Merge branch 'main' into autoware_msg

autoware_launch/config/control/trajectory_follower/lateral/mpc.param.yaml

@@ -74,3 +74,5 @@
       update_steer_threshold: 0.035
       average_num: 1000
       steering_offset_limit: 0.02
+
+    debug_publish_predicted_trajectory: false  # publish debug predicted trajectory in Frenet coordinate

autoware_launch/config/localization/ekf_localizer.param.yaml

@@ -4,6 +4,7 @@
     enable_yaw_bias_estimation: true
     predict_frequency: 50.0
     tf_rate: 50.0
+    publish_tf: true
     extend_state_step: 50
 
     # for Pose measurement
@@ -22,6 +23,10 @@
     proc_stddev_vx_c: 10.0
     proc_stddev_wz_c: 5.0
 
+    #Simple1DFilter parameters
+    z_filter_proc_dev: 1.0
+    roll_filter_proc_dev: 0.01
+    pitch_filter_proc_dev: 0.01
     # for diagnostics
     pose_no_update_count_threshold_warn: 50
     pose_no_update_count_threshold_error: 100

autoware_launch/config/localization/ndt_scan_matcher.param.yaml

@@ -1,106 +1,114 @@
 /**:
   ros__parameters:
-    # Use dynamic map loading
-    use_dynamic_map_loading: true
+    frame:
+      # Vehicle reference frame
+      base_frame: "base_link"
 
-    # Vehicle reference frame
-    base_frame: "base_link"
+      # NDT reference frame
+      ndt_base_frame: "ndt_base_link"
 
-    # NDT reference frame
-    ndt_base_frame: "ndt_base_link"
+      # Map frame
+      map_frame: "map"
 
-    # map frame
-    map_frame: "map"
 
-    # Subscriber queue size
-    input_sensor_points_queue_size: 1
+    ndt:
+      # The maximum difference between two consecutive
+      # transformations in order to consider convergence
+      trans_epsilon: 0.01
 
-    # The maximum difference between two consecutive
-    # transformations in order to consider convergence
-    trans_epsilon: 0.01
+      # The newton line search maximum step length
+      step_size: 0.1
 
-    # The newton line search maximum step length
-    step_size: 0.1
+      # The ND voxel grid resolution
+      resolution: 2.0
 
-    # The ND voxel grid resolution
-    resolution: 2.0
+      # The number of iterations required to calculate alignment
+      max_iterations: 30
 
-    # The number of iterations required to calculate alignment
-    max_iterations: 30
+      # Number of threads used for parallel computing
+      num_threads: 4
 
-    # Converged param type
-    # 0=TRANSFORM_PROBABILITY, 1=NEAREST_VOXEL_TRANSFORMATION_LIKELIHOOD
-    converged_param_type: 1
+      regularization:
+        enable: false
 
-    # If converged_param_type is 0
-    # Threshold for deciding whether to trust the estimation result
-    converged_param_transform_probability: 3.0
+        # Regularization scale factor
+        scale_factor: 0.01
 
-    # If converged_param_type is 1
-    # Threshold for deciding whether to trust the estimation result
-    converged_param_nearest_voxel_transformation_likelihood: 2.3
 
-    # The number of particles to estimate initial pose
-    initial_estimate_particles_num: 200
+    initial_pose_estimation:
+      # The number of particles to estimate initial pose
+      particles_num: 200
 
-    # The number of initial random trials in the TPE (Tree-Structured Parzen Estimator).
-    # This value should be equal to or less than 'initial_estimate_particles_num' and more than 0.
-    # If it is equal to 'initial_estimate_particles_num', the search will be the same as a full random search.
-    n_startup_trials: 20
+      # The number of initial random trials in the TPE (Tree-Structured Parzen Estimator).
+      # This value should be equal to or less than 'initial_estimate_particles_num' and more than 0.
+      # If it is equal to 'initial_estimate_particles_num', the search will be the same as a full random search.
+      n_startup_trials: 20
 
-    # Tolerance of timestamp difference between current time and sensor pointcloud. [sec]
-    lidar_topic_timeout_sec: 1.0
 
-    # Tolerance of timestamp difference between initial_pose and sensor pointcloud. [sec]
-    initial_pose_timeout_sec: 1.0
+    validation:
+      # Tolerance of timestamp difference between current time and sensor pointcloud. [sec]
+      lidar_topic_timeout_sec: 1.0
 
-    # Tolerance of distance difference between two initial poses used for linear interpolation. [m]
-    initial_pose_distance_tolerance_m: 10.0
+      # Tolerance of timestamp difference between initial_pose and sensor pointcloud. [sec]
+      initial_pose_timeout_sec: 1.0
 
-    # Number of threads used for parallel computing
-    num_threads: 4
+      # Tolerance of distance difference between two initial poses used for linear interpolation. [m]
+      initial_pose_distance_tolerance_m: 10.0
 
-    # The covariance of output pose
-    # Note that this covariance matrix is empirically derived
-    output_pose_covariance:
-      [
-        0.0225, 0.0,   0.0,   0.0,      0.0,      0.0,
-        0.0,   0.0225, 0.0,   0.0,      0.0,      0.0,
-        0.0,   0.0,   0.0225, 0.0,      0.0,      0.0,
-        0.0,   0.0,   0.0,   0.000625, 0.0,      0.0,
-        0.0,   0.0,   0.0,   0.0,      0.000625, 0.0,
-        0.0,   0.0,   0.0,   0.0,      0.0,      0.000625,
-      ]
+      # The execution time which means probably NDT cannot matches scans properly. [ms]
+      critical_upper_bound_exe_time_ms: 100.0
 
-    # 2D Real-time covariance estimation with multiple searches (output_pose_covariance is the minimum value)
-    use_covariance_estimation: false
 
-    # Offset arrangement in covariance estimation [m]
-    # initial_pose_offset_model_x & initial_pose_offset_model_y must have the same number of elements.
-    initial_pose_offset_model_x: [0.0, 0.0, 0.5, -0.5, 1.0, -1.0]
-    initial_pose_offset_model_y: [0.5, -0.5, 0.0, 0.0, 0.0, 0.0]
+    score_estimation:
+      # Converged param type
+      # 0=TRANSFORM_PROBABILITY, 1=NEAREST_VOXEL_TRANSFORMATION_LIKELIHOOD
+      converged_param_type: 1
 
-    # Regularization switch
-    regularization_enabled: false
+      # If converged_param_type is 0
+      # Threshold for deciding whether to trust the estimation result
+      converged_param_transform_probability: 3.0
 
-    # Regularization scale factor
-    regularization_scale_factor: 0.01
+      # If converged_param_type is 1
+      # Threshold for deciding whether to trust the estimation result
+      converged_param_nearest_voxel_transformation_likelihood: 2.3
 
-    # Dynamic map loading distance
-    dynamic_map_loading_update_distance: 20.0
+      # Scan matching score based on no ground LiDAR scan
+      no_ground_points:
+        enable: false
 
-    # Dynamic map loading loading radius
-    dynamic_map_loading_map_radius: 150.0
+        # If lidar_point.z - base_link.z <= this threshold , the point will be removed
+        z_margin_for_ground_removal: 0.8
 
-    # Radius of input LiDAR range (used for diagnostics of dynamic map loading)
-    lidar_radius: 100.0
 
-    # cspell: ignore degrounded
-    # A flag for using scan matching score based on de-grounded LiDAR scan
-    estimate_scores_for_degrounded_scan: false
+    covariance:
+      # The covariance of output pose
+      # Note that this covariance matrix is empirically derived
+      output_pose_covariance:
+        [
+          0.0225, 0.0,   0.0,   0.0,      0.0,      0.0,
+          0.0,   0.0225, 0.0,   0.0,      0.0,      0.0,
+          0.0,   0.0,   0.0225, 0.0,      0.0,      0.0,
+          0.0,   0.0,   0.0,   0.000625, 0.0,      0.0,
+          0.0,   0.0,   0.0,   0.0,      0.000625, 0.0,
+          0.0,   0.0,   0.0,   0.0,      0.0,      0.000625,
+        ]
 
-    # If lidar_point.z - base_link.z <= this threshold , the point will be removed
-    z_margin_for_ground_removal: 0.8
+      # 2D Real-time covariance estimation with multiple searches (output_pose_covariance is the minimum value)
+      covariance_estimation:
+        enable: false
 
-    # The execution time which means probably NDT cannot matches scans properly. [ms]
-    critical_upper_bound_exe_time_ms: 100
+        # Offset arrangement in covariance estimation [m]
+        # initial_pose_offset_model_x & initial_pose_offset_model_y must have the same number of elements.
+        initial_pose_offset_model_x: [0.0, 0.0, 0.5, -0.5, 1.0, -1.0]
+        initial_pose_offset_model_y: [0.5, -0.5, 0.0, 0.0, 0.0, 0.0]
+
+
+    dynamic_map_loading:
+      # Dynamic map loading distance
+      update_distance: 20.0
+
+      # Dynamic map loading loading radius
+      map_radius: 150.0
+
+      # Radius of input LiDAR range (used for diagnostics of dynamic map loading)
+      lidar_radius: 100.0

autoware_launch/config/localization/twist2accel.param.yaml

@@ -0,0 +1,4 @@
+/**:
+  ros__parameters:
+    use_odom: true
+    accel_lowpass_gain: 0.9

autoware_launch/config/map/map_projection_loader.param.yaml

@@ -0,0 +1,4 @@
+/**:
+  ros__parameters:
+    map_projector_info_path: $(var map_projector_info_path)
+    lanelet2_map_path: $(var lanelet2_map_path)

autoware_launch/config/map/pointcloud_map_loader.param.yaml

@@ -3,7 +3,6 @@
     enable_whole_load: true
     enable_downsampled_whole_load: false
     enable_partial_load: true
-    enable_differential_load: true
     enable_selected_load: false
 
     # only used when downsample_whole_load enabled

autoware_launch/config/perception/object_recognition/detection/detection_by_tracker/detection_by_tracker.param.yaml

@@ -1,11 +1,10 @@
 /**:
   ros__parameters:
-    tracker_ignore_label:
-      UNKNOWN : true
-      CAR : false
-      TRUCK : false
-      BUS : false
-      TRAILER : false
-      MOTORCYCLE : false
-      BICYCLE : false
-      PEDESTRIAN : false
+    tracker_ignore_label.UNKNOWN : true
+    tracker_ignore_label.CAR : false
+    tracker_ignore_label.TRUCK : false
+    tracker_ignore_label.BUS : false
+    tracker_ignore_label.TRAILER : false
+    tracker_ignore_label.MOTORCYCLE : false
+    tracker_ignore_label.BICYCLE : false
+    tracker_ignore_label.PEDESTRIAN : false

autoware_launch/config/perception/object_recognition/detection/image_projection_based_fusion/roi_cluster_fusion.param.yaml

@@ -0,0 +1,12 @@
+/**:
+  ros__parameters:
+    fusion_distance: 100.0
+    trust_object_distance: 100.0
+    trust_object_iou_mode: "iou"
+    non_trust_object_iou_mode: "iou_x"
+    use_cluster_semantic_type: false
+    only_allow_inside_cluster: true
+    roi_scale_factor: 1.1
+    iou_threshold: 0.65
+    unknown_iou_threshold: 0.1
+    remove_unknown: false

autoware_launch/config/perception/object_recognition/detection/image_projection_based_fusion/roi_detected_object_fusion.param.yaml

@@ -0,0 +1,19 @@
+/**:
+  ros__parameters:
+    # UNKNOWN, CAR, TRUCK, BUS, TRAILER, MOTORBIKE, BICYCLE, PEDESTRIAN
+    passthrough_lower_bound_probability_thresholds: [0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.50]
+    trust_distances: [50.0, 100.0, 100.0, 100.0, 100.0, 50.0, 50.0, 50.0]
+    min_iou_threshold: 0.5
+    use_roi_probability: false
+    roi_probability_threshold: 0.5
+
+    can_assign_matrix:
+      #UNKNOWN, CAR, TRUCK, BUS, TRAILER, MOTORBIKE, BICYCLE, PEDESTRIAN <-roi_msg
+      [1,       0,   0,     0,   0,       0,         0,       0,         # UNKNOWN <-detected_objects
+       0,       1,   1,     1,   1,       0,         0,       0,         # CAR
+       0,       1,   1,     1,   1,       0,         0,       0,         # TRUCK
+       0,       1,   1,     1,   1,       0,         0,       0,         # BUS
+       0,       1,   1,     1,   1,       0,         0,       0,         # TRAILER
+       0,       0,   0,     0,   0,       1,         1,       1,         # MOTORBIKE
+       0,       0,   0,     0,   0,       1,         1,       1,         # BICYCLE
+       0,       0,   0,     0,   0,       1,         1,       1]         # PEDESTRIAN

autoware_launch/config/perception/object_recognition/detection/image_projection_based_fusion/roi_pointcloud_fusion.param.yaml

@@ -0,0 +1,5 @@
+/**:
+  ros__parameters:
+    fuse_unknown_only: true
+    min_cluster_size: 2
+    cluster_2d_tolerance: 0.5

autoware_launch/config/perception/object_recognition/detection/image_projection_based_fusion/roi_sync.param.yaml

@@ -3,6 +3,8 @@
     input_offset_ms: [61.67, 111.67, 45.0, 28.33, 78.33, 95.0]
     timeout_ms: 70.0
     match_threshold_ms: 50.0
+    image_buffer_size: 15
+    debug_mode: false
     filter_scope_min_x: -100.0
     filter_scope_min_y: -100.0
     filter_scope_min_z: -100.0

autoware_launch/config/perception/object_recognition/detection/lidar_model/detection_class_remapper.param.yaml

@@ -29,9 +29,9 @@
     max_area_matrix:
       #UNKNOWN, CAR,   TRUCK,  BUS,    TRAILER,   MOTORBIKE, BICYCLE, PEDESTRIAN
       [ 0.000,  0.000,  0.000,  0.000,   0.000,   0.000,     0.000,   0.000,      #UNKNOWN
-        0.000,  0.000, 36.000,  0.000,     inf,   0.000,     0.000,   0.000,      #CAR
-        0.000,  0.000,  0.000,  0.000,     inf,   0.000,     0.000,   0.000,      #TRUCK
-        0.000,  0.000,  0.000,  0.000,     inf,   0.000,     0.000,   0.000,      #BUS
+        0.000,  0.000, 36.000,  0.000, 999.999,   0.000,     0.000,   0.000,      #CAR
+        0.000,  0.000,  0.000,  0.000, 999.999,   0.000,     0.000,   0.000,      #TRUCK
+        0.000,  0.000,  0.000,  0.000, 999.999,   0.000,     0.000,   0.000,      #BUS
         0.000,  0.000,  0.000,  0.000,   0.000,   0.000,     0.000,   0.000,      #TRAILER
         0.000,  0.000,  0.000,  0.000,   0.000,   0.000,     0.000,   0.000,      #MOTORBIKE
         0.000,  0.000,  0.000,  0.000,   0.000,   0.000,     0.000,   0.000,      #BICYCLE

autoware_launch/config/perception/object_recognition/detection/lidar_model/pointpainting.param.yaml

@@ -1,18 +1,33 @@
 /**:
   ros__parameters:
-    class_names: ["CAR", "TRUCK", "BUS", "BICYCLE", "PEDESTRIAN"]
-    paint_class_names: ["CAR", "BICYCLE", "PEDESTRIAN"]
-    point_feature_size: 7  # x, y, z, time-lag and car, pedestrian, bicycle
-    max_voxel_size: 40000
-    point_cloud_range: [-121.6, -76.8, -3.0, 121.6, 76.8, 5.0]
-    voxel_size: [0.32, 0.32, 8.0]
-    downsample_factor: 1
-    encoder_in_feature_size: 12
-    yaw_norm_thresholds: [0.3, 0.3, 0.3, 0.3, 0.0]
-    # post-process params
-    circle_nms_dist_threshold: 0.3
-    iou_nms_target_class_names: ["CAR"]
-    iou_nms_search_distance_2d: 10.0
-    iou_nms_threshold: 0.1
-    # omp params
-    omp_num_threads: 1
+    trt_precision: fp16
+    build_only: false
+    encoder_onnx_path: "$(var model_path)/pts_voxel_encoder_$(var model_name).onnx"
+    encoder_engine_path: "$(var model_path)/pts_voxel_encoder_$(var model_name).engine"
+    head_onnx_path: "$(var model_path)/pts_backbone_neck_head_$(var model_name).onnx"
+    head_engine_path: "$(var model_path)/pts_backbone_neck_head_$(var model_name).engine"
+
+    model_params:
+      class_names: ["CAR", "TRUCK", "BUS", "BICYCLE", "PEDESTRIAN"]
+      paint_class_names: ["CAR", "BICYCLE", "PEDESTRIAN"]
+      point_feature_size: 7 # x, y, z, time-lag and car, pedestrian, bicycle
+      max_voxel_size: 40000
+      point_cloud_range: [-121.6, -76.8, -3.0, 121.6, 76.8, 5.0]
+      voxel_size: [0.32, 0.32, 8.0]
+      downsample_factor: 1
+      encoder_in_feature_size: 12
+      yaw_norm_thresholds: [0.3, 0.3, 0.3, 0.3, 0.0]
+      has_twist: false
+    densification_params:
+      world_frame_id: "map"
+      num_past_frames: 0
+    post_process_params:
+      # post-process params
+      circle_nms_dist_threshold: 0.3
+      iou_nms_target_class_names: ["CAR"]
+      iou_nms_search_distance_2d: 10.0
+      iou_nms_threshold: 0.1
+      score_threshold: 0.4
+    omp_params:
+      # omp params
+      num_threads: 1

autoware_launch/config/perception/object_recognition/detection/object_range_splitter/object_range_splitter_radar.param.yaml

@@ -0,0 +1,3 @@
+/**:
+  ros__parameters:
+    split_range: 80.0

autoware_launch/config/perception/object_recognition/detection/object_range_splitter/object_range_splitter_radar_fusion.param.yaml

@@ -0,0 +1,3 @@
+/**:
+  ros__parameters:
+    split_range: 70.0

autoware_launch/config/perception/object_recognition/detection/object_velocity_splitter/object_velocity_splitter_radar.param.yaml

@@ -0,0 +1,3 @@
+/**:
+  ros__parameters:
+    velocity_threshold: 5.5

autoware_launch/config/perception/object_recognition/detection/object_velocity_splitter/object_velocity_splitter_radar_fusion.param.yaml

@@ -0,0 +1,3 @@
+/**:
+  ros__parameters:
+    velocity_threshold: 4.5

autoware_launch/config/perception/object_recognition/detection/radar_crossing_objects_noise_filter/radar_crossing_objects_noise_filter.param.yaml

@@ -0,0 +1,4 @@
+/**:
+  ros__parameters:
+    angle_threshold: 1.2210
+    velocity_threshold: 1.5

autoware_launch/config/perception/object_recognition/prediction/map_based_prediction.param.yaml

@@ -13,6 +13,13 @@
     sigma_yaw_angle_deg: 5.0 #[angle degree]
     object_buffer_time_length: 2.0 #[s]
     history_time_length: 1.0 #[s]
+    check_lateral_acceleration_constraints: false # whether to check if the predicted path complies with lateral acceleration constraints
+    max_lateral_accel: 2.0 # [m/ss] max acceptable lateral acceleration for predicted vehicle paths
+    min_acceleration_before_curve: -2.0 # [m/ss] min acceleration a vehicle might take to decelerate before a curve
+    use_vehicle_acceleration: false # whether to consider current vehicle acceleration when predicting paths or not
+    speed_limit_multiplier: 1.5 # When using vehicle acceleration. Set vehicle's maximum predicted speed as the legal speed limit in that lanelet times this value
+    acceleration_exponential_half_life: 2.5 # [s] When using vehicle acceleration. The decaying acceleration model considers that the current vehicle acceleration will be halved after this many seconds
+    use_crosswalk_signal: true
     # parameter for shoulder lane prediction
     prediction_time_horizon_rate_for_validate_shoulder_lane_length: 0.8