_dist_from_obstacle_to_traj = + autoware::motion_utils::calcLateralOffset(traj_points, obstacle_point); + const auto min_lat_dist_to_traj_poly = + std::abs(current_lat_dist_from_obstacle_to_traj) - vehicle_info_.vehicle_width_m; + + if (min_lat_dist_to_traj_poly < max_lat_margin) { + const auto current_ego_to_obstacle_distance = + calcDistanceToFrontVehicle(traj_points, ego_idx, obstacle_point); + if (current_ego_to_obstacle_distance) { + ego_to_obstacle_distance = + std::min(ego_to_obstacle_distance, *current_ego_to_obstacle_distance); + } else { + continue; + } + + lat_dist_from_obstacle_to_traj = + std::min(lat_dist_from_obstacle_to_traj, current_lat_dist_from_obstacle_to_traj); + + if (min_lat_dist_to_traj_poly < p.max_lat_margin_for_stop_against_unknown) { + if (*current_ego_to_obstacle_distance < ego_to_stop_collision_distance) { + stop_collision_point = obstacle_point; + ego_to_stop_collision_distance = *current_ego_to_obstacle_distance; + } + } else if (min_lat_dist_to_traj_poly < p.max_lat_margin_for_slow_down) { + if (*current_ego_to_obstacle_distance < ego_to_slow_down_front_collision_distance) { + slow_down_front_collision_point = obstacle_point; + ego_to_slow_down_front_collision_distance = *current_ego_to_obstacle_distance; + } else if ( + *current_ego_to_obstacle_distance > ego_to_slow_down_back_collision_distance) { + slow_down_back_collision_point = obstacle_point; + ego_to_slow_down_back_collision_distance = *current_ego_to_obstacle_distance; + } + } + } + } + + if (stop_collision_point || slow_down_front_collision_point) { + target_obstacles.emplace_back( + pointcloud.header.stamp, stop_collision_point, slow_down_front_collision_point, + slow_down_back_collision_point, ego_to_obstacle_distance, lat_dist_from_obstacle_to_traj); + } + } + } + + const double calculation_time = stop_watch_.toc(__func__); + RCLCPP_INFO_EXPRESSION( + rclcpp::get_logger("ObstacleCruisePlanner"), enable_debug_info_, " %s := %f [ms]", __func__, + calculation_time); + + return target_obstacles; +} + bool ObstacleCruisePlannerNode::isStopObstacle(const uint8_t label) const { const auto & types = stop_obstacle_types_; @@ -760,7 +962,7 @@ bool ObstacleCruisePlannerNode::isFrontCollideObstacle( } std::tuple, std::vector, std::vector> -ObstacleCruisePlannerNode::determineEgoBehaviorAgainstObstacles( +ObstacleCruisePlannerNode::determineEgoBehaviorAgainstPredictedObjectObstacles( const Odometry & odometry, const PredictedObjects & objects, const std::vector & traj_points, const std::vector & obstacles) { @@ -778,7 +980,7 @@ ObstacleCruisePlannerNode::determineEgoBehaviorAgainstObstacles( std::vector slow_down_obstacles; slow_down_condition_counter_.resetCurrentUuids(); for (const auto & obstacle : obstacles) { - const auto obstacle_poly = obstacle.toPolygon(); + const auto obstacle_poly = autoware::universe_utils::toPolygon2d(obstacle.pose, obstacle.shape); // Calculate distance between trajectory and obstacle first double precise_lat_dist = std::numeric_limits::max(); @@ -794,14 +996,14 @@ ObstacleCruisePlannerNode::determineEgoBehaviorAgainstObstacles( cruise_obstacles.push_back(*cruise_obstacle); continue; } - const auto stop_obstacle = createStopObstacle( + const auto stop_obstacle = createStopObstacleForPredictedObject( odometry, decimated_traj_points, decimated_traj_polys, obstacle, precise_lat_dist); if (stop_obstacle) { stop_obstacles.push_back(*stop_obstacle); continue; } - const auto slow_down_obstacle = - createSlowDownObstacle(odometry, decimated_traj_points, obstacle, precise_lat_dist); + const auto slow_down_obstacle = createSlowDownObstacleForPredictedObject( + odometry, decimated_traj_points, obstacle, precise_lat_dist); if (slow_down_obstacle) { slow_down_obstacles.push_back(*slow_down_obstacle); continue; @@ -842,6 +1044,47 @@ ObstacleCruisePlannerNode::determineEgoBehaviorAgainstObstacles( return {stop_obstacles, cruise_obstacles, slow_down_obstacles}; } +std::tuple, std::vector, std::vector> +ObstacleCruisePlannerNode::determineEgoBehaviorAgainstPointCloudObstacles( + const Odometry & odometry, const std::vector & traj_points, + const std::vector & obstacles) +{ + stop_watch_.tic(__func__); + + // calculated decimated trajectory points and trajectory polygon + const auto decimated_traj_points = decimateTrajectoryPoints(odometry, traj_points); + const auto decimated_traj_polys = + createOneStepPolygons(decimated_traj_points, vehicle_info_, odometry.pose.pose); + debug_data_ptr_->detection_polygons = decimated_traj_polys; + + // determine ego's behavior from stop and slow down + std::vector stop_obstacles; + std::vector slow_down_obstacles; + for (const auto & obstacle : obstacles) { + const auto & precise_lat_dist = obstacle.lat_dist_from_obstacle_to_traj; + + // Filter obstacles for stop and slow down + const auto stop_obstacle = + createStopObstacleForPointCloud(decimated_traj_points, obstacle, precise_lat_dist); + if (stop_obstacle) { + stop_obstacles.push_back(*stop_obstacle); + continue; + } + const auto slow_down_obstacle = createSlowDownObstacleForPointCloud(obstacle, precise_lat_dist); + if (slow_down_obstacle) { + slow_down_obstacles.push_back(*slow_down_obstacle); + continue; + } + } + + const double calculation_time = stop_watch_.toc(__func__); + RCLCPP_INFO_EXPRESSION( + rclcpp::get_logger("ObstacleCruisePlanner"), enable_calculation_time_info_, " %s := %f [ms]", + __func__, calculation_time); + + return {stop_obstacles, {}, slow_down_obstacles}; +} + std::vector ObstacleCruisePlannerNode::decimateTrajectoryPoints( const Odometry & odometry, const std::vector & traj_points) const { @@ -1173,7 +1416,7 @@ ObstacleCruisePlannerNode::createCollisionPointsForOutsideCruiseObstacle( return collision_points; } -std::optional ObstacleCruisePlannerNode::createStopObstacle( +std::optional ObstacleCruisePlannerNode::createStopObstacleForPredictedObject( const Odometry & odometry, const std::vector & traj_points, const std::vector & traj_polys, const Obstacle & obstacle, const double precise_lat_dist) const @@ -1209,8 +1452,8 @@ std::optional ObstacleCruisePlannerNode::createStopObstacle( // NOTE: Dynamic obstacles for stop are crossing ego's trajectory with high speed, // and the collision between ego and obstacles are within the margin threshold. const bool is_obstacle_crossing = isObstacleCrossing(traj_points, obstacle); - const double has_high_speed = p.crossing_obstacle_velocity_threshold < - std::hypot(obstacle.twist.linear.x, obstacle.twist.linear.y); + const bool has_high_speed = p.crossing_obstacle_velocity_threshold < + std::hypot(obstacle.twist.linear.x, obstacle.twist.linear.y); if (is_obstacle_crossing && has_high_speed) { // Get highest confidence predicted path const auto resampled_predicted_path = resampleHighestConfidencePredictedPath( @@ -1264,7 +1507,45 @@ std::optional ObstacleCruisePlannerNode::createStopObstacle( normal_vel, collision_point->first, collision_point->second}; } -std::optional ObstacleCruisePlannerNode::createSlowDownObstacle( +std::optional ObstacleCruisePlannerNode::createStopObstacleForPointCloud( + const std::vector & traj_points, const Obstacle & obstacle, + const double precise_lat_dist) const +{ + const auto & p = behavior_determination_param_; + + if (!use_pointcloud_for_stop_) { + return std::nullopt; + } + + if (!obstacle.stop_collision_point) { + return std::nullopt; + } + + const double max_lat_margin_for_stop = + (obstacle.classification.label == ObjectClassification::UNKNOWN) + ? p.max_lat_margin_for_stop_against_unknown + : p.max_lat_margin_for_stop; + + if (precise_lat_dist > std::max(max_lat_margin_for_stop, 1e-3)) { + return std::nullopt; + } + + const auto dist_to_collide_on_traj = + autoware::motion_utils::calcSignedArcLength(traj_points, 0, *obstacle.stop_collision_point); + + return StopObstacle{ + obstacle.uuid, + obstacle.stamp, + obstacle.classification, + obstacle.pose, + obstacle.shape, + {}, + {}, + *obstacle.stop_collision_point, + dist_to_collide_on_traj}; +} + +std::optional ObstacleCruisePlannerNode::createSlowDownObstacleForPredictedObject( const Odometry & odometry, const std::vector & traj_points, const Obstacle & obstacle, const double precise_lat_dist) { @@ -1315,7 +1596,7 @@ std::optional ObstacleCruisePlannerNode::createSlowDownObstacl return std::nullopt; } - const auto obstacle_poly = obstacle.toPolygon(); + const auto obstacle_poly = autoware::universe_utils::toPolygon2d(obstacle.pose, obstacle.shape); // calculate collision points with trajectory with lateral stop margin // NOTE: For additional margin, hysteresis is not divided by two. @@ -1388,6 +1669,26 @@ std::optional ObstacleCruisePlannerNode::createSlowDownObstacl precise_lat_dist, front_collision_point, back_collision_point}; } +std::optional ObstacleCruisePlannerNode::createSlowDownObstacleForPointCloud( + const Obstacle & obstacle, const double precise_lat_dist) +{ + if (!enable_slow_down_planning_ || !use_pointcloud_for_slow_down_) { + return std::nullopt; + } + + if (!obstacle.slow_down_front_collision_point) { + return std::nullopt; + } + + auto front_collision_point = *obstacle.slow_down_front_collision_point; + auto back_collision_point = + obstacle.slow_down_back_collision_point.value_or(front_collision_point); + + return SlowDownObstacle{ + obstacle.uuid, obstacle.stamp, obstacle.classification, obstacle.pose, {}, {}, + precise_lat_dist, front_collision_point, back_collision_point}; +} + void ObstacleCruisePlannerNode::checkConsistency( const rclcpp::Time & current_time, const PredictedObjects & predicted_objects, std::vector & stop_obstacles) diff --git a/planning/autoware_obstacle_cruise_planner/src/optimization_based_planner/optimization_based_planner.cpp b/planning/autoware_obstacle_cruise_planner/src/optimization_based_planner/optimization_based_planner.cpp index d256a64984667..b3a0fc014c410 100644 --- a/planning/autoware_obstacle_cruise_planner/src/optimization_based_planner/optimization_based_planner.cpp +++ b/planning/autoware_obstacle_cruise_planner/src/optimization_based_planner/optimization_based_planner.cpp @@ -339,16 +339,21 @@ std::tuple OptimizationBasedPlanner::calcInitialMotion( const auto stop_dist = autoware::motion_utils::calcDistanceToForwardStopPoint( input_traj_points, ego_pose, ego_nearest_param_.dist_threshold, ego_nearest_param_.yaw_threshold); - if ((stop_dist && *stop_dist > stop_dist_to_prohibit_engage_) || !stop_dist) { - initial_vel = engage_velocity_; - initial_acc = engage_acceleration_; + if (!stop_dist.has_value()) { + RCLCPP_DEBUG( + rclcpp::get_logger("ObstacleCruisePlanner::OptimizationBasedPlanner"), + "calcInitialMotion : vehicle speed is low (%.3f), and desired speed is high (%.3f). Use " + "engage speed (%.3f) until vehicle speed reaches engage_vel_thr (%.3f)", + vehicle_speed, target_vel, engage_velocity_, engage_vel_thr); + return std::make_tuple(engage_velocity_, engage_acceleration_); + } else if (stop_dist.value() > stop_dist_to_prohibit_engage_) { RCLCPP_DEBUG( rclcpp::get_logger("ObstacleCruisePlanner::OptimizationBasedPlanner"), "calcInitialMotion : vehicle speed is low (%.3f), and desired speed is high (%.3f). Use " "engage speed (%.3f) until vehicle speed reaches engage_vel_thr (%.3f). stop_dist = %.3f", vehicle_speed, target_vel, engage_velocity_, engage_vel_thr, stop_dist.value()); - return std::make_tuple(initial_vel, initial_acc); - } else if (stop_dist) { + return std::make_tuple(engage_velocity_, engage_acceleration_); + } else { RCLCPP_DEBUG( rclcpp::get_logger("ObstacleCruisePlanner::OptimizationBasedPlanner"), "calcInitialMotion : stop point is close (%.3f[m]). no engage.", stop_dist.value()); @@ -702,7 +707,6 @@ void OptimizationBasedPlanner::publishDebugTrajectory( const SBoundaries & s_boundaries, const VelocityOptimizer::OptimizationResult & opt_result) { const auto & current_time = planner_data.current_time; - const std::vector time = opt_result.t; // Publish optimized result and boundary Trajectory boundary_traj; boundary_traj.header.stamp = current_time; diff --git a/planning/autoware_obstacle_cruise_planner/src/pid_based_planner/pid_based_planner.cpp b/planning/autoware_obstacle_cruise_planner/src/pid_based_planner/pid_based_planner.cpp index 345265b1d20b0..d0736c6158a7a 100644 --- a/planning/autoware_obstacle_cruise_planner/src/pid_based_planner/pid_based_planner.cpp +++ b/planning/autoware_obstacle_cruise_planner/src/pid_based_planner/pid_based_planner.cpp @@ -509,7 +509,6 @@ std::vector PIDBasedPlanner::getAccelerationLimitedTrajectory( const double v0, const double a0, const double target_acc, const double target_jerk_ratio) const { // calculate vt function - const auto & i = longitudinal_info_; const auto vt = [&]( const double v0, const double a0, const double jerk, const double t, const double target_acc) { @@ -541,8 +540,8 @@ std::vector PIDBasedPlanner::getAccelerationLimitedTrajectory( // Therefore, we calculate v1 (velocity at next step) and use it for initial velocity. const double v1 = v0; // + 0.1; // a0 * 0.1; // + jerk * 0.1 * 0.1 / 2.0; const double a1 = a0; // + jerk * 0.1; - const double jerk = - target_acc > a1 ? i.max_jerk * target_jerk_ratio : i.min_jerk * target_jerk_ratio; + const double jerk = target_acc > a1 ? longitudinal_info_.max_jerk * target_jerk_ratio + : longitudinal_info_.min_jerk * target_jerk_ratio; double t_current = 0.0; std::vector s_vec{0.0}; diff --git a/planning/autoware_obstacle_cruise_planner/src/planner_interface.cpp b/planning/autoware_obstacle_cruise_planner/src/planner_interface.cpp index ab6d4650eb3d5..0413d4bbf3529 100644 --- a/planning/autoware_obstacle_cruise_planner/src/planner_interface.cpp +++ b/planning/autoware_obstacle_cruise_planner/src/planner_interface.cpp @@ -143,7 +143,7 @@ double findReachTime( return j * t * t * t / 6.0 + a * t * t / 2.0 + v * t - d; }; if (f(min, j, a, v, d) > 0 || f(max, j, a, v, d) < 0) { - std::logic_error("[obstacle_cruise_planner](findReachTime): search range is invalid"); + throw std::logic_error("[obstacle_cruise_planner](findReachTime): search range is invalid"); } const double eps = 1e-5; const int warn_iter = 100; @@ -175,7 +175,7 @@ double calcDecelerationVelocityFromDistanceToTarget( const double current_velocity, const double distance_to_target) { if (max_slowdown_jerk > 0 || max_slowdown_accel > 0) { - std::logic_error("max_slowdown_jerk and max_slowdown_accel should be negative"); + throw std::logic_error("max_slowdown_jerk and max_slowdown_accel should be negative"); } // case0: distance to target is behind ego if (distance_to_target <= 0) return current_velocity; @@ -439,7 +439,7 @@ std::vector PlannerInterface::generateStopTrajectory( double PlannerInterface::calculateMarginFromObstacleOnCurve( const PlannerData & planner_data, const StopObstacle & stop_obstacle) const { - if (!enable_approaching_on_curve_) { + if (!enable_approaching_on_curve_ || use_pointcloud_) { return longitudinal_info_.safe_distance_margin; } @@ -598,8 +598,9 @@ std::vector PlannerInterface::generateSlowDownTrajectory( obstacle.uuid.c_str()); continue; } - const auto [dist_to_slow_down_start, dist_to_slow_down_end, feasible_slow_down_vel] = - *dist_vec_to_slow_down; + const auto dist_to_slow_down_start = std::get<0>(*dist_vec_to_slow_down); + const auto dist_to_slow_down_end = std::get<1>(*dist_vec_to_slow_down); + const auto feasible_slow_down_vel = std::get<2>(*dist_vec_to_slow_down); // calculate slow down end distance, and insert slow down velocity // NOTE: slow_down_start_idx will not be wrong since inserted back point is after inserted @@ -622,9 +623,9 @@ std::vector PlannerInterface::generateSlowDownTrajectory( }(); // insert slow down velocity between slow start and end - for (size_t i = (slow_down_start_idx ? *slow_down_start_idx : 0); i <= *slow_down_end_idx; - ++i) { - auto & traj_point = slow_down_traj_points.at(i); + for (size_t j = (slow_down_start_idx ? *slow_down_start_idx : 0); j <= *slow_down_end_idx; + ++j) { + auto & traj_point = slow_down_traj_points.at(j); traj_point.longitudinal_velocity_mps = std::min(traj_point.longitudinal_velocity_mps, static_cast(stable_slow_down_vel)); } @@ -636,7 +637,7 @@ std::vector PlannerInterface::generateSlowDownTrajectory( slow_down_debug_multi_array_.data.push_back(feasible_slow_down_vel); slow_down_debug_multi_array_.data.push_back(stable_slow_down_vel); slow_down_debug_multi_array_.data.push_back(slow_down_start_idx ? *slow_down_start_idx : -1.0); - slow_down_debug_multi_array_.data.push_back(slow_down_end_idx ? *slow_down_end_idx : -1.0); + slow_down_debug_multi_array_.data.push_back(*slow_down_end_idx); // add virtual wall if (slow_down_start_idx && slow_down_end_idx) { diff --git a/planning/obstacle_stop_planner/CMakeLists.txt b/planning/autoware_obstacle_stop_planner/CMakeLists.txt similarity index 60% rename from planning/obstacle_stop_planner/CMakeLists.txt rename to planning/autoware_obstacle_stop_planner/CMakeLists.txt index 5b7edcd559fe5..9e663820b02f1 100644 --- a/planning/obstacle_stop_planner/CMakeLists.txt +++ b/planning/autoware_obstacle_stop_planner/CMakeLists.txt @@ -1,5 +1,5 @@ cmake_minimum_required(VERSION 3.14) -project(obstacle_stop_planner) +project(autoware_obstacle_stop_planner) find_package(autoware_cmake REQUIRED) autoware_package() @@ -7,26 +7,26 @@ autoware_package() find_package(Eigen3 REQUIRED) find_package(PCL REQUIRED) -ament_auto_add_library(obstacle_stop_planner SHARED - src/debug_marker.cpp - src/node.cpp - src/planner_utils.cpp - src/adaptive_cruise_control.cpp +ament_auto_add_library(${PROJECT_NAME} SHARED + DIRECTORY + src ) -target_include_directories(obstacle_stop_planner +target_include_directories(${PROJECT_NAME} SYSTEM PUBLIC ${PCL_INCLUDE_DIRS} ${EIGEN3_INCLUDE_DIR} + INTERFACE + src ) -target_link_libraries(obstacle_stop_planner +target_link_libraries(${PROJECT_NAME} ${PCL_LIBRARIES} ) -rclcpp_components_register_node(obstacle_stop_planner +rclcpp_components_register_node(${PROJECT_NAME} PLUGIN "motion_planning::ObstacleStopPlannerNode" - EXECUTABLE obstacle_stop_planner_node + EXECUTABLE ${PROJECT_NAME}_node ) if(BUILD_TESTING) diff --git a/planning/obstacle_stop_planner/README.md b/planning/autoware_obstacle_stop_planner/README.md similarity index 100% rename from planning/obstacle_stop_planner/README.md rename to planning/autoware_obstacle_stop_planner/README.md diff --git a/planning/obstacle_stop_planner/config/adaptive_cruise_control.param.yaml b/planning/autoware_obstacle_stop_planner/config/adaptive_cruise_control.param.yaml similarity index 100% rename from planning/obstacle_stop_planner/config/adaptive_cruise_control.param.yaml rename to planning/autoware_obstacle_stop_planner/config/adaptive_cruise_control.param.yaml diff --git a/planning/obstacle_stop_planner/config/common.param.yaml b/planning/autoware_obstacle_stop_planner/config/common.param.yaml similarity index 100% rename from planning/obstacle_stop_planner/config/common.param.yaml rename to planning/autoware_obstacle_stop_planner/config/common.param.yaml diff --git a/planning/obstacle_stop_planner/config/obstacle_stop_planner.param.yaml b/planning/autoware_obstacle_stop_planner/config/obstacle_stop_planner.param.yaml similarity index 100% rename from planning/obstacle_stop_planner/config/obstacle_stop_planner.param.yaml rename to planning/autoware_obstacle_stop_planner/config/obstacle_stop_planner.param.yaml diff --git a/planning/obstacle_stop_planner/config/plot_juggler_adaptive_cruise.xml b/planning/autoware_obstacle_stop_planner/config/plot_juggler_adaptive_cruise.xml similarity index 100% rename from planning/obstacle_stop_planner/config/plot_juggler_adaptive_cruise.xml rename to planning/autoware_obstacle_stop_planner/config/plot_juggler_adaptive_cruise.xml diff --git a/planning/obstacle_stop_planner/config/plot_juggler_slow_down.xml b/planning/autoware_obstacle_stop_planner/config/plot_juggler_slow_down.xml similarity index 100% rename from planning/obstacle_stop_planner/config/plot_juggler_slow_down.xml rename to planning/autoware_obstacle_stop_planner/config/plot_juggler_slow_down.xml diff --git a/planning/obstacle_stop_planner/config/rqt_multiplot_adaptive_cruise.xml b/planning/autoware_obstacle_stop_planner/config/rqt_multiplot_adaptive_cruise.xml similarity index 100% rename from planning/obstacle_stop_planner/config/rqt_multiplot_adaptive_cruise.xml rename to planning/autoware_obstacle_stop_planner/config/rqt_multiplot_adaptive_cruise.xml diff --git a/planning/obstacle_stop_planner/docs/adaptive_cruise.drawio.svg b/planning/autoware_obstacle_stop_planner/docs/adaptive_cruise.drawio.svg similarity index 100% rename from planning/obstacle_stop_planner/docs/adaptive_cruise.drawio.svg rename to planning/autoware_obstacle_stop_planner/docs/adaptive_cruise.drawio.svg diff --git a/planning/obstacle_stop_planner/docs/collision_parameters.svg b/planning/autoware_obstacle_stop_planner/docs/collision_parameters.svg similarity index 100% rename from planning/obstacle_stop_planner/docs/collision_parameters.svg rename to planning/autoware_obstacle_stop_planner/docs/collision_parameters.svg diff --git a/planning/obstacle_stop_planner/docs/keep_stopping.svg b/planning/autoware_obstacle_stop_planner/docs/keep_stopping.svg similarity index 100% rename from planning/obstacle_stop_planner/docs/keep_stopping.svg rename to planning/autoware_obstacle_stop_planner/docs/keep_stopping.svg diff --git a/planning/obstacle_stop_planner/docs/min_longitudinal_margin.svg b/planning/autoware_obstacle_stop_planner/docs/min_longitudinal_margin.svg similarity index 100% rename from planning/obstacle_stop_planner/docs/min_longitudinal_margin.svg rename to planning/autoware_obstacle_stop_planner/docs/min_longitudinal_margin.svg diff --git a/planning/obstacle_stop_planner/docs/restart.svg b/planning/autoware_obstacle_stop_planner/docs/restart.svg similarity index 100% rename from planning/obstacle_stop_planner/docs/restart.svg rename to planning/autoware_obstacle_stop_planner/docs/restart.svg diff --git a/planning/obstacle_stop_planner/docs/restart_prevention.svg b/planning/autoware_obstacle_stop_planner/docs/restart_prevention.svg similarity index 100% rename from planning/obstacle_stop_planner/docs/restart_prevention.svg rename to planning/autoware_obstacle_stop_planner/docs/restart_prevention.svg diff --git a/planning/obstacle_stop_planner/docs/slow_down_parameters.svg b/planning/autoware_obstacle_stop_planner/docs/slow_down_parameters.svg similarity index 100% rename from planning/obstacle_stop_planner/docs/slow_down_parameters.svg rename to planning/autoware_obstacle_stop_planner/docs/slow_down_parameters.svg diff --git a/planning/obstacle_stop_planner/docs/slow_down_target.svg b/planning/autoware_obstacle_stop_planner/docs/slow_down_target.svg similarity index 100% rename from planning/obstacle_stop_planner/docs/slow_down_target.svg rename to planning/autoware_obstacle_stop_planner/docs/slow_down_target.svg diff --git a/planning/obstacle_stop_planner/docs/stop_target.svg b/planning/autoware_obstacle_stop_planner/docs/stop_target.svg similarity index 100% rename from planning/obstacle_stop_planner/docs/stop_target.svg rename to planning/autoware_obstacle_stop_planner/docs/stop_target.svg diff --git a/planning/obstacle_stop_planner/launch/obstacle_stop_planner.launch.xml b/planning/autoware_obstacle_stop_planner/launch/obstacle_stop_planner.launch.xml similarity index 100% rename from planning/obstacle_stop_planner/launch/obstacle_stop_planner.launch.xml rename to planning/autoware_obstacle_stop_planner/launch/obstacle_stop_planner.launch.xml diff --git a/planning/obstacle_stop_planner/package.xml b/planning/autoware_obstacle_stop_planner/package.xml similarity index 94% rename from planning/obstacle_stop_planner/package.xml rename to planning/autoware_obstacle_stop_planner/package.xml index 5aca5efb36384..71eec208f7fdc 100644 --- a/planning/obstacle_stop_planner/package.xml +++ b/planning/autoware_obstacle_stop_planner/package.xml @@ -1,9 +1,9 @@ - obstacle_stop_planner + autoware_obstacle_stop_planner 0.1.0 - The obstacle_stop_planner package + The autoware_obstacle_stop_planner package Satoshi Ota Taiki Tanaka diff --git a/planning/obstacle_stop_planner/src/adaptive_cruise_control.cpp b/planning/autoware_obstacle_stop_planner/src/adaptive_cruise_control.cpp similarity index 99% rename from planning/obstacle_stop_planner/src/adaptive_cruise_control.cpp rename to planning/autoware_obstacle_stop_planner/src/adaptive_cruise_control.cpp index e9058f234e98f..c5fcf29466936 100644 --- a/planning/obstacle_stop_planner/src/adaptive_cruise_control.cpp +++ b/planning/autoware_obstacle_stop_planner/src/adaptive_cruise_control.cpp @@ -12,7 +12,7 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "obstacle_stop_planner/adaptive_cruise_control.hpp" +#include "adaptive_cruise_control.hpp" #include "autoware/motion_utils/trajectory/trajectory.hpp" @@ -35,6 +35,7 @@ #include #include #include +#include #include namespace bg = boost::geometry; @@ -122,7 +123,7 @@ constexpr double sign(const double value) } } // namespace -namespace motion_planning +namespace autoware::motion_planning { AdaptiveCruiseController::AdaptiveCruiseController( rclcpp::Node * node, const double vehicle_width, const double vehicle_length, @@ -639,14 +640,11 @@ double AdaptiveCruiseController::calcTargetVelocity_D( return 0.0; } - double add_vel_d = 0; - - add_vel_d = diff_vel; + double add_vel_d = diff_vel; if (add_vel_d >= 0) { - diff_vel *= param_.d_coeff_pos; - } - if (add_vel_d < 0) { - diff_vel *= param_.d_coeff_neg; + add_vel_d *= param_.d_coeff_pos; + } else { + add_vel_d *= param_.d_coeff_neg; } add_vel_d = boost::algorithm::clamp(add_vel_d, -param_.d_max_vel_norm, param_.d_max_vel_norm); @@ -786,4 +784,4 @@ double AdaptiveCruiseController::lowpass_filter( return gain * prev_value + (1.0 - gain) * current_value; } -} // namespace motion_planning +} // namespace autoware::motion_planning diff --git a/planning/obstacle_stop_planner/include/obstacle_stop_planner/adaptive_cruise_control.hpp b/planning/autoware_obstacle_stop_planner/src/adaptive_cruise_control.hpp similarity index 97% rename from planning/obstacle_stop_planner/include/obstacle_stop_planner/adaptive_cruise_control.hpp rename to planning/autoware_obstacle_stop_planner/src/adaptive_cruise_control.hpp index 55501cf596b07..25664b14320c1 100644 --- a/planning/obstacle_stop_planner/include/obstacle_stop_planner/adaptive_cruise_control.hpp +++ b/planning/autoware_obstacle_stop_planner/src/adaptive_cruise_control.hpp @@ -12,8 +12,8 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef OBSTACLE_STOP_PLANNER__ADAPTIVE_CRUISE_CONTROL_HPP_ -#define OBSTACLE_STOP_PLANNER__ADAPTIVE_CRUISE_CONTROL_HPP_ +#ifndef ADAPTIVE_CRUISE_CONTROL_HPP_ +#define ADAPTIVE_CRUISE_CONTROL_HPP_ #include @@ -29,7 +29,7 @@ #include #include -namespace motion_planning +namespace autoware::motion_planning { using autoware_planning_msgs::msg::TrajectoryPoint; using TrajectoryPoints = std::vector; @@ -232,6 +232,6 @@ class AdaptiveCruiseController static constexpr unsigned int num_debug_values_ = 10; }; -} // namespace motion_planning +} // namespace autoware::motion_planning -#endif // OBSTACLE_STOP_PLANNER__ADAPTIVE_CRUISE_CONTROL_HPP_ +#endif // ADAPTIVE_CRUISE_CONTROL_HPP_ diff --git a/planning/obstacle_stop_planner/src/debug_marker.cpp b/planning/autoware_obstacle_stop_planner/src/debug_marker.cpp similarity index 95% rename from planning/obstacle_stop_planner/src/debug_marker.cpp rename to planning/autoware_obstacle_stop_planner/src/debug_marker.cpp index bbb8dadbcee9d..75d25f3f8db80 100644 --- a/planning/obstacle_stop_planner/src/debug_marker.cpp +++ b/planning/autoware_obstacle_stop_planner/src/debug_marker.cpp @@ -12,7 +12,7 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "obstacle_stop_planner/debug_marker.hpp" +#include "debug_marker.hpp" #include #include @@ -24,6 +24,7 @@ #include #endif +#include #include #include @@ -38,7 +39,7 @@ using autoware::universe_utils::createMarkerColor; using autoware::universe_utils::createMarkerScale; using autoware::universe_utils::createPoint; -namespace motion_planning +namespace autoware::motion_planning { ObstacleStopPlannerDebugNode::ObstacleStopPlannerDebugNode( rclcpp::Node * node, const double base_link2front) @@ -458,39 +459,35 @@ MarkerArray ObstacleStopPlannerDebugNode::makeVisualizationMarker() } if (stop_obstacle_point_ptr_ != nullptr) { - auto marker = createDefaultMarker( + auto marker1 = createDefaultMarker( "map", current_time, "stop_obstacle_point", 0, Marker::SPHERE, createMarkerScale(0.25, 0.25, 0.25), createMarkerColor(1.0, 0.0, 0.0, 0.999)); - marker.pose.position = *stop_obstacle_point_ptr_; - msg.markers.push_back(marker); - } + marker1.pose.position = *stop_obstacle_point_ptr_; + msg.markers.push_back(marker1); - if (stop_obstacle_point_ptr_ != nullptr) { - auto marker = createDefaultMarker( + auto marker2 = createDefaultMarker( "map", current_time, "stop_obstacle_text", 0, Marker::TEXT_VIEW_FACING, createMarkerScale(0.0, 0.0, 1.0), createMarkerColor(1.0, 1.0, 1.0, 0.999)); - marker.pose.position = *stop_obstacle_point_ptr_; - marker.pose.position.z += 2.0; - marker.text = "!"; - msg.markers.push_back(marker); + marker2.pose.position = *stop_obstacle_point_ptr_; + marker2.pose.position.z += 2.0; + marker2.text = "!"; + msg.markers.push_back(marker2); } if (slow_down_obstacle_point_ptr_ != nullptr) { - auto marker = createDefaultMarker( + auto marker1 = createDefaultMarker( "map", current_time, "slow_down_obstacle_point", 0, Marker::SPHERE, createMarkerScale(0.25, 0.25, 0.25), createMarkerColor(1.0, 0.0, 0.0, 0.999)); - marker.pose.position = *slow_down_obstacle_point_ptr_; - msg.markers.push_back(marker); - } + marker1.pose.position = *slow_down_obstacle_point_ptr_; + msg.markers.push_back(marker1); - if (slow_down_obstacle_point_ptr_ != nullptr) { - auto marker = createDefaultMarker( + auto marker2 = createDefaultMarker( "map", current_time, "slow_down_obstacle_text", 0, Marker::TEXT_VIEW_FACING, createMarkerScale(0.0, 0.0, 1.0), createMarkerColor(1.0, 1.0, 1.0, 0.999)); - marker.pose.position = *slow_down_obstacle_point_ptr_; - marker.pose.position.z += 2.0; - marker.text = "!"; - msg.markers.push_back(marker); + marker2.pose.position = *slow_down_obstacle_point_ptr_; + marker2.pose.position.z += 2.0; + marker2.text = "!"; + msg.markers.push_back(marker2); } return msg; @@ -542,4 +539,4 @@ VelocityFactorArray ObstacleStopPlannerDebugNode::makeVelocityFactorArray() return velocity_factor_array; } -} // namespace motion_planning +} // namespace autoware::motion_planning diff --git a/planning/obstacle_stop_planner/include/obstacle_stop_planner/debug_marker.hpp b/planning/autoware_obstacle_stop_planner/src/debug_marker.hpp similarity index 96% rename from planning/obstacle_stop_planner/include/obstacle_stop_planner/debug_marker.hpp rename to planning/autoware_obstacle_stop_planner/src/debug_marker.hpp index 2585c8a1141de..4fc8386e92b44 100644 --- a/planning/obstacle_stop_planner/include/obstacle_stop_planner/debug_marker.hpp +++ b/planning/autoware_obstacle_stop_planner/src/debug_marker.hpp @@ -11,8 +11,8 @@ // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. -#ifndef OBSTACLE_STOP_PLANNER__DEBUG_MARKER_HPP_ -#define OBSTACLE_STOP_PLANNER__DEBUG_MARKER_HPP_ +#ifndef DEBUG_MARKER_HPP_ +#define DEBUG_MARKER_HPP_ #include @@ -34,7 +34,7 @@ #include #include #include -namespace motion_planning +namespace autoware::motion_planning { using geometry_msgs::msg::Point; @@ -154,6 +154,6 @@ class ObstacleStopPlannerDebugNode DebugValues debug_values_; }; -} // namespace motion_planning +} // namespace autoware::motion_planning -#endif // OBSTACLE_STOP_PLANNER__DEBUG_MARKER_HPP_ +#endif // DEBUG_MARKER_HPP_ diff --git a/planning/obstacle_stop_planner/src/node.cpp b/planning/autoware_obstacle_stop_planner/src/node.cpp similarity index 99% rename from planning/obstacle_stop_planner/src/node.cpp rename to planning/autoware_obstacle_stop_planner/src/node.cpp index f3814a2a06a52..dde218c94e77c 100644 --- a/planning/obstacle_stop_planner/src/node.cpp +++ b/planning/autoware_obstacle_stop_planner/src/node.cpp @@ -20,8 +20,8 @@ #include #define EIGEN_MPL2_ONLY -#include "obstacle_stop_planner/node.hpp" -#include "obstacle_stop_planner/planner_utils.hpp" +#include "node.hpp" +#include "planner_utils.hpp" #include #include @@ -38,7 +38,7 @@ #include #endif -namespace motion_planning +namespace autoware::motion_planning { using autoware::motion_utils::calcLongitudinalOffsetPose; using autoware::motion_utils::calcLongitudinalOffsetToSegment; @@ -1601,7 +1601,7 @@ void ObstacleStopPlannerNode::publishDebugData( debug_ptr_->publish(); } -} // namespace motion_planning +} // namespace autoware::motion_planning #include -RCLCPP_COMPONENTS_REGISTER_NODE(motion_planning::ObstacleStopPlannerNode) +RCLCPP_COMPONENTS_REGISTER_NODE(autoware::motion_planning::ObstacleStopPlannerNode) diff --git a/planning/obstacle_stop_planner/include/obstacle_stop_planner/node.hpp b/planning/autoware_obstacle_stop_planner/src/node.hpp similarity index 97% rename from planning/obstacle_stop_planner/include/obstacle_stop_planner/node.hpp rename to planning/autoware_obstacle_stop_planner/src/node.hpp index 6d4f341de6071..fba24061896b6 100644 --- a/planning/obstacle_stop_planner/include/obstacle_stop_planner/node.hpp +++ b/planning/autoware_obstacle_stop_planner/src/node.hpp @@ -12,14 +12,14 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef OBSTACLE_STOP_PLANNER__NODE_HPP_ -#define OBSTACLE_STOP_PLANNER__NODE_HPP_ +#ifndef NODE_HPP_ +#define NODE_HPP_ +#include "adaptive_cruise_control.hpp" #include "autoware/universe_utils/ros/logger_level_configure.hpp" #include "autoware/universe_utils/system/stop_watch.hpp" -#include "obstacle_stop_planner/adaptive_cruise_control.hpp" -#include "obstacle_stop_planner/debug_marker.hpp" -#include "obstacle_stop_planner/planner_data.hpp" +#include "debug_marker.hpp" +#include "planner_data.hpp" #include #include @@ -62,7 +62,7 @@ #include #include -namespace motion_planning +namespace autoware::motion_planning { namespace bg = boost::geometry; @@ -319,6 +319,6 @@ class ObstacleStopPlannerNode : public rclcpp::Node std::unique_ptr published_time_publisher_; }; -} // namespace motion_planning +} // namespace autoware::motion_planning -#endif // OBSTACLE_STOP_PLANNER__NODE_HPP_ +#endif // NODE_HPP_ diff --git a/planning/obstacle_stop_planner/include/obstacle_stop_planner/planner_data.hpp b/planning/autoware_obstacle_stop_planner/src/planner_data.hpp similarity index 97% rename from planning/obstacle_stop_planner/include/obstacle_stop_planner/planner_data.hpp rename to planning/autoware_obstacle_stop_planner/src/planner_data.hpp index 1d1e8a3a9844d..bb9cbd59423e9 100644 --- a/planning/obstacle_stop_planner/include/obstacle_stop_planner/planner_data.hpp +++ b/planning/autoware_obstacle_stop_planner/src/planner_data.hpp @@ -12,8 +12,8 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef OBSTACLE_STOP_PLANNER__PLANNER_DATA_HPP_ -#define OBSTACLE_STOP_PLANNER__PLANNER_DATA_HPP_ +#ifndef PLANNER_DATA_HPP_ +#define PLANNER_DATA_HPP_ #include @@ -27,7 +27,7 @@ #include -namespace motion_planning +namespace autoware::motion_planning { using diagnostic_msgs::msg::DiagnosticStatus; @@ -282,6 +282,6 @@ struct PlannerData bool enable_adaptive_cruise{false}; }; -} // namespace motion_planning +} // namespace autoware::motion_planning -#endif // OBSTACLE_STOP_PLANNER__PLANNER_DATA_HPP_ +#endif // PLANNER_DATA_HPP_ diff --git a/planning/obstacle_stop_planner/src/planner_utils.cpp b/planning/autoware_obstacle_stop_planner/src/planner_utils.cpp similarity index 99% rename from planning/obstacle_stop_planner/src/planner_utils.cpp rename to planning/autoware_obstacle_stop_planner/src/planner_utils.cpp index fd8922c500318..bff8f9588215e 100644 --- a/planning/obstacle_stop_planner/src/planner_utils.cpp +++ b/planning/autoware_obstacle_stop_planner/src/planner_utils.cpp @@ -12,7 +12,7 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "obstacle_stop_planner/planner_utils.hpp" +#include "planner_utils.hpp" #include #include @@ -29,7 +29,10 @@ #include -namespace motion_planning +#include +#include + +namespace autoware::motion_planning { using autoware::motion_utils::calcDecelDistWithJerkAndAccConstraints; @@ -720,4 +723,4 @@ double calcObstacleMaxLength(const autoware_perception_msgs::msg::Shape & shape) throw std::logic_error("The shape type is not supported in obstacle_cruise_planner."); } -} // namespace motion_planning +} // namespace autoware::motion_planning diff --git a/planning/obstacle_stop_planner/include/obstacle_stop_planner/planner_utils.hpp b/planning/autoware_obstacle_stop_planner/src/planner_utils.hpp similarity index 95% rename from planning/obstacle_stop_planner/include/obstacle_stop_planner/planner_utils.hpp rename to planning/autoware_obstacle_stop_planner/src/planner_utils.hpp index c195ff0cc55f3..21943c46f5ca5 100644 --- a/planning/obstacle_stop_planner/include/obstacle_stop_planner/planner_utils.hpp +++ b/planning/autoware_obstacle_stop_planner/src/planner_utils.hpp @@ -13,10 +13,10 @@ // See the License for the specific language governing permissions and // limitations under the License. -#ifndef OBSTACLE_STOP_PLANNER__PLANNER_UTILS_HPP_ -#define OBSTACLE_STOP_PLANNER__PLANNER_UTILS_HPP_ +#ifndef PLANNER_UTILS_HPP_ +#define PLANNER_UTILS_HPP_ -#include "obstacle_stop_planner/planner_data.hpp" +#include "planner_data.hpp" #include #include @@ -33,7 +33,7 @@ #include #include -namespace motion_planning +namespace autoware::motion_planning { namespace bg = boost::geometry; @@ -149,6 +149,6 @@ double calcObstacleMaxLength(const autoware_perception_msgs::msg::Shape & shape) rclcpp::SubscriptionOptions createSubscriptionOptions(rclcpp::Node * node_ptr); -} // namespace motion_planning +} // namespace autoware::motion_planning -#endif // OBSTACLE_STOP_PLANNER__PLANNER_UTILS_HPP_ +#endif // PLANNER_UTILS_HPP_ diff --git a/planning/obstacle_stop_planner/test/test_obstacle_stop_planner_node_interface.cpp b/planning/autoware_obstacle_stop_planner/test/test_autoware_obstacle_stop_planner_node_interface.cpp similarity index 94% rename from planning/obstacle_stop_planner/test/test_obstacle_stop_planner_node_interface.cpp rename to planning/autoware_obstacle_stop_planner/test/test_autoware_obstacle_stop_planner_node_interface.cpp index 474ed9a338725..99a1ab46d2304 100644 --- a/planning/obstacle_stop_planner/test/test_obstacle_stop_planner_node_interface.cpp +++ b/planning/autoware_obstacle_stop_planner/test/test_autoware_obstacle_stop_planner_node_interface.cpp @@ -12,7 +12,7 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "obstacle_stop_planner/node.hpp" +#include "node.hpp" #include #include @@ -22,8 +22,8 @@ #include +using autoware::motion_planning::ObstacleStopPlannerNode; using autoware::planning_test_manager::PlanningInterfaceTestManager; -using motion_planning::ObstacleStopPlannerNode; std::shared_ptr generateTestManager() { @@ -41,7 +41,7 @@ std::shared_ptr generateNode() const auto autoware_test_utils_dir = ament_index_cpp::get_package_share_directory("autoware_test_utils"); const auto obstacle_stop_planner_dir = - ament_index_cpp::get_package_share_directory("obstacle_stop_planner"); + ament_index_cpp::get_package_share_directory("autoware_obstacle_stop_planner"); node_options.append_parameter_override("enable_slow_down", false); @@ -53,7 +53,7 @@ std::shared_ptr generateNode() obstacle_stop_planner_dir + "/config/adaptive_cruise_control.param.yaml", "--params-file", obstacle_stop_planner_dir + "/config/obstacle_stop_planner.param.yaml"}); - return std::make_shared(node_options); + return std::make_shared(node_options); } void publishMandatoryTopics( diff --git a/planning/autoware_path_optimizer/src/mpt_optimizer.cpp b/planning/autoware_path_optimizer/src/mpt_optimizer.cpp index a5da50862c9fb..b5fd266006a48 100644 --- a/planning/autoware_path_optimizer/src/mpt_optimizer.cpp +++ b/planning/autoware_path_optimizer/src/mpt_optimizer.cpp @@ -954,7 +954,6 @@ void MPTOptimizer::keepMinimumBoundsWidth(std::vector & ref_poin // much. b.lower_bound = std::min(b.lower_bound, original_b.upper_bound - min_dynamic_drivable_width_vec.at(p_idx)); - continue; } // extend longitudinal if it overlaps out_of_upper_bound_sections if (upper_bound_start_idx) { @@ -1004,7 +1003,6 @@ void MPTOptimizer::keepMinimumBoundsWidth(std::vector & ref_poin // much. b.upper_bound = std::max(b.upper_bound, original_b.lower_bound + min_dynamic_drivable_width_vec.at(p_idx)); - continue; } // extend longitudinal if it overlaps out_of_lower_bound_sections if (lower_bound_start_idx) { diff --git a/planning/autoware_planning_test_manager/package.xml b/planning/autoware_planning_test_manager/package.xml index 416528257fe3b..f2aa5513ee81a 100644 --- a/planning/autoware_planning_test_manager/package.xml +++ b/planning/autoware_planning_test_manager/package.xml @@ -14,6 +14,7 @@ autoware_cmake autoware_control_msgs + autoware_lanelet2_extension autoware_map_msgs autoware_motion_utils autoware_perception_msgs @@ -24,7 +25,6 @@ autoware_vehicle_msgs component_interface_specs component_interface_utils - lanelet2_extension lanelet2_io nav_msgs rclcpp diff --git a/planning/autoware_remaining_distance_time_calculator/package.xml b/planning/autoware_remaining_distance_time_calculator/package.xml index 4e8fc23ffe391..aec6695cae5fd 100644 --- a/planning/autoware_remaining_distance_time_calculator/package.xml +++ b/planning/autoware_remaining_distance_time_calculator/package.xml @@ -12,13 +12,13 @@ autoware_cmake autoware_internal_msgs + autoware_lanelet2_extension autoware_motion_utils autoware_planning_msgs autoware_route_handler autoware_test_utils autoware_universe_utils geometry_msgs - lanelet2_extension nav_msgs rclcpp rclcpp_components diff --git a/planning/autoware_remaining_distance_time_calculator/src/remaining_distance_time_calculator_node.cpp b/planning/autoware_remaining_distance_time_calculator/src/remaining_distance_time_calculator_node.cpp index da1292fa862c6..e507cc910cf86 100644 --- a/planning/autoware_remaining_distance_time_calculator/src/remaining_distance_time_calculator_node.cpp +++ b/planning/autoware_remaining_distance_time_calculator/src/remaining_distance_time_calculator_node.cpp @@ -15,9 +15,9 @@ #include "remaining_distance_time_calculator_node.hpp" #include -#include -#include -#include +#include +#include +#include #include #include diff --git a/planning/autoware_route_handler/package.xml b/planning/autoware_route_handler/package.xml index f7d10a519ed4e..3c0c14da72817 100644 --- a/planning/autoware_route_handler/package.xml +++ b/planning/autoware_route_handler/package.xml @@ -23,12 +23,12 @@ ament_lint_auto autoware_lint_common + autoware_lanelet2_extension autoware_map_msgs autoware_planning_msgs autoware_test_utils autoware_universe_utils geometry_msgs - lanelet2_extension rclcpp rclcpp_components tf2_ros diff --git a/planning/autoware_route_handler/src/route_handler.cpp b/planning/autoware_route_handler/src/route_handler.cpp index d534ecc4c84a8..0540fb1758802 100644 --- a/planning/autoware_route_handler/src/route_handler.cpp +++ b/planning/autoware_route_handler/src/route_handler.cpp @@ -15,11 +15,11 @@ #include "autoware/route_handler/route_handler.hpp" #include +#include +#include +#include +#include #include -#include -#include -#include -#include #include #include diff --git a/planning/autoware_route_handler/test/test_route_handler.cpp b/planning/autoware_route_handler/test/test_route_handler.cpp index 35a2fe3ef45da..9a4081c51be3b 100644 --- a/planning/autoware_route_handler/test/test_route_handler.cpp +++ b/planning/autoware_route_handler/test/test_route_handler.cpp @@ -49,10 +49,11 @@ TEST_F(TestRouteHandler, getGoalLaneId) TEST_F(TestRouteHandler, getLaneletSequenceWhenOverlappingRoute) { - set_route_handler("/test_map/overlap_map.osm"); + set_route_handler("overlap_map.osm"); ASSERT_FALSE(route_handler_->isHandlerReady()); - geometry_msgs::msg::Pose start_pose, goal_pose; + geometry_msgs::msg::Pose start_pose; + geometry_msgs::msg::Pose goal_pose; start_pose.position = autoware::universe_utils::createPoint(3728.870361, 73739.281250, 0); start_pose.orientation = autoware::universe_utils::createQuaternion(0, 0, -0.513117, 0.858319); goal_pose.position = autoware::universe_utils::createPoint(3729.961182, 73727.328125, 0); @@ -79,11 +80,12 @@ TEST_F(TestRouteHandler, getLaneletSequenceWhenOverlappingRoute) TEST_F(TestRouteHandler, getClosestRouteLaneletFromLaneletWhenOverlappingRoute) { - set_route_handler("/test_map/overlap_map.osm"); - set_test_route("/test_route/overlap_test_route.yaml"); + set_route_handler("overlap_map.osm"); + set_test_route("overlap_test_route.yaml"); ASSERT_TRUE(route_handler_->isHandlerReady()); - geometry_msgs::msg::Pose reference_pose, search_pose; + geometry_msgs::msg::Pose reference_pose; + geometry_msgs::msg::Pose search_pose; lanelet::ConstLanelet reference_lanelet; reference_pose.position = autoware::universe_utils::createPoint(3730.88, 73735.3, 0); @@ -102,71 +104,189 @@ TEST_F(TestRouteHandler, getClosestRouteLaneletFromLaneletWhenOverlappingRoute) ASSERT_EQ(closest_lanelet.id(), 345); found_lanelet = route_handler_->getClosestRouteLaneletFromLanelet( - search_pose, reference_lanelet, &closest_lanelet, 3.0, 1.046); + search_pose, reference_lanelet, &closest_lanelet, dist_threshold, yaw_threshold); ASSERT_TRUE(found_lanelet); ASSERT_EQ(closest_lanelet.id(), 277); } -// TEST_F(TestRouteHandler, getClosestLaneletWithinRouteWhenPointsInRoute) -// { -// lanelet::ConstLanelet closest_lane; - -// Pose search_pose; - -// search_pose.position = autoware::universe_utils::createPoint(-1.0, 1.75, 0); -// search_pose.orientation = autoware::universe_utils::createQuaternion(0.0, 0.0, 0.0, 1.0); -// const auto closest_lane_obtained7 = -// route_handler_->getClosestLaneletWithinRoute(search_pose, &closest_lane); - -// ASSERT_TRUE(closest_lane_obtained7); -// ASSERT_EQ(closest_lane.id(), 4775); - -// search_pose.position = autoware::universe_utils::createPoint(-0.5, 1.75, 0); -// const auto closest_lane_obtained = -// search_pose.orientation = autoware::universe_utils::createQuaternion(0.0, 0.0, 0.0, 1.0); -// route_handler_->getClosestLaneletWithinRoute(search_pose, &closest_lane); - -// ASSERT_TRUE(closest_lane_obtained); -// ASSERT_EQ(closest_lane.id(), 4775); - -// search_pose.position = autoware::universe_utils::createPoint(-.01, 1.75, 0); -// search_pose.orientation = autoware::universe_utils::createQuaternion(0.0, 0.0, 0.0, 1.0); -// const auto closest_lane_obtained3 = -// route_handler_->getClosestLaneletWithinRoute(search_pose, &closest_lane); +TEST_F(TestRouteHandler, CheckLaneIsInGoalRouteSection) +{ + const auto lane = route_handler_->getLaneletsFromId(4785); + const auto is_lane_in_goal_route_section = route_handler_->isInGoalRouteSection(lane); + ASSERT_TRUE(is_lane_in_goal_route_section); +} -// ASSERT_TRUE(closest_lane_obtained3); -// ASSERT_EQ(closest_lane.id(), 4775); +TEST_F(TestRouteHandler, CheckLaneIsNotInGoalRouteSection) +{ + const auto lane = route_handler_->getLaneletsFromId(4780); + const auto is_lane_in_goal_route_section = route_handler_->isInGoalRouteSection(lane); + ASSERT_FALSE(is_lane_in_goal_route_section); +} -// search_pose.position = autoware::universe_utils::createPoint(0.0, 1.75, 0); -// search_pose.orientation = autoware::universe_utils::createQuaternion(0.0, 0.0, 0.0, 1.0); -// const auto closest_lane_obtained1 = -// route_handler_->getClosestLaneletWithinRoute(search_pose, &closest_lane); +TEST_F(TestRouteHandler, checkGetLaneletSequence) +{ + const auto current_pose = autoware::test_utils::createPose(-50.0, 1.75, 0.0, 0.0, 0.0, 0.0); -// ASSERT_TRUE(closest_lane_obtained1); -// ASSERT_EQ(closest_lane.id(), 4775); + lanelet::ConstLanelet closest_lanelet; + const auto found_closest_lanelet = route_handler_->getClosestLaneletWithConstrainsWithinRoute( + current_pose, &closest_lanelet, dist_threshold, yaw_threshold); + ASSERT_TRUE(found_closest_lanelet); + ASSERT_EQ(closest_lanelet.id(), 4765ul); + + const auto current_lanes = route_handler_->getLaneletSequence( + closest_lanelet, current_pose, backward_path_length, forward_path_length); + + ASSERT_EQ(current_lanes.size(), 6ul); + ASSERT_EQ(current_lanes.at(0).id(), 4765ul); + ASSERT_EQ(current_lanes.at(1).id(), 4770ul); + ASSERT_EQ(current_lanes.at(2).id(), 4775ul); + ASSERT_EQ(current_lanes.at(3).id(), 4424ul); + ASSERT_EQ(current_lanes.at(4).id(), 4780ul); + ASSERT_EQ(current_lanes.at(5).id(), 4785ul); +} -// search_pose.position = autoware::universe_utils::createPoint(0.01, 1.75, 0); -// search_pose.orientation = autoware::universe_utils::createQuaternion(0.0, 0.0, 0.0, 1.0); -// const auto closest_lane_obtained2 = -// route_handler_->getClosestLaneletWithinRoute(search_pose, &closest_lane); +TEST_F(TestRouteHandler, checkLateralIntervalToPreferredLaneWhenLaneChangeToRight) +{ + const auto current_lanes = get_current_lanes(); + + // The input is within expectation. + // this lane is of preferred lane type + std::for_each(current_lanes.begin(), current_lanes.begin() + 3, [&](const auto & lane) { + const auto result = route_handler_->getLateralIntervalsToPreferredLane(lane, Direction::RIGHT); + ASSERT_EQ(result.size(), 0ul); + }); + + // The input is within expectation. + // this alternative lane is a subset of preferred lane route section + std::for_each(current_lanes.begin() + 3, current_lanes.end(), [&](const auto & lane) { + const auto result = route_handler_->getLateralIntervalsToPreferredLane(lane, Direction::RIGHT); + ASSERT_EQ(result.size(), 1ul); + EXPECT_DOUBLE_EQ(result.at(0), -3.5); + }); + + // The input is within expectation. + // Although Direction::NONE, the function should still return result similar to + // Direction::RIGHT. + std::for_each(current_lanes.begin(), current_lanes.begin() + 3, [&](const auto & lane) { + const auto result = route_handler_->getLateralIntervalsToPreferredLane(lane, Direction::NONE); + ASSERT_EQ(result.size(), 0ul); + }); + + // The input is within expectation. + // Although Direction::NONE is provided, the function should behave similarly to + // Direction::RIGHT. + std::for_each(current_lanes.begin() + 3, current_lanes.end(), [&](const auto & lane) { + const auto result = route_handler_->getLateralIntervalsToPreferredLane(lane, Direction::NONE); + ASSERT_EQ(result.size(), 1ul); + EXPECT_DOUBLE_EQ(result.at(0), -3.5); + }); +} -// ASSERT_TRUE(closest_lane_obtained2); -// ASSERT_EQ(closest_lane.id(), 4424); +TEST_F(TestRouteHandler, checkLateralIntervalToPreferredLaneUsingUnexpectedResults) +{ + const auto current_lanes = get_current_lanes(); -// search_pose.position = autoware::universe_utils::createPoint(0.5, 1.75, 0); -// search_pose.orientation = autoware::universe_utils::createQuaternion(0.0, 0.0, 0.0, 1.0); -// const auto closest_lane_obtained4 = -// route_handler_->getClosestLaneletWithinRoute(search_pose, &closest_lane); + std::for_each(current_lanes.begin(), current_lanes.end(), [&](const auto & lane) { + const auto result = route_handler_->getLateralIntervalsToPreferredLane(lane, Direction::LEFT); + ASSERT_EQ(result.size(), 0ul); + }); +} -// ASSERT_TRUE(closest_lane_obtained4); -// ASSERT_EQ(closest_lane.id(), 4424); +TEST_F(TestRouteHandler, testGetCenterLinePath) +{ + const auto current_lanes = route_handler_->getLaneletsFromIds({4424, 4780, 4785}); + { + // The input is within expectation. + const auto center_line_path = route_handler_->getCenterLinePath(current_lanes, 0.0, 50.0); + ASSERT_EQ(center_line_path.points.size(), 51); // 26 + 26 - 1(overlapped) + ASSERT_EQ(center_line_path.points.back().lane_ids.size(), 2); + ASSERT_EQ(center_line_path.points.back().lane_ids.at(0), 4780); + ASSERT_EQ(center_line_path.points.back().lane_ids.at(1), 4785); + } + { + // The input is broken. + // s_start is negative, and s_end is over the boundary. + const auto center_line_path = route_handler_->getCenterLinePath(current_lanes, -1.0, 200.0); + ASSERT_EQ(center_line_path.points.size(), 76); // 26 + 26 + 26 - 2(overlapped) + ASSERT_EQ(center_line_path.points.front().lane_ids.size(), 1); + ASSERT_EQ(center_line_path.points.front().lane_ids.at(0), 4424); + ASSERT_EQ(center_line_path.points.back().lane_ids.size(), 1); + ASSERT_EQ(center_line_path.points.back().lane_ids.at(0), 4785); + } +} +TEST_F(TestRouteHandler, DISABLED_testGetCenterLinePathWhenLanesIsNotConnected) +{ + // broken current lanes. 4424 and 4785 are not connected directly. + const auto current_lanes = route_handler_->getLaneletsFromIds({4424, 4780, 4785}); + + // The input is broken. Test is disabled because it doesn't pass. + const auto center_line_path = route_handler_->getCenterLinePath(current_lanes, 0.0, 75.0); + ASSERT_EQ(center_line_path.points.size(), 26); // 26 + 26 + 26 - 2(overlapped) + ASSERT_EQ(center_line_path.points.front().lane_ids.size(), 1); + ASSERT_EQ(center_line_path.points.front().lane_ids.at(0), 4424); + ASSERT_EQ(center_line_path.points.back().lane_ids.size(), 1); + ASSERT_EQ(center_line_path.points.back().lane_ids.at(0), 4424); +} -// search_pose.position = autoware::universe_utils::createPoint(1.0, 1.75, 0); -// search_pose.orientation = autoware::universe_utils::createQuaternion(0.0, 0.0, 0.0, 1.0); -// const auto closest_lane_obtained5 = -// route_handler_->getClosestLaneletWithinRoute(search_pose, &closest_lane); +TEST_F(TestRouteHandler, getClosestLaneletWithinRouteWhenPointsInRoute) +{ + auto get_closest_lanelet_within_route = + [&](double x, double y, double z) -> std::optional { + const auto pose = autoware::test_utils::createPose(x, y, z, 0.0, 0.0, 0.0); + lanelet::ConstLanelet closest_lanelet; + const auto closest_lane_obtained = + route_handler_->getClosestLaneletWithinRoute(pose, &closest_lanelet); + if (!closest_lane_obtained) { + return std::nullopt; + } + return closest_lanelet.id(); + }; + + ASSERT_TRUE(get_closest_lanelet_within_route(-0.5, 1.75, 0).has_value()); + ASSERT_EQ(get_closest_lanelet_within_route(-0.5, 1.75, 0).value(), 4775ul); + + ASSERT_TRUE(get_closest_lanelet_within_route(-0.01, 1.75, 0).has_value()); + ASSERT_EQ(get_closest_lanelet_within_route(-0.01, 1.75, 0).value(), 4775ul); + + ASSERT_TRUE(get_closest_lanelet_within_route(0.0, 1.75, 0).has_value()); + ASSERT_EQ(get_closest_lanelet_within_route(0.0, 1.75, 0).value(), 4775ul); + + ASSERT_TRUE(get_closest_lanelet_within_route(0.01, 1.75, 0).has_value()); + ASSERT_EQ(get_closest_lanelet_within_route(0.01, 1.75, 0).value(), 4424ul); + + ASSERT_TRUE(get_closest_lanelet_within_route(0.5, 1.75, 0).has_value()); + ASSERT_EQ(get_closest_lanelet_within_route(0.5, 1.75, 0).value(), 4424ul); +} -// ASSERT_TRUE(closest_lane_obtained5); -// ASSERT_EQ(closest_lane.id(), 4424); -// } +TEST_F(TestRouteHandler, testGetLaneChangeTargetLanes) +{ + { + // The input is within expectation. + // There exist no lane changing lane since both 4770 and 4775 are preferred lane. + const auto current_lanes = route_handler_->getLaneletsFromIds({4770, 4775}); + const auto lane_change_lane = + route_handler_->getLaneChangeTarget(current_lanes, Direction::RIGHT); + ASSERT_FALSE(lane_change_lane.has_value()); + } + + { + // The input is within expectation. + // There exist lane changing lane since 4424 is subset of preferred lane 9598. + const auto current_lanes = route_handler_->getLaneletsFromIds({4775, 4424}); + const auto lane_change_lane = + route_handler_->getLaneChangeTarget(current_lanes, Direction::RIGHT); + EXPECT_TRUE(lane_change_lane.has_value()); + ASSERT_EQ(lane_change_lane.value().id(), 9598ul); + } + + { + // The input is within expectation. + // There is a lane-changing lane. Within the maximum current lanes, there is an alternative lane + // to the preferred lane. Therefore, the lane-changing lane exists. + const auto current_lanes = get_current_lanes(); + const auto lane_change_lane = route_handler_->getLaneChangeTarget(current_lanes); + ASSERT_TRUE(lane_change_lane.has_value()); + ASSERT_EQ(lane_change_lane.value().id(), 9598ul); + } +} } // namespace autoware::route_handler::test diff --git a/planning/autoware_route_handler/test/test_route_handler.hpp b/planning/autoware_route_handler/test/test_route_handler.hpp index 86f7461fc7538..cd7813243c25e 100644 --- a/planning/autoware_route_handler/test/test_route_handler.hpp +++ b/planning/autoware_route_handler/test/test_route_handler.hpp @@ -15,16 +15,15 @@ #ifndef TEST_ROUTE_HANDLER_HPP_ #define TEST_ROUTE_HANDLER_HPP_ -#include "ament_index_cpp/get_package_share_directory.hpp" #include "autoware_test_utils/autoware_test_utils.hpp" #include "autoware_test_utils/mock_data_parser.hpp" #include "gtest/gtest.h" #include -#include -#include -#include -#include +#include +#include +#include +#include #include #include @@ -37,16 +36,16 @@ namespace autoware::route_handler::test { +using autoware::test_utils::get_absolute_path_to_lanelet_map; +using autoware::test_utils::get_absolute_path_to_route; using autoware_map_msgs::msg::LaneletMapBin; - class TestRouteHandler : public ::testing::Test { public: TestRouteHandler() { - autoware_test_utils_dir = ament_index_cpp::get_package_share_directory("autoware_test_utils"); - set_route_handler("/test_map/2km_test.osm"); - set_test_route("/test_route/lane_change_test_route.yaml"); + set_route_handler("2km_test.osm"); + set_test_route(lane_change_right_test_route_filename); } TestRouteHandler(const TestRouteHandler &) = delete; @@ -55,26 +54,44 @@ class TestRouteHandler : public ::testing::Test TestRouteHandler & operator=(TestRouteHandler &&) = delete; ~TestRouteHandler() override = default; - void set_route_handler(const std::string & relative_path) + void set_route_handler(const std::string & lanelet_map_filename) { route_handler_.reset(); - const auto lanelet2_path = autoware_test_utils_dir + relative_path; + const auto lanelet2_path = + get_absolute_path_to_lanelet_map(autoware_test_utils_dir, lanelet_map_filename); const auto map_bin_msg = autoware::test_utils::make_map_bin_msg(lanelet2_path, center_line_resolution); route_handler_ = std::make_shared(map_bin_msg); } - void set_test_route(const std::string & route_path) + void set_test_route(const std::string & route_filename) { - const auto route_handler_dir = - ament_index_cpp::get_package_share_directory("autoware_route_handler"); - const auto rh_test_route = route_handler_dir + route_path; + const auto rh_test_route = + get_absolute_path_to_route(autoware_route_handler_dir, route_filename); route_handler_->setRoute(autoware::test_utils::parse_lanelet_route_file(rh_test_route)); } + lanelet::ConstLanelets get_current_lanes() + { + const auto current_pose = autoware::test_utils::createPose(-50.0, 1.75, 0.0, 0.0, 0.0, 0.0); + lanelet::ConstLanelet closest_lanelet; + [[maybe_unused]] const auto found_closest_lanelet = + route_handler_->getClosestLaneletWithConstrainsWithinRoute( + current_pose, &closest_lanelet, dist_threshold, yaw_threshold); + return route_handler_->getLaneletSequence( + closest_lanelet, current_pose, backward_path_length, forward_path_length); + } + std::shared_ptr route_handler_; - std::string autoware_test_utils_dir; - static constexpr double center_line_resolution = 5.0; + std::string autoware_test_utils_dir{"autoware_test_utils"}; + std::string autoware_route_handler_dir{"autoware_route_handler"}; + std::string lane_change_right_test_route_filename{"lane_change_test_route.yaml"}; + + static constexpr double center_line_resolution{5.0}; + static constexpr double dist_threshold{3.0}; + static constexpr double yaw_threshold{1.045}; + static constexpr double backward_path_length{5.0}; + static constexpr double forward_path_length{300.0}; }; } // namespace autoware::route_handler::test diff --git a/planning/autoware_rtc_interface/README.md b/planning/autoware_rtc_interface/README.md index 19d200592bcf3..0446ca2d3ca4d 100644 --- a/planning/autoware_rtc_interface/README.md +++ b/planning/autoware_rtc_interface/README.md @@ -185,6 +185,10 @@ Return `true` if `uuid` is registered. If `uuid` is registered, return `true`. If not, return `false`. +## Debugging Tools + +There is a debugging tool called [RTC Replayer](https://autowarefoundation.github.io/autoware_tools/main/planning/autoware_rtc_replayer/) for the RTC interface. + ## Assumptions / Known limits ## Future extensions / Unimplemented parts diff --git a/planning/autoware_scenario_selector/package.xml b/planning/autoware_scenario_selector/package.xml index 4a3c0c5c99eaf..c076ea2a5401d 100644 --- a/planning/autoware_scenario_selector/package.xml +++ b/planning/autoware_scenario_selector/package.xml @@ -16,12 +16,12 @@ ament_cmake_auto autoware_cmake + autoware_lanelet2_extension autoware_map_msgs autoware_planning_msgs autoware_planning_test_manager autoware_route_handler autoware_universe_utils - lanelet2_extension nav_msgs rclcpp rclcpp_components diff --git a/planning/autoware_scenario_selector/src/node.cpp b/planning/autoware_scenario_selector/src/node.cpp index 4dcf1b6adb111..0e204ae82146c 100644 --- a/planning/autoware_scenario_selector/src/node.cpp +++ b/planning/autoware_scenario_selector/src/node.cpp @@ -14,8 +14,8 @@ #include "autoware/scenario_selector/node.hpp" -#include -#include +#include +#include #include #include diff --git a/planning/autoware_static_centerline_generator/package.xml b/planning/autoware_static_centerline_generator/package.xml index 1362f04e720e3..6473c3150dcab 100644 --- a/planning/autoware_static_centerline_generator/package.xml +++ b/planning/autoware_static_centerline_generator/package.xml @@ -16,6 +16,7 @@ rosidl_default_generators autoware_behavior_path_planner_common + autoware_lanelet2_extension autoware_map_msgs autoware_mission_planner autoware_motion_utils @@ -30,7 +31,6 @@ geometry_msgs global_parameter_loader interpolation - lanelet2_extension map_loader map_projection_loader osqp_interface diff --git a/planning/autoware_static_centerline_generator/src/static_centerline_generator_node.cpp b/planning/autoware_static_centerline_generator/src/static_centerline_generator_node.cpp index de89cf4d12a87..4b9c5a73e81bc 100644 --- a/planning/autoware_static_centerline_generator/src/static_centerline_generator_node.cpp +++ b/planning/autoware_static_centerline_generator/src/static_centerline_generator_node.cpp @@ -18,12 +18,12 @@ #include "autoware/motion_utils/trajectory/conversion.hpp" #include "autoware/universe_utils/geometry/geometry.hpp" #include "autoware/universe_utils/ros/parameter.hpp" +#include "autoware_lanelet2_extension/utility/message_conversion.hpp" +#include "autoware_lanelet2_extension/utility/query.hpp" +#include "autoware_lanelet2_extension/utility/utilities.hpp" #include "autoware_static_centerline_generator/msg/points_with_lane_id.hpp" #include "centerline_source/bag_ego_trajectory_based_centerline.hpp" #include "interpolation/spline_interpolation_points_2d.hpp" -#include "lanelet2_extension/utility/message_conversion.hpp" -#include "lanelet2_extension/utility/query.hpp" -#include "lanelet2_extension/utility/utilities.hpp" #include "map_loader/lanelet2_map_loader_node.hpp" #include "map_projection_loader/load_info_from_lanelet2_map.hpp" #include "map_projection_loader/map_projection_loader.hpp" diff --git a/planning/autoware_velocity_smoother/src/node.cpp b/planning/autoware_velocity_smoother/src/node.cpp index aefbd9ef20f84..5c66f5b906bba 100644 --- a/planning/autoware_velocity_smoother/src/node.cpp +++ b/planning/autoware_velocity_smoother/src/node.cpp @@ -1013,7 +1013,6 @@ VelocitySmootherNode::AlgorithmType VelocitySmootherNode::getAlgorithmType( } throw std::domain_error("[VelocitySmootherNode] undesired algorithm is selected."); - return AlgorithmType::INVALID; } double VelocitySmootherNode::calcTravelDistance() const diff --git a/planning/autoware_velocity_smoother/src/trajectory_utils.cpp b/planning/autoware_velocity_smoother/src/trajectory_utils.cpp index 87ad64d3ff976..ea0453370e03a 100644 --- a/planning/autoware_velocity_smoother/src/trajectory_utils.cpp +++ b/planning/autoware_velocity_smoother/src/trajectory_utils.cpp @@ -300,7 +300,6 @@ bool calcStopDistWithJerkConstraints( } double x, v, a, j; - std::tuple state; switch (type) { case AccelerationType::TRAPEZOID: { diff --git a/planning/behavior_path_planner/autoware_behavior_path_avoidance_by_lane_change_module/package.xml b/planning/behavior_path_planner/autoware_behavior_path_avoidance_by_lane_change_module/package.xml index e9a4ba5094310..aa3a207ea5be9 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_avoidance_by_lane_change_module/package.xml +++ b/planning/behavior_path_planner/autoware_behavior_path_avoidance_by_lane_change_module/package.xml @@ -22,10 +22,10 @@ autoware_behavior_path_planner autoware_behavior_path_planner_common autoware_behavior_path_static_obstacle_avoidance_module + autoware_lanelet2_extension autoware_motion_utils autoware_rtc_interface autoware_universe_utils - lanelet2_extension pluginlib rclcpp tier4_planning_msgs diff --git a/planning/behavior_path_planner/autoware_behavior_path_avoidance_by_lane_change_module/src/scene.cpp b/planning/behavior_path_planner/autoware_behavior_path_avoidance_by_lane_change_module/src/scene.cpp index a19cbda6ccad7..856681d538db0 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_avoidance_by_lane_change_module/src/scene.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_avoidance_by_lane_change_module/src/scene.cpp @@ -22,7 +22,7 @@ #include #include -#include +#include #include #include @@ -40,7 +40,6 @@ using autoware::behavior_path_planner::LaneChangeModuleType; using autoware::behavior_path_planner::ObjectInfo; using autoware::behavior_path_planner::Point2d; using autoware::behavior_path_planner::utils::lane_change::debug::createExecutionArea; -namespace utils = autoware::behavior_path_planner::utils; AvoidanceByLaneChange::AvoidanceByLaneChange( const std::shared_ptr & parameters, diff --git a/planning/behavior_path_planner/autoware_behavior_path_dynamic_obstacle_avoidance_module/package.xml b/planning/behavior_path_planner/autoware_behavior_path_dynamic_obstacle_avoidance_module/package.xml index 6dd430ce35f44..af9a042cc4a79 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_dynamic_obstacle_avoidance_module/package.xml +++ b/planning/behavior_path_planner/autoware_behavior_path_dynamic_obstacle_avoidance_module/package.xml @@ -18,6 +18,7 @@ autoware_behavior_path_planner autoware_behavior_path_planner_common + autoware_lanelet2_extension autoware_motion_utils autoware_perception_msgs autoware_planning_msgs @@ -25,7 +26,6 @@ autoware_vehicle_msgs geometry_msgs lanelet2_core - lanelet2_extension object_recognition_utils pluginlib rclcpp diff --git a/planning/behavior_path_planner/autoware_behavior_path_dynamic_obstacle_avoidance_module/src/scene.cpp b/planning/behavior_path_planner/autoware_behavior_path_dynamic_obstacle_avoidance_module/src/scene.cpp index 8b411340e4ddc..7fa9ed012b2b9 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_dynamic_obstacle_avoidance_module/src/scene.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_dynamic_obstacle_avoidance_module/src/scene.cpp @@ -20,7 +20,7 @@ #include "object_recognition_utils/predicted_path_utils.hpp" #include "signal_processing/lowpass_filter_1d.hpp" -#include +#include #include #include @@ -1471,7 +1471,7 @@ DynamicObstacleAvoidanceModule::calcMinMaxLateralOffsetToAvoidRegulatedObject( return prev_object->lat_offset_to_avoid->min_value; }(); const double filtered_min_bound_lat_offset = - (prev_min_lat_avoid_to_offset.has_value() & enable_lowpass_filter) + (prev_min_lat_avoid_to_offset.has_value() && enable_lowpass_filter) ? signal_processing::lowpassFilter( min_bound_lat_offset, *prev_min_lat_avoid_to_offset, parameters_->lpf_gain_for_lat_avoid_to_offset) @@ -1556,7 +1556,7 @@ DynamicObstacleAvoidanceModule::calcMinMaxLateralOffsetToAvoidUnregulatedObject( return prev_object->lat_offset_to_avoid->min_value; }(); const double filtered_min_bound_pos = - (prev_min_lat_avoid_to_offset.has_value() & enable_lowpass_filter) + (prev_min_lat_avoid_to_offset.has_value() && enable_lowpass_filter) ? signal_processing::lowpassFilter( bound_pos.min_value, *prev_min_lat_avoid_to_offset, parameters_->lpf_gain_for_lat_avoid_to_offset) diff --git a/planning/behavior_path_planner/autoware_behavior_path_external_request_lane_change_module/src/scene.cpp b/planning/behavior_path_planner/autoware_behavior_path_external_request_lane_change_module/src/scene.cpp index ce2a75d0eebce..d5aaaca50d942 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_external_request_lane_change_module/src/scene.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_external_request_lane_change_module/src/scene.cpp @@ -14,7 +14,7 @@ #include "scene.hpp" -#include +#include #include diff --git a/planning/behavior_path_planner/autoware_behavior_path_goal_planner_module/src/default_fixed_goal_planner.cpp b/planning/behavior_path_planner/autoware_behavior_path_goal_planner_module/src/default_fixed_goal_planner.cpp index 10e0137978bd6..2709dbe8635d3 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_goal_planner_module/src/default_fixed_goal_planner.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_goal_planner_module/src/default_fixed_goal_planner.cpp @@ -18,8 +18,8 @@ #include "autoware/behavior_path_planner_common/utils/path_utils.hpp" #include "autoware/behavior_path_planner_common/utils/utils.hpp" -#include -#include +#include +#include #include diff --git a/planning/behavior_path_planner/autoware_behavior_path_goal_planner_module/src/geometric_pull_over.cpp b/planning/behavior_path_planner/autoware_behavior_path_goal_planner_module/src/geometric_pull_over.cpp index 50cb090ca04d8..ffae76bff1abe 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_goal_planner_module/src/geometric_pull_over.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_goal_planner_module/src/geometric_pull_over.cpp @@ -17,8 +17,8 @@ #include "autoware/behavior_path_goal_planner_module/util.hpp" #include "autoware/behavior_path_planner_common/utils/drivable_area_expansion/static_drivable_area.hpp" -#include -#include +#include +#include #include #include diff --git a/planning/behavior_path_planner/autoware_behavior_path_goal_planner_module/src/goal_planner_module.cpp b/planning/behavior_path_planner/autoware_behavior_path_goal_planner_module/src/goal_planner_module.cpp index 6caeb411f6680..00510a8725e32 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_goal_planner_module/src/goal_planner_module.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_goal_planner_module/src/goal_planner_module.cpp @@ -25,9 +25,9 @@ #include "autoware/universe_utils/geometry/boost_polygon_utils.hpp" #include "autoware/universe_utils/math/unit_conversion.hpp" -#include -#include -#include +#include +#include +#include #include #include diff --git a/planning/behavior_path_planner/autoware_behavior_path_goal_planner_module/src/goal_searcher.cpp b/planning/behavior_path_planner/autoware_behavior_path_goal_planner_module/src/goal_searcher.cpp index 04ee5278912ac..9c501b7b2ae82 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_goal_planner_module/src/goal_searcher.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_goal_planner_module/src/goal_searcher.cpp @@ -19,9 +19,9 @@ #include "autoware/behavior_path_planner_common/utils/path_utils.hpp" #include "autoware/behavior_path_planner_common/utils/utils.hpp" #include "autoware/universe_utils/geometry/boost_polygon_utils.hpp" -#include "lanelet2_extension/regulatory_elements/no_parking_area.hpp" -#include "lanelet2_extension/regulatory_elements/no_stopping_area.hpp" -#include "lanelet2_extension/utility/utilities.hpp" +#include "autoware_lanelet2_extension/regulatory_elements/no_parking_area.hpp" +#include "autoware_lanelet2_extension/regulatory_elements/no_stopping_area.hpp" +#include "autoware_lanelet2_extension/utility/utilities.hpp" #include @@ -161,9 +161,7 @@ GoalCandidates GoalSearcher::search( original_search_poses.push_back(original_search_pose); // for createAreaPolygon Pose search_pose{}; // search goal_pose in lateral direction - double lateral_offset = 0.0; for (double dy = 0; dy <= max_lateral_offset; dy += lateral_offset_interval) { - lateral_offset = dy; search_pose = calcOffsetPose(original_search_pose, 0, sign * dy, 0); const auto transformed_vehicle_footprint = @@ -185,7 +183,7 @@ GoalCandidates GoalSearcher::search( GoalCandidate goal_candidate{}; goal_candidate.goal_pose = search_pose; - goal_candidate.lateral_offset = lateral_offset; + goal_candidate.lateral_offset = dy; goal_candidate.id = goal_id; goal_id++; // use longitudinal_distance as distance_from_original_goal diff --git a/planning/behavior_path_planner/autoware_behavior_path_goal_planner_module/src/shift_pull_over.cpp b/planning/behavior_path_planner/autoware_behavior_path_goal_planner_module/src/shift_pull_over.cpp index beb27c67833ea..3d2bf9500bc78 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_goal_planner_module/src/shift_pull_over.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_goal_planner_module/src/shift_pull_over.cpp @@ -18,8 +18,8 @@ #include "autoware/behavior_path_planner_common/utils/drivable_area_expansion/static_drivable_area.hpp" #include "autoware/behavior_path_planner_common/utils/path_utils.hpp" -#include -#include +#include +#include #include #include diff --git a/planning/behavior_path_planner/autoware_behavior_path_goal_planner_module/src/util.cpp b/planning/behavior_path_planner/autoware_behavior_path_goal_planner_module/src/util.cpp index 99bef9c243508..d6f03f79186d8 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_goal_planner_module/src/util.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_goal_planner_module/src/util.cpp @@ -18,9 +18,9 @@ #include "autoware/behavior_path_planner_common/utils/utils.hpp" #include -#include -#include -#include +#include +#include +#include #include #include diff --git a/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/include/autoware/behavior_path_lane_change_module/scene.hpp b/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/include/autoware/behavior_path_lane_change_module/scene.hpp index 69e9e90a236b9..be62492b7c2cc 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/include/autoware/behavior_path_lane_change_module/scene.hpp +++ b/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/include/autoware/behavior_path_lane_change_module/scene.hpp @@ -31,10 +31,10 @@ using autoware::behavior_path_planner::utils::path_safety_checker:: using autoware::behavior_path_planner::utils::path_safety_checker::PoseWithVelocityStamped; using autoware::behavior_path_planner::utils::path_safety_checker::PredictedPathWithPolygon; using autoware::route_handler::Direction; -using data::lane_change::LanesPolygon; using geometry_msgs::msg::Point; using geometry_msgs::msg::Pose; using geometry_msgs::msg::Twist; +using lane_change::LanesPolygon; using tier4_planning_msgs::msg::PathWithLaneId; using utils::path_safety_checker::ExtendedPredictedObjects; diff --git a/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/include/autoware/behavior_path_lane_change_module/utils/base_class.hpp b/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/include/autoware/behavior_path_lane_change_module/utils/base_class.hpp index 229f46f89377f..e8e83a0703a76 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/include/autoware/behavior_path_lane_change_module/utils/base_class.hpp +++ b/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/include/autoware/behavior_path_lane_change_module/utils/base_class.hpp @@ -38,10 +38,10 @@ namespace autoware::behavior_path_planner { using autoware::route_handler::Direction; using autoware::universe_utils::StopWatch; -using data::lane_change::PathSafetyStatus; using geometry_msgs::msg::Point; using geometry_msgs::msg::Pose; using geometry_msgs::msg::Twist; +using lane_change::PathSafetyStatus; using tier4_planning_msgs::msg::PathWithLaneId; class LaneChangeBase @@ -126,7 +126,7 @@ class LaneChangeBase const LaneChangeStatus & getLaneChangeStatus() const { return status_; } - const data::lane_change::Debug & getDebugData() const { return lane_change_debug_; } + const lane_change::Debug & getDebugData() const { return lane_change_debug_; } const Pose & getEgoPose() const { return planner_data_->self_odometry->pose.pose; } @@ -233,7 +233,7 @@ class LaneChangeBase LaneChangeModuleType type_{LaneChangeModuleType::NORMAL}; mutable StopWatch stop_watch_; - mutable data::lane_change::Debug lane_change_debug_; + mutable lane_change::Debug lane_change_debug_; rclcpp::Logger logger_ = utils::lane_change::getLogger(getModuleTypeStr()); mutable rclcpp::Clock clock_{RCL_ROS_TIME}; diff --git a/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/include/autoware/behavior_path_lane_change_module/utils/data_structs.hpp b/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/include/autoware/behavior_path_lane_change_module/utils/data_structs.hpp index a65f3baff01d7..ffd2754acc38f 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/include/autoware/behavior_path_lane_change_module/utils/data_structs.hpp +++ b/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/include/autoware/behavior_path_lane_change_module/utils/data_structs.hpp @@ -190,13 +190,6 @@ struct LaneChangeInfo double terminal_lane_changing_velocity{0.0}; }; -struct LaneChangeTargetObjectIndices -{ - std::vector current_lane{}; - std::vector target_lane{}; - std::vector other_lane{}; -}; - struct LaneChangeLanesFilteredObjects { utils::path_safety_checker::ExtendedPredictedObjects current_lane{}; @@ -211,7 +204,7 @@ enum class LaneChangeModuleType { }; } // namespace autoware::behavior_path_planner -namespace autoware::behavior_path_planner::data::lane_change +namespace autoware::behavior_path_planner::lane_change { struct PathSafetyStatus { @@ -225,6 +218,6 @@ struct LanesPolygon std::optional target; std::vector target_backward; }; -} // namespace autoware::behavior_path_planner::data::lane_change +} // namespace autoware::behavior_path_planner::lane_change #endif // AUTOWARE__BEHAVIOR_PATH_LANE_CHANGE_MODULE__UTILS__DATA_STRUCTS_HPP_ diff --git a/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/include/autoware/behavior_path_lane_change_module/utils/debug_structs.hpp b/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/include/autoware/behavior_path_lane_change_module/utils/debug_structs.hpp index 3b8159dc3ad47..1890b9f308e8e 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/include/autoware/behavior_path_lane_change_module/utils/debug_structs.hpp +++ b/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/include/autoware/behavior_path_lane_change_module/utils/debug_structs.hpp @@ -26,7 +26,7 @@ #include #include -namespace autoware::behavior_path_planner::data::lane_change +namespace autoware::behavior_path_planner::lane_change { using utils::path_safety_checker::CollisionCheckDebugMap; struct Debug @@ -71,6 +71,6 @@ struct Debug is_abort = false; } }; -} // namespace autoware::behavior_path_planner::data::lane_change +} // namespace autoware::behavior_path_planner::lane_change #endif // AUTOWARE__BEHAVIOR_PATH_LANE_CHANGE_MODULE__UTILS__DEBUG_STRUCTS_HPP_ diff --git a/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/include/autoware/behavior_path_lane_change_module/utils/markers.hpp b/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/include/autoware/behavior_path_lane_change_module/utils/markers.hpp index c40db47adf280..91d1f1db15cbc 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/include/autoware/behavior_path_lane_change_module/utils/markers.hpp +++ b/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/include/autoware/behavior_path_lane_change_module/utils/markers.hpp @@ -30,7 +30,7 @@ namespace marker_utils::lane_change_markers { using autoware::behavior_path_planner::LaneChangePath; -using autoware::behavior_path_planner::data::lane_change::Debug; +using autoware::behavior_path_planner::lane_change::Debug; using autoware::behavior_path_planner::utils::path_safety_checker::ExtendedPredictedObjects; using visualization_msgs::msg::MarkerArray; MarkerArray showAllValidLaneChangePath( diff --git a/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/include/autoware/behavior_path_lane_change_module/utils/utils.hpp b/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/include/autoware/behavior_path_lane_change_module/utils/utils.hpp index 4071c39568987..e25b67bdb73d8 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/include/autoware/behavior_path_lane_change_module/utils/utils.hpp +++ b/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/include/autoware/behavior_path_lane_change_module/utils/utils.hpp @@ -48,8 +48,8 @@ using autoware::universe_utils::Polygon2d; using autoware_perception_msgs::msg::PredictedObject; using autoware_perception_msgs::msg::PredictedObjects; using autoware_perception_msgs::msg::PredictedPath; -using data::lane_change::LanesPolygon; -using data::lane_change::PathSafetyStatus; +using behavior_path_planner::lane_change::LanesPolygon; +using behavior_path_planner::lane_change::PathSafetyStatus; using geometry_msgs::msg::Point; using geometry_msgs::msg::Pose; using geometry_msgs::msg::Twist; diff --git a/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/src/scene.cpp b/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/src/scene.cpp index ebfa1e7ac260c..f3e05eca1e92d 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/src/scene.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/src/scene.cpp @@ -23,8 +23,8 @@ #include "autoware/behavior_path_planner_common/utils/utils.hpp" #include -#include -#include +#include +#include #include #include diff --git a/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/src/utils/markers.cpp b/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/src/utils/markers.cpp index 9a553cf97af5c..85840b2bb3dd6 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/src/utils/markers.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/src/utils/markers.cpp @@ -18,7 +18,7 @@ #include #include -#include +#include #include #include diff --git a/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/src/utils/utils.cpp b/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/src/utils/utils.cpp index d989a1e6d301d..ad789975cbcd7 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/src/utils/utils.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_lane_change_module/src/utils/utils.cpp @@ -30,9 +30,9 @@ #include #include #include +#include +#include #include -#include -#include #include #include diff --git a/planning/behavior_path_planner/autoware_behavior_path_planner/config/scene_module_manager.param.yaml b/planning/behavior_path_planner/autoware_behavior_path_planner/config/scene_module_manager.param.yaml index 6cf8719b2ef83..ef6cb82d1412a 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_planner/config/scene_module_manager.param.yaml +++ b/planning/behavior_path_planner/autoware_behavior_path_planner/config/scene_module_manager.param.yaml @@ -9,7 +9,6 @@ enable_simultaneous_execution_as_candidate_module: true keep_last: false priority: 6 - max_module_size: 1 external_request_lane_change_right: enable_rtc: false @@ -17,7 +16,6 @@ enable_simultaneous_execution_as_candidate_module: true keep_last: false priority: 6 - max_module_size: 1 lane_change_left: enable_rtc: false @@ -25,7 +23,6 @@ enable_simultaneous_execution_as_candidate_module: true keep_last: false priority: 5 - max_module_size: 1 lane_change_right: enable_rtc: false @@ -33,7 +30,6 @@ enable_simultaneous_execution_as_candidate_module: true keep_last: false priority: 5 - max_module_size: 1 start_planner: enable_rtc: false @@ -41,7 +37,6 @@ enable_simultaneous_execution_as_candidate_module: false keep_last: false priority: 0 - max_module_size: 1 side_shift: enable_rtc: false @@ -49,7 +44,6 @@ enable_simultaneous_execution_as_candidate_module: false keep_last: false priority: 2 - max_module_size: 1 goal_planner: enable_rtc: false @@ -57,7 +51,6 @@ enable_simultaneous_execution_as_candidate_module: false keep_last: true priority: 1 - max_module_size: 1 static_obstacle_avoidance: enable_rtc: false @@ -65,7 +58,6 @@ enable_simultaneous_execution_as_candidate_module: false keep_last: false priority: 4 - max_module_size: 1 avoidance_by_lane_change: enable_rtc: false @@ -73,7 +65,6 @@ enable_simultaneous_execution_as_candidate_module: false keep_last: false priority: 3 - max_module_size: 1 dynamic_obstacle_avoidance: enable_rtc: false @@ -81,7 +72,6 @@ enable_simultaneous_execution_as_candidate_module: true keep_last: false priority: 7 - max_module_size: 1 sampling_planner: enable_module: true @@ -90,4 +80,3 @@ enable_simultaneous_execution_as_candidate_module: false keep_last: true priority: 16 - max_module_size: 1 diff --git a/planning/behavior_path_planner/autoware_behavior_path_planner/docs/behavior_path_planner_manager_design.md b/planning/behavior_path_planner/autoware_behavior_path_planner/docs/behavior_path_planner_manager_design.md index 5ffdcafdb2497..09627d2d5d91e 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_planner/docs/behavior_path_planner_manager_design.md +++ b/planning/behavior_path_planner/autoware_behavior_path_planner/docs/behavior_path_planner_manager_design.md @@ -88,7 +88,6 @@ struct ModuleConfigParameters bool enable_simultaneous_execution_as_approved_module{false}; bool enable_simultaneous_execution_as_candidate_module{false}; uint8_t priority{0}; - uint8_t max_module_size{0}; }; ``` @@ -101,7 +100,6 @@ Code is [here](https://github.com/autowarefoundation/autoware.universe/blob/b173 | `enable_simultaneous_execution_as_candidate_module` | bool | if true, the manager allows its scene modules to run with other scene modules as **candidate module**. | | `enable_simultaneous_execution_as_approved_module` | bool | if true, the manager allows its scene modules to run with other scene modules as **approved module**. | | `priority` | uint8_t | the manager decides execution priority based on this parameter. The smaller the number is, the higher the priority is. | -| `max_module_size` | uint8_t | the sub-manager can run some modules simultaneously. this parameter set the maximum number of the launched modules. | ### Scene modules diff --git a/planning/behavior_path_planner/autoware_behavior_path_planner/package.xml b/planning/behavior_path_planner/autoware_behavior_path_planner/package.xml index a4eeba6051b61..981051a363029 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_planner/package.xml +++ b/planning/behavior_path_planner/autoware_behavior_path_planner/package.xml @@ -41,6 +41,7 @@ autoware_freespace_planning_algorithms autoware_frenet_planner autoware_lane_departure_checker + autoware_lanelet2_extension autoware_motion_utils autoware_path_sampler autoware_perception_msgs @@ -52,7 +53,6 @@ behaviortree_cpp_v3 geometry_msgs interpolation - lanelet2_extension libboost-dev libopencv-dev magic_enum diff --git a/planning/behavior_path_planner/autoware_behavior_path_planner/src/planner_manager.cpp b/planning/behavior_path_planner/autoware_behavior_path_planner/src/planner_manager.cpp index e66eec2a1e0a4..05dcc2163d14a 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_planner/src/planner_manager.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_planner/src/planner_manager.cpp @@ -20,7 +20,7 @@ #include "autoware/universe_utils/ros/debug_publisher.hpp" #include "autoware/universe_utils/system/stop_watch.hpp" -#include +#include #include #include diff --git a/planning/behavior_path_planner/autoware_behavior_path_planner_common/include/autoware/behavior_path_planner_common/data_manager.hpp b/planning/behavior_path_planner/autoware_behavior_path_planner_common/include/autoware/behavior_path_planner_common/data_manager.hpp index 0ec7841f77cfe..1b0bb240cabf2 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_planner_common/include/autoware/behavior_path_planner_common/data_manager.hpp +++ b/planning/behavior_path_planner/autoware_behavior_path_planner_common/include/autoware/behavior_path_planner_common/data_manager.hpp @@ -21,8 +21,8 @@ #include "autoware/motion_utils/trajectory/trajectory.hpp" #include -#include -#include +#include +#include #include #include diff --git a/planning/behavior_path_planner/autoware_behavior_path_planner_common/include/autoware/behavior_path_planner_common/interface/scene_module_manager_interface.hpp b/planning/behavior_path_planner/autoware_behavior_path_planner_common/include/autoware/behavior_path_planner_common/interface/scene_module_manager_interface.hpp index 5b9c0389e29c5..fab9a6ba45113 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_planner_common/include/autoware/behavior_path_planner_common/interface/scene_module_manager_interface.hpp +++ b/planning/behavior_path_planner/autoware_behavior_path_planner_common/include/autoware/behavior_path_planner_common/interface/scene_module_manager_interface.hpp @@ -205,7 +205,16 @@ class SceneModuleManagerInterface }); } - bool canLaunchNewModule() const { return observers_.size() < config_.max_module_size; } + /** + * Determine if a new module can be launched. It ensures that only one instance of a particular + * scene module type is registered at a time. + * + * When this returns true: + * - A new instance of the scene module can be launched. + * - No other instance of the same name of scene module is currently active or registered. + * + */ + bool canLaunchNewModule() const { return observers_.empty(); } /** * Determine if the module is always executable, regardless of the state of other modules. @@ -290,7 +299,6 @@ class SceneModuleManagerInterface *node, ns + "enable_simultaneous_execution_as_candidate_module"); config_.keep_last = getOrDeclareParameter(*node, ns + "keep_last"); config_.priority = getOrDeclareParameter(*node, ns + "priority"); - config_.max_module_size = getOrDeclareParameter(*node, ns + "max_module_size"); } // init rtc configuration diff --git a/planning/behavior_path_planner/autoware_behavior_path_planner_common/include/autoware/behavior_path_planner_common/parameters.hpp b/planning/behavior_path_planner/autoware_behavior_path_planner_common/include/autoware/behavior_path_planner_common/parameters.hpp index bbc249f5a0daf..a45e8366f45d8 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_planner_common/include/autoware/behavior_path_planner_common/parameters.hpp +++ b/planning/behavior_path_planner/autoware_behavior_path_planner_common/include/autoware/behavior_path_planner_common/parameters.hpp @@ -25,7 +25,6 @@ struct ModuleConfigParameters bool enable_simultaneous_execution_as_candidate_module{false}; bool keep_last{false}; uint8_t priority{0}; - uint8_t max_module_size{0}; }; struct BehaviorPathPlannerParameters diff --git a/planning/behavior_path_planner/autoware_behavior_path_planner_common/include/autoware/behavior_path_planner_common/turn_signal_decider.hpp b/planning/behavior_path_planner/autoware_behavior_path_planner_common/include/autoware/behavior_path_planner_common/turn_signal_decider.hpp index 94ed8af3c82b4..bc41c77a07cea 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_planner_common/include/autoware/behavior_path_planner_common/turn_signal_decider.hpp +++ b/planning/behavior_path_planner/autoware_behavior_path_planner_common/include/autoware/behavior_path_planner_common/turn_signal_decider.hpp @@ -22,7 +22,7 @@ #include #include #include -#include +#include #include #include diff --git a/planning/behavior_path_planner/autoware_behavior_path_planner_common/include/autoware/behavior_path_planner_common/utils/traffic_light_utils.hpp b/planning/behavior_path_planner/autoware_behavior_path_planner_common/include/autoware/behavior_path_planner_common/utils/traffic_light_utils.hpp index ef8465988ad73..bd892c68b0ada 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_planner_common/include/autoware/behavior_path_planner_common/utils/traffic_light_utils.hpp +++ b/planning/behavior_path_planner/autoware_behavior_path_planner_common/include/autoware/behavior_path_planner_common/utils/traffic_light_utils.hpp @@ -15,9 +15,9 @@ #define AUTOWARE__BEHAVIOR_PATH_PLANNER_COMMON__UTILS__TRAFFIC_LIGHT_UTILS_HPP_ #include -#include -#include -#include +#include +#include +#include #include #include diff --git a/planning/behavior_path_planner/autoware_behavior_path_planner_common/package.xml b/planning/behavior_path_planner/autoware_behavior_path_planner_common/package.xml index 46ee55c07fe09..7a189250c088d 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_planner_common/package.xml +++ b/planning/behavior_path_planner/autoware_behavior_path_planner_common/package.xml @@ -45,6 +45,7 @@ autoware_adapi_v1_msgs autoware_freespace_planning_algorithms autoware_lane_departure_checker + autoware_lanelet2_extension autoware_motion_utils autoware_objects_of_interest_marker_interface autoware_perception_msgs @@ -55,7 +56,6 @@ autoware_vehicle_info_utils geometry_msgs interpolation - lanelet2_extension magic_enum object_recognition_utils rclcpp diff --git a/planning/behavior_path_planner/autoware_behavior_path_planner_common/src/turn_signal_decider.cpp b/planning/behavior_path_planner/autoware_behavior_path_planner_common/src/turn_signal_decider.cpp index 73f81ce59844c..2a9d3fb4d1d13 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_planner_common/src/turn_signal_decider.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_planner_common/src/turn_signal_decider.cpp @@ -23,8 +23,8 @@ #include #include #include -#include -#include +#include +#include #include #include diff --git a/planning/behavior_path_planner/autoware_behavior_path_planner_common/src/utils/drivable_area_expansion/static_drivable_area.cpp b/planning/behavior_path_planner/autoware_behavior_path_planner_common/src/utils/drivable_area_expansion/static_drivable_area.cpp index a2d701c3d3871..d861f3459a3e6 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_planner_common/src/utils/drivable_area_expansion/static_drivable_area.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_planner_common/src/utils/drivable_area_expansion/static_drivable_area.cpp @@ -19,9 +19,9 @@ #include #include #include -#include -#include -#include +#include +#include +#include #include @@ -357,7 +357,6 @@ std::vector generatePolygonInsideBounds( // Here is if (!is_prev_outside && is_curr_outside). inside_poly.push_back(prev_poly); inside_poly.push_back(intersect_point); - continue; } if (has_intersection) { return inside_poly; @@ -746,9 +745,9 @@ std::vector cutOverlappedLanes( } // Step2. pick up only path points within drivable lanes - for (const auto & lanes : shorten_lanes) { + for (const auto & drivable_lanes : shorten_lanes) { for (size_t i = start_point_idx; i < original_points.size(); ++i) { - if (is_point_in_drivable_lanes(lanes, original_points.at(i))) { + if (is_point_in_drivable_lanes(drivable_lanes, original_points.at(i))) { path.points.push_back(original_points.at(i)); continue; } diff --git a/planning/behavior_path_planner/autoware_behavior_path_planner_common/src/utils/parking_departure/geometric_parallel_parking.cpp b/planning/behavior_path_planner/autoware_behavior_path_planner_common/src/utils/parking_departure/geometric_parallel_parking.cpp index 113b87f4c0032..7afce28019c3f 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_planner_common/src/utils/parking_departure/geometric_parallel_parking.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_planner_common/src/utils/parking_departure/geometric_parallel_parking.cpp @@ -20,8 +20,8 @@ #include "autoware/universe_utils/geometry/geometry.hpp" #include "autoware/universe_utils/math/unit_conversion.hpp" +#include #include -#include #include diff --git a/planning/behavior_path_planner/autoware_behavior_path_planner_common/src/utils/parking_departure/utils.cpp b/planning/behavior_path_planner/autoware_behavior_path_planner_common/src/utils/parking_departure/utils.cpp index 69b0deb8c4c5d..7ca11e7d7fd20 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_planner_common/src/utils/parking_departure/utils.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_planner_common/src/utils/parking_departure/utils.cpp @@ -17,7 +17,7 @@ #include "autoware/behavior_path_planner_common/utils/utils.hpp" #include -#include +#include namespace autoware::behavior_path_planner::utils::parking_departure { diff --git a/planning/behavior_path_planner/autoware_behavior_path_planner_common/src/utils/path_shifter/path_shifter.cpp b/planning/behavior_path_planner/autoware_behavior_path_planner_common/src/utils/path_shifter/path_shifter.cpp index 9572e5a92605f..f88efa49f10ff 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_planner_common/src/utils/path_shifter/path_shifter.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_planner_common/src/utils/path_shifter/path_shifter.cpp @@ -17,8 +17,8 @@ #include "autoware/behavior_path_planner_common/utils/path_utils.hpp" #include +#include #include -#include #include #include @@ -194,7 +194,7 @@ void PathShifter::applyLinearShifter(ShiftedPath * shifted_path) const double ith_shift_length = 0.0; if (i < shift_line.start_idx) { ith_shift_length = 0.0; - } else if (shift_line.start_idx <= i && i <= shift_line.end_idx) { + } else if (i <= shift_line.end_idx) { auto dist_from_start = arclength_arr.at(i) - arclength_arr.at(shift_line.start_idx); ith_shift_length = (dist_from_start / shifting_arclength) * delta_shift; } else { diff --git a/planning/behavior_path_planner/autoware_behavior_path_planner_common/src/utils/path_utils.cpp b/planning/behavior_path_planner/autoware_behavior_path_planner_common/src/utils/path_utils.cpp index 46ca155d04daa..79b4ba8e16344 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_planner_common/src/utils/path_utils.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_planner_common/src/utils/path_utils.cpp @@ -20,9 +20,9 @@ #include #include #include +#include +#include #include -#include -#include #include @@ -471,14 +471,15 @@ std::vector interpolatePose( { std::vector interpolated_poses{}; // output - const std::vector base_s{ - 0, autoware::universe_utils::calcDistance2d(start_pose.position, end_pose.position)}; + const double distance = + autoware::universe_utils::calcDistance2d(start_pose.position, end_pose.position); + const std::vector base_s{0.0, distance}; const std::vector base_x{start_pose.position.x, end_pose.position.x}; const std::vector base_y{start_pose.position.y, end_pose.position.y}; std::vector new_s; constexpr double eps = 0.3; // prevent overlapping - for (double s = eps; s < base_s.back() - eps; s += resample_interval) { + for (double s = eps; s < distance - eps; s += resample_interval) { new_s.push_back(s); } @@ -491,7 +492,7 @@ std::vector interpolatePose( for (size_t i = 0; i < interpolated_x.size(); ++i) { Pose pose{}; pose = autoware::universe_utils::calcInterpolatedPose( - end_pose, start_pose, (base_s.back() - new_s.at(i)) / base_s.back()); + end_pose, start_pose, (distance - new_s.at(i)) / distance); pose.position.x = interpolated_x.at(i); pose.position.y = interpolated_y.at(i); pose.position.z = end_pose.position.z; diff --git a/planning/behavior_path_planner/autoware_behavior_path_planner_common/src/utils/utils.cpp b/planning/behavior_path_planner/autoware_behavior_path_planner_common/src/utils/utils.cpp index 55a6c6ff39d30..b7955da9a44f1 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_planner_common/src/utils/utils.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_planner_common/src/utils/utils.cpp @@ -21,9 +21,9 @@ #include #include #include -#include -#include -#include +#include +#include +#include #include @@ -734,12 +734,11 @@ double getDistanceToCrosswalk( boost::geometry::intersection(centerline, polygon, points_intersection); for (const auto & point : points_intersection) { - lanelet::ConstLanelets lanelets = {llt}; Pose pose_point; pose_point.position.x = point.x(); pose_point.position.y = point.y(); const lanelet::ArcCoordinates & arc_crosswalk = - lanelet::utils::getArcCoordinates(lanelets, pose_point); + lanelet::utils::getArcCoordinates({llt}, pose_point); const double distance_to_crosswalk = arc_crosswalk.length; if (distance_to_crosswalk < min_distance_to_crosswalk) { diff --git a/planning/behavior_path_planner/autoware_behavior_path_planner_common/test/test_drivable_area_expansion.cpp b/planning/behavior_path_planner/autoware_behavior_path_planner_common/test/test_drivable_area_expansion.cpp index bf69647304833..b6fee5efb3ef8 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_planner_common/test/test_drivable_area_expansion.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_planner_common/test/test_drivable_area_expansion.cpp @@ -16,7 +16,7 @@ #include "autoware/behavior_path_planner_common/utils/drivable_area_expansion/drivable_area_expansion.hpp" #include "autoware/behavior_path_planner_common/utils/drivable_area_expansion/path_projection.hpp" #include "autoware/behavior_path_planner_common/utils/drivable_area_expansion/types.hpp" -#include "lanelet2_extension/utility/message_conversion.hpp" +#include "autoware_lanelet2_extension/utility/message_conversion.hpp" #include #include diff --git a/planning/behavior_path_planner/autoware_behavior_path_planner_common/test/test_turn_signal.cpp b/planning/behavior_path_planner/autoware_behavior_path_planner_common/test/test_turn_signal.cpp index f5996809dc057..b6149e3b5f99c 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_planner_common/test/test_turn_signal.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_planner_common/test/test_turn_signal.cpp @@ -355,7 +355,6 @@ TEST(BehaviorPathPlanningTurnSignal, Condition3) TurnSignalInfo behavior_signal_info; behavior_signal_info.turn_signal.command = TurnIndicatorsCommand::ENABLE_RIGHT; - behavior_signal_info.turn_signal.command = TurnIndicatorsCommand::ENABLE_RIGHT; behavior_signal_info.desired_start_point.position = createPoint(5.0, 0.0, 0.0); behavior_signal_info.desired_start_point.orientation = createQuaternionFromYaw(0.0); behavior_signal_info.desired_end_point.position = createPoint(70.0, 0.0, 0.0); diff --git a/planning/behavior_path_planner/autoware_behavior_path_sampling_planner_module/include/autoware/behavior_path_sampling_planner_module/sampling_planner_module.hpp b/planning/behavior_path_planner/autoware_behavior_path_sampling_planner_module/include/autoware/behavior_path_sampling_planner_module/sampling_planner_module.hpp index 2b6f52998cb23..e82c4fd287432 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_sampling_planner_module/include/autoware/behavior_path_sampling_planner_module/sampling_planner_module.hpp +++ b/planning/behavior_path_planner/autoware_behavior_path_sampling_planner_module/include/autoware/behavior_path_sampling_planner_module/sampling_planner_module.hpp @@ -31,14 +31,14 @@ #include "autoware/universe_utils/system/stop_watch.hpp" #include "autoware_bezier_sampler/bezier_sampling.hpp" #include "autoware_frenet_planner/frenet_planner.hpp" +#include "autoware_lanelet2_extension/utility/query.hpp" +#include "autoware_lanelet2_extension/utility/utilities.hpp" #include "autoware_sampler_common/constraints/footprint.hpp" #include "autoware_sampler_common/constraints/hard_constraint.hpp" #include "autoware_sampler_common/constraints/soft_constraint.hpp" #include "autoware_sampler_common/structures.hpp" #include "autoware_sampler_common/transform/spline_transform.hpp" #include "autoware_vehicle_info_utils/vehicle_info_utils.hpp" -#include "lanelet2_extension/utility/query.hpp" -#include "lanelet2_extension/utility/utilities.hpp" #include "rclcpp/rclcpp.hpp" #include "tier4_planning_msgs/msg/lateral_offset.hpp" diff --git a/planning/behavior_path_planner/autoware_behavior_path_sampling_planner_module/package.xml b/planning/behavior_path_planner/autoware_behavior_path_sampling_planner_module/package.xml index aab393f4bdb5a..9ede8342856a6 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_sampling_planner_module/package.xml +++ b/planning/behavior_path_planner/autoware_behavior_path_sampling_planner_module/package.xml @@ -16,6 +16,7 @@ autoware_behavior_path_planner_common autoware_bezier_sampler autoware_frenet_planner + autoware_lanelet2_extension autoware_motion_utils autoware_path_sampler autoware_perception_msgs @@ -27,7 +28,6 @@ autoware_vehicle_msgs geometry_msgs interpolation - lanelet2_extension pluginlib rclcpp rclcpp_components diff --git a/planning/behavior_path_planner/autoware_behavior_path_side_shift_module/src/scene.cpp b/planning/behavior_path_planner/autoware_behavior_path_side_shift_module/src/scene.cpp index bcb972bf5dc8b..929e890edc204 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_side_shift_module/src/scene.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_side_shift_module/src/scene.cpp @@ -20,7 +20,7 @@ #include "autoware/behavior_path_planner_common/utils/utils.hpp" #include "autoware/behavior_path_side_shift_module/utils.hpp" -#include +#include #include #include diff --git a/planning/behavior_path_planner/autoware_behavior_path_start_planner_module/src/freespace_pull_out.cpp b/planning/behavior_path_planner/autoware_behavior_path_start_planner_module/src/freespace_pull_out.cpp index 137a2c53321fc..9c9c084f823c4 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_start_planner_module/src/freespace_pull_out.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_start_planner_module/src/freespace_pull_out.cpp @@ -19,7 +19,7 @@ #include "autoware/behavior_path_planner_common/utils/utils.hpp" #include "autoware/behavior_path_start_planner_module/util.hpp" -#include +#include #include #include diff --git a/planning/behavior_path_planner/autoware_behavior_path_start_planner_module/src/geometric_pull_out.cpp b/planning/behavior_path_planner/autoware_behavior_path_start_planner_module/src/geometric_pull_out.cpp index f3239d41bc1b9..4470026bc8dda 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_start_planner_module/src/geometric_pull_out.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_start_planner_module/src/geometric_pull_out.cpp @@ -20,7 +20,7 @@ #include "autoware/behavior_path_start_planner_module/util.hpp" #include "autoware/universe_utils/geometry/boost_polygon_utils.hpp" -#include +#include using autoware::motion_utils::findNearestIndex; using autoware::universe_utils::calcDistance2d; diff --git a/planning/behavior_path_planner/autoware_behavior_path_start_planner_module/src/shift_pull_out.cpp b/planning/behavior_path_planner/autoware_behavior_path_start_planner_module/src/shift_pull_out.cpp index 971c9a48e8b40..7c78f62192105 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_start_planner_module/src/shift_pull_out.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_start_planner_module/src/shift_pull_out.cpp @@ -22,7 +22,7 @@ #include "autoware/motion_utils/trajectory/path_with_lane_id.hpp" #include "autoware/universe_utils/geometry/boost_polygon_utils.hpp" -#include +#include #include #include diff --git a/planning/behavior_path_planner/autoware_behavior_path_start_planner_module/src/start_planner_module.cpp b/planning/behavior_path_planner/autoware_behavior_path_start_planner_module/src/start_planner_module.cpp index 86e35aa52095a..00b6649851f87 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_start_planner_module/src/start_planner_module.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_start_planner_module/src/start_planner_module.cpp @@ -22,10 +22,10 @@ #include "autoware/behavior_path_start_planner_module/util.hpp" #include "autoware/motion_utils/trajectory/trajectory.hpp" -#include -#include -#include -#include +#include +#include +#include +#include #include #include @@ -889,20 +889,16 @@ PriorityOrder StartPlannerModule::determinePriorityOrder( order_priority.emplace_back(i, planner); } } - return order_priority; - } - - if (search_priority == "short_back_distance") { + } else if (search_priority == "short_back_distance") { for (size_t i = 0; i < start_pose_candidates_num; i++) { for (const auto & planner : start_planners_) { order_priority.emplace_back(i, planner); } } - return order_priority; + } else { + RCLCPP_ERROR(getLogger(), "Invalid search_priority: %s", search_priority.c_str()); + throw std::domain_error("[start_planner] invalid search_priority"); } - - RCLCPP_ERROR(getLogger(), "Invalid search_priority: %s", search_priority.c_str()); - throw std::domain_error("[start_planner] invalid search_priority"); return order_priority; } diff --git a/planning/behavior_path_planner/autoware_behavior_path_start_planner_module/src/util.cpp b/planning/behavior_path_planner/autoware_behavior_path_start_planner_module/src/util.cpp index 3fe9524e34a17..0d4a4366b2714 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_start_planner_module/src/util.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_start_planner_module/src/util.cpp @@ -20,7 +20,7 @@ #include #include -#include +#include #include #include diff --git a/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/README.md b/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/README.md index bc70c1fdd5c6a..40d36898b2309 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/README.md +++ b/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/README.md @@ -8,7 +8,7 @@ This is a rule-based avoidance module, which is running based on perception outp ![fig](./images/purpose/avoidance.png) -This module has [RTC interface](../autoware_rtc_interface/README.md), and user can select operation mode from MANUAL/AUTO depending on the vehicle sensor performance. If user selects MANUAL mode, this module outputs avoidance path as candidate and waits operator approval. In the case where the sensor/perception performance is not enough and false positive maybe occurs, we recommend to use this module with MANUAL mode in order to prevent unnecessary avoidance maneuver. +This module has [RTC interface](../../autoware_rtc_interface/README.md), and user can select operation mode from MANUAL/AUTO depending on the vehicle sensor performance. If user selects MANUAL mode, this module outputs avoidance path as candidate and waits operator approval. In the case where the sensor/perception performance is not enough and false positive maybe occurs, we recommend to use this module with MANUAL mode in order to prevent unnecessary avoidance maneuver. On the other hand, if user selects AUTO mode, this module modifies current following path without operator approval. If the sensor/perception performance is good enough, user can use this module with AUTO mode. @@ -83,7 +83,7 @@ partition fillEgoStatus() { note right This module has following status: - RUNNING: target object is still remaining. Or, the ego hasn't returned original lane. - - CANCEL: taget obejct has gone. And, the ego hasn't initiated avoidance maneuver. + - CANCEL: target object has gone. And, the ego hasn't initiated avoidance maneuver. - SUCCEEDED: the ego finishes avoiding all objects and returns original lane. end note @@ -122,7 +122,7 @@ if (Is there object that is potentially avoidable?) then (yes) :return true; note right Sometimes, we meet the situation where there is enough space to avoid - but ego speed is to high to avoid target obejct under lateral jerk constraints. + but ego speed is to high to avoid target object under lateral jerk constraints. This module keeps running in this case in order to decelerate ego speed. end note stop @@ -485,11 +485,6 @@ title Filtering flow for vehicle type objects start partition isSatisfiedWithVehicleCodition() { -if(object is force avoidance target ?) then (yes) -#FF006C :return true; -stop -else (\n no) - if(isNeverAvoidanceTarget()) then (yes) #00FFB1 :return false; stop @@ -537,7 +532,6 @@ endif endif endif endif -endif #00FFB1 :return false; stop } @@ -747,6 +741,59 @@ The longitudinal positions depends on envelope polygon, ego vehicle specificatio ![fig](./images/path_generation/margin.png) +### Lateral margin + +As mentioned above, user can adjust lateral margin by changing following two types parameter. The `soft_margin` is a soft constraint parameter for lateral margin. The `hard_margin` and `hard_margin_for_parked_vehicle` are hard constraint parameter. + +```yaml + car: + ... + lateral_margin: + soft_margin: 0.3 # [m] + hard_margin: 0.2 # [m] + hard_margin_for_parked_vehicle: 0.7 # [m] +``` + +Basically, this module tries to generate avoidance path in order to keep lateral distance, which is sum of `soft_margin` and `hard_margin`/`hard_margin_for_parked_vehicle`, from avoidance target object. + +![fig](./images/path_generation/soft_hard.png) + +But if there isn't enough space to keep `soft_margin` distance, this module shortens soft constraint lateral margin. The parameter `soft_margin` is a maximum value of soft constraint, and actual soft margin can be a value between 0.0 and `soft_margin`. On the other hand, this module definitely keeps `hard_margin` or `hard_margin_for_parked_vehicle` depending on the situation. Thus, the minimum value of total lateral margin is `hard_margin`/`hard_margin_for_parked_vehicle`, and the maximum value is the sum of `hard_margin`/`hard_margin_for_parked_vehicle` and `soft_margin`. + +Following figure shows the situation where this module shortens lateral soft constraint in order not to drive opposite direction lane when user set a parameter `use_lane_type` to `same_direction_lane`. + +![fig](./images/path_generation/adjust_margin.png) + +This module avoids not only parked vehicle but also non-parked vehicle which stops temporarily for some reason (e.g. waiting for traffic light to change red to green.). Additionally, this module has two types hard margin parameters, `hard_margin` and `hard_margin_for_parked_vehicle` and judges if it's a parked vehicle or not for each vehicles because it takes the risk of vehicle doors opening suddenly and people getting out from parked vehicle into consideration. + +Basically, user had better make `hard_margin_for_parked_vehicle` larger than `hard_margin` to prevent collision with doors or people who suddenly get out from vehicle. + +On the other hand, this module has only one parameter `soft_margin` for soft lateral margin constraint. + +![fig](./images/path_generation/hard_margin.png) + +As the hard margin parameters the distance which the user definitely want to keep, they are used in the logic to check whether the ego can pass side of the target object without avoidance maneuver as well. + +If the lateral distance is less than `hard_margin`/`hard_margin_for_parked_vehicle` when assuming that the ego follows current lane without avoidance maneuver, this module thinks the ego can not pass the side of the object safely and the ego must avoid it. In this case, this module inserts stop point until the avoidance maneuver is allowed to execute so that the ego can avoid the object after approval. (e.g. The ego keeps stopping in front of such a object until operator approves avoidance maneuver if user uses this module in MANUAL mode.) + +![fig](./images/path_generation/must_avoid.png) + +On the other hand, if the lateral distance is larger than `hard_margin`/`hard_margin_for_parked_vehicle`, this module doesn't insert stop point even when it's waiting approval because it thinks it's possible to pass the side of the object safely. + +![fig](./images/path_generation/pass_through.png) + +### When there is not enough space + +This module inserts stop point only when the ego can potentially avoid the object. So, if it is not able to keep distance more than `hard_margin`/`hard_margin_for_parked_vehicle`, this module does nothing. Following figure shows the situation where this module is not able to keep enough lateral distance when user set a parameter `use_lane_type` to `same_direction_lane`. + +![fig](./images/path_generation/do_nothing.png) + +!!! info + + In this situation, obstacle stop feature in [obstacle_cruise_planner](../../autoware_obstacle_cruise_planner/README.md) is responsible for ego vehicle safety. + +![fig](./images/path_generation/insufficient_drivable_space.png) + ### Shift length calculation The lateral shift length is sum of `overhang_distance`, lateral margin, whose value is set in config file, and the half of ego vehicle width defined in `vehicle_info.param.yaml`. On the other hand, the module limits the shift length depending on the space which the module can use for avoidance maneuver and the parameters `soft_drivable_bound_margin` `hard_drivable_bound_margin`. Basically, the shift length is limited so that the ego doesn't get closer than `soft_drivable_bound_margin` to drivable boundary. But it allows to relax the threshold `soft_drivable_bound_margin` to `hard_drivable_bound_margin` when the road is narrow. @@ -757,15 +804,19 @@ Usable lane for avoidance module can be selected by config file. ```yaml ... - use_adjacent_lane: true - use_opposite_lane: true + # drivable lane setting. this module is able to use not only current lane but also right/left lane + # if the current lane(=lanelt::Lanelet) and the rignt/left lane share the boundary(=lanelet::Linestring) in HDMap. + # "current_lane" : use only current lane. this module doesn't use adjacent lane to avoid object. + # "same_direction_lane" : this module uses same direction lane to avoid object if need. + # "opposite_direction_lane": this module uses both same direction and opposite direction lane. + use_lane_type: "opposite_direction_lane" ``` -When user set parameter both `use_adjacent_lane` and `use_opposite_lane` to `true`, it is possible to use opposite lane. +When user set parameter `use_lane_type` to `opposite_direction_lane`, it is possible to use opposite lane. ![fig](./images/path_generation/opposite_direction.png) -When user only set parameter `use_adjacent_lane` to `true`, the module doesn't create path that overlaps opposite lane. +When user set parameter `use_lane_type` to `same_direction_lane`, the module doesn't create path that overlaps opposite lane. ![fig](./images/path_generation/same_direction.png) @@ -804,6 +855,34 @@ The `prepare_length` is calculated as the product of ego speed and `max_prepare_ ![fig](./images/path_generation/shift_line.png) +## Planning at RED traffic light + +This module takes traffic light information into account so that the ego can behave properly. Sometimes, the ego straddles lane boundary but we want to prevent the ego from stopping in front of red traffic signal in such a situation. This is because the ego will block adjacent lane and it's inconvenient for other vehicles. + +![fig](./images/traffic_light/traffic_light.png) + +So, this module controls shift length and shift start/end point in order to prevent above situation. + +### Control shift length + +At first, if the ego hasn't initiated avoidance maneuver yet, this module limits maximum shift length and uses **ONLY** current lane during red traffic signal. This prevents the ego from blocking other vehicles even if this module executes avoidance maneuver and the ego is caught by red traffic signal. + +![fig](./images/traffic_light/limit_shift_length.png) + +### Control avoidance shift start point + +Additionally, if the target object is farther than stop line for traffic light, this module set avoidance shift start point on the stop line in order to prevent the ego from stopping by red traffic signal in middle of avoidance maneuver. + +![fig](./images/traffic_light/shift_from_current_pos.png) +![fig](./images/traffic_light/shift_from_stop_line.png) + +### Control return shift end point + +If the ego has already initiated avoidance maneuver, this module tries to set return shift end point on the stop line. + +![fig](./images/traffic_light/return_after_stop_line.png) +![fig](./images/traffic_light/return_before_stop_line.png) + ## Safety check This feature can be enable by setting following parameter to `true`. @@ -897,33 +976,28 @@ This module supports drivable area expansion for following polygons defined in H Please set the flags to `true` when user wants to make it possible to use those areas in avoidance maneuver. ```yaml +# drivable lane setting. this module is able to use not only current lane but also right/left lane +# if the current lane(=lanelt::Lanelet) and the rignt/left lane share the boundary(=lanelet::Linestring) in HDMap. +# "current_lane" : use only current lane. this module doesn't use adjacent lane to avoid object. +# "same_direction_lane" : this module uses same direction lane to avoid object if need. +# "opposite_direction_lane": this module uses both same direction and opposite direction lane. +use_lane_type: "opposite_direction_lane" # drivable area setting -use_adjacent_lane: true -use_opposite_lane: true use_intersection_areas: true use_hatched_road_markings: true use_freespace_areas: true ``` -| | | | -| --------------------- | ---------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| adjacent lane | ![fig](./images/advanced/avoidance_same_direction.png) | | -| opposite lane | ![fig](./images/advanced/avoidance_opposite_direction.png) | | -| intersection area | ![fig](./images/advanced/avoidance_intersection.png) | The intersection area is defined on Lanelet map. See [here](https://github.com/autowarefoundation/autoware_common/blob/main/tmp/lanelet2_extension/docs/lanelet2_format_extension.md) | -| hatched road markings | ![fig](./images/advanced/avoidance_zebra.png) | The hatched road marking is defined on Lanelet map. See [here](https://github.com/autowarefoundation/autoware_common/blob/main/tmp/lanelet2_extension/docs/lanelet2_format_extension.md#hatched-road-markings-area) | -| freespace area | ![fig](./images/advanced/avoidance_freespace.png) | The freespace area is defined on Lanelet map. (unstable) | +| | | | +| -------------------------------------- | ---------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| use_lane_type: same_direction_lane | ![fig](./images/advanced/avoidance_same_direction.png) | | +| use_lane_type: opposite_direction_lane | ![fig](./images/advanced/avoidance_opposite_direction.png) | | +| intersection area | ![fig](./images/advanced/avoidance_intersection.png) | The intersection area is defined on Lanelet map. See [here](https://github.com/autowarefoundation/autoware_lanelet2_extension/blob/main/autoware_lanelet2_extension/docs/lanelet2_format_extension.md) | +| hatched road markings | ![fig](./images/advanced/avoidance_zebra.png) | The hatched road marking is defined on Lanelet map. See [here](https://github.com/autowarefoundation/autoware_lanelet2_extension/blob/main/autoware_lanelet2_extension/docs/lanelet2_format_extension.md#hatched-road-markings-area) | +| freespace area | ![fig](./images/advanced/avoidance_freespace.png) | The freespace area is defined on Lanelet map. (unstable) | ## Future extensions / Unimplemented parts -- **Planning on the intersection** - - If it is known that the ego vehicle is going to stop in the middle of avoidance execution (for example, at a red traffic light), sometimes the avoidance should not be executed until the vehicle is ready to move. This is because it is impossible to predict how the environment will change during the stop. This is especially important at intersections. - -![fig](./images/intersection_problem.drawio.svg) - -- **Safety Check** - - - In the current implementation, it is only the jerk limit that permits the avoidance execution. It is needed to consider the collision with other vehicles when change the path shape. - - **Consideration of the speed of the avoidance target** - In the current implementation, only stopped vehicle is targeted as an avoidance target. It is needed to support the overtaking function for low-speed vehicles, such as a bicycle. (It is actually possible to overtake the low-speed objects by changing the parameter, but the logic is not supported and thus the safety cannot be guaranteed.) @@ -1146,4 +1220,4 @@ The shift points are modified by a filtering process in order to get the expecte The avoidance specific parameter configuration file can be located at `src/autoware/launcher/planning_launch/config/scenario_planning/lane_driving/behavior_planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/static_obstacle_avoidance.param.yaml`. -{{ json_to_markdown("planning/autoware_behavior_path_static_obstacle_avoidance_module/schema/static_obstacle_avoidance.schema.json") }} +{{ json_to_markdown("planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/schema/static_obstacle_avoidance.schema.json") }} diff --git a/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/config/static_obstacle_avoidance.param.yaml b/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/config/static_obstacle_avoidance.param.yaml index fb8b02f1f0158..3087ccc93934b 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/config/static_obstacle_avoidance.param.yaml +++ b/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/config/static_obstacle_avoidance.param.yaml @@ -5,13 +5,13 @@ resample_interval_for_planning: 0.3 # [m] FOR DEVELOPER resample_interval_for_output: 4.0 # [m] FOR DEVELOPER - # avoidance module common setting - enable_bound_clipping: false - disable_path_update: false - + # drivable lane setting. this module is able to use not only current lane but also right/left lane + # if the current lane(=lanelet::Lanelet) and the right/left lane share the boundary(=lanelet::Linestring) in HDMap. + # "current_lane" : use only current lane. this module doesn't use adjacent lane to avoid object. + # "same_direction_lane" : this module uses same direction lane to avoid object if need. + # "opposite_direction_lane": this module uses both same direction and opposite direction lane. + use_lane_type: "opposite_direction_lane" # drivable area setting - use_adjacent_lane: true - use_opposite_lane: true use_intersection_areas: true use_hatched_road_markings: true use_freespace_areas: true @@ -285,6 +285,19 @@ max_acceleration: 0.5 # [m/ss] min_velocity_to_limit_max_acceleration: 2.78 # [m/ss] + # path generation method. select "shift_line_base" or "optimization_base" or "both". + # "shift_line_base" : Create avoidance path based on shift line. + # User can control avoidance maneuver execution via RTC. + # However, this method doesn't support complex avoidance scenario (e.g. S-shape maneuver). + # "optimization_base": This module selects avoidance target object + # and bpp module clips drivable area based on avoidance target object polygon shape. + # But this module doesn't modify the path shape. + # On the other hand, autoware_path_optimizer module optimizes path shape instead of this module + # so that the path can be within drivable area. This method is able to deal with complex avoidance scenario. + # However, user can't control avoidance manuever execution. + # "both" : Use both method. + path_generation_method: "shift_line_base" + shift_line_pipeline: trim: quantize_size: 0.1 diff --git a/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/images/avoidance_design.fig.drawio.svg b/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/images/avoidance_design.fig.drawio.svg deleted file mode 100644 index d0563e9f2f542..0000000000000 --- a/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/images/avoidance_design.fig.drawio.svg +++ /dev/null @@ -1,668 +0,0 @@ - - - - - - - - - - - - - -
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- - Each shift points... - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Text is not SVG - cannot display - - - diff --git a/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/images/intersection_problem.drawio.svg b/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/images/intersection_problem.drawio.svg deleted file mode 100644 index 67d21dfd78a09..0000000000000 --- a/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/images/intersection_problem.drawio.svg +++ /dev/null @@ -1,179 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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- // enable avoidance to be perform only in lane with same direction - bool use_adjacent_lane{true}; - - // enable avoidance to be perform in opposite lane direction - // to use this, enable_avoidance_over_same_direction need to be set to true. - bool use_opposite_lane{true}; + // drivable lane config + std::string use_lane_type{"current_lane"}; // if this param is true, it reverts avoidance path when the path is no longer needed. bool enable_cancel_maneuver{false}; @@ -120,9 +116,6 @@ struct AvoidanceParameters // enable yield maneuver. bool enable_yield_maneuver_during_shifting{false}; - // disable path update - bool disable_path_update{false}; - // use hatched road markings for avoidance bool use_hatched_road_markings{false}; @@ -315,6 +308,9 @@ struct AvoidanceParameters // policy std::string policy_lateral_margin{"best_effort"}; + // path generation method. + std::string path_generation_method{"shift_line_base"}; + // target velocity matrix std::vector velocity_map; @@ -336,9 +332,6 @@ struct AvoidanceParameters // rss parameters utils::path_safety_checker::RSSparams rss_params{}; - // clip left and right bounds for objects - bool enable_bound_clipping{false}; - // debug bool enable_other_objects_marker{false}; bool enable_other_objects_info{false}; @@ -440,6 +433,9 @@ struct ObjectData // avoidance target // is ambiguous stopped vehicle. bool is_ambiguous{false}; + // is clip targe. + bool is_clip_target{false}; + // object direction. Direction direction{Direction::NONE}; diff --git a/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/include/autoware/behavior_path_static_obstacle_avoidance_module/parameter_helper.hpp b/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/include/autoware/behavior_path_static_obstacle_avoidance_module/parameter_helper.hpp index d7335165e5622..84cf7c4e33d26 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/include/autoware/behavior_path_static_obstacle_avoidance_module/parameter_helper.hpp +++ b/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/include/autoware/behavior_path_static_obstacle_avoidance_module/parameter_helper.hpp @@ -39,15 +39,14 @@ AvoidanceParameters getParameter(rclcpp::Node * node) getOrDeclareParameter(*node, ns + "resample_interval_for_planning"); p.resample_interval_for_output = getOrDeclareParameter(*node, ns + "resample_interval_for_output"); - p.enable_bound_clipping = getOrDeclareParameter(*node, ns + "enable_bound_clipping"); - p.disable_path_update = getOrDeclareParameter(*node, ns + "disable_path_update"); + p.path_generation_method = + getOrDeclareParameter(*node, ns + "path_generation_method"); } // drivable area { const std::string ns = "avoidance."; - p.use_adjacent_lane = getOrDeclareParameter(*node, ns + "use_adjacent_lane"); - p.use_opposite_lane = getOrDeclareParameter(*node, ns + "use_opposite_lane"); + p.use_lane_type = getOrDeclareParameter(*node, ns + "use_lane_type"); p.use_intersection_areas = getOrDeclareParameter(*node, ns + "use_intersection_areas"); p.use_hatched_road_markings = getOrDeclareParameter(*node, ns + "use_hatched_road_markings"); diff --git a/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/include/autoware/behavior_path_static_obstacle_avoidance_module/scene.hpp b/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/include/autoware/behavior_path_static_obstacle_avoidance_module/scene.hpp index f1769a45be530..635edb7c84f40 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/include/autoware/behavior_path_static_obstacle_avoidance_module/scene.hpp +++ b/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/include/autoware/behavior_path_static_obstacle_avoidance_module/scene.hpp @@ -436,6 +436,8 @@ class StaticObstacleAvoidanceModule : public SceneModuleInterface UUID candidate_uuid_; + ObjectDataArray clip_objects_; + // TODO(Satoshi OTA) create detected object manager. ObjectDataArray registered_objects_; diff --git a/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/include/autoware/behavior_path_static_obstacle_avoidance_module/utils.hpp b/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/include/autoware/behavior_path_static_obstacle_avoidance_module/utils.hpp index 76f8b1e53955a..3797fe0d55147 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/include/autoware/behavior_path_static_obstacle_avoidance_module/utils.hpp +++ b/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/include/autoware/behavior_path_static_obstacle_avoidance_module/utils.hpp @@ -34,7 +34,8 @@ using autoware::behavior_path_planner::utils::path_safety_checker::PoseWithVeloc using autoware::behavior_path_planner::utils::path_safety_checker::PredictedPathWithPolygon; static constexpr const char * logger_namespace = - "planning.scenario_planning.lane_driving.behavior_planning.behavior_path_planner.avoidance.utils"; + "planning.scenario_planning.lane_driving.behavior_planning.behavior_path_planner.static_obstacle_" + "avoidance.utils"; bool isOnRight(const ObjectData & obj); @@ -121,6 +122,8 @@ void updateRegisteredObject( ObjectDataArray & registered_objects, const ObjectDataArray & now_objects, const std::shared_ptr & parameters); +void updateClipObject(ObjectDataArray & clip_objects, AvoidancePlanningData & data); + void compensateDetectionLost( const ObjectDataArray & registered_objects, ObjectDataArray & now_objects, ObjectDataArray & other_objects); diff --git a/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/package.xml b/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/package.xml index ea6d405f80f41..f997b30da9ffa 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/package.xml +++ b/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/package.xml @@ -23,6 +23,7 @@ autoware_behavior_path_planner autoware_behavior_path_planner_common + autoware_lanelet2_extension autoware_motion_utils autoware_objects_of_interest_marker_interface autoware_perception_msgs @@ -31,7 +32,6 @@ autoware_universe_utils autoware_vehicle_info_utils geometry_msgs - lanelet2_extension magic_enum pluginlib rclcpp diff --git a/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/schema/static_obstacle_avoidance.schema.json b/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/schema/static_obstacle_avoidance.schema.json index ce36cbf949637..db3215fa8d238 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/schema/static_obstacle_avoidance.schema.json +++ b/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/schema/static_obstacle_avoidance.schema.json @@ -16,25 +16,17 @@ "description": "Path resample interval for output path. Too short interval increases computational cost for latter modules.", "default": "4.0" }, - "enable_bound_clipping": { - "type": "boolean", - "description": "Enable clipping left and right bound of drivable area when obstacles are in the drivable area.", - "default": "false" - }, - "disable_path_update": { - "type": "boolean", - "description": "Disable path update.", - "default": "false" - }, - "use_adjacent_lane": { - "type": "boolean", - "description": "Extend avoidance trajectory to adjacent lanes that has same direction. If false, avoidance only happen in current lane.", - "default": "true" + "path_generation_method": { + "type": "string", + "enum": ["shift_line_base", "optimization_base", "both"], + "description": "Path generation method.", + "default": "shift_line_base" }, - "use_opposite_lane": { - "type": "boolean", - "description": "Extend avoidance trajectory to opposite direction lane. `use_adjacent_lane` must be `true` to take effects.", - "default": "true" + "use_lane_type": { + "type": "string", + "enum": ["current_lane", "same_direction_lane", "opposite_direction_lane"], + "description": "Drivable lane configuration.", + "default": "opposite_direction_lane" }, "use_hatched_road_markings": { "type": "boolean", @@ -1450,9 +1442,8 @@ "required": [ "resample_interval_for_planning", "resample_interval_for_output", - "enable_bound_clipping", - "use_adjacent_lane", - "use_opposite_lane", + "path_generation_method", + "use_lane_type", "use_hatched_road_markings", "use_intersection_areas", "use_freespace_areas", diff --git a/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/src/debug.cpp b/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/src/debug.cpp index 03b17e7729ed9..9c771224e89eb 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/src/debug.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/src/debug.cpp @@ -17,7 +17,7 @@ #include "autoware/behavior_path_planner_common/marker_utils/utils.hpp" #include "autoware/behavior_path_planner_common/utils/utils.hpp" -#include +#include #include #include @@ -147,6 +147,7 @@ MarkerArray createObjectInfoMarkerArray( string_stream << std::fixed << std::setprecision(2) << std::boolalpha; string_stream << "ratio:" << object.shiftable_ratio << " [-]\n" << "lateral:" << object.to_centerline << " [m]\n" + << "clip:" << object.is_clip_target << " [-]\n" << "necessity:" << object.avoid_required << " [-]\n" << "stoppable:" << object.is_stoppable << " [-]\n" << "stop_factor:" << object.to_stop_factor_distance << " [m]\n" @@ -573,55 +574,45 @@ MarkerArray createDebugMarkerArray( addObjects(data.other_objects, ObjectInfo::AMBIGUOUS_STOPPED_VEHICLE); } - // shift line pre-process if (parameters->enable_shift_line_marker) { + // shift line pre-process addAvoidLine(debug.step1_registered_shift_line, "step1_registered_shift_line", 0.2, 0.2, 1.0); addAvoidLine(debug.step1_current_shift_line, "step1_current_shift_line", 0.2, 0.4, 0.8, 0.3); addAvoidLine(debug.step1_merged_shift_line, "step1_merged_shift_line", 0.2, 0.6, 0.6, 0.3); addAvoidLine(debug.step1_filled_shift_line, "step1_filled_shift_line", 0.2, 0.8, 0.4, 0.3); addAvoidLine(debug.step1_return_shift_line, "step1_return_shift_line", 0.2, 1.0, 0.2, 0.3); - } - // merge process - if (parameters->enable_shift_line_marker) { + // merge process addAvoidLine(debug.step2_merged_shift_line, "step2_merged_shift_line", 0.2, 1.0, 0.0, 0.3); - } - // trimming process - if (parameters->enable_shift_line_marker) { + // trimming process addAvoidLine(debug.step3_grad_filtered_1st, "step3_grad_filtered_1st", 0.2, 0.8, 0.0, 0.3); addAvoidLine(debug.step3_grad_filtered_2nd, "step3_grad_filtered_2nd", 0.4, 0.6, 0.0, 0.3); addAvoidLine(debug.step3_grad_filtered_3rd, "step3_grad_filtered_3rd", 0.6, 0.4, 0.0, 0.3); - } - // registering process - if (parameters->enable_shift_line_marker) { + // registering process addShiftLine(shifter.getShiftLines(), "step4_old_shift_line", 1.0, 1.0, 0.0, 0.3); addAvoidLine(data.new_shift_line, "step4_new_shift_line", 1.0, 0.0, 0.0, 0.3); - } - - // safety check - if (parameters->enable_safety_check_marker) { - add(showSafetyCheckInfo(debug.collision_check, "object_debug_info")); - add(showPredictedPath(debug.collision_check, "ego_predicted_path")); - add(showPolygon(debug.collision_check, "ego_and_target_polygon_relation")); - } - // shift length - if (parameters->enable_shift_line_marker) { + // shift length addShiftLength(debug.pos_shift, "merged_length_pos", 0.0, 0.7, 0.5); addShiftLength(debug.neg_shift, "merged_length_neg", 0.0, 0.5, 0.7); addShiftLength(debug.total_shift, "merged_length_total", 0.99, 0.4, 0.2); - } - // shift grad - if (parameters->enable_shift_line_marker) { + // shift grad addShiftGrad(debug.pos_shift_grad, debug.pos_shift, "merged_grad_pos", 0.0, 0.7, 0.5); addShiftGrad(debug.neg_shift_grad, debug.neg_shift, "merged_grad_neg", 0.0, 0.5, 0.7); addShiftGrad(debug.total_forward_grad, debug.total_shift, "grad_forward", 0.99, 0.4, 0.2); addShiftGrad(debug.total_backward_grad, debug.total_shift, "grad_backward", 0.4, 0.2, 0.9); } + // safety check + if (parameters->enable_safety_check_marker) { + add(showSafetyCheckInfo(debug.collision_check, "object_debug_info")); + add(showPredictedPath(debug.collision_check, "ego_predicted_path")); + add(showPolygon(debug.collision_check, "ego_and_target_polygon_relation")); + } + // detection area if (parameters->enable_detection_area_marker) { size_t i = 0; diff --git a/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/src/scene.cpp b/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/src/scene.cpp index 922883f289de8..b0e7d68c75d44 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/src/scene.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/src/scene.cpp @@ -24,8 +24,8 @@ #include "autoware/behavior_path_static_obstacle_avoidance_module/debug.hpp" #include "autoware/behavior_path_static_obstacle_avoidance_module/utils.hpp" -#include -#include +#include +#include #include #include @@ -294,6 +294,7 @@ void StaticObstacleAvoidanceModule::fillFundamentalData( registered_objects_, data.target_objects, parameters_); utils::static_obstacle_avoidance::compensateDetectionLost( registered_objects_, data.target_objects, data.other_objects); + utils::static_obstacle_avoidance::updateClipObject(clip_objects_, data); // sort object order by longitudinal distance std::sort(data.target_objects.begin(), data.target_objects.end(), [](auto a, auto b) { @@ -653,7 +654,7 @@ void StaticObstacleAvoidanceModule::fillDebugData( void StaticObstacleAvoidanceModule::updateEgoBehavior( const AvoidancePlanningData & data, ShiftedPath & path) { - if (parameters_->disable_path_update) { + if (parameters_->path_generation_method == "optimization_base") { return; } @@ -999,19 +1000,14 @@ BehaviorModuleOutput StaticObstacleAvoidanceModule::plan() // expand freespace areas current_drivable_area_info.enable_expanding_freespace_areas = parameters_->use_freespace_areas; // generate obstacle polygons - if (parameters_->enable_bound_clipping) { - ObjectDataArray clip_objects; - // If avoidance is executed by both behavior and motion, only non-avoidable object will be - // extracted from the drivable area. - std::for_each( - data.target_objects.begin(), data.target_objects.end(), [&](const auto & object) { - if (!object.is_avoidable) clip_objects.push_back(object); - }); + current_drivable_area_info.obstacles.clear(); + + if ( + parameters_->path_generation_method == "optimization_base" || + parameters_->path_generation_method == "both") { current_drivable_area_info.obstacles = utils::static_obstacle_avoidance::generateObstaclePolygonsForDrivableArea( - clip_objects, parameters_, planner_data_->parameters.vehicle_width / 2.0); - } else { - current_drivable_area_info.obstacles.clear(); + clip_objects_, parameters_, planner_data_->parameters.vehicle_width / 2.0); } output.drivable_area_info = utils::combineDrivableAreaInfo( @@ -1078,7 +1074,7 @@ BehaviorModuleOutput StaticObstacleAvoidanceModule::planWaitingApproval() void StaticObstacleAvoidanceModule::updatePathShifter(const AvoidLineArray & shift_lines) { - if (parameters_->disable_path_update) { + if (parameters_->path_generation_method == "optimization_base") { return; } diff --git a/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/src/utils.cpp b/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/src/utils.cpp index 05561318039b7..03fe48c6ab441 100644 --- a/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/src/utils.cpp +++ b/planning/behavior_path_planner/autoware_behavior_path_static_obstacle_avoidance_module/src/utils.cpp @@ -21,7 +21,7 @@ #include "autoware/behavior_path_static_obstacle_avoidance_module/data_structs.hpp" #include "autoware/behavior_path_static_obstacle_avoidance_module/utils.hpp" -#include +#include #include #include @@ -1690,6 +1690,59 @@ void compensateDetectionLost( } } +void updateClipObject(ObjectDataArray & clip_objects, AvoidancePlanningData & data) +{ + std::for_each(data.target_objects.begin(), data.target_objects.end(), [](auto & o) { + if (o.is_avoidable) { + o.is_clip_target = true; + } + }); + + const auto itr = + std::remove_if(clip_objects.begin(), clip_objects.end(), [&data](const auto & clip_object) { + const auto id = clip_object.object.object_id; + + // update target objects + { + const auto same_id_obj = std::find_if( + data.target_objects.begin(), data.target_objects.end(), + [&id](const auto & o) { return o.object.object_id == id; }); + if (same_id_obj != data.target_objects.end()) { + same_id_obj->is_clip_target = true; + return false; + } + } + + // update other objects + { + const auto same_id_obj = std::find_if( + data.other_objects.begin(), data.other_objects.end(), + [&id](const auto & o) { return o.object.object_id == id; }); + if (same_id_obj != data.other_objects.end()) { + same_id_obj->is_clip_target = true; + return false; + } + } + + return true; + }); + + clip_objects.erase(itr, clip_objects.end()); + + for (const auto & object : data.target_objects) { + const auto id = object.object.object_id; + const auto same_id_obj = std::find_if( + clip_objects.begin(), clip_objects.end(), + [&id](const auto & o) { return o.object.object_id == id; }); + + if (same_id_obj != clip_objects.end()) { + continue; + } + + clip_objects.push_back(object); + } +} + void updateRoadShoulderDistance( AvoidancePlanningData & data, const std::shared_ptr & planner_data, const std::shared_ptr & parameters) @@ -2185,14 +2238,16 @@ DrivableLanes generateExpandedDrivableLanes( current_drivable_lanes.left_lane = lanelet; current_drivable_lanes.right_lane = lanelet; - if (!parameters->use_adjacent_lane) { + if (parameters->use_lane_type == "current_lane") { return current_drivable_lanes; } + const auto use_opposite_lane = parameters->use_lane_type == "opposite_direction_lane"; + // 1. get left/right side lanes const auto update_left_lanelets = [&](const lanelet::ConstLanelet & target_lane) { - const auto all_left_lanelets = route_handler->getAllLeftSharedLinestringLanelets( - target_lane, parameters->use_opposite_lane, true); + const auto all_left_lanelets = + route_handler->getAllLeftSharedLinestringLanelets(target_lane, use_opposite_lane, true); if (!all_left_lanelets.empty()) { current_drivable_lanes.left_lane = all_left_lanelets.back(); // leftmost lanelet pushUniqueVector( @@ -2201,8 +2256,8 @@ DrivableLanes generateExpandedDrivableLanes( } }; const auto update_right_lanelets = [&](const lanelet::ConstLanelet & target_lane) { - const auto all_right_lanelets = route_handler->getAllRightSharedLinestringLanelets( - target_lane, parameters->use_opposite_lane, true); + const auto all_right_lanelets = + route_handler->getAllRightSharedLinestringLanelets(target_lane, use_opposite_lane, true); if (!all_right_lanelets.empty()) { current_drivable_lanes.right_lane = all_right_lanelets.back(); // rightmost lanelet pushUniqueVector( diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_blind_spot_module/package.xml b/planning/behavior_velocity_planner/autoware_behavior_velocity_blind_spot_module/package.xml index dd4858dabe293..0b3959b7f77cd 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_blind_spot_module/package.xml +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_blind_spot_module/package.xml @@ -17,13 +17,13 @@ autoware_cmake autoware_behavior_velocity_planner_common + autoware_lanelet2_extension autoware_motion_utils autoware_perception_msgs autoware_planning_msgs autoware_route_handler autoware_universe_utils geometry_msgs - lanelet2_extension pluginlib rclcpp tf2 diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_blind_spot_module/src/scene.cpp b/planning/behavior_velocity_planner/autoware_behavior_velocity_blind_spot_module/src/scene.cpp index f7e859a9085c7..1880adb045b42 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_blind_spot_module/src/scene.cpp +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_blind_spot_module/src/scene.cpp @@ -18,9 +18,9 @@ #include #include #include -#include -#include -#include +#include +#include +#include #include #include diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_crosswalk_module/README.md b/planning/behavior_velocity_planner/autoware_behavior_velocity_crosswalk_module/README.md index 84d3e0f77a281..60601299daf8b 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_crosswalk_module/README.md +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_crosswalk_module/README.md @@ -183,7 +183,7 @@ In the `stuck_vehicle` namespace, the following parameters are defined. In the current autoware implementation, if no target object is detected around a crosswalk, the ego vehicle will not slow down for the crosswalk. However, it may be desirable to slow down in situations, for example, where there are blind spots. -Such a situation can be handled by setting some tags to the related crosswalk as instructed in the [lanelet2_format_extension.md](https://github.com/autowarefoundation/autoware_common/blob/main/tmp/lanelet2_extension/docs/lanelet2_format_extension.md) +Such a situation can be handled by setting some tags to the related crosswalk as instructed in the [lanelet2_format_extension.md](https://github.com/autowarefoundation/autoware_lanelet2_extension/blob/main/autoware_lanelet2_extension/docs/lanelet2_format_extension.md) document. | Parameter | | Type | Description | diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_crosswalk_module/package.xml b/planning/behavior_velocity_planner/autoware_behavior_velocity_crosswalk_module/package.xml index 85a19411405a7..cd9c63fc0367f 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_crosswalk_module/package.xml +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_crosswalk_module/package.xml @@ -23,6 +23,7 @@ autoware_behavior_velocity_planner_common autoware_grid_map_utils + autoware_lanelet2_extension autoware_motion_utils autoware_perception_msgs autoware_planning_msgs @@ -34,7 +35,6 @@ grid_map_core grid_map_ros interpolation - lanelet2_extension libboost-dev nav_msgs pcl_conversions diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_crosswalk_module/src/manager.hpp b/planning/behavior_velocity_planner/autoware_behavior_velocity_crosswalk_module/src/manager.hpp index 8ce2adea56a4c..693f6c27a380d 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_crosswalk_module/src/manager.hpp +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_crosswalk_module/src/manager.hpp @@ -20,7 +20,7 @@ #include #include #include -#include +#include #include #include diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_crosswalk_module/src/scene_crosswalk.hpp b/planning/behavior_velocity_planner/autoware_behavior_velocity_crosswalk_module/src/scene_crosswalk.hpp index 491e0dadd4d80..b7dc2fea16794 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_crosswalk_module/src/scene_crosswalk.hpp +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_crosswalk_module/src/scene_crosswalk.hpp @@ -20,7 +20,7 @@ #include #include #include -#include +#include #include #include diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_crosswalk_module/src/util.cpp b/planning/behavior_velocity_planner/autoware_behavior_velocity_crosswalk_module/src/util.cpp index 93b9780445479..6389637ca1526 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_crosswalk_module/src/util.cpp +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_crosswalk_module/src/util.cpp @@ -19,7 +19,7 @@ #include #include #include -#include +#include #include @@ -40,7 +40,7 @@ #define EIGEN_MPL2_ONLY #include #include -#include +#include #include #include diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_detection_area_module/package.xml b/planning/behavior_velocity_planner/autoware_behavior_velocity_detection_area_module/package.xml index 5a15c78c9dee2..c707e2fbfdbde 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_detection_area_module/package.xml +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_detection_area_module/package.xml @@ -18,13 +18,13 @@ eigen3_cmake_module autoware_behavior_velocity_planner_common + autoware_lanelet2_extension autoware_motion_utils autoware_planning_msgs autoware_route_handler autoware_universe_utils eigen geometry_msgs - lanelet2_extension libboost-dev pluginlib rclcpp diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_detection_area_module/src/manager.cpp b/planning/behavior_velocity_planner/autoware_behavior_velocity_detection_area_module/src/manager.cpp index 4887fd7423817..6206bb5c2d25c 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_detection_area_module/src/manager.cpp +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_detection_area_module/src/manager.cpp @@ -16,7 +16,7 @@ #include #include -#include +#include #include diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_detection_area_module/src/scene.cpp b/planning/behavior_velocity_planner/autoware_behavior_velocity_detection_area_module/src/scene.cpp index 7abd88d59f398..9cbe962e88cae 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_detection_area_module/src/scene.cpp +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_detection_area_module/src/scene.cpp @@ -83,7 +83,7 @@ bool DetectionAreaModule::modifyPathVelocity(PathWithLaneId * path, StopReason * // Get stop point const auto stop_point = arc_lane_utils::createTargetPoint( - original_path, stop_line, lane_id_, planner_param_.stop_margin, + original_path, stop_line, planner_param_.stop_margin, planner_data_->vehicle_info_.max_longitudinal_offset_m); if (!stop_point) { return true; @@ -128,7 +128,7 @@ bool DetectionAreaModule::modifyPathVelocity(PathWithLaneId * path, StopReason * if (planner_param_.use_dead_line) { // Use '-' for margin because it's the backward distance from stop line const auto dead_line_point = arc_lane_utils::createTargetPoint( - original_path, stop_line, lane_id_, -planner_param_.dead_line_margin, + original_path, stop_line, -planner_param_.dead_line_margin, planner_data_->vehicle_info_.max_longitudinal_offset_m); if (dead_line_point) { diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_detection_area_module/src/scene.hpp b/planning/behavior_velocity_planner/autoware_behavior_velocity_detection_area_module/src/scene.hpp index 5b15cc92f57b1..efc1c1809302a 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_detection_area_module/src/scene.hpp +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_detection_area_module/src/scene.hpp @@ -25,7 +25,7 @@ #include #include #include -#include +#include #include #include diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/README.md b/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/README.md index 4e13a1fa169de..6ba8f8b929146 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/README.md +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/README.md @@ -18,7 +18,7 @@ This module is activated when the path contains the lanes with turn_direction ta ## Requirements/Limitations -- The HDMap needs to have the information of turn_direction tag (which should be one of straight, left, right) for all the lanes in intersections and right_of_way tag for specific lanes (refer to [RightOfWay](#how-towhy-set-rightofway-tag) section for more details). See [lanelet2_extension document](https://github.com/autowarefoundation/autoware_common/blob/main/tmp/lanelet2_extension/docs/lanelet2_format_extension.md) for more detail. +- The HDMap needs to have the information of turn_direction tag (which should be one of straight, left, right) for all the lanes in intersections and right_of_way tag for specific lanes (refer to [RightOfWay](#how-towhy-set-rightofway-tag) section for more details). See [autoware_lanelet2_extension document](https://github.com/autowarefoundation/autoware_lanelet2_extension/blob/main/autoware_lanelet2_extension/docs/lanelet2_format_extension.md) for more detail. - WIP(perception requirements/limitations) - WIP(sensor visibility requirements/limitations) diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/package.xml b/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/package.xml index e240722e66a5b..7772235342266 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/package.xml +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/package.xml @@ -19,6 +19,7 @@ autoware_cmake autoware_behavior_velocity_planner_common + autoware_lanelet2_extension autoware_motion_utils autoware_perception_msgs autoware_planning_msgs @@ -29,7 +30,6 @@ fmt geometry_msgs interpolation - lanelet2_extension libopencv-dev magic_enum nav_msgs diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/manager.cpp b/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/manager.cpp index 3fa2dc3efb5b1..43df89b49b268 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/manager.cpp +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/manager.cpp @@ -17,7 +17,7 @@ #include #include #include -#include +#include #include diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/object_manager.cpp b/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/object_manager.cpp index 650363c602b90..6ead1df3eb2d1 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/object_manager.cpp +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/object_manager.cpp @@ -16,7 +16,7 @@ #include #include // for toPolygon2d -#include +#include #include #include diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/scene_intersection.cpp b/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/scene_intersection.cpp index 274bbe5627b22..ac157fb3ab6dc 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/scene_intersection.cpp +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/scene_intersection.cpp @@ -23,8 +23,8 @@ #include // for toPolygon2d #include #include -#include -#include +#include +#include #include diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/scene_intersection_collision.cpp b/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/scene_intersection_collision.cpp index 739b1122be6a1..ec6610048dc79 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/scene_intersection_collision.cpp +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/scene_intersection_collision.cpp @@ -20,7 +20,7 @@ #include #include // for toPolygon2d #include -#include +#include #include #include @@ -265,7 +265,9 @@ void IntersectionModule::updateObjectInfoManagerCollision( continue; } const auto & object_passage_interval = object_passage_interval_opt.value(); - const auto [object_enter_time, object_exit_time] = object_passage_interval.interval_time; + const auto object_enter_exit_time = object_passage_interval.interval_time; + const auto object_enter_time = std::get<0>(object_enter_exit_time); + const auto object_exit_time = std::get<1>(object_enter_exit_time); const auto ego_start_itr = std::lower_bound( time_distance_array.begin(), time_distance_array.end(), object_enter_time - collision_start_margin_time, diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/scene_intersection_prepare_data.cpp b/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/scene_intersection_prepare_data.cpp index 1390104d98175..a4c4e1b41688f 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/scene_intersection_prepare_data.cpp +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/scene_intersection_prepare_data.cpp @@ -18,10 +18,10 @@ #include // for to_bg2d #include // for planning_utils:: #include +#include // for lanelet::autoware::RoadMarking +#include +#include #include -#include // for lanelet::autoware::RoadMarking -#include -#include #include #include diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/scene_intersection_stuck.cpp b/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/scene_intersection_stuck.cpp index 14baf6faa581a..78365bc5e500f 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/scene_intersection_stuck.cpp +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/scene_intersection_stuck.cpp @@ -17,7 +17,7 @@ #include // for toGeomPoly #include -#include +#include #include #include diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/scene_merge_from_private_road.cpp b/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/scene_merge_from_private_road.cpp index ddfd3b7d4cacd..d5cd9ca587716 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/scene_merge_from_private_road.cpp +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/scene_merge_from_private_road.cpp @@ -19,8 +19,8 @@ #include #include #include -#include -#include +#include +#include #include #include diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/util.cpp b/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/util.cpp index 893ec646dba73..308483e5b0869 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/util.cpp +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/util.cpp @@ -20,7 +20,7 @@ #include #include #include -#include +#include #include #include diff --git a/planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/CMakeLists.txt b/planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/CMakeLists.txt similarity index 85% rename from planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/CMakeLists.txt rename to planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/CMakeLists.txt index 265b5b637e7ce..cee9e8ca039c9 100644 --- a/planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/CMakeLists.txt +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/CMakeLists.txt @@ -1,5 +1,5 @@ cmake_minimum_required(VERSION 3.14) -project(behavior_velocity_no_drivable_lane_module) +project(autoware_behavior_velocity_no_drivable_lane_module) find_package(autoware_cmake REQUIRED) autoware_package() diff --git a/planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/README.md b/planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/README.md similarity index 100% rename from planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/README.md rename to planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/README.md diff --git a/planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/config/no_drivable_lane.param.yaml b/planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/config/no_drivable_lane.param.yaml similarity index 100% rename from planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/config/no_drivable_lane.param.yaml rename to planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/config/no_drivable_lane.param.yaml diff --git a/planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/docs/no_drivable_lane_design.svg b/planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/docs/no_drivable_lane_design.svg similarity index 100% rename from planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/docs/no_drivable_lane_design.svg rename to planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/docs/no_drivable_lane_design.svg diff --git a/planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/docs/no_drivable_lane_scenarios.svg b/planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/docs/no_drivable_lane_scenarios.svg similarity index 100% rename from planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/docs/no_drivable_lane_scenarios.svg rename to planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/docs/no_drivable_lane_scenarios.svg diff --git a/planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/package.xml b/planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/package.xml similarity index 88% rename from planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/package.xml rename to planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/package.xml index e03a05cfc57c7..b674478ffb228 100644 --- a/planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/package.xml +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/package.xml @@ -1,9 +1,9 @@ - behavior_velocity_no_drivable_lane_module + autoware_behavior_velocity_no_drivable_lane_module 0.1.0 - The behavior_velocity_no_drivable_lane_module package + The autoware_behavior_velocity_no_drivable_lane_module package Ahmed Ebrahim Tomoya Kimura @@ -20,13 +20,13 @@ eigen3_cmake_module autoware_behavior_velocity_planner_common + autoware_lanelet2_extension autoware_motion_utils autoware_planning_msgs autoware_route_handler autoware_universe_utils eigen geometry_msgs - lanelet2_extension libboost-dev pluginlib rclcpp diff --git a/planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/plugins.xml b/planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/plugins.xml similarity index 70% rename from planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/plugins.xml rename to planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/plugins.xml index e3e9efb254fba..26635e8e24bc1 100644 --- a/planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/plugins.xml +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/plugins.xml @@ -1,3 +1,3 @@ - + diff --git a/planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/src/debug.cpp b/planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/src/debug.cpp similarity index 100% rename from planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/src/debug.cpp rename to planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/src/debug.cpp diff --git a/planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/src/manager.cpp b/planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/src/manager.cpp similarity index 100% rename from planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/src/manager.cpp rename to planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/src/manager.cpp diff --git a/planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/src/manager.hpp b/planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/src/manager.hpp similarity index 100% rename from planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/src/manager.hpp rename to planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/src/manager.hpp diff --git a/planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/src/scene.cpp b/planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/src/scene.cpp similarity index 100% rename from planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/src/scene.cpp rename to planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/src/scene.cpp diff --git a/planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/src/scene.hpp b/planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/src/scene.hpp similarity index 100% rename from planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/src/scene.hpp rename to planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/src/scene.hpp diff --git a/planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/src/util.cpp b/planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/src/util.cpp similarity index 74% rename from planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/src/util.cpp rename to planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/src/util.cpp index 1339fee207416..25315fd397aa4 100644 --- a/planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/src/util.cpp +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/src/util.cpp @@ -85,29 +85,37 @@ PathWithNoDrivableLanePolygonIntersection getPathIntersectionWithNoDrivableLaneP bg::within(first_path_point, polygon); auto const & is_last_path_point_inside_polygon = bg::within(last_path_point, polygon); - if ( - intersects.empty() && - path_no_drivable_lane_polygon_intersection.is_first_path_point_inside_polygon && - is_last_path_point_inside_polygon) { - path_no_drivable_lane_polygon_intersection.is_path_inside_of_polygon = true; - } else { - // classify first and second intersection points - for (size_t i = 0; i < intersects.size(); ++i) { - const auto & p = intersects.at(i); - if ( - (intersects.size() == 2 && i == 0) || - (intersects.size() == 1 && is_last_path_point_inside_polygon)) { - path_no_drivable_lane_polygon_intersection.first_intersection_point = - createPoint(p.x(), p.y(), ego_pos.z); - } else if ( - (intersects.size() == 2 && i == 1) || - (intersects.size() == 1 && - path_no_drivable_lane_polygon_intersection.is_first_path_point_inside_polygon)) { - path_no_drivable_lane_polygon_intersection.second_intersection_point = - createPoint(p.x(), p.y(), ego_pos.z); - } + if (intersects.empty()) { + if ( + path_no_drivable_lane_polygon_intersection.is_first_path_point_inside_polygon && + is_last_path_point_inside_polygon) { + path_no_drivable_lane_polygon_intersection.is_path_inside_of_polygon = true; + } else { + // do nothing + } + } else if (intersects.size() == 1) { + const auto & p = intersects.at(0); + if (is_last_path_point_inside_polygon) { + path_no_drivable_lane_polygon_intersection.first_intersection_point = + createPoint(p.x(), p.y(), ego_pos.z); + } else if (path_no_drivable_lane_polygon_intersection.is_first_path_point_inside_polygon) { + path_no_drivable_lane_polygon_intersection.second_intersection_point = + createPoint(p.x(), p.y(), ego_pos.z); + } else { + // do nothing } + } else if (intersects.size() == 2) { + // classify first and second intersection points + const auto & p0 = intersects.at(0); + const auto & p1 = intersects.at(1); + path_no_drivable_lane_polygon_intersection.first_intersection_point = + createPoint(p0.x(), p0.y(), ego_pos.z); + path_no_drivable_lane_polygon_intersection.second_intersection_point = + createPoint(p1.x(), p1.y(), ego_pos.z); + } else { + // do nothing } + return path_no_drivable_lane_polygon_intersection; } } // namespace autoware::behavior_velocity_planner diff --git a/planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/src/util.hpp b/planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/src/util.hpp similarity index 100% rename from planning/behavior_velocity_planner/behavior_velocity_no_drivable_lane_module/src/util.hpp rename to planning/behavior_velocity_planner/autoware_behavior_velocity_no_drivable_lane_module/src/util.hpp diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_no_stopping_area_module/package.xml b/planning/behavior_velocity_planner/autoware_behavior_velocity_no_stopping_area_module/package.xml index eab1a13d7ebf1..b630f988af662 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_no_stopping_area_module/package.xml +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_no_stopping_area_module/package.xml @@ -18,6 +18,7 @@ eigen3_cmake_module autoware_behavior_velocity_planner_common + autoware_lanelet2_extension autoware_motion_utils autoware_perception_msgs autoware_planning_msgs @@ -27,7 +28,6 @@ eigen geometry_msgs interpolation - lanelet2_extension libboost-dev pluginlib rclcpp diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_no_stopping_area_module/src/manager.cpp b/planning/behavior_velocity_planner/autoware_behavior_velocity_no_stopping_area_module/src/manager.cpp index 4ee711297b494..14a79d64339d6 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_no_stopping_area_module/src/manager.cpp +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_no_stopping_area_module/src/manager.cpp @@ -16,7 +16,7 @@ #include #include -#include +#include #include diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_no_stopping_area_module/src/scene_no_stopping_area.cpp b/planning/behavior_velocity_planner/autoware_behavior_velocity_no_stopping_area_module/src/scene_no_stopping_area.cpp index d007416dccb47..0478aea34a44f 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_no_stopping_area_module/src/scene_no_stopping_area.cpp +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_no_stopping_area_module/src/scene_no_stopping_area.cpp @@ -125,7 +125,7 @@ bool NoStoppingAreaModule::modifyPathVelocity(PathWithLaneId * path, StopReason return true; } const auto stop_point = arc_lane_utils::createTargetPoint( - original_path, stop_line.value(), lane_id_, planner_param_.stop_margin, + original_path, stop_line.value(), planner_param_.stop_margin, planner_data_->vehicle_info_.max_longitudinal_offset_m); if (!stop_point) { setSafe(true); diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_no_stopping_area_module/src/scene_no_stopping_area.hpp b/planning/behavior_velocity_planner/autoware_behavior_velocity_no_stopping_area_module/src/scene_no_stopping_area.hpp index ae297d96c5afd..2c553ac457ef0 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_no_stopping_area_module/src/scene_no_stopping_area.hpp +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_no_stopping_area_module/src/scene_no_stopping_area.hpp @@ -21,7 +21,7 @@ #include #include #include -#include +#include #include #include diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_occlusion_spot_module/package.xml b/planning/behavior_velocity_planner/autoware_behavior_velocity_occlusion_spot_module/package.xml index 34fd712d2229c..22da467ba9fac 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_occlusion_spot_module/package.xml +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_occlusion_spot_module/package.xml @@ -18,6 +18,7 @@ autoware_behavior_velocity_planner_common autoware_grid_map_utils + autoware_lanelet2_extension autoware_motion_utils autoware_perception_msgs autoware_planning_msgs @@ -27,7 +28,6 @@ geometry_msgs grid_map_ros interpolation - lanelet2_extension libboost-dev libopencv-dev nav_msgs diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_occlusion_spot_module/src/occlusion_spot_utils.hpp b/planning/behavior_velocity_planner/autoware_behavior_velocity_occlusion_spot_module/src/occlusion_spot_utils.hpp index 189a5be5abe95..992cc5787bf58 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_occlusion_spot_module/src/occlusion_spot_utils.hpp +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_occlusion_spot_module/src/occlusion_spot_utils.hpp @@ -19,9 +19,9 @@ #include #include -#include -#include -#include +#include +#include +#include #include #include diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_occlusion_spot_module/src/scene_occlusion_spot.cpp b/planning/behavior_velocity_planner/autoware_behavior_velocity_occlusion_spot_module/src/scene_occlusion_spot.cpp index b55869be8c902..24b9d10e09830 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_occlusion_spot_module/src/scene_occlusion_spot.cpp +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_occlusion_spot_module/src/scene_occlusion_spot.cpp @@ -21,8 +21,8 @@ #include #include #include -#include -#include +#include +#include #include diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_occlusion_spot_module/test/src/utils.hpp b/planning/behavior_velocity_planner/autoware_behavior_velocity_occlusion_spot_module/test/src/utils.hpp index 4b8a7ae3b2f3f..39a887ab16476 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_occlusion_spot_module/test/src/utils.hpp +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_occlusion_spot_module/test/src/utils.hpp @@ -86,7 +86,7 @@ inline void generatePossibleCollisions( // intersection geometry_msgs::msg::Pose intersection_pose{}; intersection_pose.position.x = x0 + x_step * i + lon; - intersection_pose.position.x = y0 + y_step * i; + intersection_pose.position.y = y0 + y_step * i; // collision path point autoware_planning_msgs::msg::PathPoint collision_with_margin{}; diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_planner/README.md b/planning/behavior_velocity_planner/autoware_behavior_velocity_planner/README.md index f688318ed051e..041e0b86e0d06 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_planner/README.md +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_planner/README.md @@ -19,7 +19,7 @@ It loads modules as plugins. Please refer to the links listed below for detail o - [Occlusion Spot](../autoware_behavior_velocity_occlusion_spot_module/README.md) - [No Stopping Area](../autoware_behavior_velocity_no_stopping_area_module/README.md) - [Run Out](../autoware_behavior_velocity_run_out_module/README.md) -- [Speed Bump](../behavior_velocity_speed_bump_module/README.md) +- [Speed Bump](../autoware_behavior_velocity_speed_bump_module/README.md) When each module plans velocity, it considers based on `base_link`(center of rear-wheel axis) pose. So for example, in order to stop at a stop line with the vehicles' front on the stop line, it calculates `base_link` position from the distance between `base_link` to front and modifies path velocity from the `base_link` position. diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_planner/package.xml b/planning/behavior_velocity_planner/autoware_behavior_velocity_planner/package.xml index 0c5088d7c4e1d..49946766ef55c 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_planner/package.xml +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_planner/package.xml @@ -35,6 +35,7 @@ rosidl_default_generators autoware_behavior_velocity_planner_common + autoware_lanelet2_extension autoware_map_msgs autoware_motion_utils autoware_perception_msgs @@ -45,7 +46,6 @@ diagnostic_msgs eigen geometry_msgs - lanelet2_extension libboost-dev pcl_conversions pluginlib @@ -66,20 +66,19 @@ ament_cmake_ros ament_lint_auto autoware_behavior_velocity_blind_spot_module - autoware_behavior_velocity_blind_spot_module autoware_behavior_velocity_crosswalk_module autoware_behavior_velocity_detection_area_module autoware_behavior_velocity_intersection_module + autoware_behavior_velocity_no_drivable_lane_module autoware_behavior_velocity_no_stopping_area_module autoware_behavior_velocity_occlusion_spot_module autoware_behavior_velocity_run_out_module + autoware_behavior_velocity_speed_bump_module autoware_behavior_velocity_stop_line_module autoware_behavior_velocity_traffic_light_module autoware_behavior_velocity_virtual_traffic_light_module autoware_behavior_velocity_walkway_module autoware_lint_common - behavior_velocity_no_drivable_lane_module - behavior_velocity_speed_bump_module rosidl_interface_packages diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_planner/src/node.cpp b/planning/behavior_velocity_planner/autoware_behavior_velocity_planner/src/node.cpp index cbacbe625238e..704059a9d7ed0 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_planner/src/node.cpp +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_planner/src/node.cpp @@ -20,7 +20,7 @@ #include #include #include -#include +#include #include #include diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_planner/test/src/test_node_interface.cpp b/planning/behavior_velocity_planner/autoware_behavior_velocity_planner/test/src/test_node_interface.cpp index a6cff987e30f0..3482d7be8ec48 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_planner/test/src/test_node_interface.cpp +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_planner/test/src/test_node_interface.cpp @@ -55,12 +55,6 @@ std::shared_ptr generateNode() ament_index_cpp::get_package_share_directory("autoware_velocity_smoother"); // TODO(esteve): delete when all the modules are migrated to autoware_behavior_velocity_* - const auto get_behavior_velocity_module_config_no_prefix = [](const std::string & module) { - const auto package_name = "behavior_velocity_" + module + "_module"; - const auto package_path = ament_index_cpp::get_package_share_directory(package_name); - return package_path + "/config/" + module + ".param.yaml"; - }; - const auto get_behavior_velocity_module_config = [](const std::string & module) { const auto package_name = "autoware_behavior_velocity_" + module + "_module"; const auto package_path = ament_index_cpp::get_package_share_directory(package_name); @@ -103,11 +97,11 @@ std::shared_ptr generateNode() get_behavior_velocity_module_config("no_stopping_area"), get_behavior_velocity_module_config("occlusion_spot"), get_behavior_velocity_module_config("run_out"), - get_behavior_velocity_module_config_no_prefix("speed_bump"), + get_behavior_velocity_module_config("speed_bump"), get_behavior_velocity_module_config("stop_line"), get_behavior_velocity_module_config("traffic_light"), get_behavior_velocity_module_config("virtual_traffic_light"), - get_behavior_velocity_module_config_no_prefix("no_drivable_lane")}); + get_behavior_velocity_module_config("no_drivable_lane")}); // TODO(Takagi, Isamu): set launch_modules // TODO(Kyoichi Sugahara) set to true launch_virtual_traffic_light diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_planner_common/include/autoware/behavior_velocity_planner_common/utilization/arc_lane_util.hpp b/planning/behavior_velocity_planner/autoware_behavior_velocity_planner_common/include/autoware/behavior_velocity_planner_common/utilization/arc_lane_util.hpp index 77b7d25f9f46e..769932259e3e6 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_planner_common/include/autoware/behavior_velocity_planner_common/utilization/arc_lane_util.hpp +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_planner_common/include/autoware/behavior_velocity_planner_common/utilization/arc_lane_util.hpp @@ -61,20 +61,9 @@ std::optional checkCollision( template std::optional findCollisionSegment( const T & path, const geometry_msgs::msg::Point & stop_line_p1, - const geometry_msgs::msg::Point & stop_line_p2, const size_t target_lane_id) + const geometry_msgs::msg::Point & stop_line_p2) { for (size_t i = 0; i < path.points.size() - 1; ++i) { - const auto & prev_lane_ids = path.points.at(i).lane_ids; - const auto & next_lane_ids = path.points.at(i + 1).lane_ids; - - const bool is_target_lane_in_prev_lane = - std::find(prev_lane_ids.begin(), prev_lane_ids.end(), target_lane_id) != prev_lane_ids.end(); - const bool is_target_lane_in_next_lane = - std::find(next_lane_ids.begin(), next_lane_ids.end(), target_lane_id) != next_lane_ids.end(); - if (!is_target_lane_in_prev_lane && !is_target_lane_in_next_lane) { - continue; - } - const auto & p1 = autoware::universe_utils::getPoint(path.points.at(i)); // Point before collision point const auto & p2 = @@ -92,12 +81,12 @@ std::optional findCollisionSegment( template std::optional findCollisionSegment( - const T & path, const LineString2d & stop_line, const size_t target_lane_id) + const T & path, const LineString2d & stop_line) { const auto stop_line_p1 = convertToGeomPoint(stop_line.at(0)); const auto stop_line_p2 = convertToGeomPoint(stop_line.at(1)); - return findCollisionSegment(path, stop_line_p1, stop_line_p2, target_lane_id); + return findCollisionSegment(path, stop_line_p1, stop_line_p2); } template @@ -194,7 +183,7 @@ geometry_msgs::msg::Pose calcTargetPose(const T & path, const PathIndexWithOffse std::optional createTargetPoint( const tier4_planning_msgs::msg::PathWithLaneId & path, const LineString2d & stop_line, - const size_t lane_id, const double margin, const double vehicle_offset); + const double margin, const double vehicle_offset); } // namespace arc_lane_utils } // namespace autoware::behavior_velocity_planner diff --git a/planning/behavior_velocity_planner/autoware_behavior_velocity_planner_common/include/autoware/behavior_velocity_planner_common/utilization/util.hpp b/planning/behavior_velocity_planner/autoware_behavior_velocity_planner_common/include/autoware/behavior_velocity_planner_common/utilization/util.hpp index 591a30928fd09..5f1c17ea1b815 100644 --- a/planning/behavior_velocity_planner/autoware_behavior_velocity_planner_common/include/autoware/behavior_velocity_planner_common/utilization/util.hpp +++ b/planning/behavior_velocity_planner/autoware_behavior_velocity_planner_common/include/autoware/behavior_velocity_planner_common/utilization/util.hpp @@ -228,17 +228,8 @@ std::set getAssociativeIntersectionLanelets( lanelet::ConstLanelet lane, const lanelet::LaneletMapPtr lanelet_map, const lanelet::routing::RoutingGraphPtr routing_graph); -template