From ca00f41e52119adcdea7da1925fbe4b22842806e Mon Sep 17 00:00:00 2001 From: Niklas Rousset Date: Wed, 30 Oct 2024 10:27:04 +0100 Subject: [PATCH 01/57] Fixed typos in motion planning documentation --- doc/planning/motion_planning.md | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/doc/planning/motion_planning.md b/doc/planning/motion_planning.md index dd05ed42..4b7709db 100644 --- a/doc/planning/motion_planning.md +++ b/doc/planning/motion_planning.md @@ -1,15 +1,14 @@ # Motion Planning **Summary:** [motion_planning.py](.../code/planning/local_planner/src/motion_planning.py): -The motion planning is responsible for collecting all the speeds from the different components and choosing the optimal one to be fowarded into the acting. It also is capabale to change the trajectory for a overtaking maneuver. +The motion planning is responsible for collecting all the speeds from the different components and choosing the optimal one to be fowarded into the acting. It also is capable to change the trajectory for an overtaking maneuver. -- [Motion Planning](#motion-planning) - - [Overview](#overview) - - [Component](#component) - - [ROS Data Interface](#ros-data-interface) - - [Subscribed Topics](#subscribed-topics) - - [Published Topics](#published-topics) - - [Node Creation + Running Tests](#node-creation--running-tests) +- [Overview](#overview) +- [Component](#component) +- [ROS Data Interface](#ros-data-interface) + - [Subscribed Topics](#subscribed-topics) + - [Published Topics](#published-topics) +- [Node Creation + Running Tests](#node-creation--running-tests) ## Overview @@ -21,7 +20,7 @@ since we only need it for the unstuck routine. It also creates an overtake traje ## Component -The Motion Planning only consists of one node that contains all subscriper and publishers. It uses some utility functions from [utils.py](../../code/planning/src/local_planner/utils.py). +The Motion Planning only consists of one node that contains all subscribers and publishers. It uses some utility functions from [utils.py](../../code/planning/src/local_planner/utils.py). ## ROS Data Interface @@ -41,7 +40,7 @@ This node subscribes to the following topics: - `/paf/hero/waypoint_distance`: Subscribes to the Carla Waypoint to get the new road option. - `/paf/hero/lane_change_distance`: Subscribes to the Carla Waypoint to check if the next Waypoint is a lane change. - `/paf/hero/collision`: Subscribes to the collision published by the Collision Check. -- `/paf/hero//Center/traffic_light_y_distance`: Subscribes to the distance the traffic light has to the upper camera bound in pixels. +- `/paf/hero/Center/traffic_light_y_distance`: Subscribes to the distance the traffic light has to the upper camera bound in pixels. - `/paf/hero/unstuck_distance`: Subscribes to the distance travelled by the unstuck maneuver. ### Published Topics From 4a1d131009a26526a66edf757e6cc3ab34fbb1e2 Mon Sep 17 00:00:00 2001 From: RoyaLxPole <124280467+RoyaLxPole@users.noreply.github.com> 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1 file changed, 20 insertions(+) create mode 100644 doc/research/paf24/segmentation.md diff --git a/doc/research/paf24/segmentation.md b/doc/research/paf24/segmentation.md new file mode 100644 index 00000000..1d60f5b0 --- /dev/null +++ b/doc/research/paf24/segmentation.md @@ -0,0 +1,20 @@ +Already implemented solutions: + +https://github.com/una-auxme/paf/blob/c3011ee70039e199e106c54aa162a8f52be241a6/code/perception/launch/perception.launch?plain=1#L59-L61 + +Carla Sensor: +https://carla.readthedocs.io/en/0.8.4/cameras_and_sensors/ +``` +camera = carla.sensor.Camera('MyCamera', PostProcessing='SemanticSegmentation') +camera.set(FOV=90.0) +camera.set_image_size(800, 600) +camera.set_position(x=0.30, y=0, z=1.30) +camera.set_rotation(pitch=0, yaw=0, roll=0) + +carla_settings.add_sensor(camera) +``` + +#Questions: +Is the already implemented solution using the "Sementic Sensor"? +How to convert the Carla Sementic Sensor into our code? (https://github.com/una-auxme/paf/blob/main/code/agent/src/agent/agent.py) +How the yolo8x-seg and deeplabv3_resnet101 was trained? 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5 insertions(+), 1 deletion(-) diff --git a/doc/research/paf24/segmentation.md b/doc/research/paf24/segmentation.md index 1d60f5b0..787ecb2d 100644 --- a/doc/research/paf24/segmentation.md +++ b/doc/research/paf24/segmentation.md @@ -13,8 +13,12 @@ camera.set_rotation(pitch=0, yaw=0, roll=0) carla_settings.add_sensor(camera) ``` +![Alt text](https://github.com/una-auxme/paf/blob/368-visionnode-and-segmentation/doc/assets/perception/Carla_Segmentation_Sensor.png)) -#Questions: + +# Questions: Is the already implemented solution using the "Sementic Sensor"? + How to convert the Carla Sementic Sensor into our code? (https://github.com/una-auxme/paf/blob/main/code/agent/src/agent/agent.py) + How the yolo8x-seg and deeplabv3_resnet101 was trained? From 4ba470668866c4da8be0b81eb467ac23419f22a7 Mon Sep 17 00:00:00 2001 From: RoyaLxPole <124280467+RoyaLxPole@users.noreply.github.com> Date: Wed, 30 Oct 2024 15:25:11 +0100 Subject: [PATCH 06/57] Update segmentation.md --- doc/research/paf24/segmentation.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/doc/research/paf24/segmentation.md b/doc/research/paf24/segmentation.md index 787ecb2d..53c9ebf4 100644 --- a/doc/research/paf24/segmentation.md +++ b/doc/research/paf24/segmentation.md @@ -13,7 +13,7 @@ camera.set_rotation(pitch=0, yaw=0, roll=0) carla_settings.add_sensor(camera) ``` -![Alt text](https://github.com/una-auxme/paf/blob/368-visionnode-and-segmentation/doc/assets/perception/Carla_Segmentation_Sensor.png)) +![Alt text](https://github.com/una-auxme/paf/blob/368-visionnode-and-segmentation/doc/assets/perception/Carla_Segmentation_Sensor.png) # Questions: @@ -21,4 +21,6 @@ Is the already implemented solution using the "Sementic Sensor"? How to convert the Carla Sementic Sensor into our code? (https://github.com/una-auxme/paf/blob/main/code/agent/src/agent/agent.py) +How are crosswalks, signs and traffic lights classiefied in the output from Carla's semantic sensor?(Create output in the simulation with sementic sensor) + How the yolo8x-seg and deeplabv3_resnet101 was trained? From c33ddcb63e14196c5dcfcb29009849d8aa2e2c99 Mon Sep 17 00:00:00 2001 From: Veronika Neumann Date: Thu, 31 Oct 2024 12:17:31 +0100 Subject: [PATCH 07/57] Basics of communication between planning and acting explained --- doc/research/paf24/planning_acting_communication.svg | 4 ++++ doc/research/paf24/planning_acting_relation.md | 8 ++++++++ 2 files changed, 12 insertions(+) create mode 100644 doc/research/paf24/planning_acting_communication.svg create mode 100644 doc/research/paf24/planning_acting_relation.md diff --git a/doc/research/paf24/planning_acting_communication.svg b/doc/research/paf24/planning_acting_communication.svg new file mode 100644 index 00000000..3d46178e --- /dev/null +++ b/doc/research/paf24/planning_acting_communication.svg @@ -0,0 +1,4 @@ + + + +
MotionPlanning
MotionPlanning
CollisionCheck
CollisionCheck
behavior_agent
behavior_agent
pure_pursuit_controller
pure_pursuit_controller
stanley_controller
stanley_controller
vehicle_controller
vehicle_controller
MotionPlanning
MotionPlanning
/paf/hero/trajectory
/paf/hero/trajectory
/paf/hero/emergency
/paf/hero/emergency
/paf/hero/curr_behavior
/paf/hero/curr_behavior
Publisher
Publisher
Topic
Topic
Subscriber
Subscriber
\ No newline at end of file diff --git a/doc/research/paf24/planning_acting_relation.md b/doc/research/paf24/planning_acting_relation.md new file mode 100644 index 00000000..3adee936 --- /dev/null +++ b/doc/research/paf24/planning_acting_relation.md @@ -0,0 +1,8 @@ +# Relation between planning and acting + +This diagram shows the basics of the communication between the planning (green) and acting (blue) components. There are three main topics which represent the communication: +* trajectory +* emergency +* curr_behavior + +![](planning_acting_communication.svg) \ No newline at end of file From fc2b6c9d43a90f0f01d3114257724b89cc3bc757 Mon Sep 17 00:00:00 2001 From: Veronika Neumann Date: Thu, 31 Oct 2024 17:30:20 +0100 Subject: [PATCH 08/57] Messages in communication between planning and acting added --- doc/research/paf24/curr_behavior_example.png | Bin 0 -> 12937 bytes doc/research/paf24/nav_msgs_Path_type.png | Bin 0 -> 36153 bytes .../paf24/planning_acting_relation.md | 36 +++++++++++++++--- doc/research/paf24/trajectory_example.png | Bin 0 -> 32823 bytes 4 files changed, 31 insertions(+), 5 deletions(-) create mode 100644 doc/research/paf24/curr_behavior_example.png create mode 100644 doc/research/paf24/nav_msgs_Path_type.png create mode 100644 doc/research/paf24/trajectory_example.png diff --git a/doc/research/paf24/curr_behavior_example.png b/doc/research/paf24/curr_behavior_example.png new 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There are three main topics which represent the communication: -* trajectory -* emergency -* curr_behavior +This diagram shows the basics of the communication between the planning (green) and acting (blue) components. The information is gained using the rqt-graph. There are three main topics which represent the communication: +* `/paf/hero/trajectory` +* `/paf/hero/emergency` +* `/paf/hero/curr_behavior` + +![](planning_acting_communication.svg) + +## Useful commands +* The types of the messages published on the mentioned topics can be found out with the command `rostopic type `. +* To have a deeper look on the message types, you can use the command `rosmsg show `. +* `rostopic echo ` shows all the messages published on the specified topic. + + +## Messages +The topic `/paf/hero/trajectory` uses the message type `nav_msgs/Path`. This type looks as follows: + +![](nav_msgs_Path_type.png) + +Every message that is published consists of several parts of this structure. One concrete example of a part of such a message can be seen here: +![](trajectory_example.png) + + +The topic `/paf/hero/emergency` uses the message type `std_msgs/Bool`, so the emergency case can either be true or false. + + +The topic `/paf/hero/curr_behavior` uses the message type `std_msgs/String`. Examples of these messages can be seen here: +![](curr_behavior_example.png) + + + + -![](planning_acting_communication.svg) \ No newline at end of file diff --git a/doc/research/paf24/trajectory_example.png b/doc/research/paf24/trajectory_example.png new file mode 100644 index 0000000000000000000000000000000000000000..8e313a14499aad4a2cdab1697904dd367129922a GIT binary patch literal 32823 zcmbTeWmp_t7Oo8;L4pQ?I|=UYke~?~+%0Hu*G7T|cZVjyfs%jyAgSuA>Z-l>TFY|h z!21pR_1sD9qbf4&2Y_r63i~&qv!tf8ik+FWn~|d_oVl%?jVZH}v7@P}t&@eF^D#o3 z2y7?jr=7$dO^uwt*x6F3ez7ryQ?+oR;AE#zGV(v?u&USVd{Px9>M{jAjAH&tx z5cK_CW@fu~U;joLe(JT3$3!VYj|&%4Oin5;j$*4DA66@IX)WrzunQp~&)y2P*qWa2 zs1_DEKb-TQClh#((Ue^Yt*MIuZ*E3`;*?3;VAmw`#nM!-(8F9iMV+1fZG=|u@mlJu zdlct>3&<{i$j`DP5*@bKF?dS3`E&fpE?TwA~hgY0kVgy*&APyB9Ls z@x=GUEqMJOx+K-5(2O?{%D=l3q49W8rQ*b!;d6EOkjk_fv4P_3k3+k|y5x z+c%E@3OBvVC0a^}-JI`+r3H7X?liq*ofa}CR|QQUCqyMO_cq6OiN~sB)kXk9nJEYb z^@i*YpTW+bBh|jlTpGa*>ENBN<=>OYau~5w|MG(3Ve65vp{$(koPVvhQBMWVGH-=< 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a/doc/research/paf24/planning_acting_relation.md b/doc/research/paf24/planning_acting_relation.md index c7b4c20a..f4de99d2 100644 --- a/doc/research/paf24/planning_acting_relation.md +++ b/doc/research/paf24/planning_acting_relation.md @@ -19,6 +19,7 @@ The topic `/paf/hero/trajectory` uses the message type `nav_msgs/Path`. This typ ![](nav_msgs_Path_type.png) Every message that is published consists of several parts of this structure. One concrete example of a part of such a message can be seen here: + ![](trajectory_example.png) @@ -26,6 +27,7 @@ The topic `/paf/hero/emergency` uses the message type `std_msgs/Bool`, so the em The topic `/paf/hero/curr_behavior` uses the message type `std_msgs/String`. 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b/doc/research/paf24/old_research_overview.md @@ -0,0 +1,56 @@ +# Research Summary + +**Summary:** The research of the previous groups is condensed into this file to make it an entry point for this years project. + +- [**Research and Resources**](#research-and-resources) +- [**Acting and Control Modules**](#acting-and-control-modules) +- [**Planning and Trajectory Generation**](#planning-and-trajectory-generation) +- [**State Machine for Decision-Making**](#state-machine-for-decision-making) +- [**OpenDrive Integration and Navigation Data**](#opendrive-integration-and-navigation-data) + +## **[Research and Resources](./README.md)** + +- This section provides an extensive foundation for the autonomous vehicle project by consolidating previous research from **[PAF22](./paf22/)** and **[PAF23](./paf23/)**. +- **PAF22**: Established core methods for autonomous vehicle control and perception, including traffic light detection and emergency braking features. It also set up the base components of the CARLA simulator integration, essential sensor configurations, and data processing pipelines. +- **PAF23**: Enhanced lane-change algorithms and expanded intersection-handling strategies. This project introduced a more robust approach to decision-making at intersections, factoring in pedestrian presence, oncoming traffic, and improved signal detection. Additionally, **PAF23** refined vehicle +- behavior for more fluid lane-change maneuvers, optimizing control responses to avoid obstacles and maintain lane positioning during highway merging and overtaking scenarios. +- The resources also include CARLA-specific tools such as the CARLA Leaderboard and ROS Bridge integration, which link CARLA’s simulation environment to the Robot Operating System (ROS). Detailed references to CARLA’s sensor suite are provided, covering RGB cameras, LIDAR, radar, GNSS, and IMU +sensors essential for perception and control. + +## **[Acting and Control Modules](./paf22/acting/implementation_acting.md)** + +- The **acting module** focuses on the vehicle’s control actions, including throttle, steering, and braking. +- **Core Controllers**: Contains controllers like the **PID controller** for longitudinal (speed) control and **Pure Pursuit** and **Stanley controllers** for lateral (steering) control. These controllers work in unison to achieve precise vehicle handling, especially on turns and at varying speeds. +- **PAF22 Contributions**: Implemented the PID-based longitudinal controller and integrated the Pure Pursuit controller for basic trajectory following, setting a solid foundation for steering and throttle control. PAF22 also laid out the preliminary **emergency braking** logic, designed to override +other controls in hazardous situations. +- **PAF23 Contributions**: Introduced refinements in lateral control, including the adaptive Stanley controller, which adjusts steering sensitivity based on vehicle speed to maintain a smooth trajectory. **PAF23** also optimized the emergency braking logic to respond more quickly to obstacles, with +improvements in lane-changing safety. +- **Sensor Integration**: The acting module subscribes to **navigation and sensor data** topics to remain updated on vehicle position and velocity, integrating sensor feedback for real-time control adjustments. + +## **[Planning and Trajectory Generation](./paf22/planning/basics.md)** + +- The **planning module** is critical for determining safe, efficient routes by combining **global and local path planning** techniques. +- **Global Planning**: Uses the **CommonRoad route planner** from TUM, creating a high-level path based on predefined waypoints. **PAF22** initially set up this planner, while **PAF23** added finer adjustments for lane selection and obstacle navigation. +- **Local Planning**: Tailored for dynamic obstacles, this component focuses on immediate adjustments to the vehicle’s path, particularly useful in urban environments with unpredictable elements. +Local planning includes **trajectory tracking** using Pure Pursuit and Stanley controllers to maintain a steady path. +- **PAF23 Enhancements**: Improved **collision avoidance** algorithms and added real-time updates to the trajectory based on sensor data. The local planner now adapts quickly to lane-change requests or route deviations due to traffic, creating a seamless flow between global and local path planning. + +## **[State Machine for Decision-Making](./paf22/planning/state_machine_design.md)** + +- This modular state machine handles various driving behaviors, including **lane changes**, **intersections**, and **traffic light responses**. +- **Core State Machines**: The **driving state machine** manages normal vehicle navigation, controlling target speed and ensuring lane compliance. The **lane-change state machine** makes safe decisions based on lane availability and traffic, +while the **intersection state machine** manages vehicle approach, stop, and turn behaviors at intersections. + +- **PAF22 Contributions**: Developed the base decision-making states, enabling lane following, simple lane-change maneuvers, and basic intersection handling. +- **PAF23 Contributions**: Significantly enhanced the state machine by adding specialized states for complex maneuvers, like responding to oncoming traffic at intersections, merging onto highways, and making priority-based decisions at roundabouts. +The **intersection state machine** now incorporates detailed behaviors for handling left turns, straight passes, and right turns, considering pedestrian zones and cross-traffic. **PAF23** also introduced an adaptive lane-change state, +which calculates safety based on vehicle speed, distance to adjacent vehicles, and road type. + +## **[OpenDrive Integration and Navigation Data](./paf22/planning/OpenDrive.md)** + +- **OpenDrive** files provide a structured road network description, detailing lanes, road segments, intersections, and traffic signals. The navigation data is published in CARLA as ROS topics containing GPS/world coordinates and route instructions. +- **PAF22 Setup**: Established OpenDrive as the core format for map data, integrating it with the CARLA simulator. Initial work involved parsing road and lane data to create accurate trajectories. +- **PAF23 Enhancements**: Improved the parsing of OpenDrive files, focusing on high-level map data relevant to the vehicle's route, such as signal placements and lane restrictions. This project also optimized the navigation data integration with ROS, +ensuring the vehicle receives consistent updates on its location relative to the route, intersections, and nearby obstacles. +- **Navigation Data Structure**: The system uses navigation data points, including **GPS coordinates, world coordinates, and high-level route instructions** (e.g., turn left, change lanes) to guide the vehicle. Each point is matched with a **road option command**, +instructing the vehicle on how to proceed at specific waypoints. \ No newline at end of file From 4c1a36501225aa02081e827a27d28e6f2336a666 Mon Sep 17 00:00:00 2001 From: Sebastian Seitz Date: Thu, 31 Oct 2024 18:18:59 +0100 Subject: [PATCH 12/57] fixed typo causing linter to fail --- doc/research/paf24/old_research_overview.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/research/paf24/old_research_overview.md b/doc/research/paf24/old_research_overview.md index 6e388ee4..aaa04d80 100644 --- a/doc/research/paf24/old_research_overview.md +++ b/doc/research/paf24/old_research_overview.md @@ -53,4 +53,4 @@ which calculates safety based on vehicle speed, distance to adjacent vehicles, a - **PAF23 Enhancements**: Improved the parsing of OpenDrive files, focusing on high-level map data relevant to the vehicle's route, such as signal placements and lane restrictions. This project also optimized the navigation data integration with ROS, ensuring the vehicle receives consistent updates on its location relative to the route, intersections, and nearby obstacles. - **Navigation Data Structure**: The system uses navigation data points, including **GPS coordinates, world coordinates, and high-level route instructions** (e.g., turn left, change lanes) to guide the vehicle. Each point is matched with a **road option command**, -instructing the vehicle on how to proceed at specific waypoints. \ No newline at end of file +instructing the vehicle on how to proceed at specific waypoints. From 72bdd7046997cead3aaf3b10ff5c35a5b5caf145 Mon Sep 17 00:00:00 2001 From: Sebastian Seitz Date: Thu, 31 Oct 2024 18:25:43 +0100 Subject: [PATCH 13/57] move to the right folder --- .../paf24/general/old_research_overview.md | 56 +++++++++++++++++++ 1 file changed, 56 insertions(+) create mode 100644 doc/research/paf24/general/old_research_overview.md diff --git a/doc/research/paf24/general/old_research_overview.md b/doc/research/paf24/general/old_research_overview.md new file mode 100644 index 00000000..aaa04d80 --- /dev/null +++ b/doc/research/paf24/general/old_research_overview.md @@ -0,0 +1,56 @@ +# Research Summary + +**Summary:** The research of the previous groups is condensed into this file to make it an entry point for this years project. + +- [**Research and Resources**](#research-and-resources) +- [**Acting and Control Modules**](#acting-and-control-modules) +- [**Planning and Trajectory Generation**](#planning-and-trajectory-generation) +- [**State Machine for Decision-Making**](#state-machine-for-decision-making) +- [**OpenDrive Integration and Navigation Data**](#opendrive-integration-and-navigation-data) + +## **[Research and Resources](./README.md)** + +- This section provides an extensive foundation for the autonomous vehicle project by consolidating previous research from **[PAF22](./paf22/)** and **[PAF23](./paf23/)**. +- **PAF22**: Established core methods for autonomous vehicle control and perception, including traffic light detection and emergency braking features. It also set up the base components of the CARLA simulator integration, essential sensor configurations, and data processing pipelines. +- **PAF23**: Enhanced lane-change algorithms and expanded intersection-handling strategies. This project introduced a more robust approach to decision-making at intersections, factoring in pedestrian presence, oncoming traffic, and improved signal detection. Additionally, **PAF23** refined vehicle +- behavior for more fluid lane-change maneuvers, optimizing control responses to avoid obstacles and maintain lane positioning during highway merging and overtaking scenarios. +- The resources also include CARLA-specific tools such as the CARLA Leaderboard and ROS Bridge integration, which link CARLA’s simulation environment to the Robot Operating System (ROS). Detailed references to CARLA’s sensor suite are provided, covering RGB cameras, LIDAR, radar, GNSS, and IMU +sensors essential for perception and control. + +## **[Acting and Control Modules](./paf22/acting/implementation_acting.md)** + +- The **acting module** focuses on the vehicle’s control actions, including throttle, steering, and braking. +- **Core Controllers**: Contains controllers like the **PID controller** for longitudinal (speed) control and **Pure Pursuit** and **Stanley controllers** for lateral (steering) control. These controllers work in unison to achieve precise vehicle handling, especially on turns and at varying speeds. +- **PAF22 Contributions**: Implemented the PID-based longitudinal controller and integrated the Pure Pursuit controller for basic trajectory following, setting a solid foundation for steering and throttle control. PAF22 also laid out the preliminary **emergency braking** logic, designed to override +other controls in hazardous situations. +- **PAF23 Contributions**: Introduced refinements in lateral control, including the adaptive Stanley controller, which adjusts steering sensitivity based on vehicle speed to maintain a smooth trajectory. **PAF23** also optimized the emergency braking logic to respond more quickly to obstacles, with +improvements in lane-changing safety. +- **Sensor Integration**: The acting module subscribes to **navigation and sensor data** topics to remain updated on vehicle position and velocity, integrating sensor feedback for real-time control adjustments. + +## **[Planning and Trajectory Generation](./paf22/planning/basics.md)** + +- The **planning module** is critical for determining safe, efficient routes by combining **global and local path planning** techniques. +- **Global Planning**: Uses the **CommonRoad route planner** from TUM, creating a high-level path based on predefined waypoints. **PAF22** initially set up this planner, while **PAF23** added finer adjustments for lane selection and obstacle navigation. +- **Local Planning**: Tailored for dynamic obstacles, this component focuses on immediate adjustments to the vehicle’s path, particularly useful in urban environments with unpredictable elements. +Local planning includes **trajectory tracking** using Pure Pursuit and Stanley controllers to maintain a steady path. +- **PAF23 Enhancements**: Improved **collision avoidance** algorithms and added real-time updates to the trajectory based on sensor data. The local planner now adapts quickly to lane-change requests or route deviations due to traffic, creating a seamless flow between global and local path planning. + +## **[State Machine for Decision-Making](./paf22/planning/state_machine_design.md)** + +- This modular state machine handles various driving behaviors, including **lane changes**, **intersections**, and **traffic light responses**. +- **Core State Machines**: The **driving state machine** manages normal vehicle navigation, controlling target speed and ensuring lane compliance. The **lane-change state machine** makes safe decisions based on lane availability and traffic, +while the **intersection state machine** manages vehicle approach, stop, and turn behaviors at intersections. + +- **PAF22 Contributions**: Developed the base decision-making states, enabling lane following, simple lane-change maneuvers, and basic intersection handling. +- **PAF23 Contributions**: Significantly enhanced the state machine by adding specialized states for complex maneuvers, like responding to oncoming traffic at intersections, merging onto highways, and making priority-based decisions at roundabouts. +The **intersection state machine** now incorporates detailed behaviors for handling left turns, straight passes, and right turns, considering pedestrian zones and cross-traffic. **PAF23** also introduced an adaptive lane-change state, +which calculates safety based on vehicle speed, distance to adjacent vehicles, and road type. + +## **[OpenDrive Integration and Navigation Data](./paf22/planning/OpenDrive.md)** + +- **OpenDrive** files provide a structured road network description, detailing lanes, road segments, intersections, and traffic signals. The navigation data is published in CARLA as ROS topics containing GPS/world coordinates and route instructions. +- **PAF22 Setup**: Established OpenDrive as the core format for map data, integrating it with the CARLA simulator. Initial work involved parsing road and lane data to create accurate trajectories. +- **PAF23 Enhancements**: Improved the parsing of OpenDrive files, focusing on high-level map data relevant to the vehicle's route, such as signal placements and lane restrictions. This project also optimized the navigation data integration with ROS, +ensuring the vehicle receives consistent updates on its location relative to the route, intersections, and nearby obstacles. +- **Navigation Data Structure**: The system uses navigation data points, including **GPS coordinates, world coordinates, and high-level route instructions** (e.g., turn left, change lanes) to guide the vehicle. Each point is matched with a **road option command**, +instructing the vehicle on how to proceed at specific waypoints. From 3ad9f537a189ce2add5d5d05df24c6ab65ac5cc4 Mon Sep 17 00:00:00 2001 From: RoyaLxPole <124280467+RoyaLxPole@users.noreply.github.com> Date: Thu, 31 Oct 2024 18:41:55 +0100 Subject: [PATCH 14/57] Update segmentation.md --- doc/research/paf24/segmentation.md | 33 ++++++++++++++++++++++-------- 1 file changed, 24 insertions(+), 9 deletions(-) diff --git a/doc/research/paf24/segmentation.md b/doc/research/paf24/segmentation.md index 53c9ebf4..7f96346a 100644 --- a/doc/research/paf24/segmentation.md +++ b/doc/research/paf24/segmentation.md @@ -1,8 +1,24 @@ -Already implemented solutions: +# Segmentation + +**Summary:** This page contains the research into the segmentation component of Autoware. + +- [Segmentation](#Segmentation) + - [Already implemended solutions](#1.-Already-implemented-solutions) + - [Implemented but dropped](#2Implemented-but-dropped) + - [Carla Sensors](#3-Carla-Sensors) + - [Follow up Question](#4-Follow-up-Question) + +## Already implemented solutions https://github.com/una-auxme/paf/blob/c3011ee70039e199e106c54aa162a8f52be241a6/code/perception/launch/perception.launch?plain=1#L59-L61 -Carla Sensor: +probably trained with the generated dataset: +https://github.com/una-auxme/paf/blob/8e8f9a1a03ae09d5ac763c1a11b398fc1ce144b0/code/perception/src/dataset_generator.py#L109-L110 + +## Implemented but dropped: +https://github.com/una-auxme/paf/blob/8c968fb5c6c44c15b2733c5a181c496eb9b244be/doc/perception/efficientps.md#efficientps + +## Carla Sensors: https://carla.readthedocs.io/en/0.8.4/cameras_and_sensors/ ``` camera = carla.sensor.Camera('MyCamera', PostProcessing='SemanticSegmentation') @@ -15,12 +31,11 @@ carla_settings.add_sensor(camera) ``` ![Alt text](https://github.com/una-auxme/paf/blob/368-visionnode-and-segmentation/doc/assets/perception/Carla_Segmentation_Sensor.png) +there is another solution implemented by the carla simulator: +https://carla.readthedocs.io/en/0.9.14/ref_sensors/#semantic-segmentation-camera:~:text=the%20object%20it.-,Semantic%20segmentation%20camera,-Blueprint%3A%20sensor -# Questions: -Is the already implemented solution using the "Sementic Sensor"? - -How to convert the Carla Sementic Sensor into our code? (https://github.com/una-auxme/paf/blob/main/code/agent/src/agent/agent.py) - -How are crosswalks, signs and traffic lights classiefied in the output from Carla's semantic sensor?(Create output in the simulation with sementic sensor) +for more context: +the pedestrian walks will be labeld as roadlines -How the yolo8x-seg and deeplabv3_resnet101 was trained? +## Follow up Question: +Why the last group used bounding boxes and not the segmentation model is it to slow or maybe not reliable? From 3a5158cc2a8e58f81b9e1b6695a5e5d2d69c623f Mon Sep 17 00:00:00 2001 From: Sebastian Seitz Date: Thu, 31 Oct 2024 19:05:54 +0100 Subject: [PATCH 15/57] documented driving score metric --- doc/research/paf24/general/driving_score.md | 82 +++++++++++++++++++++ 1 file changed, 82 insertions(+) create mode 100644 doc/research/paf24/general/driving_score.md diff --git a/doc/research/paf24/general/driving_score.md b/doc/research/paf24/general/driving_score.md new file mode 100644 index 00000000..d65c72c1 --- /dev/null +++ b/doc/research/paf24/general/driving_score.md @@ -0,0 +1,82 @@ +# Driving Score Computation + +**Summary:** The Driving score is the main performance metric of the agent and therefore has to be examined. + +- [A Starting Point for Metrics and Measurements](#a-starting-point-for-metrics-and-measurements) +- [Driving score](#driving-score) +- [Route completion](#route-completion) +- [Infraction penalty](#infraction-penalty) +- [Infractions](#infractions) +- [Off-road driving](#off-road-driving) +- [Additional Events](#additional-events) +- [Sources:](#sources) + +## A Starting Point for Metrics and Measurements + +The CARLA Leaderboard sets a public example for evaluation and comparison. +The driving proficiency of an agent can be characterized by multiple metrics. For this leaderboard, the CARLA team selected a set of metrics that help understand different aspects of driving. + +## Driving score + +\[\frac{1}{N}\sum^i_N R_i P_i\] + +- The main metric of the leaderboard, serving as an aggregate of the average route completion and the number of traffic infractions. Here $N$ stands for the number of routes, $R_i$ is the percentage of completion of the $i$-th route, and $P_i$ is the infraction penalty of the $i$-th route. + +## Route completion + +\[\frac{1}{N}\sum^i_N R_i\] + +- Percentage of route distance completed by an agent, averaged across $N$ routes. + +## Infraction penalty + +\[\prod_j^{ped, veh, ... stop} (p_j^i)^{n_{infractions}}\] + +- Aggregates the number of infractions triggered by an agent as a geometric series. Agents start with an ideal 1.0 base score, which is reduced by a penalty coefficient for every instance of these. + +## Infractions + +The CARLA leaderboard offers individual metrics for a series of infractions. Each of these has a penalty coefficient that will be applied every time it happens. Ordered by severity, the infractions are the following. + +- Collisions with pedestrians $0.50$ +- Collisions with other vehicles $0.60$ +- Collisions with static elements $0.65$ +- Running a red light $0.70$ +- Running a stop sign $0.80$ + +Some scenarios feature behaviors that can block the ego-vehicle indefinitely. These scenarios will have a timeout of 4 minutes after which the ego-vehicle will be released to continue the route. However, a penalty is applied when the time limit is breached + +- Scenario Timeout $0.70$ + +The agent is expected to maintain a minimum speed in keeping with nearby traffic. The agent’s speed will be compared with the speed of nearby vehicles. Failure to maintain a suitable speed will result in a penalty. +The penalty applied is dependent on the magnitude of the speed difference, up to the following value: + +- Failure to maintain speed $0.70$ + +The agent should yield to emergency vehicles coming from behind. Failure to allow the emergency vehicle to pass will incur a penalty: + +- Failure to yield to emergency vehicle $0.70$ + +Besides these, there is one additional infraction which has no coefficient, and instead affects the computation of route completion $(R_i)$. + +## Off-road driving + +If an agent drives off-road, that percentage of the route will not be considered towards the computation of the route completion score. + +## Additional Events + +Some events will interrupt the simulation, preventing the agent to continue. + +- Route deviation +If an agent deviates more than $30$ meters from the assigned route. +- Agent blocked +If an agent is blocked in traffic without taking any actions for $180$ simulation seconds. +- Simulation timeout +If no client-server communication can be established in $60$ seconds. +- Route timeout +This timeout is triggered if the simulation of a route takes more than the allowed time. This allowed time is computed by multiplying the route distance in meters by a factor of $0.8$. + +## Sources: + +- [Alpha Drive Carla Leaderboard Case Study](https://alphadrive.ai/industries/automotive/carla-leaderboard-case-study/) +- [Carla Leaderboard](https://leaderboard.carla.org/) From fcb6783bd503c9b1c2645d0ec3b3b2e57d60027e Mon Sep 17 00:00:00 2001 From: Sebastian Seitz Date: Thu, 31 Oct 2024 19:09:03 +0100 Subject: [PATCH 16/57] make sure linting passes --- doc/research/paf24/general/driving_score.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/research/paf24/general/driving_score.md b/doc/research/paf24/general/driving_score.md index d65c72c1..a9b68181 100644 --- a/doc/research/paf24/general/driving_score.md +++ b/doc/research/paf24/general/driving_score.md @@ -9,7 +9,7 @@ - [Infractions](#infractions) - [Off-road driving](#off-road-driving) - [Additional Events](#additional-events) -- [Sources:](#sources) +- [Sources](#sources) ## A Starting Point for Metrics and Measurements @@ -76,7 +76,7 @@ If no client-server communication can be established in $60$ seconds. - Route timeout This timeout is triggered if the simulation of a route takes more than the allowed time. This allowed time is computed by multiplying the route distance in meters by a factor of $0.8$. -## Sources: +## Sources - [Alpha Drive Carla Leaderboard Case Study](https://alphadrive.ai/industries/automotive/carla-leaderboard-case-study/) - [Carla Leaderboard](https://leaderboard.carla.org/) From 5ccfbedfda40b2f7b4e3875de98a122be6013428 Mon Sep 17 00:00:00 2001 From: Ludwig Holl Date: Thu, 31 Oct 2024 19:30:56 +0100 Subject: [PATCH 17/57] Added architecture documentation and current version of the ros nodes / topics --- doc/assets/research_assets/node_path_ros.png | Bin 0 -> 346357 bytes doc/assets/research_assets/rosgraph.svg | 10221 ++++++++++++++++ .../research_assets/rosgraph_leaf_topics.svg | 4669 +++++++ .../system/architecture_documentation.md | 23 + 4 files changed, 14913 insertions(+) create mode 100644 doc/assets/research_assets/node_path_ros.png create mode 100644 doc/assets/research_assets/rosgraph.svg create mode 100644 doc/assets/research_assets/rosgraph_leaf_topics.svg 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As it is a crucial part of the project to understand the component interactions, especially the up-to-date version of it, in this document I will give a brief overview of the existing documentation and some links to the up-to-date versions. ## Existing architecture documentation from the previous semester +The main architecture documentation can be found [here](/ddoc/general/architecture.md). +It contains information to most nodes and what they subscribe / publish. + A miro board with the existing architecture (Perception, planning, acting) is existing. ![Architecture overview](/doc/assets/overview.jpg) The miro-board can be found [here](https://miro.com/welcomeonboard/a1F0d1dya2FneWNtbVk4cTBDU1NiN3RiZUIxdGhHNzJBdk5aS3N4VmdBM0R5c2Z1VXZIUUN4SkkwNHpuWlk2ZXwzNDU4NzY0NTMwNjYwNzAyODIzfDI=?share_link_id=785020837509). -This miro board does contain the main architecture details and flows of information but when comparing it to the rosgraph of the nodes and topics it seems like the diagram is not complete. +This miro board does contain the main architecture details and flows of information but when comparing it to the +rosgraph of the nodes and topics it seems like the diagram is not complete. -### Rosgraph of the nodes and topics of the project: +### Current Rosgraph of the nodes and topics of the project: [//]: # "![Up to date ros graph](/doc/assets/research_assets/rosgraph.svg)" ![Up to date ros graph](/doc/assets/research_assets/rosgraph_leaf_topics.svg) @@ -21,7 +25,8 @@ There you can see the nodes and the in- and outputs. In represents the subscribe This should now be extended by what logic is happening in which node and how the nodes are connected. ## Perception architecture - - TODO + - Extended information of how the perception works can be found [here](/doc/perception/README.md) ## Planning architecture - + - Extended information of how the planning works can be found [here](/doc/planning/README.md) ## Acting architecture + - Extended information of how the acting works can be found [here](/doc/acting/architecture_documentation.md) From 86ed795cc5003e30636d84a6a7edd4ace4f9042a Mon Sep 17 00:00:00 2001 From: Ludwig Holl Date: Fri, 1 Nov 2024 09:25:05 +0100 Subject: [PATCH 26/57] reformated markdown --- .../system/architecture_documentation.md | 26 ++++++++++++++----- 1 file changed, 19 insertions(+), 7 deletions(-) diff --git a/doc/research/paf24/system/architecture_documentation.md b/doc/research/paf24/system/architecture_documentation.md index 3164cf77..5cc1859c 100644 --- a/doc/research/paf24/system/architecture_documentation.md +++ b/doc/research/paf24/system/architecture_documentation.md @@ -1,32 +1,44 @@ # Research about the existing architecture documentation -The repository already holds various documents about how the architecture was planned and how the architecture should look or is + +The repository already holds various documents about how the architecture was planned and how the architecture should +look or is looking. As it is a crucial part of the project to understand the component interactions, especially the up-to-date -version of it, in this document I will give a brief overview of the existing documentation and some links to the up-to-date versions. +version of it, in this document I will give a brief overview of the existing documentation and some links to the +up-to-date versions. ## Existing architecture documentation from the previous semester + The main architecture documentation can be found [here](/ddoc/general/architecture.md). It contains information to most nodes and what they subscribe / publish. A miro board with the existing architecture (Perception, planning, acting) is existing. ![Architecture overview](/doc/assets/overview.jpg) -The miro-board can be found [here](https://miro.com/welcomeonboard/a1F0d1dya2FneWNtbVk4cTBDU1NiN3RiZUIxdGhHNzJBdk5aS3N4VmdBM0R5c2Z1VXZIUUN4SkkwNHpuWlk2ZXwzNDU4NzY0NTMwNjYwNzAyODIzfDI=?share_link_id=785020837509). +The miro-board can be +found [here](https://miro.com/welcomeonboard/a1F0d1dya2FneWNtbVk4cTBDU1NiN3RiZUIxdGhHNzJBdk5aS3N4VmdBM0R5c2Z1VXZIUUN4SkkwNHpuWlk2ZXwzNDU4NzY0NTMwNjYwNzAyODIzfDI=?share_link_id=785020837509). -This miro board does contain the main architecture details and flows of information but when comparing it to the +This miro board does contain the main architecture details and flows of information but when comparing it to the rosgraph of the nodes and topics it seems like the diagram is not complete. ### Current Rosgraph of the nodes and topics of the project: + [//]: # "![Up to date ros graph](/doc/assets/research_assets/rosgraph.svg)" ![Up to date ros graph](/doc/assets/research_assets/rosgraph_leaf_topics.svg) ### Rewritten rosgraph divided into perception, planning and acting: + ![RosGraphDrawIO](/doc/assets/research_assets/node_path_ros.png) There you can see the nodes and the in- and outputs. In represents the subscribed topics and out the published topics. This should now be extended by what logic is happening in which node and how the nodes are connected. ## Perception architecture - - Extended information of how the perception works can be found [here](/doc/perception/README.md) + +- Extended information of how the perception works can be found [here](/doc/perception/README.md) + ## Planning architecture - - Extended information of how the planning works can be found [here](/doc/planning/README.md) + +- Extended information of how the planning works can be found [here](/doc/planning/README.md) + ## Acting architecture - - Extended information of how the acting works can be found [here](/doc/acting/architecture_documentation.md) + +- Extended information of how the acting works can be found [here](/doc/acting/architecture_documentation.md) From d30e990e034b79fe42c1a40275bdea14940cbddc Mon Sep 17 00:00:00 2001 From: Ludwig Holl Date: Fri, 1 Nov 2024 09:27:29 +0100 Subject: [PATCH 27/57] removed trailing punctuation in heading --- doc/research/paf24/system/architecture_documentation.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/research/paf24/system/architecture_documentation.md b/doc/research/paf24/system/architecture_documentation.md index 5cc1859c..6d6fe371 100644 --- a/doc/research/paf24/system/architecture_documentation.md +++ b/doc/research/paf24/system/architecture_documentation.md @@ -19,12 +19,12 @@ found [here](https://miro.com/welcomeonboard/a1F0d1dya2FneWNtbVk4cTBDU1NiN3RiZUI This miro board does contain the main architecture details and flows of information but when comparing it to the rosgraph of the nodes and topics it seems like the diagram is not complete. -### Current Rosgraph of the nodes and topics of the project: +### Current Rosgraph of the nodes and topics of the project [//]: # "![Up to date ros graph](/doc/assets/research_assets/rosgraph.svg)" ![Up to date ros graph](/doc/assets/research_assets/rosgraph_leaf_topics.svg) -### Rewritten rosgraph divided into perception, planning and acting: +### Rewritten rosgraph divided into perception, planning and acting ![RosGraphDrawIO](/doc/assets/research_assets/node_path_ros.png) From 5c8a602baf43e5e481bdf32ce3e32b711817aeb5 Mon Sep 17 00:00:00 2001 From: Ludwig Holl Date: Fri, 1 Nov 2024 09:30:40 +0100 Subject: [PATCH 28/57] fixed link --- doc/research/paf24/system/architecture_documentation.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/research/paf24/system/architecture_documentation.md b/doc/research/paf24/system/architecture_documentation.md index 6d6fe371..57b65e7d 100644 --- a/doc/research/paf24/system/architecture_documentation.md +++ b/doc/research/paf24/system/architecture_documentation.md @@ -8,7 +8,7 @@ up-to-date versions. ## Existing architecture documentation from the previous semester -The main architecture documentation can be found [here](/ddoc/general/architecture.md). +The main architecture documentation can be found [here](/doc/general/architecture.md). It contains information to most nodes and what they subscribe / publish. A miro board with the existing architecture (Perception, planning, acting) is existing. 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zbSXJ`-w&awAzwPB;r5FAHQ?v-#|hOb+-|s0=&A7-G;4IWMz>EIUz18_VPfFN=m9`3!yu6nd?Tz9ox~b zbSbnQ*$27vXtN|CPcRM3SDKoW(02=%Vb!`D6RA1z86=5)>w*w_8--3 zOFno3?t>|bEcMTvB#?|QQ;El8eSj@#92e+pbE(IO`&iyVt!yoHXWb9kv((X7_nbM* zCMO1a#=^-!P1%zK&4`EFy{p&=h1xe+jYc+{YutuSaIp`(Vc0Mx1NFjW2+AGAueV?e$V5DX54{r;Ip;uSV}vo z)dNKUm8I}-!A;0tMbI4!u`H&O=REJuDWW@q1)VUZ+hceWT;l#L&ZuYnAs67C7S z=Q)%SH@@ByHSEK7qbK8QpbM?hLm@}#{|ZY$U=smJxbw@>?L9|CneqkM+S0&C(IeqT z37Q#`6ppLp64%cz`TxWF#TzRebQ37;(ApD@zmlrc8hh0AuiCy&RYZKXU{YCX9$Vsr zR zm=Q5S%A=j|Uc5ixNf8BBeC4bWzPYEe70_UQCyy|jfKT;XV-=?tDnO$9A17Ax;? zwh`mNU;cFq^n*pLSpJaMBo$5x8_xihlq&Z{V22yLs9cL6h7`8CE%SQsO=Pc=vWvgC z$vL@7Y_B!4fwp}q998E(*7Po335*4^Mv<3_n-hP);a+RM94O5y{o|1bvN@!ML{Z1G;meMHUE*`uNr5 zdA*m#k4|=51+0LAnm~Kl>calcpdqH6QRlpgZcDK+;p!HV`2MiU0X3r^&EMm3m5CUG z#l&1Ef%jNs$s5yl%?Og-=#k~cB~fVZ&ciuU9sP%8RM@(oYC~%zvQnMskG1`a^0dnT zo5>|{QHn?XCIaSW?Qucu+!Tkv@6MZir@H*&k7>&J5{|ZRM%kmm0_(`BlXb*j7(VKA ztF>pt(f6SrV=Ct#Q`E?jst16z%r@wF7@*S;X!X|!6?qQ)ce2l0*XyhCSebFvVa&jb zpPjsT$FatBck!xpozc5;PnKD5dDqcP&BFtoQO18&mb8oE+u>wfjJyCCPwwL-cpo|~ YCh9$ISQ<2_jtBm!-O##@R5TC%Kl{tKC;$Ke From 38bc7eb332f3f2ee99fbe1c8662e7d995dd4c3ed Mon Sep 17 00:00:00 2001 From: Anastasiia Bilinska Date: Fri, 1 Nov 2024 15:15:35 +0100 Subject: [PATCH 30/57] docu --- .../paf24/planning/Unstuck_Overtake Behavior | 90 +++++++++++++++++++ 1 file changed, 90 insertions(+) create mode 100644 doc/research/paf24/planning/Unstuck_Overtake Behavior diff --git a/doc/research/paf24/planning/Unstuck_Overtake Behavior b/doc/research/paf24/planning/Unstuck_Overtake Behavior new file mode 100644 index 00000000..8a8e5d70 --- /dev/null +++ b/doc/research/paf24/planning/Unstuck_Overtake Behavior @@ -0,0 +1,90 @@ +This dicument analyze current unstuck/overtake behavior and reveal several critical issues with the current implementation + +1. Unstuck Behavior: + The "unstuck" behavior is designed to address situations where the vehicle becomes stuck, perhaps due to obstacles or other vehicles blocking its path. + 1.1 Key components: + self.unstuck_distance: Tracks the distance to the object causing the blockage, helping the algorithm to determine if an alternative route (overtaking) might be required. + self.unstuck_overtake_flag: Prevents repeated or "spam" overtakes by ensuring that the unstuck overtake only happens once within a specified distance (UNSTUCK_OVERTAKE_FLAG_CLEAR_DISTANCE). + self.init_overtake_pos: Saves the position where the unstuck behavior is initiated to prevent excessive overtaking within a short range. + Overtaking Logic: The method overtake_fallback is called within __get_speed_unstuck, where a new trajectory is created by offsetting the path to one side, allowing the vehicle to bypass the obstacle and get "unstuck." + + + 1.2 How Unstuck is Triggered: + The method __get_speed_unstuck checks the current behavior (e.g., us_unstuck), adjusting speed accordingly and invoking overtake_fallback if an obstacle is detected and the vehicle is in an "unstuck" situation. + UNSTUCK_OVERTAKE_FLAG_CLEAR_DISTANCE ensures that once an unstuck maneuver is initiated, it won't be repeated until a certain distance is cleared + +2. Overtake Behavior: + The overtake behavior allows the vehicle to safely overtake a slower-moving or stationary vehicle ahead by modifying its trajectory. + 2.1 Key components: + self.__overtake_status: Manages the status of overtaking, where 1 indicates an active overtake, while -1 signals that overtaking isn't required. + change_trajectory: Initiates the overtake by calling overtake_fallback when it detects a collision point or slow-moving vehicle within a certain range. This method also publishes the updated __overtake_status to signal other systems of the change. + overtake_fallback: Calculates a new trajectory path that offsets the vehicle by a certain distance (normal_x_offset) to safely pass the obstacle. The adjusted path uses the Rotation library to align the offset with the vehicle’s heading, creating a smooth path around the obstacle. + + 2.3 How Overtake is Triggered: + The method __set_curr_behavior monitors the vehicle’s behavior, triggering an overtake when ot_enter_init behavior is detected and if an obstacle or collision point is near. It then calls change_trajectory to modify the route. + Speed adjustments specific to overtaking are handled in __get_speed_overtake, where the vehicle might slow down (e.g., ot_enter_slow) or proceed at normal speed after overtaking (e.g., ot_leave). + +3. Relevant rostopics + overtake_success + unstuck_distance + ... + #TODO:need to continue + + + +4. Methods overview + 4.1 How the function def __set_curr_behavior(self, data: String) works: + + If beggins overtake manuever: + if no obsticle to overtake: + unsuccessfull or canceled overtake + publish abandonment of the overtake attempt. + else: + change trajectory + + Infinity as "No Collision Detected": Setting self.__collision_point to infinity (np.inf) would mean "no collision detected." So, checking if np.isinf(self.__collision_point) effectively asks, "Is there no obstacle or point that necessitates an overtake?" (??? not sure abou it) + + Questions: + 1. who tells the function the behaviour status? + 2. If the function has already become an overtake status, why should we check if there is actually an object to overtake? + + 4.2 How the function change_trajectory(self, distynce_obj) works: + update trajectory for overtaking and convert it to a new Path message. Args:distance_obj (float): distance to overtake object + Publish overate success status + + Questions: + 1. What does the success status for overtake mean? It seems like it oublishs than an overtake takes place but does not notify that the overtake manoeuvre was successful. + + 4.3 How the function def overtake_fallback(self, distance, pose_list, unstuck=False) works: + This method constructs a temporary path around an obstacle based on the current location, intended distance to overtake, and a choice between two different lateral offsets depending on whether the vehicle is in an “unstuck” situation. + + Pick Path Points Around the Obstacle: It selects a section of the path around the vehicle’s current position and the obstacle it needs to overtake. If the vehicle is stuck, it picks a larger section to give it more room to maneuver. + Shift Path to the Side: It moves this section of the path slightly to the side (left or right, depending on the vehicle's heading) to avoid the obstacle. If the vehicle is in a “stuck” situation, it shifts it a bit more to give it extra clearance. + Create the New Path: It converts the shifted points into a path format that the vehicle’s navigation system can understand and follow. + Combine with Original Path: it merges this temporary bypass with the original path, so the vehicle can return to its route after it passes the obstacle. + +5. Overtake Behavior Issues + 5.1 Aggressive Lane Changes: The vehicle exhibits aggressive lane changes, leading to emergency stops to avoid oncoming traffic. This behavior suggests that the decision-making logic for overtaking does not adequately assess the surrounding traffic conditions before executing maneuvers. + + 5.2 Inadequate Collision Detection: The vehicle fails to avoid obstacles, such as open car doors or stationary vehicles, indicating that the collision detection mechanisms are either insufficient or not effectively integrated into the overtaking logic. + + 5.3 Improper Trajectory Planning: The trajectory generated for overtaking often leads to incorrect paths, such as attempting to overtake trees or parked cars without considering oncoming traffic. A better filtering and validation of potential overtaking targets is needed. + +6. Unstuck Behavior Issues + 6.1 The vehicle gets stuck multiple times and exhibits erratic behavior when trying to get unstuck, + 6.2 The vehicle's aggressive lane-holding behavior after being unstuck + 6.3 After collisions, the vehicle often fails to resume movement, indicating a lack of recovery logic post-collision + + +7. Potential improvements + + 7.1 'def change trajektory' : consider implementing a more sophisticated trajectory planning algorithm that takes into account dynamic obstacles and traffic conditions rather than relying on a fallback method. Verify that the area into which the vehicle is moving is clear of obstacles and oncoming traffic. + 7.2 'def overtake_fallback' : instead of fixed offsets for unstuck and normal situations, consider dynamically calculating offsets based on current speed, vehicle dimensions, and surrounding traffic conditions. + 7.3 '__get_speed_unstuck' : includ checks for nearby vehicles and their speeds. Make UNSTUCK_OVERTAKE_FLAG_CLEAR_DISTANCE dynamic. + 7.4 '__check_emergency' : Now it only considers whether the vehicle is in a parking behavior state. Expand this method to evaluate various emergency conditions (e.g., obstacles detected by sensors) and initiate appropriate responses (e.g., stopping or rerouting). + 7.5 'get_speed_by_behavior' : consider feedback from sensors regarding current traffic conditions. + 7.6 '__calc_corner_points' : thinking about somathing smarter rather than a simple angle thresholds. + + + + From 3183e4e497469f3e6c07a25a65f42634f84112e3 Mon Sep 17 00:00:00 2001 From: Niklas Rousset Date: Fri, 1 Nov 2024 17:10:15 +0100 Subject: [PATCH 31/57] Removes outdated topic from subscription list --- doc/planning/motion_planning.md | 127 ++++++++++++++++---------------- 1 file changed, 63 insertions(+), 64 deletions(-) diff --git a/doc/planning/motion_planning.md b/doc/planning/motion_planning.md index 4b7709db..031e73fc 100644 --- a/doc/planning/motion_planning.md +++ b/doc/planning/motion_planning.md @@ -1,64 +1,63 @@ -# Motion Planning - -**Summary:** [motion_planning.py](.../code/planning/local_planner/src/motion_planning.py): -The motion planning is responsible for collecting all the speeds from the different components and choosing the optimal one to be fowarded into the acting. It also is capable to change the trajectory for an overtaking maneuver. - -- [Overview](#overview) -- [Component](#component) -- [ROS Data Interface](#ros-data-interface) - - [Subscribed Topics](#subscribed-topics) - - [Published Topics](#published-topics) -- [Node Creation + Running Tests](#node-creation--running-tests) - -## Overview - -This module is responsible for adjusting the current speed and the current trajectory according to the traffic situation. It subscribes to topics that provide information about the current speed of the vehicle, the current heading and many more to navigate safely in the simulation. -It publishes topics that provide information about the target speed, trajectoy changes, current waypoint and if an overtake was successful. - -This file is also responsible for providing a ```target_speed of -3``` for acting, whenever we need to use the Unstuck Behavior. -3 is the only case we can drive backwards right now, -since we only need it for the unstuck routine. It also creates an overtake trajectory, whenever the unstuck behavior calls for it. - -## Component - -The Motion Planning only consists of one node that contains all subscribers and publishers. It uses some utility functions from [utils.py](../../code/planning/src/local_planner/utils.py). - -## ROS Data Interface - -### Subscribed Topics - -This node subscribes to the following topics: - -- `/paf/hero/Spawn_car`: Can spawn a car on the first straight in the dev environment, if this message is manually published. -- `/paf/hero/speed_limit`: Subscribes to the speed Limit. -- `/carla/hero/Speed`: Subscribes to the current speed. -- `/paf/hero/current_heading`: Subscribes to the filtered heading of the ego vehicle. -- `/paf/hero/trajectory_global`: Subscribes to the global trajectory, which is calculated at the start of the simulation. -- `/paf/hero/current_pos`: Subscribes to the filtered position of our car. -- `/paf/hero/curr_behavior`: Subscribes to Current Behavior pubished by the Decision Making. -- `/paf/hero/unchecked_emergency`: Subscribes to check if the emergency brake is not triggered. -- `/paf/hero/acc_velocity`: Subscribes to the speed published by the acc. -- `/paf/hero/waypoint_distance`: Subscribes to the Carla Waypoint to get the new road option. -- `/paf/hero/lane_change_distance`: Subscribes to the Carla Waypoint to check if the next Waypoint is a lane change. -- `/paf/hero/collision`: Subscribes to the collision published by the Collision Check. -- `/paf/hero/Center/traffic_light_y_distance`: Subscribes to the distance the traffic light has to the upper camera bound in pixels. -- `/paf/hero/unstuck_distance`: Subscribes to the distance travelled by the unstuck maneuver. - -### Published Topics - -This node publishes the following topics: - -- `/paf/hero/trajectory`: Publishes the new adjusted trajectory. -- `/paf/hero/target_velocity`: Publishes the new calcualted Speed. -- `/paf/hero/current_wp`: Publishes according to our position the index of the current point on the trajectory. -- `/paf/hero/overtake_success`: Publishes if an overtake was successful. - -## Node Creation + Running Tests - -To run this node insert the following statement in the [planning.launch](../../code/planning/launch/planning.launch) file: - -```xml - - - - -``` +# Motion Planning + +**Summary:** [motion_planning.py](.../code/planning/local_planner/src/motion_planning.py): +The motion planning is responsible for collecting all the speeds from the different components and choosing the optimal one to be fowarded into the acting. It also is capable to change the trajectory for an overtaking maneuver. + +- [Overview](#overview) +- [Component](#component) +- [ROS Data Interface](#ros-data-interface) + - [Subscribed Topics](#subscribed-topics) + - [Published Topics](#published-topics) +- [Node Creation + Running Tests](#node-creation--running-tests) + +## Overview + +This module is responsible for adjusting the current speed and the current trajectory according to the traffic situation. It subscribes to topics that provide information about the current speed of the vehicle, the current heading and many more to navigate safely in the simulation. +It publishes topics that provide information about the target speed, trajectoy changes, current waypoint and if an overtake was successful. + +This file is also responsible for providing a ```target_speed of -3``` for acting, whenever we need to use the Unstuck Behavior. -3 is the only case we can drive backwards right now, +since we only need it for the unstuck routine. It also creates an overtake trajectory, whenever the unstuck behavior calls for it. + +## Component + +The Motion Planning only consists of one node that contains all subscribers and publishers. It uses some utility functions from [utils.py](../../code/planning/src/local_planner/utils.py). + +## ROS Data Interface + +### Subscribed Topics + +This node subscribes to the following topics: + +- `/paf/hero/Spawn_car`: Can spawn a car on the first straight in the dev environment, if this message is manually published. +- `/paf/hero/speed_limit`: Subscribes to the speed Limit. +- `/carla/hero/Speed`: Subscribes to the current speed. +- `/paf/hero/current_heading`: Subscribes to the filtered heading of the ego vehicle. +- `/paf/hero/trajectory_global`: Subscribes to the global trajectory, which is calculated at the start of the simulation. +- `/paf/hero/current_pos`: Subscribes to the filtered position of our car. +- `/paf/hero/curr_behavior`: Subscribes to Current Behavior pubished by the Decision Making. +- `/paf/hero/acc_velocity`: Subscribes to the speed published by the acc. +- `/paf/hero/waypoint_distance`: Subscribes to the Carla Waypoint to get the new road option. +- `/paf/hero/lane_change_distance`: Subscribes to the Carla Waypoint to check if the next Waypoint is a lane change. +- `/paf/hero/collision`: Subscribes to the collision published by the Collision Check. +- `/paf/hero/Center/traffic_light_y_distance`: Subscribes to the distance the traffic light has to the upper camera bound in pixels. +- `/paf/hero/unstuck_distance`: Subscribes to the distance travelled by the unstuck maneuver. + +### Published Topics + +This node publishes the following topics: + +- `/paf/hero/trajectory`: Publishes the new adjusted trajectory. +- `/paf/hero/target_velocity`: Publishes the new calcualted Speed. +- `/paf/hero/current_wp`: Publishes according to our position the index of the current point on the trajectory. +- `/paf/hero/overtake_success`: Publishes if an overtake was successful. + +## Node Creation + Running Tests + +To run this node insert the following statement in the [planning.launch](../../code/planning/launch/planning.launch) file: + +```xml + + + + +``` From e6d69e16571d64b4cd83ba14119d78ffeaf333c4 Mon Sep 17 00:00:00 2001 From: Niklas Rousset Date: Fri, 1 Nov 2024 17:10:43 +0100 Subject: [PATCH 32/57] Move debugging topic subcription to test section in motion_planning docs --- doc/planning/motion_planning.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/doc/planning/motion_planning.md b/doc/planning/motion_planning.md index 031e73fc..8b2613dd 100644 --- a/doc/planning/motion_planning.md +++ b/doc/planning/motion_planning.md @@ -28,7 +28,6 @@ The Motion Planning only consists of one node that contains all subscribers and This node subscribes to the following topics: -- `/paf/hero/Spawn_car`: Can spawn a car on the first straight in the dev environment, if this message is manually published. - `/paf/hero/speed_limit`: Subscribes to the speed Limit. - `/carla/hero/Speed`: Subscribes to the current speed. - `/paf/hero/current_heading`: Subscribes to the filtered heading of the ego vehicle. @@ -61,3 +60,7 @@ To run this node insert the following statement in the [planning.launch](../../c ``` + +The motion planning node listens to the following debugging topics: + +- `/paf/hero/Spawn_car`: Can spawn a car on the first straight in the dev environment, if this message is manually published. From a19e7656131cf0f5d7b12f17eb194b34b133df26 Mon Sep 17 00:00:00 2001 From: johannaschwarz Date: Fri, 1 Nov 2024 18:23:43 +0100 Subject: [PATCH 33/57] Graph and Global Planning desc --- .../src/global_planner/global_planner.py | 16 +++++++-------- .../research_assets/planning_internal.png | Bin 0 -> 132805 bytes doc/planning/Global_Planner.md | 18 ++++++++--------- .../paf24/planning/local_v_global_planning.md | 19 ++++++++++++++++++ 4 files changed, 34 insertions(+), 19 deletions(-) create mode 100644 doc/assets/research_assets/planning_internal.png create mode 100644 doc/research/paf24/planning/local_v_global_planning.md diff --git a/code/planning/src/global_planner/global_planner.py b/code/planning/src/global_planner/global_planner.py index 291e161b..2d39ab33 100755 --- a/code/planning/src/global_planner/global_planner.py +++ b/code/planning/src/global_planner/global_planner.py @@ -1,17 +1,15 @@ #!/usr/bin/env python -import rospy -import tf.transformations -import ros_compatibility as roscomp -from ros_compatibility.node import CompatibleNode from xml.etree import ElementTree as eTree -from geometry_msgs.msg import PoseStamped, Pose, Point, Quaternion +import ros_compatibility as roscomp +import rospy +import tf.transformations from carla_msgs.msg import CarlaRoute # , CarlaWorldInfo +from geometry_msgs.msg import Point, Pose, PoseStamped, Quaternion from nav_msgs.msg import Path -from std_msgs.msg import String -from std_msgs.msg import Float32MultiArray - from preplanning_trajectory import OpenDriveConverter +from ros_compatibility.node import CompatibleNode +from std_msgs.msg import Float32MultiArray, String RIGHT = 1 LEFT = 2 @@ -29,7 +27,7 @@ class PrePlanner(CompatibleNode): - global Plan: /carla/{role_name}/global_plan - current agent position: /paf/{role_name}/current_pos Published topics: - - preplanned trajectory: /paf/{role_name}/trajectory + - preplanned trajectory: /paf/{role_name}/trajectory_global - prevailing speed 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This component and so most of the documentation was taken from the previous project PAF22 (Authors: Simon Erlbacher, Niklas Vogel) -- [Global Planner](#global-planner) - - [Getting started](#getting-started) - - [Description](#description) - - [Inputs](#inputs) - - [Outputs](#outputs) - - [Testing](#testing) +- [Getting started](#getting-started) +- [Description](#description) + - [Inputs](#inputs) + - [Outputs](#outputs) +- [Testing](#testing) ## Getting started @@ -25,8 +24,7 @@ No extra installation needed. ## Description -First the global planner is responsible for collecting and preparing all data from the leaderboard and other intern -components that is needed for the preplanning component. +First the global planner is responsible for collecting and preparing all data from the leaderboard and other internal components that is needed for the preplanning component. To get an instance of the OpenDriveConverter (ODC) the received OpenDrive Map prevailing in String format has to be converted. In our case we use the @@ -56,8 +54,8 @@ The received agent spawn position is valid if it´s closer to the first waypoint parameter expresses. This is necessary to prevent unwanted behaviour in the startup phase where the current agent position is faulty. -When the ODC got initialised, the current agent position is received and the global plan is obtained from -the leaderboard the trajectory can be calculated by iterating through the global route and passing it to the ODC. +When the ODC is initialised, the current agent position is received and the global plan is obtained from +the leaderboard. The trajectory can be calculated by iterating through the global route and passing it to the ODC. After smaller outliners are removed the x and y coordinates as well as the yaw-orientation and the prevailing speed limits can be acquired from the ODC in the following form: diff --git a/doc/research/paf24/planning/local_v_global_planning.md b/doc/research/paf24/planning/local_v_global_planning.md new file mode 100644 index 00000000..8ba49e63 --- /dev/null +++ b/doc/research/paf24/planning/local_v_global_planning.md @@ -0,0 +1,19 @@ +# Relation beteween local and global planning + +Communication between Global and Local Planning is done via ros topics. Here is a graph showing the relation between the nodes. + +- Global Planning: PrePlanner +- Local Planning: ACC (Adaptive Cruise Control), CollisionCheck, MotionPlanning + +![Graph relation](/doc/assets/research_assets/planning_internal.png) + +## Global Planning + +Global planning consits of the PrePlanner Node and the OpenDriveConverter. + +- OpenDriveConverter: The class is primarily used to process OpenDrive maps and extract relevant information needed for trajectory planning. +- PrePlanner Node: The PrePlanner node is responsible for creating a trajectory out of an OpenDrive Map with the belonging road options. + - the `/paf/hero/trajectory_global` topic published is used internally for the preplanned trajectory. + +## Local Planning + From 26133f0595b69891046bd1436a32e7b8a1e7340c Mon Sep 17 00:00:00 2001 From: johannaschwarz Date: Fri, 1 Nov 2024 18:44:36 +0100 Subject: [PATCH 34/57] Update docu --- doc/planning/Collision_Check.md | 11 +++++------ doc/planning/Global_Planner.md | 5 ++--- doc/planning/README.md | 5 ++--- .../paf24/planning/local_v_global_planning.md | 12 ++++++++++++ 4 files changed, 21 insertions(+), 12 deletions(-) diff --git a/doc/planning/Collision_Check.md b/doc/planning/Collision_Check.md index d3e88572..bbf6032c 100644 --- a/doc/planning/Collision_Check.md +++ b/doc/planning/Collision_Check.md @@ -3,12 +3,11 @@ **Summary:** This module is responsible for detecting collisions and reporting them. It subscribes to topics that provide information about the current speed of the vehicle and the distances to objects detected by a LIDAR sensor. It publishes topics that provide information about emergency stops, the distance to collisions, the distance to oncoming traffic, and the approximated speed of the obstacle in front. -- [Collision Check](#collision-check) - - [Component](#component) - - [ROS Data Interface](#ros-data-interface) - - [Published Topics](#published-topics) - - [Subscribed Topics](#subscribed-topics) - - [Node Creation + Running Tests](#node-creation--running-tests) +- [Component](#component) +- [ROS Data Interface](#ros-data-interface) + - [Published Topics](#published-topics) + - [Subscribed Topics](#subscribed-topics) +- [Node Creation + Running Tests](#node-creation--running-tests) ## Component diff --git a/doc/planning/Global_Planner.md b/doc/planning/Global_Planner.md index 74d55072..08a00760 100644 --- a/doc/planning/Global_Planner.md +++ b/doc/planning/Global_Planner.md @@ -1,11 +1,10 @@ # Global Planner **Summary:** [global_planner.py](.../code/planning/global_planner/src/global_planner.py): -The global planner is responsible for collecting and preparing all data from the leaderboard and other intern +The global planner is responsible for collecting and preparing all data from the leaderboard and other internal components that is needed for the preplanning component. After finishing that this node initiates the calculation of a trajectory based on the OpenDriveConverter -from preplanning_trajectory.py. In the end the computed trajectory and prevailing speed limits are published -to the other components of this project (acting, decision making,...). +from preplanning_trajectory.py. In the end the computed trajectory and prevailing speed limits are published. This component and so most of the documentation was taken from the previous project PAF22 (Authors: Simon Erlbacher, Niklas Vogel) diff --git a/doc/planning/README.md b/doc/planning/README.md index 378d94bd..6a70faf9 100644 --- a/doc/planning/README.md +++ b/doc/planning/README.md @@ -10,11 +10,10 @@ from the leaderboard with the belonging actions. For example action number 3 mea ### [Global plan](./Global_Planner.md) -The global planner is responsible for collecting and preparing all data from the leaderboard and other intern +The global planner is responsible for collecting and preparing all data from the leaderboard and other internal components that is needed for the preplanning component. After finishing that this node initiates the calculation of a trajectory based on the OpenDriveConverter -from preplanning_trajectory.py. In the end the computed trajectory and prevailing speed limits are published -to the other components of this project (acting, decision making,...). +from preplanning_trajectory.py. In the end the computed trajectory and prevailing speed limits are published. ![img.png](../assets/Global_Plan.png) diff --git a/doc/research/paf24/planning/local_v_global_planning.md b/doc/research/paf24/planning/local_v_global_planning.md index 8ba49e63..56042fe1 100644 --- a/doc/research/paf24/planning/local_v_global_planning.md +++ b/doc/research/paf24/planning/local_v_global_planning.md @@ -17,3 +17,15 @@ Global planning consits of the PrePlanner Node and the OpenDriveConverter. ## Local Planning +Local planning consists of the ACC, CollisionCheck and MotionPlanning nodes. It uses information from the Global Planning to make decisions in the local environment of the ego vehicle. It creates a trajectory based on the global trajectory and the current position of the vehicle that is used by the acting component. + +- ACC: The Adaptive Cruise Control node is responsible for calculating the desired speed of the vehicle based on the current speed, trajectory, and speed limits. + - receives Speed from `/carla/hero/Speed` + - receives speed limits from `/paf/hero/speed_limits_OpenDrive` + - receives global trajectory from `/paf/hero/trajectory_global` +- CollisionCheck: The Collision Check node is responsible for detecting collisions and reporting them. + - receives current speed from `/carla/hero/Speed` + - receives distances to objects detected by a LIDAR sensor from `/paf/hero/Center/object_distance` +- MotionPlanning: The Motion Planning node is responsible for calculating the desired trajectory based on the current position of the vehicle and the global trajectory. + - receives speed information from ACC + - receives global trajectory from `/paf/hero/trajectory_global` \ No newline at end of file From d6be1b527443386a4cb3a2df13655ab3e856705a Mon Sep 17 00:00:00 2001 From: Niklas Rousset Date: Fri, 1 Nov 2024 18:51:25 +0100 Subject: [PATCH 35/57] Update motion planning docs --- doc/planning/motion_planning.md | 39 ++++++++++++++++----------------- 1 file changed, 19 insertions(+), 20 deletions(-) diff --git a/doc/planning/motion_planning.md b/doc/planning/motion_planning.md index 8b2613dd..83a6960b 100644 --- a/doc/planning/motion_planning.md +++ b/doc/planning/motion_planning.md @@ -1,10 +1,9 @@ # Motion Planning **Summary:** [motion_planning.py](.../code/planning/local_planner/src/motion_planning.py): -The motion planning is responsible for collecting all the speeds from the different components and choosing the optimal one to be fowarded into the acting. It also is capable to change the trajectory for an overtaking maneuver. +The motion planning is responsible for collecting all the velocity recommendations from the different components and choosing the optimal one to be fowarded into the acting. It also is capable to change the trajectory for an overtaking maneuver. - [Overview](#overview) -- [Component](#component) - [ROS Data Interface](#ros-data-interface) - [Subscribed Topics](#subscribed-topics) - [Published Topics](#published-topics) @@ -12,15 +11,15 @@ The motion planning is responsible for collecting all the speeds from the differ ## Overview -This module is responsible for adjusting the current speed and the current trajectory according to the traffic situation. It subscribes to topics that provide information about the current speed of the vehicle, the current heading and many more to navigate safely in the simulation. -It publishes topics that provide information about the target speed, trajectoy changes, current waypoint and if an overtake was successful. +This module contains one ROS node and is responsible for adjusting and publishing the target velocity and the target trajectory according to the traffic situation. +It subscribes to topics that provide information about the current velocity of the vehicle, the current heading and many more to navigate safely in the simulation. +It also publishes a topic that indicates wether an overtake maneuver was successful or not. -This file is also responsible for providing a ```target_speed of -3``` for acting, whenever we need to use the Unstuck Behavior. -3 is the only case we can drive backwards right now, -since we only need it for the unstuck routine. It also creates an overtake trajectory, whenever the unstuck behavior calls for it. +This component is also responsible for providing a target_speed of `-3` for acting, whenever we need to use the Unstuck Behavior. This is currently the only behavior that allows the car to drive backwards. -## Component - -The Motion Planning only consists of one node that contains all subscribers and publishers. It uses some utility functions from [utils.py](../../code/planning/src/local_planner/utils.py). +The trajectory is calculated by the global planner and is adjusted by the motion planning node. +When the decision making node decides that an overtake maneuver is necessary, the motion planning node will adjust the trajectory accordingly. +Otherwise the received trajectory is published without any changes. ## ROS Data Interface @@ -28,27 +27,27 @@ The Motion Planning only consists of one node that contains all subscribers and This node subscribes to the following topics: -- `/paf/hero/speed_limit`: Subscribes to the speed Limit. - `/carla/hero/Speed`: Subscribes to the current speed. +- `/paf/hero/acc_velocity`: Subscribes to the speed published by the acc. +- `/paf/hero/Center/traffic_light_y_distance`: Subscribes to the distance the traffic light has to the upper camera bound in pixels. +- `/paf/hero/collision`: Subscribes to the collision published by the Collision Check. +- `/paf/hero/curr_behavior`: Subscribes to Current Behavior pubished by the Decision Making. - `/paf/hero/current_heading`: Subscribes to the filtered heading of the ego vehicle. -- `/paf/hero/trajectory_global`: Subscribes to the global trajectory, which is calculated at the start of the simulation. - `/paf/hero/current_pos`: Subscribes to the filtered position of our car. -- `/paf/hero/curr_behavior`: Subscribes to Current Behavior pubished by the Decision Making. -- `/paf/hero/acc_velocity`: Subscribes to the speed published by the acc. -- `/paf/hero/waypoint_distance`: Subscribes to the Carla Waypoint to get the new road option. +- `/paf/hero/current_wp`: Subscribes to the current waypoint. - `/paf/hero/lane_change_distance`: Subscribes to the Carla Waypoint to check if the next Waypoint is a lane change. -- `/paf/hero/collision`: Subscribes to the collision published by the Collision Check. -- `/paf/hero/Center/traffic_light_y_distance`: Subscribes to the distance the traffic light has to the upper camera bound in pixels. +- `/paf/hero/speed_limit`: Subscribes to the speed limit. +- `/paf/hero/trajectory_global`: Subscribes to the global trajectory, which is calculated at the start of the simulation. - `/paf/hero/unstuck_distance`: Subscribes to the distance travelled by the unstuck maneuver. +- `/paf/hero/waypoint_distance`: Subscribes to the Carla Waypoint to get the new road option. ### Published Topics This node publishes the following topics: -- `/paf/hero/trajectory`: Publishes the new adjusted trajectory. -- `/paf/hero/target_velocity`: Publishes the new calcualted Speed. -- `/paf/hero/current_wp`: Publishes according to our position the index of the current point on the trajectory. -- `/paf/hero/overtake_success`: Publishes if an overtake was successful. +- `/paf/hero/overtake_success`: Publishes if an overtake was successful. ([std_msgs/Float32](http://docs.ros.org/en/api/std_msgs/html/msg/Float32.html)) +- `/paf/hero/target_velocity`: Publishes the target velocity in m/s. ([std_msgs/Float32](http://docs.ros.org/en/api/std_msgs/html/msg/Float32.html)) +- `/paf/hero/trajectory`: Publishes the new adjusted trajectory. ([nav_msgs/Path](https://docs.ros.org/en/lunar/api/nav_msgs/html/msg/Path.html)) ## Node Creation + Running Tests From c65875eeff8c2cc03273eb51878172de349bf6a3 Mon Sep 17 00:00:00 2001 From: Niklas Rousset Date: Fri, 1 Nov 2024 18:51:47 +0100 Subject: [PATCH 36/57] Update motion planning topics in architecture doc --- doc/general/architecture.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/general/architecture.md b/doc/general/architecture.md index 72eacdce..f8eaf839 100644 --- a/doc/general/architecture.md +++ b/doc/general/architecture.md @@ -198,12 +198,12 @@ Subscriptions: - ```collision``` ([std_msgs/Float32MultiArray](https://docs.ros.org/en/api/std_msgs/html/msg/Float32MultiArray.html)) - ```traffic_light_y_distance``` ([std_msgs/Int16](https://docs.ros.org/en/api/std_msgs/html/msg/Int16.html)) - ```unstuck_distance``` ([std_msgs/Float32](https://docs.ros.org/en/api/std_msgs/html/msg/Float32.html)) +- ```current_wp``` ([std_msgs/Float32](https://docs.ros.org/en/api/std_msgs/html/msg/Float32.html)) Publishes: - ```trajectory``` ([nav_msgs/Path Message](http://docs.ros.org/en/noetic/api/nav_msgs/html/msg/Path.html)) - ```target_velocity``` ([std_msgs/Float32](https://docs.ros.org/en/api/std_msgs/html/msg/Float32.html)) -- ```current_wp``` ([std_msgs/Float32](https://docs.ros.org/en/api/std_msgs/html/msg/Float32.html)) - ```overtake_success``` ([std_msgs/Float32](https://docs.ros.org/en/api/std_msgs/html/msg/Float32.html)) ## Acting From 4c5fdc352d35c5a2f58a57b15e546d56abbb9039 Mon Sep 17 00:00:00 2001 From: Niklas Rousset Date: Fri, 1 Nov 2024 18:53:56 +0100 Subject: [PATCH 37/57] Add doc strings to motion planning module --- .../src/local_planner/motion_planning.py | 82 ++++++++++++++----- 1 file changed, 62 insertions(+), 20 deletions(-) diff --git a/code/planning/src/local_planner/motion_planning.py b/code/planning/src/local_planner/motion_planning.py index 48abf69b..74aac44d 100755 --- a/code/planning/src/local_planner/motion_planning.py +++ b/code/planning/src/local_planner/motion_planning.py @@ -30,10 +30,8 @@ class MotionPlanning(CompatibleNode): """ - This node selects speeds according to the behavior in the Decision Tree - and the ACC. - Later this Node should compute a local Trajectory and forward - it to the Acting. + This node selects speeds based on the current behaviour and ACC to forward to the acting components. + It also handles the generation of trajectories for overtaking maneuvers. """ def __init__(self): @@ -235,12 +233,27 @@ def change_trajectory(self, distance_obj): pose_list = self.trajectory.poses # Only use fallback - self.overtake_fallback(distance_obj, pose_list) + self.generate_overtake_trajectory(distance_obj, pose_list) self.__overtake_status = 1 self.overtake_success_pub.publish(self.__overtake_status) return - def overtake_fallback(self, distance, pose_list, unstuck=False): + def generate_overtake_trajectory(self, distance, pose_list, unstuck=False): + """ + Generates a trajectory for overtaking maneuvers. + + This method creates a new trajectory for the vehicle to follow when an overtaking maneuver is required. + It adjusts the waypoints based on the current waypoint and distance, and applies an offset to the waypoints + to create a path that avoids obstacles or gets the vehicle unstuck. + + Args: + distance (int): The distance over which the overtaking maneuver should be planned. + pose_list (list): A list of PoseStamped objects representing the current planned path. + unstuck (bool, optional): A flag indicating whether the vehicle is stuck and requires a larger offset to get unstuck. Defaults to False. + + Returns: + None: The method updates the self.trajectory attribute with the new path. + """ currentwp = self.current_wp normal_x_offset = 2 unstuck_x_offset = 3 # could need adjustment with better steering @@ -306,9 +319,21 @@ def __set_trajectory(self, data: Path): """ self.trajectory = data self.loginfo("Trajectory received") + self.__corners = self.__calc_corner_points() - def __calc_corner_points(self): + def __calc_corner_points(self) -> list[list[np.ndarray]]: + """ + Calculate the corner points of the trajectory. + + This method converts the poses in the trajectory to an array of coordinates, + calculates the angles between consecutive points, and identifies the points + where there is a significant change in the angle, indicating a corner or curve. + The corner points are then grouped by proximity and returned. + + Returns: + list: A list of lists, where each sublist contains 2D points that form a corner. + """ coords = self.convert_pose_to_array(np.array(self.trajectory.poses)) x_values = np.array([point[0] for point in coords]) y_values = np.array([point[1] for point in coords]) @@ -320,13 +345,24 @@ def __calc_corner_points(self): threshold = 1 # in degree curve_change_indices = np.where(np.abs(np.diff(angles)) > threshold)[0] - sublist = self.create_sublists(curve_change_indices, proximity=5) + sublist = self.group_points_by_proximity(curve_change_indices, proximity=5) coords_of_curve = [coords[i] for i in sublist] return coords_of_curve - def create_sublists(self, points, proximity=5): + def group_points_by_proximity(self, points, proximity=5): + """ + Groups a list of points into sublists based on their proximity to each other. + + Args: + points (list): A list of points to be grouped. + proximity (int, optional): The maximum distance between points in a sublist. Defaults to 5. + + Returns: + list: A list of sublists, where each sublist contains points that are within the specified proximity of each other. + Sublists with only one point are filtered out. + """ sublists = [] current_sublist = [] @@ -395,14 +431,14 @@ def map_corner(dist): return self.__get_speed_cruise() @staticmethod - def convert_pose_to_array(poses: np.array): - """convert pose array to numpy array + def convert_pose_to_array(poses: np.ndarray) -> np.ndarray: + """Convert an array of PoseStamped objects to a numpy array of positions. Args: - poses (np.array): pose array + poses (np.ndarray): Array of PoseStamped objects. Returns: - np.array: numpy array + np.ndarray: Numpy array of shape (n, 2) containing the x and y positions. """ result_array = np.empty((len(poses), 2)) for pose in range(len(poses)): @@ -424,6 +460,11 @@ def __check_emergency(self, data: Bool): self.emergency_pub.publish(data) def update_target_speed(self, acc_speed, behavior): + """ + Updates the target velocity based on the current behavior and ACC velocity and overtake status and publishes it. + The unit of the velocity is m/s. + """ + be_speed = self.get_speed_by_behavior(behavior) if behavior == bs.parking.name or self.__overtake_status == 1: self.target_speed = be_speed @@ -439,6 +480,10 @@ def __set_acc_speed(self, data: Float32): self.__acc_speed = data.data def __set_curr_behavior(self, data: String): + """ + Sets the received current behavior of the vehicle. + If the behavior is an overtake behavior, a trajectory change is triggered. + """ self.__curr_behavior = data.data if data.data == bs.ot_enter_init.name: if np.isinf(self.__collision_point): @@ -507,7 +552,9 @@ def __get_speed_unstuck(self, behavior: str) -> float: # create overtake trajectory starting 6 meteres before # the obstacle # 6 worked well in tests, but can be adjusted - self.overtake_fallback(self.unstuck_distance, pose_list, unstuck=True) + self.generate_overtake_trajectory( + self.unstuck_distance, pose_list, unstuck=True + ) self.logfatal("Overtake Trajectory while unstuck!") self.unstuck_overtake_flag = True self.init_overtake_pos = self.current_pos[:2] @@ -628,8 +675,7 @@ def __calc_virtual_overtake(self) -> float: def run(self): """ - Control loop - :return: + Control loop that updates the target speed and publishes the target trajectory and speed over ROS topics. """ def loop(timer_event=None): @@ -649,10 +695,6 @@ def loop(timer_event=None): if __name__ == "__main__": - """ - main function starts the MotionPlanning node - :param args: - """ roscomp.init("MotionPlanning") try: node = MotionPlanning() From 265958199b49bf9dec03ae07bbddd3d46f4a9a6f Mon Sep 17 00:00:00 2001 From: Niklas Rousset Date: Fri, 1 Nov 2024 18:55:04 +0100 Subject: [PATCH 38/57] add TODOs for motion planning refactoring (#416) --- .../src/local_planner/motion_planning.py | 35 +++++++++++-------- 1 file changed, 21 insertions(+), 14 deletions(-) diff --git a/code/planning/src/local_planner/motion_planning.py b/code/planning/src/local_planner/motion_planning.py index 74aac44d..f959bdea 100755 --- a/code/planning/src/local_planner/motion_planning.py +++ b/code/planning/src/local_planner/motion_planning.py @@ -1,23 +1,21 @@ #!/usr/bin/env python # import tf.transformations -import ros_compatibility as roscomp -import rospy -import sys +import math import os +import sys +import numpy as np +import ros_compatibility as roscomp +import rospy +from carla_msgs.msg import CarlaSpeedometer +from geometry_msgs.msg import Point, Pose, PoseStamped, Quaternion +from nav_msgs.msg import Path +from perception.msg import LaneChange, Waypoint from ros_compatibility.node import CompatibleNode from rospy import Publisher, Subscriber -from std_msgs.msg import String, Float32, Bool, Float32MultiArray, Int16 -from nav_msgs.msg import Path -from geometry_msgs.msg import PoseStamped, Pose, Point, Quaternion -from carla_msgs.msg import CarlaSpeedometer -import numpy as np from scipy.spatial.transform import Rotation -import math - -from perception.msg import Waypoint, LaneChange - -from utils import convert_to_ms, spawn_car, NUM_WAYPOINTS, TARGET_DISTANCE_TO_STOP +from std_msgs.msg import Bool, Float32, Float32MultiArray, Int16, String +from utils import NUM_WAYPOINTS, TARGET_DISTANCE_TO_STOP, convert_to_ms, spawn_car sys.path.append(os.path.abspath(sys.path[0] + "/../../planning/src/behavior_agent")) from behaviours import behavior_speed as bs # type: ignore # noqa: E402 @@ -39,12 +37,14 @@ def __init__(self): self.role_name = self.get_param("role_name", "hero") self.control_loop_rate = self.get_param("control_loop_rate", 0.05) + # TODO: add type hints self.target_speed = 0.0 self.__curr_behavior = None self.__acc_speed = 0.0 self.__stopline = None # (Distance, isStopline) self.__change_point = None # (Distance, isLaneChange, roadOption) self.__collision_point = None + # TODO: clarify what the overtake_status values mean (by using an enum or similar) self.__overtake_status = -1 self.published = False self.current_pos = None @@ -160,9 +160,11 @@ def __init__(self): Float32, f"/paf/{self.role_name}/target_velocity", qos_profile=1 ) + # TODO move up to subscribers self.wp_subs = self.new_subscription( Float32, f"/paf/{self.role_name}/current_wp", self.__set_wp, qos_profile=1 ) + self.overtake_success_pub = self.new_publisher( Float32, f"/paf/{self.role_name}/overtake_success", qos_profile=1 ) @@ -335,6 +337,8 @@ def __calc_corner_points(self) -> list[list[np.ndarray]]: list: A list of lists, where each sublist contains 2D points that form a corner. """ coords = self.convert_pose_to_array(np.array(self.trajectory.poses)) + + # TODO: refactor by using numpy functions x_values = np.array([point[0] for point in coords]) y_values = np.array([point[1] for point in coords]) @@ -381,6 +385,7 @@ def group_points_by_proximity(self, points, proximity=5): if current_sublist: sublists.append(current_sublist) + # TODO: Check if it is intended to filter out sublists with only one point filtered_list = [in_list for in_list in sublists if len(in_list) > 1] return filtered_list @@ -390,6 +395,7 @@ def get_cornering_speed(self): pos = self.current_pos[:2] def euclid_dist(vector1, vector2): + # TODO replace with numpy function point1 = np.array(vector1) point2 = np.array(vector2) @@ -405,7 +411,7 @@ def map_corner(dist): elif dist < 50: return 7 else: - 8 + 8 # TODO add return distance_corner = 0 for i in range(len(corner) - 1): @@ -525,6 +531,7 @@ def get_speed_by_behavior(self, behavior: str) -> float: return speed def __get_speed_unstuck(self, behavior: str) -> float: + # TODO check if this 'global' is necessary global UNSTUCK_OVERTAKE_FLAG_CLEAR_DISTANCE speed = 0.0 if behavior == bs.us_unstuck.name: From df2d0a0d2d386edaa2c1c14de355aac65fcea172 Mon Sep 17 00:00:00 2001 From: Niklas Rousset Date: Fri, 1 Nov 2024 19:16:51 +0100 Subject: [PATCH 39/57] fix line lengths in motion planning --- .../src/local_planner/motion_planning.py | 40 +++++++++++-------- 1 file changed, 24 insertions(+), 16 deletions(-) diff --git a/code/planning/src/local_planner/motion_planning.py b/code/planning/src/local_planner/motion_planning.py index f959bdea..8c1514cb 100755 --- a/code/planning/src/local_planner/motion_planning.py +++ b/code/planning/src/local_planner/motion_planning.py @@ -28,8 +28,9 @@ class MotionPlanning(CompatibleNode): """ - This node selects speeds based on the current behaviour and ACC to forward to the acting components. - It also handles the generation of trajectories for overtaking maneuvers. + This node selects speeds based on the current behaviour and ACC to forward to the + acting components. It also handles the generation of trajectories for overtaking + maneuvers. """ def __init__(self): @@ -44,7 +45,7 @@ def __init__(self): self.__stopline = None # (Distance, isStopline) self.__change_point = None # (Distance, isLaneChange, roadOption) self.__collision_point = None - # TODO: clarify what the overtake_status values mean (by using an enum or similar) + # TODO: clarify what the overtake_status values mean (by using an enum or ...) self.__overtake_status = -1 self.published = False self.current_pos = None @@ -244,14 +245,18 @@ def generate_overtake_trajectory(self, distance, pose_list, unstuck=False): """ Generates a trajectory for overtaking maneuvers. - This method creates a new trajectory for the vehicle to follow when an overtaking maneuver is required. - It adjusts the waypoints based on the current waypoint and distance, and applies an offset to the waypoints - to create a path that avoids obstacles or gets the vehicle unstuck. + This method creates a new trajectory for the vehicle to follow when an + overtaking maneuver is required. It adjusts the waypoints based on the current + waypoint and distance, and applies an offset to the waypoints to create a path + that avoids obstacles or gets the vehicle unstuck. Args: - distance (int): The distance over which the overtaking maneuver should be planned. - pose_list (list): A list of PoseStamped objects representing the current planned path. - unstuck (bool, optional): A flag indicating whether the vehicle is stuck and requires a larger offset to get unstuck. Defaults to False. + distance (int): The distance over which the overtaking maneuver should be + planned. + pose_list (list): A list of PoseStamped objects representing the current + planned path. + unstuck (bool, optional): A flag indicating whether the vehicle is stuck + and requires a larger offset to get unstuck. Defaults to False. Returns: None: The method updates the self.trajectory attribute with the new path. @@ -334,7 +339,8 @@ def __calc_corner_points(self) -> list[list[np.ndarray]]: The corner points are then grouped by proximity and returned. Returns: - list: A list of lists, where each sublist contains 2D points that form a corner. + list: A list of lists, where each sublist contains 2D points that form a + corner. """ coords = self.convert_pose_to_array(np.array(self.trajectory.poses)) @@ -361,11 +367,12 @@ def group_points_by_proximity(self, points, proximity=5): Args: points (list): A list of points to be grouped. - proximity (int, optional): The maximum distance between points in a sublist. Defaults to 5. + proximity (int, optional): The maximum distance between points in a sublist Returns: - list: A list of sublists, where each sublist contains points that are within the specified proximity of each other. - Sublists with only one point are filtered out. + list: A list of sublists, where each sublist contains points that are within + the specified proximity of each other. Sublists with only one point are + filtered out. """ sublists = [] current_sublist = [] @@ -467,8 +474,8 @@ def __check_emergency(self, data: Bool): def update_target_speed(self, acc_speed, behavior): """ - Updates the target velocity based on the current behavior and ACC velocity and overtake status and publishes it. - The unit of the velocity is m/s. + Updates the target velocity based on the current behavior and ACC velocity and + overtake status and publishes it. The unit of the velocity is m/s. """ be_speed = self.get_speed_by_behavior(behavior) @@ -682,7 +689,8 @@ def __calc_virtual_overtake(self) -> float: def run(self): """ - Control loop that updates the target speed and publishes the target trajectory and speed over ROS topics. + Control loop that updates the target speed and publishes the target trajectory + and speed over ROS topics. """ def loop(timer_event=None): From 45855c7c6b36679879ad5d14a87f8636d2268a0f Mon Sep 17 00:00:00 2001 From: victor-42 Date: Fri, 1 Nov 2024 19:23:53 +0100 Subject: [PATCH 40/57] Made some final improvements to the current planning arch diagram. 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ZO-&DMK8}+$`XYdT;vzE7iiEU%{s;4SN2&k- From 109933af035031b54b392148557767df779d315b Mon Sep 17 00:00:00 2001 From: victor-42 Date: Fri, 1 Nov 2024 19:28:21 +0100 Subject: [PATCH 41/57] Made some final improvements to the current planning arch diagram. 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doc/research/paf24/general/driving_score.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/doc/research/paf24/general/driving_score.md b/doc/research/paf24/general/driving_score.md index a9b68181..4eaeea7e 100644 --- a/doc/research/paf24/general/driving_score.md +++ b/doc/research/paf24/general/driving_score.md @@ -18,19 +18,19 @@ The driving proficiency of an agent can be characterized by multiple metrics. Fo ## Driving score -\[\frac{1}{N}\sum^i_N R_i P_i\] +$\frac{1}{N}\sum^i_N R_i P_i$ - The main metric of the leaderboard, serving as an aggregate of the average route completion and the number of traffic infractions. Here $N$ stands for the number of routes, $R_i$ is the percentage of completion of the $i$-th route, and $P_i$ is the infraction penalty of the $i$-th route. ## Route completion -\[\frac{1}{N}\sum^i_N R_i\] +$\frac{1}{N}\sum^i_N R_i$ - Percentage of route distance completed by an agent, averaged across $N$ routes. ## Infraction penalty -\[\prod_j^{ped, veh, ... stop} (p_j^i)^{n_{infractions}}\] +$\prod_j^{ped, veh, ... stop} (p_j^i)^{n_{infractions}}$ - Aggregates the number of infractions triggered by an agent as a geometric series. Agents start with an ideal 1.0 base score, which is reduced by a penalty coefficient for every instance of these. From ccbad810690d4472ea6aed3bfc93f42acc982ea0 Mon Sep 17 00:00:00 2001 From: Sebastian Seitz Date: Fri, 1 Nov 2024 21:39:35 +0100 Subject: [PATCH 43/57] mention behavior tree integration --- doc/research/paf24/general/old_research_overview.md | 1 + 1 file changed, 1 insertion(+) diff --git a/doc/research/paf24/general/old_research_overview.md b/doc/research/paf24/general/old_research_overview.md index 71bef29c..db2627b9 100644 --- a/doc/research/paf24/general/old_research_overview.md +++ b/doc/research/paf24/general/old_research_overview.md @@ -34,6 +34,7 @@ improvements in lane-changing safety. - **Local Planning**: Tailored for dynamic obstacles, this component focuses on immediate adjustments to the vehicle’s path, particularly useful in urban environments with unpredictable elements. Local planning includes **trajectory tracking** using Pure Pursuit and Stanley controllers to maintain a steady path. - **PAF23 Enhancements**: Improved **collision avoidance** algorithms and added real-time updates to the trajectory based on sensor data. The local planner now adapts quickly to lane-change requests or route deviations due to traffic, creating a seamless flow between global and local path planning. +Furthermore, the integration of **behavior trees** has been researched, offering some advantages in computing power and explainability. Its drawbacks in uncertain situations and complex environments have been presented. ## State Machine for Decision-Making From f3ddad048c40a32d6fcb41ba1c7d8dfc51c6a15d Mon Sep 17 00:00:00 2001 From: Anastasiia Bilinska Date: Sat, 2 Nov 2024 21:47:21 +0100 Subject: [PATCH 44/57] syntax --- .../src/local_planner/motion_planning.py | 29 +++--- .../paf24/planning/Unstuck_Overtake Behavior | 96 ++++++++++++------- 2 files changed, 76 insertions(+), 49 deletions(-) diff --git a/code/planning/src/local_planner/motion_planning.py b/code/planning/src/local_planner/motion_planning.py index 48abf69b..583a64b9 100755 --- a/code/planning/src/local_planner/motion_planning.py +++ b/code/planning/src/local_planner/motion_planning.py @@ -232,15 +232,16 @@ def change_trajectory(self, distance_obj): Args: distance_obj (float): distance to overtake object """ - pose_list = self.trajectory.poses + pose_list = self.trajectory.poses #vehicle's planned path. # Only use fallback - self.overtake_fallback(distance_obj, pose_list) - self.__overtake_status = 1 + self.overtake_fallback(distance_obj, pose_list) #likely implements a simpler, predefined maneuver for overtaking, which adjusts the trajectory if more sophisticated planning is unavailable or as a safety fallback option. + self.__overtake_status = 1 #overtake successfully planed self.overtake_success_pub.publish(self.__overtake_status) return - def overtake_fallback(self, distance, pose_list, unstuck=False): + def overtake_fallback(self, distance, pose_list, unstuck=False): #his method constructs a temporary path around an obstacle based on the current location, intended distance to overtake, and a choice between two different lateral offsets depending on whether the vehicle is in an “unstuck” situation. + #codepart for overtake behaviour currentwp = self.current_wp normal_x_offset = 2 unstuck_x_offset = 3 # could need adjustment with better steering @@ -280,6 +281,8 @@ def overtake_fallback(self, distance, pose_list, unstuck=False): pos.header.frame_id = "global" pos.pose = pose result.append(pos) + + #code part for returning back on the lane path = Path() path.header.stamp = rospy.Time.now() path.header.frame_id = "global" @@ -440,10 +443,10 @@ def __set_acc_speed(self, data: Float32): def __set_curr_behavior(self, data: String): self.__curr_behavior = data.data - if data.data == bs.ot_enter_init.name: - if np.isinf(self.__collision_point): - self.__overtake_status = -1 - self.overtake_success_pub.publish(self.__overtake_status) + if data.data == bs.ot_enter_init.name: #is beggins overtake manuever + if np.isinf(self.__collision_point): # if no obsticle to overtake + self.__overtake_status = -1 #unsuccessfull or canceled overtake + self.overtake_success_pub.publish(self.__overtake_status) #abandonment of the overtake attempt. return self.change_trajectory(self.__collision_point) @@ -479,6 +482,7 @@ def get_speed_by_behavior(self, behavior: str) -> float: speed = self.__get_speed_cruise() return speed + #manage the speed of the vehicle when it encounters situations where it becomes "stuck" def __get_speed_unstuck(self, behavior: str) -> float: global UNSTUCK_OVERTAKE_FLAG_CLEAR_DISTANCE speed = 0.0 @@ -488,7 +492,7 @@ def __get_speed_unstuck(self, behavior: str) -> float: speed = bs.us_stop.speed elif behavior == bs.us_overtake.name: pose_list = self.trajectory.poses - if self.unstuck_distance is None: + if self.unstuck_distance is None: # Without knowing the distance to the obstacle, the vehicle cannot safely plan an overtake trajectory. self.logfatal("Unstuck distance not set") return speed @@ -498,16 +502,16 @@ def __get_speed_unstuck(self, behavior: str) -> float: ) # self.logfatal(f"Unstuck Distance in mp: {distance}") # clear distance to last unstuck -> avoid spamming overtake - if distance > UNSTUCK_OVERTAKE_FLAG_CLEAR_DISTANCE: + if distance > UNSTUCK_OVERTAKE_FLAG_CLEAR_DISTANCE: #The method checks if the vehicle has moved a significant distance (UNSTUCK_OVERTAKE_FLAG_CLEAR_DISTANCE) since the last overtake. This prevents repeated overtake attempts when they are unnecessary or unsafe. self.unstuck_overtake_flag = False self.logwarn("Unstuck Overtake Flag Cleared") # to avoid spamming the overtake_fallback - if self.unstuck_overtake_flag is False: + if self.unstuck_overtake_flag is False: # create overtake trajectory starting 6 meteres before # the obstacle # 6 worked well in tests, but can be adjusted - self.overtake_fallback(self.unstuck_distance, pose_list, unstuck=True) + self.overtake_fallback(self.unstuck_distance, pose_list, unstuck=True) #A fallback overtaking path is created to move around the obstacle when necessary. self.logfatal("Overtake Trajectory while unstuck!") self.unstuck_overtake_flag = True self.init_overtake_pos = self.current_pos[:2] @@ -661,3 +665,4 @@ def loop(timer_event=None): pass finally: roscomp.shutdown() + diff --git a/doc/research/paf24/planning/Unstuck_Overtake Behavior b/doc/research/paf24/planning/Unstuck_Overtake Behavior index 8a8e5d70..d2e89a11 100644 --- a/doc/research/paf24/planning/Unstuck_Overtake Behavior +++ b/doc/research/paf24/planning/Unstuck_Overtake Behavior @@ -1,39 +1,60 @@ -This dicument analyze current unstuck/overtake behavior and reveal several critical issues with the current implementation - -1. Unstuck Behavior: +# Unstuck/Overtake Behavio + +**Summary:** This document analyze current unstuck/overtake behavior and reveal several critical issues with the current implementation +- [Unstuck behavior](#unstuck-behavior) + -[Key components](#key-components) + -[How unstuck is triggered](#how-unstuck-is-triggered) +-[Overtake behavior](#overtake-behavior) + -[Key components](#key-components) + -[How overtake is triggered](#how-overtake-is-triggered) +- [Relevant rostopics](#relevant-rostopics) +- [Methods overview](#methods-overview) + -[def __set_curr_behavior](#__set_curr_behavior) + -[def change_trajectory](#change_trajectory) + -[def overtake_fallback](#overtake_fallback) +- [Overtake behavior issues](#overtake-behavior-issues) + -[Aggressive lane changes](#aggressive-lane-changes) + -[Inadequate collision detection](#inadequate-collision-detection) + -[Improper trajectory planning](#improper-trajectory-planning) +- [Unstuck behavior issues](#unstuck-behavior-issues) +- [Potential improvements](#potential-improvements) + + + +## Unstuck behavior: The "unstuck" behavior is designed to address situations where the vehicle becomes stuck, perhaps due to obstacles or other vehicles blocking its path. - 1.1 Key components: + ### Key components: self.unstuck_distance: Tracks the distance to the object causing the blockage, helping the algorithm to determine if an alternative route (overtaking) might be required. self.unstuck_overtake_flag: Prevents repeated or "spam" overtakes by ensuring that the unstuck overtake only happens once within a specified distance (UNSTUCK_OVERTAKE_FLAG_CLEAR_DISTANCE). self.init_overtake_pos: Saves the position where the unstuck behavior is initiated to prevent excessive overtaking within a short range. Overtaking Logic: The method overtake_fallback is called within __get_speed_unstuck, where a new trajectory is created by offsetting the path to one side, allowing the vehicle to bypass the obstacle and get "unstuck." - 1.2 How Unstuck is Triggered: + ### How unstuck is triggered: The method __get_speed_unstuck checks the current behavior (e.g., us_unstuck), adjusting speed accordingly and invoking overtake_fallback if an obstacle is detected and the vehicle is in an "unstuck" situation. UNSTUCK_OVERTAKE_FLAG_CLEAR_DISTANCE ensures that once an unstuck maneuver is initiated, it won't be repeated until a certain distance is cleared -2. Overtake Behavior: +## Overtake behavior: The overtake behavior allows the vehicle to safely overtake a slower-moving or stationary vehicle ahead by modifying its trajectory. - 2.1 Key components: + ### Key components: self.__overtake_status: Manages the status of overtaking, where 1 indicates an active overtake, while -1 signals that overtaking isn't required. change_trajectory: Initiates the overtake by calling overtake_fallback when it detects a collision point or slow-moving vehicle within a certain range. This method also publishes the updated __overtake_status to signal other systems of the change. overtake_fallback: Calculates a new trajectory path that offsets the vehicle by a certain distance (normal_x_offset) to safely pass the obstacle. The adjusted path uses the Rotation library to align the offset with the vehicle’s heading, creating a smooth path around the obstacle. - 2.3 How Overtake is Triggered: + ### How overtake is triggered: The method __set_curr_behavior monitors the vehicle’s behavior, triggering an overtake when ot_enter_init behavior is detected and if an obstacle or collision point is near. It then calls change_trajectory to modify the route. Speed adjustments specific to overtaking are handled in __get_speed_overtake, where the vehicle might slow down (e.g., ot_enter_slow) or proceed at normal speed after overtaking (e.g., ot_leave). -3. Relevant rostopics - overtake_success +## Relevant rostopics + overtake_success unstuck_distance ... #TODO:need to continue -4. Methods overview - 4.1 How the function def __set_curr_behavior(self, data: String) works: +## Methods overview + ## How the function def __set_curr_behavior(self, data: String) works: If beggins overtake manuever: if no obsticle to overtake: @@ -42,48 +63,49 @@ This dicument analyze current unstuck/overtake behavior and reveal several criti else: change trajectory - Infinity as "No Collision Detected": Setting self.__collision_point to infinity (np.inf) would mean "no collision detected." So, checking if np.isinf(self.__collision_point) effectively asks, "Is there no obstacle or point that necessitates an overtake?" (??? not sure abou it) + Infinity as "No Collision Detected": Setting self.__collision_point to infinity (np.inf) would mean "no collision detected." So, checking if np.isinf(self.__collision_point) effectively asks, "Is there no obstacle or point that needs an overtake?" (??? not sure about it) + self.__overtake_status = -1 #unsuccessfull or canceled overtake + self.__overtake_status = 1 #overtake should take place - Questions: + **Questions:** 1. who tells the function the behaviour status? 2. If the function has already become an overtake status, why should we check if there is actually an object to overtake? - 4.2 How the function change_trajectory(self, distynce_obj) works: + ## How the function change_trajectory(self, distynce_obj) works: update trajectory for overtaking and convert it to a new Path message. Args:distance_obj (float): distance to overtake object Publish overate success status - Questions: + **Questions:** 1. What does the success status for overtake mean? It seems like it oublishs than an overtake takes place but does not notify that the overtake manoeuvre was successful. - 4.3 How the function def overtake_fallback(self, distance, pose_list, unstuck=False) works: + ## How the function def overtake_fallback(self, distance, pose_list, unstuck=False) works: This method constructs a temporary path around an obstacle based on the current location, intended distance to overtake, and a choice between two different lateral offsets depending on whether the vehicle is in an “unstuck” situation. - Pick Path Points Around the Obstacle: It selects a section of the path around the vehicle’s current position and the obstacle it needs to overtake. If the vehicle is stuck, it picks a larger section to give it more room to maneuver. - Shift Path to the Side: It moves this section of the path slightly to the side (left or right, depending on the vehicle's heading) to avoid the obstacle. If the vehicle is in a “stuck” situation, it shifts it a bit more to give it extra clearance. - Create the New Path: It converts the shifted points into a path format that the vehicle’s navigation system can understand and follow. - Combine with Original Path: it merges this temporary bypass with the original path, so the vehicle can return to its route after it passes the obstacle. + **Pick Path Points Around the Obstacle:** It selects a section of the path around the vehicle’s current position and the obstacle it needs to overtake. If the vehicle is stuck, it picks a larger section to give it more room to maneuver. + **Shift Path to the Side:** It moves this section of the path slightly to the side (left or right, depending on the vehicle's heading) to avoid the obstacle. If the vehicle is in a “stuck” situation, it shifts it a bit more to give it extra clearance. + **Create the New Path:** It converts the shifted points into a path format that the vehicle’s navigation system can understand and follow. + **Combine with Original Path:** it merges this temporary bypass with the original path, so the vehicle can return to its route after it passes the obstacle. -5. Overtake Behavior Issues - 5.1 Aggressive Lane Changes: The vehicle exhibits aggressive lane changes, leading to emergency stops to avoid oncoming traffic. This behavior suggests that the decision-making logic for overtaking does not adequately assess the surrounding traffic conditions before executing maneuvers. +## Overtake behavior issues + ### Aggressive lane changes: The vehicle exhibits aggressive lane changes, leading to emergency stops to avoid oncoming traffic. This behavior suggests that the decision-making logic for overtaking does not adequately assess the surrounding traffic conditions before executing maneuvers. - 5.2 Inadequate Collision Detection: The vehicle fails to avoid obstacles, such as open car doors or stationary vehicles, indicating that the collision detection mechanisms are either insufficient or not effectively integrated into the overtaking logic. + ### Inadequate collision detection: The vehicle fails to avoid obstacles, such as open car doors or stationary vehicles, indicating that the collision detection mechanisms are either insufficient or not effectively integrated into the overtaking logic. - 5.3 Improper Trajectory Planning: The trajectory generated for overtaking often leads to incorrect paths, such as attempting to overtake trees or parked cars without considering oncoming traffic. A better filtering and validation of potential overtaking targets is needed. + ### Improper trajectory planning: The trajectory generated for overtaking often leads to incorrect paths, such as attempting to overtake trees or parked cars without considering oncoming traffic. A better filtering and validation of potential overtaking targets is needed. -6. Unstuck Behavior Issues - 6.1 The vehicle gets stuck multiple times and exhibits erratic behavior when trying to get unstuck, - 6.2 The vehicle's aggressive lane-holding behavior after being unstuck - 6.3 After collisions, the vehicle often fails to resume movement, indicating a lack of recovery logic post-collision +## Unstuck behavior issues + The vehicle gets stuck multiple times and exhibits erratic behavior when trying to get unstuck, + The vehicle's aggressive lane-holding behavior after being unstuck + After collisions, the vehicle often fails to resume movement, indicating a lack of recovery logic post-collision -7. Potential improvements - - 7.1 'def change trajektory' : consider implementing a more sophisticated trajectory planning algorithm that takes into account dynamic obstacles and traffic conditions rather than relying on a fallback method. Verify that the area into which the vehicle is moving is clear of obstacles and oncoming traffic. - 7.2 'def overtake_fallback' : instead of fixed offsets for unstuck and normal situations, consider dynamically calculating offsets based on current speed, vehicle dimensions, and surrounding traffic conditions. - 7.3 '__get_speed_unstuck' : includ checks for nearby vehicles and their speeds. Make UNSTUCK_OVERTAKE_FLAG_CLEAR_DISTANCE dynamic. - 7.4 '__check_emergency' : Now it only considers whether the vehicle is in a parking behavior state. Expand this method to evaluate various emergency conditions (e.g., obstacles detected by sensors) and initiate appropriate responses (e.g., stopping or rerouting). - 7.5 'get_speed_by_behavior' : consider feedback from sensors regarding current traffic conditions. - 7.6 '__calc_corner_points' : thinking about somathing smarter rather than a simple angle thresholds. +## Potential improvements + 'def change trajektory' : consider implementing a more sophisticated trajectory planning algorithm that takes into account dynamic obstacles and traffic conditions rather than relying on a fallback method. Verify that the area into which the vehicle is moving is clear of obstacles and oncoming traffic. + 'def overtake_fallback' : instead of fixed offsets for unstuck and normal situations, consider dynamically calculating offsets based on current speed, vehicle dimensions, and surrounding traffic conditions. + '__get_speed_unstuck' : includ checks for nearby vehicles and their speeds. Make UNSTUCK_OVERTAKE_FLAG_CLEAR_DISTANCE dynamic. + '__check_emergency' : Now it only considers whether the vehicle is in a parking behavior state. Expand this method to evaluate various emergency conditions (e.g., obstacles detected by sensors) and initiate appropriate responses (e.g., stopping or rerouting). + 'get_speed_by_behavior' : consider feedback from sensors regarding current traffic conditions. + '__calc_corner_points' : thinking about somathing smarter rather than a simple angle thresholds. From 2215fd0080fe147e4058aa52f3267f3dbc732cb0 Mon Sep 17 00:00:00 2001 From: Anastasiia Bilinska Date: Sat, 2 Nov 2024 21:49:02 +0100 Subject: [PATCH 45/57] syntax --- .../{Unstuck_Overtake Behavior => Unstuck_Overtake Behavior.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename doc/research/paf24/planning/{Unstuck_Overtake Behavior => Unstuck_Overtake Behavior.md} (100%) diff --git a/doc/research/paf24/planning/Unstuck_Overtake Behavior b/doc/research/paf24/planning/Unstuck_Overtake Behavior.md similarity index 100% rename from doc/research/paf24/planning/Unstuck_Overtake Behavior rename to doc/research/paf24/planning/Unstuck_Overtake Behavior.md From 8ff02f12a0d673eee8ad6d1bcb6c515e0879bdeb Mon Sep 17 00:00:00 2001 From: Anastasiia Bilinska Date: Sat, 2 Nov 2024 21:53:07 +0100 Subject: [PATCH 46/57] syntax .md --- .../planning/Unstuck_Overtake Behavior.md | 201 ++++++++++-------- 1 file changed, 118 insertions(+), 83 deletions(-) diff --git a/doc/research/paf24/planning/Unstuck_Overtake Behavior.md b/doc/research/paf24/planning/Unstuck_Overtake Behavior.md index d2e89a11..513376cf 100644 --- a/doc/research/paf24/planning/Unstuck_Overtake Behavior.md +++ b/doc/research/paf24/planning/Unstuck_Overtake Behavior.md @@ -1,112 +1,147 @@ -# Unstuck/Overtake Behavio +# Unstuck/Overtake Behavior + +**Summary:** This document analyzes the current unstuck/overtake behavior and reveals several critical issues with the existing implementation. -**Summary:** This document analyze current unstuck/overtake behavior and reveal several critical issues with the current implementation - [Unstuck behavior](#unstuck-behavior) - -[Key components](#key-components) - -[How unstuck is triggered](#how-unstuck-is-triggered) --[Overtake behavior](#overtake-behavior) - -[Key components](#key-components) - -[How overtake is triggered](#how-overtake-is-triggered) + - [Key components](#key-components) + - [How unstuck is triggered](#how-unstuck-is-triggered) +- [Overtake behavior](#overtake-behavior) + - [Key components](#key-components) + - [How overtake is triggered](#how-overtake-is-triggered) - [Relevant rostopics](#relevant-rostopics) - [Methods overview](#methods-overview) - -[def __set_curr_behavior](#__set_curr_behavior) - -[def change_trajectory](#change_trajectory) - -[def overtake_fallback](#overtake_fallback) + - [def \_\_set_curr_behavior](#def-set_curr_behavior) + - [def change_trajectory](#def-change_trajectory) + - [def overtake_fallback](#def-overtake_fallback) - [Overtake behavior issues](#overtake-behavior-issues) - -[Aggressive lane changes](#aggressive-lane-changes) - -[Inadequate collision detection](#inadequate-collision-detection) - -[Improper trajectory planning](#improper-trajectory-planning) + - [Aggressive lane changes](#aggressive-lane-changes) + - [Inadequate collision detection](#inadequate-collision-detection) + - [Improper trajectory planning](#improper-trajectory-planning) - [Unstuck behavior issues](#unstuck-behavior-issues) - [Potential improvements](#potential-improvements) +--- + +## Unstuck behavior + +The "unstuck" behavior is designed to address situations where the vehicle becomes stuck, perhaps due to obstacles or other vehicles blocking its path. + +### Key components + +- `self.unstuck_distance`: Tracks the distance to the object causing the blockage, helping the algorithm to determine if an alternative route (overtaking) might be required. +- `self.unstuck_overtake_flag`: Prevents repeated or "spam" overtakes by ensuring that the unstuck overtake only happens once within a specified distance (`UNSTUCK_OVERTAKE_FLAG_CLEAR_DISTANCE`). +- `self.init_overtake_pos`: Saves the position where the unstuck behavior is initiated to prevent excessive overtaking within a short range. +- **Overtaking Logic**: The method `overtake_fallback` is called within `__get_speed_unstuck`, where a new trajectory is created by offsetting the path to one side, allowing the vehicle to bypass the obstacle and get "unstuck." + +### How unstuck is triggered + +The method `__get_speed_unstuck` checks the current behavior (e.g., `us_unstuck`), adjusting speed accordingly and invoking `overtake_fallback` if an obstacle is detected and the vehicle is in an "unstuck" situation. `UNSTUCK_OVERTAKE_FLAG_CLEAR_DISTANCE` ensures that once an unstuck maneuver is initiated, it won't be repeated until a certain distance is cleared. + +--- + +## Overtake behavior +The overtake behavior allows the vehicle to safely overtake a slower-moving or stationary vehicle ahead by modifying its trajectory. -## Unstuck behavior: - The "unstuck" behavior is designed to address situations where the vehicle becomes stuck, perhaps due to obstacles or other vehicles blocking its path. - ### Key components: - self.unstuck_distance: Tracks the distance to the object causing the blockage, helping the algorithm to determine if an alternative route (overtaking) might be required. - self.unstuck_overtake_flag: Prevents repeated or "spam" overtakes by ensuring that the unstuck overtake only happens once within a specified distance (UNSTUCK_OVERTAKE_FLAG_CLEAR_DISTANCE). - self.init_overtake_pos: Saves the position where the unstuck behavior is initiated to prevent excessive overtaking within a short range. - Overtaking Logic: The method overtake_fallback is called within __get_speed_unstuck, where a new trajectory is created by offsetting the path to one side, allowing the vehicle to bypass the obstacle and get "unstuck." +### Key components +- `self.__overtake_status`: Manages the status of overtaking, where `1` indicates an active overtake, while `-1` signals that overtaking isn't required. +- `change_trajectory`: Initiates the overtake by calling `overtake_fallback` when it detects a collision point or slow-moving vehicle within a certain range. This method also publishes the updated `__overtake_status` to signal other systems of the change. +- `overtake_fallback`: Calculates a new trajectory path that offsets the vehicle by a certain distance (`normal_x_offset`) to safely pass the obstacle. The adjusted path uses the Rotation library to align the offset with the vehicle’s heading, creating a smooth path around the obstacle. - ### How unstuck is triggered: - The method __get_speed_unstuck checks the current behavior (e.g., us_unstuck), adjusting speed accordingly and invoking overtake_fallback if an obstacle is detected and the vehicle is in an "unstuck" situation. - UNSTUCK_OVERTAKE_FLAG_CLEAR_DISTANCE ensures that once an unstuck maneuver is initiated, it won't be repeated until a certain distance is cleared +### How overtake is triggered -## Overtake behavior: - The overtake behavior allows the vehicle to safely overtake a slower-moving or stationary vehicle ahead by modifying its trajectory. - ### Key components: - self.__overtake_status: Manages the status of overtaking, where 1 indicates an active overtake, while -1 signals that overtaking isn't required. - change_trajectory: Initiates the overtake by calling overtake_fallback when it detects a collision point or slow-moving vehicle within a certain range. This method also publishes the updated __overtake_status to signal other systems of the change. - overtake_fallback: Calculates a new trajectory path that offsets the vehicle by a certain distance (normal_x_offset) to safely pass the obstacle. The adjusted path uses the Rotation library to align the offset with the vehicle’s heading, creating a smooth path around the obstacle. +The method `__set_curr_behavior` monitors the vehicle’s behavior, triggering an overtake when `ot_enter_init` behavior is detected and if an obstacle or collision point is near. It then calls `change_trajectory` to modify the route. Speed adjustments specific to overtaking are handled in `__get_speed_overtake`, where the vehicle might slow down (e.g., `ot_enter_slow`) or proceed at normal speed after overtaking (e.g., `ot_leave`). - ### How overtake is triggered: - The method __set_curr_behavior monitors the vehicle’s behavior, triggering an overtake when ot_enter_init behavior is detected and if an obstacle or collision point is near. It then calls change_trajectory to modify the route. - Speed adjustments specific to overtaking are handled in __get_speed_overtake, where the vehicle might slow down (e.g., ot_enter_slow) or proceed at normal speed after overtaking (e.g., ot_leave). - -## Relevant rostopics - overtake_success - unstuck_distance - ... - #TODO:need to continue +--- +## Relevant rostopics +- `overtake_success` +- `unstuck_distance` +- _#TODO: need to continue_ + +--- ## Methods overview - ## How the function def __set_curr_behavior(self, data: String) works: - - If beggins overtake manuever: - if no obsticle to overtake: - unsuccessfull or canceled overtake - publish abandonment of the overtake attempt. - else: - change trajectory - - Infinity as "No Collision Detected": Setting self.__collision_point to infinity (np.inf) would mean "no collision detected." So, checking if np.isinf(self.__collision_point) effectively asks, "Is there no obstacle or point that needs an overtake?" (??? not sure about it) - self.__overtake_status = -1 #unsuccessfull or canceled overtake - self.__overtake_status = 1 #overtake should take place - - **Questions:** - 1. who tells the function the behaviour status? - 2. If the function has already become an overtake status, why should we check if there is actually an object to overtake? - - ## How the function change_trajectory(self, distynce_obj) works: - update trajectory for overtaking and convert it to a new Path message. Args:distance_obj (float): distance to overtake object - Publish overate success status - - **Questions:** - 1. What does the success status for overtake mean? It seems like it oublishs than an overtake takes place but does not notify that the overtake manoeuvre was successful. - - ## How the function def overtake_fallback(self, distance, pose_list, unstuck=False) works: - This method constructs a temporary path around an obstacle based on the current location, intended distance to overtake, and a choice between two different lateral offsets depending on whether the vehicle is in an “unstuck” situation. - - **Pick Path Points Around the Obstacle:** It selects a section of the path around the vehicle’s current position and the obstacle it needs to overtake. If the vehicle is stuck, it picks a larger section to give it more room to maneuver. - **Shift Path to the Side:** It moves this section of the path slightly to the side (left or right, depending on the vehicle's heading) to avoid the obstacle. If the vehicle is in a “stuck” situation, it shifts it a bit more to give it extra clearance. - **Create the New Path:** It converts the shifted points into a path format that the vehicle’s navigation system can understand and follow. - **Combine with Original Path:** it merges this temporary bypass with the original path, so the vehicle can return to its route after it passes the obstacle. + +### `def __set_curr_behavior(self, data: String)` + +**How it works:** + +- **If beginning an overtake maneuver:** + - If no obstacle to overtake: + - Unsuccessful or canceled overtake + - Publish abandonment of the overtake attempt. + - Otherwise: + - Change trajectory + +- **Infinity as "No Collision Detected"**: Setting `self.__collision_point` to infinity (`np.inf`) means "no collision detected." So, checking if `np.isinf(self.__collision_point)` effectively asks, "Is there no obstacle or point that needs an overtake?" + + - `self.__overtake_status = -1`: Unsuccessful or canceled overtake + - `self.__overtake_status = 1`: Overtake should take place + +**Questions:** + +1. Who tells the function the behavior status? +2. If the function has already become an overtake status, why should we check if there is actually an object to overtake? + +--- + +### `def change_trajectory(self, distance_obj)` + +**Description**: Updates trajectory for overtaking and converts it to a new `Path` message. + +- **Args**: `distance_obj` (float): Distance to overtake object +- **Outcome**: Publishes overtake success status + +**Questions:** + +1. What does the success status for overtake mean? It seems like it publishes that an overtake takes place but does not notify if the maneuver was successful. + +--- + +### `def overtake_fallback(self, distance, pose_list, unstuck=False)` + +**Description**: Constructs a temporary path around an obstacle based on the current location, intended distance to overtake, and a choice between two different lateral offsets depending on whether the vehicle is in an "unstuck" situation. + +1. **Pick Path Points Around the Obstacle**: Selects a section of the path around the vehicle’s current position and the obstacle it needs to overtake. If the vehicle is stuck, it picks a larger section to give it more room to maneuver. +2. **Shift Path to the Side**: Moves this section of the path slightly to the side (left or right, depending on the vehicle's heading) to avoid the obstacle. If the vehicle is in a “stuck” situation, it shifts it a bit more to give it extra clearance. +3. **Create the New Path**: Converts the shifted points into a path format that the vehicle’s navigation system can understand and follow. +4. **Combine with Original Path**: Merges this temporary bypass with the original path, so the vehicle can return to its route after it passes the obstacle. + +--- ## Overtake behavior issues - ### Aggressive lane changes: The vehicle exhibits aggressive lane changes, leading to emergency stops to avoid oncoming traffic. This behavior suggests that the decision-making logic for overtaking does not adequately assess the surrounding traffic conditions before executing maneuvers. - ### Inadequate collision detection: The vehicle fails to avoid obstacles, such as open car doors or stationary vehicles, indicating that the collision detection mechanisms are either insufficient or not effectively integrated into the overtaking logic. +### Aggressive lane changes + +The vehicle exhibits aggressive lane changes, leading to emergency stops to avoid oncoming traffic. This behavior suggests that the decision-making logic for overtaking does not adequately assess the surrounding traffic conditions before executing maneuvers. + +### Inadequate collision detection + +The vehicle fails to avoid obstacles, such as open car doors or stationary vehicles, indicating that the collision detection mechanisms are either insufficient or not effectively integrated into the overtaking logic. - ### Improper trajectory planning: The trajectory generated for overtaking often leads to incorrect paths, such as attempting to overtake trees or parked cars without considering oncoming traffic. A better filtering and validation of potential overtaking targets is needed. +### Improper trajectory planning + +The trajectory generated for overtaking often leads to incorrect paths, such as attempting to overtake trees or parked cars without considering oncoming traffic. A better filtering and validation of potential overtaking targets is needed. + +--- ## Unstuck behavior issues - The vehicle gets stuck multiple times and exhibits erratic behavior when trying to get unstuck, - The vehicle's aggressive lane-holding behavior after being unstuck - After collisions, the vehicle often fails to resume movement, indicating a lack of recovery logic post-collision - -## Potential improvements - 'def change trajektory' : consider implementing a more sophisticated trajectory planning algorithm that takes into account dynamic obstacles and traffic conditions rather than relying on a fallback method. Verify that the area into which the vehicle is moving is clear of obstacles and oncoming traffic. - 'def overtake_fallback' : instead of fixed offsets for unstuck and normal situations, consider dynamically calculating offsets based on current speed, vehicle dimensions, and surrounding traffic conditions. - '__get_speed_unstuck' : includ checks for nearby vehicles and their speeds. Make UNSTUCK_OVERTAKE_FLAG_CLEAR_DISTANCE dynamic. - '__check_emergency' : Now it only considers whether the vehicle is in a parking behavior state. Expand this method to evaluate various emergency conditions (e.g., obstacles detected by sensors) and initiate appropriate responses (e.g., stopping or rerouting). - 'get_speed_by_behavior' : consider feedback from sensors regarding current traffic conditions. - '__calc_corner_points' : thinking about somathing smarter rather than a simple angle thresholds. +- The vehicle gets stuck multiple times and exhibits erratic behavior when trying to get unstuck. +- The vehicle's aggressive lane-holding behavior after being unstuck. +- After collisions, the vehicle often fails to resume movement, indicating a lack of recovery logic post-collision. +--- +## Potential improvements +- `def change_trajectory`: Consider implementing a more sophisticated trajectory planning algorithm that considers dynamic obstacles and traffic conditions rather than relying on a fallback method. Verify that the area into which the vehicle is moving is clear of obstacles and oncoming traffic. +- `def overtake_fallback`: Instead of fixed offsets for unstuck and normal situations, consider dynamically calculating offsets based on current speed, vehicle dimensions, and surrounding traffic conditions. +- `__get_speed_unstuck`: Include checks for nearby vehicles and their speeds. Make `UNSTUCK_OVERTAKE_FLAG_CLEAR_DISTANCE` dynamic. +- `__check_emergency`: Currently, it only considers whether the vehicle is in a parking behavior state. Expand this method to evaluate various emergency conditions (e.g., obstacles detected by sensors) and initiate appropriate responses (e.g., stopping or rerouting). +- `get_speed_by_behavior`: Consider feedback from sensors regarding current traffic conditions. +- `__calc_corner_points`: Consider a more intelligent approach rather than relying on simple angle thresholds. From cff40c8b271568247818dd2d9d7242b6893ed387 Mon Sep 17 00:00:00 2001 From: Anastasiia Bilinska Date: Sat, 2 Nov 2024 22:10:10 +0100 Subject: [PATCH 47/57] .md length --- .../src/local_planner/motion_planning.py | 26 +++++++------------ .../planning/Unstuck_Overtake Behavior.md | 9 ++++--- 2 files changed, 14 insertions(+), 21 deletions(-) diff --git a/code/planning/src/local_planner/motion_planning.py b/code/planning/src/local_planner/motion_planning.py index 87ff244e..c57e8dd9 100755 --- a/code/planning/src/local_planner/motion_planning.py +++ b/code/planning/src/local_planner/motion_planning.py @@ -233,13 +233,11 @@ def change_trajectory(self, distance_obj): Args: distance_obj (float): distance to overtake object """ - pose_list = self.trajectory.poses #vehicle's planned path. + pose_list = self.trajectory.poses # Only use fallback self.generate_overtake_trajectory(distance_obj, pose_list) self.__overtake_status = 1 - self.overtake_fallback(distance_obj, pose_list) #likely implements a simpler, predefined maneuver for overtaking, which adjusts the trajectory if more sophisticated planning is unavailable or as a safety fallback option. - self.__overtake_status = 1 #overtake successfully planed self.overtake_success_pub.publish(self.__overtake_status) return @@ -263,8 +261,6 @@ def generate_overtake_trajectory(self, distance, pose_list, unstuck=False): Returns: None: The method updates the self.trajectory attribute with the new path. """ - def overtake_fallback(self, distance, pose_list, unstuck=False): #his method constructs a temporary path around an obstacle based on the current location, intended distance to overtake, and a choice between two different lateral offsets depending on whether the vehicle is in an “unstuck” situation. - #codepart for overtake behaviour currentwp = self.current_wp normal_x_offset = 2 unstuck_x_offset = 3 # could need adjustment with better steering @@ -304,8 +300,6 @@ def overtake_fallback(self, distance, pose_list, unstuck=False): #his method con pos.header.frame_id = "global" pos.pose = pose result.append(pos) - - #code part for returning back on the lane path = Path() path.header.stamp = rospy.Time.now() path.header.frame_id = "global" @@ -504,10 +498,10 @@ def __set_curr_behavior(self, data: String): If the behavior is an overtake behavior, a trajectory change is triggered. """ self.__curr_behavior = data.data - if data.data == bs.ot_enter_init.name: #is beggins overtake manuever - if np.isinf(self.__collision_point): # if no obsticle to overtake - self.__overtake_status = -1 #unsuccessfull or canceled overtake - self.overtake_success_pub.publish(self.__overtake_status) #abandonment of the overtake attempt. + if data.data == bs.ot_enter_init.name: + if np.isinf(self.__collision_point): + self.__overtake_status = -1 + self.overtake_success_pub.publish(self.__overtake_status) return self.change_trajectory(self.__collision_point) @@ -543,7 +537,6 @@ def get_speed_by_behavior(self, behavior: str) -> float: speed = self.__get_speed_cruise() return speed - #manage the speed of the vehicle when it encounters situations where it becomes "stuck" def __get_speed_unstuck(self, behavior: str) -> float: # TODO check if this 'global' is necessary global UNSTUCK_OVERTAKE_FLAG_CLEAR_DISTANCE @@ -554,7 +547,7 @@ def __get_speed_unstuck(self, behavior: str) -> float: speed = bs.us_stop.speed elif behavior == bs.us_overtake.name: pose_list = self.trajectory.poses - if self.unstuck_distance is None: # Without knowing the distance to the obstacle, the vehicle cannot safely plan an overtake trajectory. + if self.unstuck_distance is None: self.logfatal("Unstuck distance not set") return speed @@ -564,12 +557,12 @@ def __get_speed_unstuck(self, behavior: str) -> float: ) # self.logfatal(f"Unstuck Distance in mp: {distance}") # clear distance to last unstuck -> avoid spamming overtake - if distance > UNSTUCK_OVERTAKE_FLAG_CLEAR_DISTANCE: #The method checks if the vehicle has moved a significant distance (UNSTUCK_OVERTAKE_FLAG_CLEAR_DISTANCE) since the last overtake. This prevents repeated overtake attempts when they are unnecessary or unsafe. + if distance > UNSTUCK_OVERTAKE_FLAG_CLEAR_DISTANCE: self.unstuck_overtake_flag = False self.logwarn("Unstuck Overtake Flag Cleared") # to avoid spamming the overtake_fallback - if self.unstuck_overtake_flag is False: + if self.unstuck_overtake_flag is False: # create overtake trajectory starting 6 meteres before # the obstacle # 6 worked well in tests, but can be adjusted @@ -724,5 +717,4 @@ def loop(timer_event=None): except KeyboardInterrupt: pass finally: - roscomp.shutdown() - + roscomp.shutdown() \ No newline at end of file diff --git a/doc/research/paf24/planning/Unstuck_Overtake Behavior.md b/doc/research/paf24/planning/Unstuck_Overtake Behavior.md index 513376cf..5e5e2c9e 100644 --- a/doc/research/paf24/planning/Unstuck_Overtake Behavior.md +++ b/doc/research/paf24/planning/Unstuck_Overtake Behavior.md @@ -35,7 +35,8 @@ The "unstuck" behavior is designed to address situations where the vehicle becom ### How unstuck is triggered -The method `__get_speed_unstuck` checks the current behavior (e.g., `us_unstuck`), adjusting speed accordingly and invoking `overtake_fallback` if an obstacle is detected and the vehicle is in an "unstuck" situation. `UNSTUCK_OVERTAKE_FLAG_CLEAR_DISTANCE` ensures that once an unstuck maneuver is initiated, it won't be repeated until a certain distance is cleared. +The method `__get_speed_unstuck` checks the current behavior (e.g., `us_unstuck`), adjusting speed accordingly and invoking `overtake_fallback` if an obstacle is detected and the vehicle is in an "unstuck" situation. +`UNSTUCK_OVERTAKE_FLAG_CLEAR_DISTANCE` ensures that once an unstuck maneuver is initiated, it won't be repeated until a certain distance is cleared. --- @@ -51,7 +52,8 @@ The overtake behavior allows the vehicle to safely overtake a slower-moving or s ### How overtake is triggered -The method `__set_curr_behavior` monitors the vehicle’s behavior, triggering an overtake when `ot_enter_init` behavior is detected and if an obstacle or collision point is near. It then calls `change_trajectory` to modify the route. Speed adjustments specific to overtaking are handled in `__get_speed_overtake`, where the vehicle might slow down (e.g., `ot_enter_slow`) or proceed at normal speed after overtaking (e.g., `ot_leave`). +The method `__set_curr_behavior` monitors the vehicle’s behavior, triggering an overtake when `ot_enter_init` behavior is detected and if an obstacle or collision point is near. It then calls `change_trajectory` to modify the route. +Speed adjustments specific to overtaking are handled in `__get_speed_overtake`, where the vehicle might slow down (e.g., `ot_enter_slow`) or proceed at normal speed after overtaking (e.g., `ot_leave`). --- @@ -143,5 +145,4 @@ The trajectory generated for overtaking often leads to incorrect paths, such as - `__get_speed_unstuck`: Include checks for nearby vehicles and their speeds. Make `UNSTUCK_OVERTAKE_FLAG_CLEAR_DISTANCE` dynamic. - `__check_emergency`: Currently, it only considers whether the vehicle is in a parking behavior state. Expand this method to evaluate various emergency conditions (e.g., obstacles detected by sensors) and initiate appropriate responses (e.g., stopping or rerouting). - `get_speed_by_behavior`: Consider feedback from sensors regarding current traffic conditions. -- `__calc_corner_points`: Consider a more intelligent approach rather than relying on simple angle thresholds. - +- `__calc_corner_points`: Consider a more intelligent approach rather than relying on simple angle thresholds. \ No newline at end of file From 8ac5f90aec8f010583125d91e4b1bb80d66b5a25 Mon Sep 17 00:00:00 2001 From: Anastasiia Bilinska Date: Sat, 2 Nov 2024 22:14:58 +0100 Subject: [PATCH 48/57] syntax --- doc/research/paf24/planning/Unstuck_Overtake Behavior.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/doc/research/paf24/planning/Unstuck_Overtake Behavior.md b/doc/research/paf24/planning/Unstuck_Overtake Behavior.md index 5e5e2c9e..8228c4e1 100644 --- a/doc/research/paf24/planning/Unstuck_Overtake Behavior.md +++ b/doc/research/paf24/planning/Unstuck_Overtake Behavior.md @@ -36,6 +36,9 @@ The "unstuck" behavior is designed to address situations where the vehicle becom ### How unstuck is triggered The method `__get_speed_unstuck` checks the current behavior (e.g., `us_unstuck`), adjusting speed accordingly and invoking `overtake_fallback` if an obstacle is detected and the vehicle is in an "unstuck" situation. + +--- + `UNSTUCK_OVERTAKE_FLAG_CLEAR_DISTANCE` ensures that once an unstuck maneuver is initiated, it won't be repeated until a certain distance is cleared. --- @@ -53,6 +56,9 @@ The overtake behavior allows the vehicle to safely overtake a slower-moving or s ### How overtake is triggered The method `__set_curr_behavior` monitors the vehicle’s behavior, triggering an overtake when `ot_enter_init` behavior is detected and if an obstacle or collision point is near. It then calls `change_trajectory` to modify the route. + +--- + Speed adjustments specific to overtaking are handled in `__get_speed_overtake`, where the vehicle might slow down (e.g., `ot_enter_slow`) or proceed at normal speed after overtaking (e.g., `ot_leave`). --- From ce40a7eaea54884f6ca03b7977c1c5ad6f2e413c Mon Sep 17 00:00:00 2001 From: Anastasiia Bilinska Date: Sat, 2 Nov 2024 22:21:55 +0100 Subject: [PATCH 49/57] formatter --- code/planning/src/local_planner/motion_planning.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/code/planning/src/local_planner/motion_planning.py b/code/planning/src/local_planner/motion_planning.py index c57e8dd9..8c1514cb 100755 --- a/code/planning/src/local_planner/motion_planning.py +++ b/code/planning/src/local_planner/motion_planning.py @@ -717,4 +717,4 @@ def loop(timer_event=None): except KeyboardInterrupt: pass finally: - roscomp.shutdown() \ No newline at end of file + roscomp.shutdown() From 968ad94a3c7414095a9aa1ea71fbcb527ce4c731 Mon Sep 17 00:00:00 2001 From: Anastasiia Bilinska Date: Sat, 2 Nov 2024 22:23:56 +0100 Subject: [PATCH 50/57] formatter --- doc/research/paf24/planning/Unstuck_Overtake Behavior.md | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/doc/research/paf24/planning/Unstuck_Overtake Behavior.md b/doc/research/paf24/planning/Unstuck_Overtake Behavior.md index 8228c4e1..76104bf7 100644 --- a/doc/research/paf24/planning/Unstuck_Overtake Behavior.md +++ b/doc/research/paf24/planning/Unstuck_Overtake Behavior.md @@ -35,7 +35,7 @@ The "unstuck" behavior is designed to address situations where the vehicle becom ### How unstuck is triggered -The method `__get_speed_unstuck` checks the current behavior (e.g., `us_unstuck`), adjusting speed accordingly and invoking `overtake_fallback` if an obstacle is detected and the vehicle is in an "unstuck" situation. +The method `__get_speed_unstuck` checks the current behavior (e.g., `us_unstuck`), adjusting speed accordingly and invoking `overtake_fallback` if an obstacle is detected and the vehicle is in an "unstuck" situation. --- @@ -55,7 +55,7 @@ The overtake behavior allows the vehicle to safely overtake a slower-moving or s ### How overtake is triggered -The method `__set_curr_behavior` monitors the vehicle’s behavior, triggering an overtake when `ot_enter_init` behavior is detected and if an obstacle or collision point is near. It then calls `change_trajectory` to modify the route. +The method `__set_curr_behavior` monitors the vehicle’s behavior, triggering an overtake when `ot_enter_init` behavior is detected and if an obstacle or collision point is near. It then calls `change_trajectory` to modify the route. --- @@ -151,4 +151,6 @@ The trajectory generated for overtaking often leads to incorrect paths, such as - `__get_speed_unstuck`: Include checks for nearby vehicles and their speeds. Make `UNSTUCK_OVERTAKE_FLAG_CLEAR_DISTANCE` dynamic. - `__check_emergency`: Currently, it only considers whether the vehicle is in a parking behavior state. Expand this method to evaluate various emergency conditions (e.g., obstacles detected by sensors) and initiate appropriate responses (e.g., stopping or rerouting). - `get_speed_by_behavior`: Consider feedback from sensors regarding current traffic conditions. -- `__calc_corner_points`: Consider a more intelligent approach rather than relying on simple angle thresholds. \ No newline at end of file +- `__calc_corner_points`: Consider a more intelligent approach rather than relying on simple angle thresholds. + +--- From 30d12b48f5aa53e2669120c4dc9f9782747e46ea Mon Sep 17 00:00:00 2001 From: johannaschwarz Date: Sun, 3 Nov 2024 10:21:24 +0100 Subject: [PATCH 51/57] Update Doc strings --- .../src/global_planner/global_planner.py | 6 +++--- .../global_planner/preplanning_trajectory.py | 10 +++++----- code/planning/src/local_planner/ACC.py | 17 ++++++++--------- doc/planning/Collision_Check.md | 11 ++++++----- doc/planning/Global_Planner.md | 11 ++++++----- .../paf24/planning/local_v_global_planning.md | 6 ++++-- 6 files changed, 32 insertions(+), 29 deletions(-) diff --git a/code/planning/src/global_planner/global_planner.py b/code/planning/src/global_planner/global_planner.py index 2d39ab33..deeb0f32 100755 --- a/code/planning/src/global_planner/global_planner.py +++ b/code/planning/src/global_planner/global_planner.py @@ -19,7 +19,7 @@ class PrePlanner(CompatibleNode): """ This node is responsible for collecting all data needed for the - preplanning and calculate a trajectory based on the OpenDriveConverter + preplanning and calculating a trajectory based on the OpenDriveConverter from preplanning_trajectory.py. Subscribed/needed topics: - OpenDrive Map: /carla/{role_name}/OpenDRIVE @@ -88,7 +88,7 @@ def global_route_callback(self, data: CarlaRoute) -> None: """ when the global route gets updated a new trajectory is calculated with the help of OpenDriveConverter and published into - '/paf/ self.role_name /trajectory' + '/paf/ self.role_name /trajectory_global' :param data: global Route """ if data is None: @@ -211,7 +211,7 @@ def global_route_callback(self, data: CarlaRoute) -> None: def world_info_callback(self, opendrive: String) -> None: """ - when the map gets updated a mew OpenDriveConverter instance is created + when the map gets updated a new OpenDriveConverter instance is created (needed for the trajectory preplanning) :param opendrive: updated CarlaWorldInformation """ diff --git a/code/planning/src/global_planner/preplanning_trajectory.py b/code/planning/src/global_planner/preplanning_trajectory.py index 478addd7..2300627e 100755 --- a/code/planning/src/global_planner/preplanning_trajectory.py +++ b/code/planning/src/global_planner/preplanning_trajectory.py @@ -1,9 +1,9 @@ import copy -from xml.etree import ElementTree as eTree -import help_functions +from math import cos, degrees, sin from typing import Tuple -from math import sin, cos, degrees +from xml.etree import ElementTree as eTree +import help_functions # Check small distance between two points SMALL_DIST = 0.001 @@ -220,7 +220,7 @@ def calculate_intervalls_id( ): """ The function assumes, that the current chosen road is not the only - one that is possible. The current raad is calculated based on all + one that is possible. The current road is calculated based on all possible scenarios not assuming that the succ and pred road are both junctions. :param agent: current position of the agent with x and y coordinate @@ -287,7 +287,7 @@ def calculate_intervalls_id( def get_special_case_id(self, road: int, current: int, agent: Tuple[float, float]): """When the function get_min_dist() returns two solutions with the - same distance, this function calculated the distance based on the + same distance, this function calculates the distance based on the interpolation of the two possible roads. :param road: id value of the successor or predecessor road :param current: id value of the current road diff --git a/code/planning/src/local_planner/ACC.py b/code/planning/src/local_planner/ACC.py index 74b681b2..2e3fa8c8 100755 --- a/code/planning/src/local_planner/ACC.py +++ b/code/planning/src/local_planner/ACC.py @@ -1,19 +1,18 @@ #!/usr/bin/env python +import numpy as np import ros_compatibility as roscomp -from ros_compatibility.node import CompatibleNode -from rospy import Subscriber, Publisher -from geometry_msgs.msg import PoseStamped from carla_msgs.msg import CarlaSpeedometer # , CarlaWorldInfo +from geometry_msgs.msg import PoseStamped from nav_msgs.msg import Path -from std_msgs.msg import Float32MultiArray, Float32, Bool -import numpy as np -from utils import interpolate_speed, calculate_rule_of_thumb +from ros_compatibility.node import CompatibleNode +from rospy import Publisher, Subscriber +from std_msgs.msg import Bool, Float32, Float32MultiArray +from utils import calculate_rule_of_thumb, interpolate_speed class ACC(CompatibleNode): - """ - This node recieves a possible collision and - """ + """ACC (Adaptive Cruise Control) calculates and publishes the desired speed based on + possible collisions, the current speed, the trajectory, and the speed limits.""" def __init__(self): super(ACC, self).__init__("ACC") diff --git a/doc/planning/Collision_Check.md b/doc/planning/Collision_Check.md index bbf6032c..d3e88572 100644 --- a/doc/planning/Collision_Check.md +++ b/doc/planning/Collision_Check.md @@ -3,11 +3,12 @@ **Summary:** This module is responsible for detecting collisions and reporting them. It subscribes to topics that provide information about the current speed of the vehicle and the distances to objects detected by a LIDAR sensor. It publishes topics that provide information about emergency stops, the distance to collisions, the distance to oncoming traffic, and the approximated speed of the obstacle in front. -- [Component](#component) -- [ROS Data Interface](#ros-data-interface) - - [Published Topics](#published-topics) - - [Subscribed Topics](#subscribed-topics) -- [Node Creation + Running Tests](#node-creation--running-tests) +- [Collision Check](#collision-check) + - [Component](#component) + - [ROS Data Interface](#ros-data-interface) + - [Published Topics](#published-topics) + - [Subscribed Topics](#subscribed-topics) + - [Node Creation + Running Tests](#node-creation--running-tests) ## Component diff --git a/doc/planning/Global_Planner.md b/doc/planning/Global_Planner.md index 08a00760..94a66612 100644 --- a/doc/planning/Global_Planner.md +++ b/doc/planning/Global_Planner.md @@ -8,11 +8,12 @@ from preplanning_trajectory.py. In the end the computed trajectory and prevailin This component and so most of the documentation was taken from the previous project PAF22 (Authors: Simon Erlbacher, Niklas Vogel) -- [Getting started](#getting-started) -- [Description](#description) - - [Inputs](#inputs) - - [Outputs](#outputs) -- [Testing](#testing) +- [Global Planner](#global-planner) + - [Getting started](#getting-started) + - [Description](#description) + - [Inputs](#inputs) + - [Outputs](#outputs) + - [Testing](#testing) ## Getting started diff --git a/doc/research/paf24/planning/local_v_global_planning.md b/doc/research/paf24/planning/local_v_global_planning.md index 56042fe1..506d7e1c 100644 --- a/doc/research/paf24/planning/local_v_global_planning.md +++ b/doc/research/paf24/planning/local_v_global_planning.md @@ -17,7 +17,8 @@ Global planning consits of the PrePlanner Node and the OpenDriveConverter. ## Local Planning -Local planning consists of the ACC, CollisionCheck and MotionPlanning nodes. It uses information from the Global Planning to make decisions in the local environment of the ego vehicle. It creates a trajectory based on the global trajectory and the current position of the vehicle that is used by the acting component. +Local planning consists of the ACC, CollisionCheck and MotionPlanning nodes. It uses information from the Global Planning to make decisions in the local environment of the ego vehicle. +It creates a trajectory based on the global trajectory and the current position of the vehicle that is used by the acting component. - ACC: The Adaptive Cruise Control node is responsible for calculating the desired speed of the vehicle based on the current speed, trajectory, and speed limits. - receives Speed from `/carla/hero/Speed` @@ -28,4 +29,5 @@ Local planning consists of the ACC, CollisionCheck and MotionPlanning nodes. It - receives distances to objects detected by a LIDAR sensor from `/paf/hero/Center/object_distance` - MotionPlanning: The Motion Planning node is responsible for calculating the desired trajectory based on the current position of the vehicle and the global trajectory. - receives speed information from ACC - - receives global trajectory from `/paf/hero/trajectory_global` \ No newline at end of file + - receives global trajectory from `/paf/hero/trajectory_global` + \ No newline at end of file From d9e11478da620f45af58979031ca201b4206ed24 Mon Sep 17 00:00:00 2001 From: Johanna Schwarz <76784140+johannaschwarz@users.noreply.github.com> Date: Sun, 3 Nov 2024 10:37:20 +0100 Subject: [PATCH 52/57] Update doc/research/paf24/planning/local_v_global_planning.md Co-authored-by: Niklas Rousset <75939868+niklasr22@users.noreply.github.com> --- doc/research/paf24/planning/local_v_global_planning.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/research/paf24/planning/local_v_global_planning.md b/doc/research/paf24/planning/local_v_global_planning.md index 506d7e1c..73383fdf 100644 --- a/doc/research/paf24/planning/local_v_global_planning.md +++ b/doc/research/paf24/planning/local_v_global_planning.md @@ -9,7 +9,7 @@ Communication between Global and Local Planning is done via ros topics. Here is ## Global Planning -Global planning consits of the PrePlanner Node and the OpenDriveConverter. +Global planning consists of the PrePlanner Node and the OpenDriveConverter. - OpenDriveConverter: The class is primarily used to process OpenDrive maps and extract relevant information needed for trajectory planning. - PrePlanner Node: The PrePlanner node is responsible for creating a trajectory out of an OpenDrive Map with the belonging road options. From 46c96d282bbe268bf7fa4b640d3c1ef30889568d Mon Sep 17 00:00:00 2001 From: Antonia Date: Sun, 3 Nov 2024 13:38:29 +0100 Subject: [PATCH 53/57] Add overview of the localization of the car --- doc/perception/README.md | 4 + doc/perception/localization.md | 132 +++++++++++++++++++++++++++++++++ 2 files changed, 136 insertions(+) create mode 100644 doc/perception/localization.md diff --git a/doc/perception/README.md b/doc/perception/README.md index 9b747565..f28f24b7 100644 --- a/doc/perception/README.md +++ b/doc/perception/README.md @@ -17,6 +17,10 @@ This folder contains further documentation of the perception components. 11. [Efficient PS](./efficientps.md) 1. not used scince paf22 and never successfully tested +## Overview Localization + +An overview over the different nodes working together to localize the vehicle is provided in the [localization](./localization.md) file. + ## Experiments - The overview of performance evaluations is located in the [experiments](./experiments/README.md) folder. diff --git a/doc/perception/localization.md b/doc/perception/localization.md new file mode 100644 index 00000000..9558091b --- /dev/null +++ b/doc/perception/localization.md @@ -0,0 +1,132 @@ +# Localization + +There are currently three nodes working together to localize the vehicle: + +- [position_heading_publisher_node](./position_heading_publisher_node.md) +- [kalman_filter](./kalman_filter.md) +- [coordinate_transformation](./coordinate_transformation.md) + +The [position_heading_filter_debug_node](./position_heading_filter_debug_node.md) node is another useful node but it does not actively localize the vehicle. +Instead it makes it possible to compare and tune different filters and the [viz.py](../../code/perception/src/experiments/Position_Heading_Datasets/viz.py) file is the recommended way to visualizes the results. + +## position_heading_publisher_node + +The [position_heading_publisher_node](./position_heading_publisher_node.md) node is responsible for publishing the position and heading of the car. It publishes raw as well as already filtered data. The goal of filtering the raw data is the elimination / reduction of noise. + +### Published / subscribed topics + +The following topics are therefore published by this node: + +- `unfiltered_pos` (raw data, subscribed to by filter nodes e.g. kalman_filter) +- `unfiltered_heading` (raw data, subscribed to by filter nodes e.g. kalman_filter) +- `current_pos` (filtered data, position of the car) +- `current_heading` (filtered data, orientation of the car around the z-axis) + +To gather the necessary information for the topics above the node subscribes the following topics: + +- OpenDrive (map information) +- IMU (Inertial Measurement Unit) +- GPS +- the topic published by the filter that is used (e.g. kalman_pos) + +As you can see the node first subscribes the filtered data (e.g. kalman_pos) and then publishes this data as the current position / heading. It merely passes along the data. + +This makes it possible for multiple filter nodes to be running and only the data produced by one filter is published as the current position / heading. Otherwise different filters would publish to the same topic (e.g. current_pos) which is not desirable. + +### Available filters + +The filter to be used is specified in the [perception.launch](../../code/perception/launch/perception.launch) file. + +The currently available filters are as follows: + +- position filter: + - Kalman (Kalman Filter) + - RunningAvg (Running Average Filter) + - None (No Filter) +- heading filter: + - Kalman (Kalman Filter) + - None (No Filter) + - Old (heading is calculated the WRONG way, for demonstration purposes only) + +To use a certain / new filter two files need to be updated: + +- First make sure that in the [perception.launch](../../code/perception/launch/perception.launch) file: + - the node of the filter you want to use is included (e.g. kalman_filter.py) + - the `pos_filter` / `heading_filter` arguments are set accordingly (e.g. "Kalman") in the code of the position_heading_publisher_node node + +- Then the according subscriber and publisher need to be added in the init function of the position_heading_publisher_node.py file. + +For further details on the position_heading_publisher_node node click [here](./position_heading_publisher_node.md). + +## kalman_filter + +The currently used filter is the (linear) Kalman Filter. It is responsible for filtering the location and heading data so the noise can be eliminated / reduced. + +### Published / subscribed topics + +Therefore the published topics are: + +- `kalman_pos` (filtered position of the vehicle) +- `kalman_heading` (filtered heading of the vehicle) + +The variables to be estimated are put together in the state vector. It consists of the following elements: + +- `x` (position on the x-axis) +- `y` (position on the y-axis) +- `v_x` (velocity in the x-direction) +- `v_y` (velocity in the y-direction) +- `yaw` (orientation, rotation around z-axis) +- `omega_z` (angular velocity) + +The z-position is currently not estimated by the Kalman Filter and is calculated using the rolling average. + +The x-/y-position is measured by the GNSS sensor. The measurement is provided by the unfiltered_pos topic. + +The velocity in x-/y-direction can be derived from the speed measured by the Carla Speedometer in combination with the current orientation. + +To get the orientation and angular velocity of the vehicle the data provided by the IMU (Inertial Measurement Unit) sensor is used. + +### Possible improvements + +In earlier experiments it was shown that the Kalman Filter performs better than using the rolling average or the unfiltered data. But it seems likely that further improvements can be made. + +For further details on the current implementation of the kalman_filter node click [here](./kalman_filter.md). + +Currently the model assumes that the vehicle drives at a constant speed. Adding acceleration in the model (as proposed [here](https://www.youtube.com/watch?v=TEKPcyBwEH8)) could ultimately improve the filter. + +It is also likely to achieve a better performance by using a non-linear filter like the Extended or Unscented Kalman Filter. +The latter is the most generic of the three options as it does not even assume a normal distribution of the system but it is also the most complex Kalman Filter. + +The localization of the vehicle could further be improved by combining the current estimate of the position with data generated by image processing. +Using the vision node and the lidar distance node it is possible to calculate the distance to detected objects (for details see [distance_to_objects.md](./distance_to_objects.md). +Objects such as signals (traffic signs, traffic lights, ...) have a specified position in the OpenDrive map. +For details see: (Disclaimer: the second source belongs to a previous version of OpenDrive but is probably still applicable) + +- [source_1](https://www.asam.net/standards/detail/opendrive/) and +- [source_2](https://www.asam.net/index.php?eID=dumpFile&t=f&f=4422&token=e590561f3c39aa2260e5442e29e93f6693d1cccd#top-016f925e-bfe2-481d-b603-da4524d7491f) (menu point "12. Signals") + +The knowledge of the map could be combined with the calculated distance to a detected object. For a better understanding look at the following example: +The car is driving on "road1". This road is 100 meters long and there is a traffic light in the middle of it. +If the program detects a traffic light next to the road with a distance of 20 meters this suggests that the vehicle is 30 meters down "road 1". +That information could be used to refine the position estimation of the vehicle. + +## coordinate_transformation + +The [coordinate_transformation](./coordinate_transformation) node provides useful helper functions such as quat_to_heading which transforms a given quaternion into the heading of the car. + +This node is used by the [position_heading_publisher_node](./position_heading_publisher_node) node as well as the [kalman_filter](./kalman_filter) node. Both nodes use the node for its quat_to_heading function and its CoordinateTransformer class. + +The node is not fully documented yet but for further details on the quat_to_heading function click [here](./coordinate_transformation.md). + +## position_heading_filter_debug_node + +This node processes the data provided by the IMU and GNSS so the errors between the is-state and the measured state can be seen. +To get the is-state the Carla API is used to get the real position and heading of the car. +Comparing the real position / heading with the position / heading estimated by a filter (e.g. Kalman Filter) the performance of a filter can be evaluated and the parameters used by the filter can be tuned. + +The recommended way to look at the results is using the mathplotlib plots provided by the [viz.py](../../code/perception/src/experiments/Position_Heading_Datasets/viz.py) file even though they can also be shown via rqt_plots. + +Because the node uses the Carla API and therefore uses the ground truth it should only be used for combaring and tuning filters and not for any other purposes. +It might be best to remove this node before submitting to the official leaderboard because otherwise the project could get disqualified. + +For more details on the node see [position_heading_filter_debug_node](./position_heading_filter_debug_node.md) and [viz.py](../../code/perception/src/experiments/Position_Heading_Datasets/viz.py). From 6b37448bfb8a7b687d18169cd5a2278d29547319 Mon Sep 17 00:00:00 2001 From: seefelke <33551476+seefelke@users.noreply.github.com> Date: Sun, 3 Nov 2024 18:35:24 +0100 Subject: [PATCH 54/57] 364 summarize leaderboard documentation (#407) * Create leaderboard_summary.md * Update leaderboard_summary.md * Update leaderboard_summary.md * Update leaderboard_summary.md * Update leaderboard_summary.md * Update leaderboard_summary.md linter * Update leaderboard_summary.md linter * Update leaderboard_summary.md * Update leaderboard_summary.md --------- Co-authored-by: seitzseb <74816319+seitzseb@users.noreply.github.com> --- .../paf24/general/leaderboard_summary.md | 131 ++++++++++++++++++ 1 file changed, 131 insertions(+) create mode 100644 doc/research/paf24/general/leaderboard_summary.md diff --git a/doc/research/paf24/general/leaderboard_summary.md b/doc/research/paf24/general/leaderboard_summary.md new file mode 100644 index 00000000..193095ca --- /dev/null +++ b/doc/research/paf24/general/leaderboard_summary.md @@ -0,0 +1,131 @@ +# Leaderboard summary + +**Summary:** This document depicts general informations regarding the leaderboard as a quick overview in a more condensed form. + +- [General Information](#general-information) +- [Traffic Scenarios](#traffic-scenarios) + - [Generic](#generic) + - [Control Loss](#control-loss) + - [Traffic negotiation](#traffic-negotiation) + - [Highway](#highway) + - [Obstacle avoidance](#obstacle-avoidance) + - [Braking and lane changing](#braking-and-lane-changing) + - [Parking](#parking) +- [Available Sensors](#available-sensors) + +## General Information + +The CARLA Leaderboard includes a variety of scenarios to test autonomous driving models in realistic urban environments. This document provides a quick overview of all possible scenarios and available sensors. +The leaderboard offers a driving score metric based on infractions happening during scenarios. + +[#366](https://github.com/una-auxme/paf/issues/366) provides a more indepth look at how this score is calculated. + +Each time an infraction takes place several details are recorded and appended to a list specific to the infraction type in question. Besides scenario and score handling, the leaderboard also provides a plethora of sensors to get data from. + +The leaderboard provides predefined routes with a starting and destination point. The route description can contain GPS style coordinates, map coordinates or route instructions. + +Two participation modalities are offered, "Sensors" and "Map". "Sensors" only offers the sensors listed down below while "Map" offers HD map data in addition to all the sensors. + +## Traffic Scenarios + +- ### **Generic:** + + - Traffic lights + - Signs (stop, speed limit, yield) + +- ### **Control Loss:** + + - The ego-vehicle loses control due to bad conditions on the road and it must recover, coming back to its original lane. + ![image](https://leaderboard.carla.org/assets/images/TR01.png) + +- ### **Traffic negotiation:** + + - The ego-vehicle is performing an unprotected left turn at an intersection, yielding to oncoming traffic. + ![image](https://leaderboard.carla.org/assets/images/TR08.png) + - The ego-vehicle is performing a right turn at an intersection, yielding to crossing traffic. + ![image](https://leaderboard.carla.org/assets/images/TR09.png) + - The ego-vehicle needs to negotiate with other vehicles to cross an unsignalized intersection. + ![image](https://leaderboard.carla.org/assets/images/TR10.png) + - The ego-vehicle is going straight at an intersection but a crossing vehicle runs a red light, forcing the ego-vehicle to avoid the collision. + ![image](https://leaderboard.carla.org/assets/images/TR07.png) + - The ego-vehicle needs to perform a turn at an intersection yielding to bicycles crossing from either the left or right. + ![image](https://leaderboard.carla.org/assets/images/TR13.png) + +- ### **Highway:** + + - The ego-vehicle merges into moving highway traffic from a highway on-ramp. + ![image](https://leaderboard.carla.org/assets/images/TR18.png) + - The ego-vehicle encounters a vehicle merging into its lane from a highway on-ramp. + ![image](https://leaderboard.carla.org/assets/images/TR19.png) + - The ego-vehicle encounters a vehicle cutting into its lane from a lane of static traffic. + ![image](https://leaderboard.carla.org/assets/images/TR20.png) + - The ego-vehicle must cross a lane of moving traffic to exit the highway at an off-ramp. + ![image](https://leaderboard.carla.org/assets/images/TR21.png) + - The ego-vehicle is approached by an emergency vehicle coming from behind. + ![image](https://leaderboard.carla.org/assets/images/TR23.png) + +- ### **Obstacle avoidance:** + + - The ego-vehicle encounters an obstacle blocking the lane and must perform a lane change into traffic moving in the same or opposite direction to avoid it. The obstacle may be a construction site, an accident or a parked vehicle. + ![image](https://leaderboard.carla.org/assets/images/TR14.png) + - The ego-vehicle encounters a parked vehicle opening a door into its lane and must maneuver to avoid it. + ![image](https://leaderboard.carla.org/assets/images/TR15.png) + - The ego-vehicle encounters a slow moving hazard (e.g. bicycles) blocking part of the lane. The ego-vehicle must brake or maneuver next to a lane of traffic moving in the same or opposite direction to avoid it. + ![image](https://leaderboard.carla.org/assets/images/TR16.png) + - The ego-vehicle encounters an oncoming vehicles invading its lane on a bend due to an obstacle. + ![image](https://leaderboard.carla.org/assets/images/TR22.png) + +- ### **Braking and lane changing:** + + - The leading vehicle decelerates suddenly due to an obstacle and the ego-vehicle must perform an emergency brake or an avoidance maneuver. + ![image](https://leaderboard.carla.org/assets/images/TR02.png) + - The ego-vehicle encounters an obstacle / unexpected entity on the road and must perform an emergency brake or an avoidance maneuver. + ![image](https://leaderboard.carla.org/assets/images/TR03.png) + - The ego-vehicle encounters an pedestrian emerging from behind a parked vehicle and advancing into the lane. + ![image](https://leaderboard.carla.org/assets/images/TR17.png) + - While performing a maneuver, the ego-vehicle encounters an obstacle in the road, either a pedestrian or a bicycle, and must perform an emergency brake or an avoidance maneuver. + ![image](https://leaderboard.carla.org/assets/images/TR04.png) + - While performing a maneuver, the ego-vehicle encounters a stopped vehicle in the road and must perform an emergency brake or an avoidance maneuver. + ![image](https://leaderboard.carla.org/assets/images/TR19a.png) + - The ego-vehicle must slow down or brake to allow a parked vehicle exiting a parallel parking bay to cut in front. + ![image](https://leaderboard.carla.org/assets/images/TR12.png) + +- ### **Parking:** + + - The ego-vehicle must exit a parallel parking bay into a flow of traffic. + ![image](https://leaderboard.carla.org/assets/images/TR11.png) + +## Available Sensors + +- **[GNSS](https://carla.readthedocs.io/en/latest/ref_sensors/#gnss-sensor)** + - GPS sensor returning geo location data. +- **[IMU](https://carla.readthedocs.io/en/latest/ref_sensors/#imu-sensor)** + - 6-axis inertial measurement unit. +- **[LIDAR](https://carla.readthedocs.io/en/latest/ref_sensors/#lidar-sensor)** + - Laser to detect obstacles. +- **[RADAR](https://carla.readthedocs.io/en/latest/ref_sensors/#radar-sensor)** + - Long-range RADAR (up to 100 meters). +- **[RGB CAMERA](https://carla.readthedocs.io/en/latest/ref_sensors/#rgb-camera)** + - Regular camera for image capture +- **[COLLISION DETECTOR](https://carla.readthedocs.io/en/latest/ref_sensors/#collision-detector)** + - Used to detect collisions with other actors. +- **[DEPTH CAMERA](https://carla.readthedocs.io/en/latest/ref_sensors/#depth-camera)** + - Provides a distance-coded picture to create a depth map of the scene. +- **[LANE INVASION DETECTOR](https://carla.readthedocs.io/en/latest/ref_sensors/#lane-invasion-detector)** + - Uses road data to detect when the vehicle crosses a lane marking. +- **[OBSTACLE DETECTOR](https://carla.readthedocs.io/en/latest/ref_sensors/#obstacle-detector)** + - Detects obstacles in front of the vehicle within a capsular shape. Works geometry based so requires obstacles to exist as geometry in the scene. (seems like a pure simulation sensor) +- **[RSS SENSOR](https://carla.readthedocs.io/en/latest/ref_sensors/#rss-sensor)** + - Integrates the responisbility sensitive safety model in CARLA. Basically a framwork for mathematical guidelines on how to react in various scenarios. Disabled by default. +- **[SEMANTIC LIDAR](https://carla.readthedocs.io/en/latest/ref_sensors/#semantic-lidar-sensor)** + - Similiar to LIDAR with a different data structure/focus. +- **[SEMANTIC SEGMENTATION CAMERA](https://carla.readthedocs.io/en/latest/ref_sensors/#semantic-segmentation-camera)** + - Classifies objects in sight by tag (seems like a pure simulation sensor). +- **[INSTANCE SEGMENTATION CAMERA](https://carla.readthedocs.io/en/latest/ref_sensors/#instance-segmentation-camera)** + - Classifies every object by class and instance ID (seems like a pure simulation sensor). +- **[DVS CAMERA](https://carla.readthedocs.io/en/latest/ref_sensors/#dvs-camera)** + - Different kind of camera that works in high-speed scenarios. Pixels asynchronously respond to local changes in brightness instead of globally by shutter. +- **[OPTICAL FLOW CAMERA](https://carla.readthedocs.io/en/latest/ref_sensors/#optical-flow-camera)** + - Captures motion(velocity per pixel) perceived from the point of view of the camera. +- **[V2X SENSOR](https://carla.readthedocs.io/en/latest/ref_sensors/#v2x-sensor)** + - Vehicle to everything sensor, allows vehicle to communicate with other vehicles and elements in the environment. More like a concept for the future, not widespread implemented yet. 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The information is gained using the rqt-graph. There are three main topics which represent the communication: + +- `/paf/hero/trajectory` +- `/paf/hero/emergency` +- `/paf/hero/curr_behavior` + +![Diagram showing the involved nodes and topics and their relation](../../../assets/research_assets/planning_acting_communication.svg) + +## Useful commands + +- The types of the messages published on the mentioned topics can be found out with the command `rostopic type `. +- To have a deeper look on the message types, you can use the command `rosmsg show `. +- `rostopic echo ` shows all the messages published on the specified topic. + +## Messages + +The topic `/paf/hero/trajectory` uses the message type `nav_msgs/Path`. This type looks as follows: + +![Format of Path messages](../../../assets/research_assets/nav_msgs_Path_type.png) + +Every message that is published consists of several parts of this structure. One concrete example of a part of such a message can be seen here: + +![Example of a Path message](../../../assets/research_assets/trajectory_example.png) + +The topic `/paf/hero/emergency` uses the message type `std_msgs/Bool`, so the emergency case can either be true or false. + +The topic `/paf/hero/curr_behavior` uses the message type `std_msgs/String`. Examples of these messages can be seen here: + +![Examples of messages posted on the topic curr_behavior](../../../assets/research_assets/curr_behavior_example.png) diff --git a/doc/research/paf24/planning_acting_communication.svg b/doc/research/paf24/planning_acting_communication.svg deleted file mode 100644 index 3d46178e..00000000 --- a/doc/research/paf24/planning_acting_communication.svg +++ /dev/null @@ -1,4 +0,0 @@ - - - -
MotionPlanning
MotionPlanning
CollisionCheck
CollisionCheck
behavior_agent
behavior_agent
pure_pursuit_controller
pure_pursuit_controller
stanley_controller
stanley_controller
vehicle_controller
vehicle_controller
MotionPlanning
MotionPlanning
/paf/hero/trajectory
/paf/hero/trajectory
/paf/hero/emergency
/paf/hero/emergency
/paf/hero/curr_behavior
/paf/hero/curr_behavior
Publisher
Publisher
Topic
Topic
Subscriber
Subscriber
\ No newline at end of file diff --git a/doc/research/paf24/planning_acting_relation.md b/doc/research/paf24/planning_acting_relation.md deleted file mode 100644 index f4de99d2..00000000 --- a/doc/research/paf24/planning_acting_relation.md +++ /dev/null @@ -1,36 +0,0 @@ -# Relation between planning and acting - -This diagram shows the basics of the communication between the planning (green) and acting (blue) components. The information is gained using the rqt-graph. There are three main topics which represent the communication: -* `/paf/hero/trajectory` -* `/paf/hero/emergency` -* `/paf/hero/curr_behavior` - -![](planning_acting_communication.svg) - -## Useful commands -* The types of the messages published on the mentioned topics can be found out with the command `rostopic type `. -* To have a deeper look on the message types, you can use the command `rosmsg show `. -* `rostopic echo ` shows all the messages published on the specified topic. - - -## Messages -The topic `/paf/hero/trajectory` uses the message type `nav_msgs/Path`. This type looks as follows: - -![](nav_msgs_Path_type.png) - -Every message that is published consists of several parts of this structure. One concrete example of a part of such a message can be seen here: - -![](trajectory_example.png) - - -The topic `/paf/hero/emergency` uses the message type `std_msgs/Bool`, so the emergency case can either be true or false. - - -The topic `/paf/hero/curr_behavior` uses the message type `std_msgs/String`. Examples of these messages can be seen here: - -![](curr_behavior_example.png) - - - - - diff --git a/doc/research/paf24/trajectory_example.png b/doc/research/paf24/trajectory_example.png deleted file mode 100644 index 8e313a14499aad4a2cdab1697904dd367129922a..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 32823 zcmbTeWmp_t7Oo8;L4pQ?I|=UYke~?~+%0Hu*G7T|cZVjyfs%jyAgSuA>Z-l>TFY|h z!21pR_1sD9qbf4&2Y_r63i~&qv!tf8ik+FWn~|d_oVl%?jVZH}v7@P}t&@eF^D#o3 z2y7?jr=7$dO^uwt*x6F3ez7ryQ?+oR;AE#zGV(v?u&USVd{Px9>M{jAjAH&tx z5cK_CW@fu~U;joLe(JT3$3!VYj|&%4Oin5;j$*4DA66@IX)WrzunQp~&)y2P*qWa2 zs1_DEKb-TQClh#((Ue^Yt*MIuZ*E3`;*?3;VAmw`#nM!-(8F9iMV+1fZG=|u@mlJu zdlct>3&<{i$j`DP5*@bKF?dS3`E&fpE?TwA~hgY0kVgy*&APyB9Ls z@x=GUEqMJOx+K-5(2O?{%D=l3q49W8rQ*b!;d6EOkjk_fv4P_3k3+k|y5x z+c%E@3OBvVC0a^}-JI`+r3H7X?liq*ofa}CR|QQUCqyMO_cq6OiN~sB)kXk9nJEYb z^@i*YpTW+bBh|jlTpGa*>ENBN<=>OYau~5w|MG(3Ve65vp{$(koPVvhQBMWVGH-=< 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insertions(+), 1 deletion(-) diff --git a/code/planning/src/local_planner/motion_planning.py b/code/planning/src/local_planner/motion_planning.py index 8c1514cb..589e6456 100755 --- a/code/planning/src/local_planner/motion_planning.py +++ b/code/planning/src/local_planner/motion_planning.py @@ -3,6 +3,7 @@ import math import os import sys +from typing import List import numpy as np import ros_compatibility as roscomp @@ -329,7 +330,7 @@ def __set_trajectory(self, data: Path): self.__corners = self.__calc_corner_points() - def __calc_corner_points(self) -> list[list[np.ndarray]]: + def __calc_corner_points(self) -> List[List[np.ndarray]]: """ Calculate the corner points of the trajectory. 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MotionPlanning
MotionPlanning
CollisionCheck
CollisionCheck
behavior_agent
behavior_agent
pure_pursuit_controller
pure_pursuit_controller
stanley_controller
stanley_controller
vehicle_controller
vehicle_controller
MotionPlanning
MotionPlanning
/paf/hero/trajectory
/paf/hero/trajectory
/paf/hero/emergency
/paf/hero/emergency
/paf/hero/curr_behavior
/paf/hero/curr_behavior
Publisher
Publisher
Topic
Topic
Subscriber
Subscriber
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As it is a crucial part of the project to understand the component interactions, especially the up-to-date +version of it, in this document I will give a brief overview of the existing documentation and some links to the up-to-date versions. + +## Existing architecture documentation from the previous semester +A miro board with the existing architecture (Perception, planning, acting) is existing. +![Architecture overview](/doc/assets/overview.jpg) +The miro-board can be found [here](https://miro.com/welcomeonboard/a1F0d1dya2FneWNtbVk4cTBDU1NiN3RiZUIxdGhHNzJBdk5aS3N4VmdBM0R5c2Z1VXZIUUN4SkkwNHpuWlk2ZXwzNDU4NzY0NTMwNjYwNzAyODIzfDI=?share_link_id=785020837509). + +This miro board does contain the main architecture details and flows of information but when comparing it to the rosgraph of the nodes and topics it seems like the diagram is not complete. + +### Rosgraph of the nodes and topics of the project: +[//]: # "![Up to date ros graph](/doc/assets/research_assets/rosgraph.svg)" +![Up to date ros graph](/doc/assets/research_assets/rosgraph_leaf_topics.svg) + +### Rewritten rosgraph divided into perception, planning and acting: +![RosGraphDrawIO](/doc/assets/research_assets/node_path_ros.png) + +There you can see the nodes and the in- and outputs. In represents the subscribed topics and out the published topics. +This should now be extended by what logic is happening in which node and how the nodes are connected. + +## TODO: Create or add additional information of the teams. This is split into other issues, which will extend this document. From 22c8434327640eb06656721d078de0c74effaf4c Mon Sep 17 00:00:00 2001 From: RoyaLxPole <124280467+RoyaLxPole@users.noreply.github.com> Date: Thu, 31 Oct 2024 20:17:50 +0100 Subject: [PATCH 18/57] Update segmentation.md --- doc/research/paf24/segmentation.md | 45 +++++++++++++++++++----------- 1 file changed, 28 insertions(+), 17 deletions(-) diff --git a/doc/research/paf24/segmentation.md b/doc/research/paf24/segmentation.md index 7f96346a..d954d794 100644 --- a/doc/research/paf24/segmentation.md +++ b/doc/research/paf24/segmentation.md @@ -3,10 +3,10 @@ **Summary:** This page contains the research into the segmentation component of Autoware. - [Segmentation](#Segmentation) - - [Already implemended solutions](#1.-Already-implemented-solutions) - - [Implemented but dropped](#2Implemented-but-dropped) - - [Carla Sensors](#3-Carla-Sensors) - - [Follow up Question](#4-Follow-up-Question) + - [Already implemented solutions](#already-implemented-solutions) + - [Implemented but dropped](#implemented-but-dropped) + - [Carla Sensors](#carla-sensors) + - [Follow-up Question](#follow-up-question) ## Already implemented solutions @@ -19,23 +19,34 @@ https://github.com/una-auxme/paf/blob/8e8f9a1a03ae09d5ac763c1a11b398fc1ce144b0/c https://github.com/una-auxme/paf/blob/8c968fb5c6c44c15b2733c5a181c496eb9b244be/doc/perception/efficientps.md#efficientps ## Carla Sensors: -https://carla.readthedocs.io/en/0.8.4/cameras_and_sensors/ -``` -camera = carla.sensor.Camera('MyCamera', PostProcessing='SemanticSegmentation') -camera.set(FOV=90.0) -camera.set_image_size(800, 600) -camera.set_position(x=0.30, y=0, z=1.30) -camera.set_rotation(pitch=0, yaw=0, roll=0) +![Alt text](https://carla.readthedocs.io/en/0.9.14/img/ref_sensors_semantic.jpg) +![Alt text](https://carla.readthedocs.io/en/0.9.14/img/tuto_sem.jpg) -carla_settings.add_sensor(camera) -``` -![Alt text](https://github.com/una-auxme/paf/blob/368-visionnode-and-segmentation/doc/assets/perception/Carla_Segmentation_Sensor.png) +for more context: +the pedestrian walks will be labled as roadlines + +example: +``` +# -------------- +# Add a new semantic segmentation camera to my ego +# -------------- +sem_cam = None +sem_bp = world.get_blueprint_library().find('sensor.camera.semantic_segmentation') +sem_bp.set_attribute("image_size_x",str(1920)) +sem_bp.set_attribute("image_size_y",str(1080)) +sem_bp.set_attribute("fov",str(105)) +sem_location = carla.Location(2,0,1) +sem_rotation = carla.Rotation(0,180,0) +sem_transform = carla.Transform(sem_location,sem_rotation) +sem_cam = world.spawn_actor(sem_bp,sem_transform,attach_to=ego_vehicle, attachment_type=carla.AttachmentType.Rigid) +# This time, a color converter is applied to the image, to get the semantic segmentation view +sem_cam.listen(lambda image: image.save_to_disk('tutorial/new_sem_output/%.6d.jpg' % image.frame,carla.ColorConverter.CityScapesPalette)) -there is another solution implemented by the carla simulator: +``` +For more information: https://carla.readthedocs.io/en/0.9.14/ref_sensors/#semantic-segmentation-camera:~:text=the%20object%20it.-,Semantic%20segmentation%20camera,-Blueprint%3A%20sensor -for more context: -the pedestrian walks will be labeld as roadlines +https://carla.readthedocs.io/en/0.9.14/tuto_G_retrieve_data/#semantic-segmentation-camera:~:text=on%20the%20right.-,Semantic%20segmentation%20camera,-The%20semantic%20segmentation ## Follow up Question: Why the last group used bounding boxes and not the segmentation model is it to slow or maybe not reliable? 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++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/doc/research/paf24/segmentation.md b/doc/research/paf24/segmentation.md index d954d794..4f81577e 100644 --- a/doc/research/paf24/segmentation.md +++ b/doc/research/paf24/segmentation.md @@ -1,3 +1,4 @@ + # Segmentation **Summary:** This page contains the research into the segmentation component of Autoware. @@ -16,9 +17,11 @@ probably trained with the generated dataset: https://github.com/una-auxme/paf/blob/8e8f9a1a03ae09d5ac763c1a11b398fc1ce144b0/code/perception/src/dataset_generator.py#L109-L110 ## Implemented but dropped: + https://github.com/una-auxme/paf/blob/8c968fb5c6c44c15b2733c5a181c496eb9b244be/doc/perception/efficientps.md#efficientps ## Carla Sensors: + ![Alt text](https://carla.readthedocs.io/en/0.9.14/img/ref_sensors_semantic.jpg) ![Alt text](https://carla.readthedocs.io/en/0.9.14/img/tuto_sem.jpg) @@ -26,7 +29,9 @@ for more context: the pedestrian walks will be labled as roadlines example: -``` + +``` + # -------------- # Add a new semantic segmentation camera to my ego # -------------- @@ -43,10 +48,12 @@ sem_cam = world.spawn_actor(sem_bp,sem_transform,attach_to=ego_vehicle, attachme sem_cam.listen(lambda image: image.save_to_disk('tutorial/new_sem_output/%.6d.jpg' % image.frame,carla.ColorConverter.CityScapesPalette)) ``` + For more information: https://carla.readthedocs.io/en/0.9.14/ref_sensors/#semantic-segmentation-camera:~:text=the%20object%20it.-,Semantic%20segmentation%20camera,-Blueprint%3A%20sensor https://carla.readthedocs.io/en/0.9.14/tuto_G_retrieve_data/#semantic-segmentation-camera:~:text=on%20the%20right.-,Semantic%20segmentation%20camera,-The%20semantic%20segmentation ## Follow up Question: + Why the last group used bounding boxes and not the segmentation model is it to slow or maybe not reliable? From 5cbcd68448ad61789b2ce6eca79634d04e0bdef5 Mon Sep 17 00:00:00 2001 From: RoyaLxPole <124280467+RoyaLxPole@users.noreply.github.com> Date: Thu, 31 Oct 2024 20:59:25 +0100 Subject: [PATCH 21/57] Update segmentation.md linter update --- doc/research/paf24/segmentation.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/doc/research/paf24/segmentation.md b/doc/research/paf24/segmentation.md index 4f81577e..674f749b 100644 --- a/doc/research/paf24/segmentation.md +++ b/doc/research/paf24/segmentation.md @@ -11,16 +11,16 @@ ## Already implemented solutions -https://github.com/una-auxme/paf/blob/c3011ee70039e199e106c54aa162a8f52be241a6/code/perception/launch/perception.launch?plain=1#L59-L61 + probably trained with the generated dataset: -https://github.com/una-auxme/paf/blob/8e8f9a1a03ae09d5ac763c1a11b398fc1ce144b0/code/perception/src/dataset_generator.py#L109-L110 + -## Implemented but dropped: +## Implemented but dropped -https://github.com/una-auxme/paf/blob/8c968fb5c6c44c15b2733c5a181c496eb9b244be/doc/perception/efficientps.md#efficientps + -## Carla Sensors: +## Carla Sensors ![Alt text](https://carla.readthedocs.io/en/0.9.14/img/ref_sensors_semantic.jpg) ![Alt text](https://carla.readthedocs.io/en/0.9.14/img/tuto_sem.jpg) @@ -30,7 +30,7 @@ the pedestrian walks will be labled as roadlines example: -``` +```Python # -------------- # Add a new semantic segmentation camera to my ego @@ -50,10 +50,10 @@ sem_cam.listen(lambda image: image.save_to_disk('tutorial/new_sem_output/%.6d.jp ``` For more information: -https://carla.readthedocs.io/en/0.9.14/ref_sensors/#semantic-segmentation-camera:~:text=the%20object%20it.-,Semantic%20segmentation%20camera,-Blueprint%3A%20sensor + -https://carla.readthedocs.io/en/0.9.14/tuto_G_retrieve_data/#semantic-segmentation-camera:~:text=on%20the%20right.-,Semantic%20segmentation%20camera,-The%20semantic%20segmentation + -## Follow up Question: +## Follow up Question Why the last group used bounding boxes and not the segmentation model is it to slow or maybe not reliable? From 852ad10dd52f9ad5a48be8f319cf9e35697bcec8 Mon Sep 17 00:00:00 2001 From: Sebastian Seitz Date: Thu, 31 Oct 2024 21:14:22 +0100 Subject: [PATCH 22/57] excessive links removed --- .../paf24/general/old_research_overview.md | 24 ++++---- doc/research/paf24/old_research_overview.md | 56 ------------------- 2 files changed, 12 insertions(+), 68 deletions(-) delete mode 100644 doc/research/paf24/old_research_overview.md diff --git a/doc/research/paf24/general/old_research_overview.md b/doc/research/paf24/general/old_research_overview.md index aaa04d80..71bef29c 100644 --- a/doc/research/paf24/general/old_research_overview.md +++ b/doc/research/paf24/general/old_research_overview.md @@ -1,23 +1,23 @@ # Research Summary -**Summary:** The research of the previous groups is condensed into this file to make it an entry point for this years project. +**Summary:** The research of the previous groups is condensed into this file to make it an entry point for this year's project. -- [**Research and Resources**](#research-and-resources) -- [**Acting and Control Modules**](#acting-and-control-modules) -- [**Planning and Trajectory Generation**](#planning-and-trajectory-generation) -- [**State Machine for Decision-Making**](#state-machine-for-decision-making) -- [**OpenDrive Integration and Navigation Data**](#opendrive-integration-and-navigation-data) +- [Research and Resources](#research-and-resources) +- [Acting and Control Modules](#acting-and-control-modules) +- [Planning and Trajectory Generation](#planning-and-trajectory-generation) +- [State Machine for Decision-Making](#state-machine-for-decision-making) +- [OpenDrive Integration and Navigation Data](#opendrive-integration-and-navigation-data) -## **[Research and Resources](./README.md)** +## Research and Resources -- This section provides an extensive foundation for the autonomous vehicle project by consolidating previous research from **[PAF22](./paf22/)** and **[PAF23](./paf23/)**. +- This section provides an extensive foundation for the autonomous vehicle project by consolidating previous research from **PAF22** and **PAF23**. - **PAF22**: Established core methods for autonomous vehicle control and perception, including traffic light detection and emergency braking features. It also set up the base components of the CARLA simulator integration, essential sensor configurations, and data processing pipelines. - **PAF23**: Enhanced lane-change algorithms and expanded intersection-handling strategies. This project introduced a more robust approach to decision-making at intersections, factoring in pedestrian presence, oncoming traffic, and improved signal detection. Additionally, **PAF23** refined vehicle - behavior for more fluid lane-change maneuvers, optimizing control responses to avoid obstacles and maintain lane positioning during highway merging and overtaking scenarios. - The resources also include CARLA-specific tools such as the CARLA Leaderboard and ROS Bridge integration, which link CARLA’s simulation environment to the Robot Operating System (ROS). Detailed references to CARLA’s sensor suite are provided, covering RGB cameras, LIDAR, radar, GNSS, and IMU sensors essential for perception and control. -## **[Acting and Control Modules](./paf22/acting/implementation_acting.md)** +## Acting and Control Modules - The **acting module** focuses on the vehicle’s control actions, including throttle, steering, and braking. - **Core Controllers**: Contains controllers like the **PID controller** for longitudinal (speed) control and **Pure Pursuit** and **Stanley controllers** for lateral (steering) control. These controllers work in unison to achieve precise vehicle handling, especially on turns and at varying speeds. @@ -27,7 +27,7 @@ other controls in hazardous situations. improvements in lane-changing safety. - **Sensor Integration**: The acting module subscribes to **navigation and sensor data** topics to remain updated on vehicle position and velocity, integrating sensor feedback for real-time control adjustments. -## **[Planning and Trajectory Generation](./paf22/planning/basics.md)** +## Planning and Trajectory Generation - The **planning module** is critical for determining safe, efficient routes by combining **global and local path planning** techniques. - **Global Planning**: Uses the **CommonRoad route planner** from TUM, creating a high-level path based on predefined waypoints. **PAF22** initially set up this planner, while **PAF23** added finer adjustments for lane selection and obstacle navigation. @@ -35,7 +35,7 @@ improvements in lane-changing safety. Local planning includes **trajectory tracking** using Pure Pursuit and Stanley controllers to maintain a steady path. - **PAF23 Enhancements**: Improved **collision avoidance** algorithms and added real-time updates to the trajectory based on sensor data. The local planner now adapts quickly to lane-change requests or route deviations due to traffic, creating a seamless flow between global and local path planning. -## **[State Machine for Decision-Making](./paf22/planning/state_machine_design.md)** +## State Machine for Decision-Making - This modular state machine handles various driving behaviors, including **lane changes**, **intersections**, and **traffic light responses**. - **Core State Machines**: The **driving state machine** manages normal vehicle navigation, controlling target speed and ensuring lane compliance. The **lane-change state machine** makes safe decisions based on lane availability and traffic, @@ -46,7 +46,7 @@ while the **intersection state machine** manages vehicle approach, stop, and tur The **intersection state machine** now incorporates detailed behaviors for handling left turns, straight passes, and right turns, considering pedestrian zones and cross-traffic. **PAF23** also introduced an adaptive lane-change state, which calculates safety based on vehicle speed, distance to adjacent vehicles, and road type. -## **[OpenDrive Integration and Navigation Data](./paf22/planning/OpenDrive.md)** +## OpenDrive Integration and Navigation Data - **OpenDrive** files provide a structured road network description, detailing lanes, road segments, intersections, and traffic signals. The navigation data is published in CARLA as ROS topics containing GPS/world coordinates and route instructions. - **PAF22 Setup**: Established OpenDrive as the core format for map data, integrating it with the CARLA simulator. Initial work involved parsing road and lane data to create accurate trajectories. diff --git a/doc/research/paf24/old_research_overview.md b/doc/research/paf24/old_research_overview.md deleted file mode 100644 index aaa04d80..00000000 --- a/doc/research/paf24/old_research_overview.md +++ /dev/null @@ -1,56 +0,0 @@ -# Research Summary - -**Summary:** The research of the previous groups is condensed into this file to make it an entry point for this years project. - -- [**Research and Resources**](#research-and-resources) -- [**Acting and Control Modules**](#acting-and-control-modules) -- [**Planning and Trajectory Generation**](#planning-and-trajectory-generation) -- [**State Machine for Decision-Making**](#state-machine-for-decision-making) -- [**OpenDrive Integration and Navigation Data**](#opendrive-integration-and-navigation-data) - -## **[Research and Resources](./README.md)** - -- This section provides an extensive foundation for the autonomous vehicle project by consolidating previous research from **[PAF22](./paf22/)** and **[PAF23](./paf23/)**. -- **PAF22**: Established core methods for autonomous vehicle control and perception, including traffic light detection and emergency braking features. It also set up the base components of the CARLA simulator integration, essential sensor configurations, and data processing pipelines. -- **PAF23**: Enhanced lane-change algorithms and expanded intersection-handling strategies. This project introduced a more robust approach to decision-making at intersections, factoring in pedestrian presence, oncoming traffic, and improved signal detection. Additionally, **PAF23** refined vehicle -- behavior for more fluid lane-change maneuvers, optimizing control responses to avoid obstacles and maintain lane positioning during highway merging and overtaking scenarios. -- The resources also include CARLA-specific tools such as the CARLA Leaderboard and ROS Bridge integration, which link CARLA’s simulation environment to the Robot Operating System (ROS). Detailed references to CARLA’s sensor suite are provided, covering RGB cameras, LIDAR, radar, GNSS, and IMU -sensors essential for perception and control. - -## **[Acting and Control Modules](./paf22/acting/implementation_acting.md)** - -- The **acting module** focuses on the vehicle’s control actions, including throttle, steering, and braking. -- **Core Controllers**: Contains controllers like the **PID controller** for longitudinal (speed) control and **Pure Pursuit** and **Stanley controllers** for lateral (steering) control. These controllers work in unison to achieve precise vehicle handling, especially on turns and at varying speeds. -- **PAF22 Contributions**: Implemented the PID-based longitudinal controller and integrated the Pure Pursuit controller for basic trajectory following, setting a solid foundation for steering and throttle control. PAF22 also laid out the preliminary **emergency braking** logic, designed to override -other controls in hazardous situations. -- **PAF23 Contributions**: Introduced refinements in lateral control, including the adaptive Stanley controller, which adjusts steering sensitivity based on vehicle speed to maintain a smooth trajectory. **PAF23** also optimized the emergency braking logic to respond more quickly to obstacles, with -improvements in lane-changing safety. -- **Sensor Integration**: The acting module subscribes to **navigation and sensor data** topics to remain updated on vehicle position and velocity, integrating sensor feedback for real-time control adjustments. - -## **[Planning and Trajectory Generation](./paf22/planning/basics.md)** - -- The **planning module** is critical for determining safe, efficient routes by combining **global and local path planning** techniques. -- **Global Planning**: Uses the **CommonRoad route planner** from TUM, creating a high-level path based on predefined waypoints. **PAF22** initially set up this planner, while **PAF23** added finer adjustments for lane selection and obstacle navigation. -- **Local Planning**: Tailored for dynamic obstacles, this component focuses on immediate adjustments to the vehicle’s path, particularly useful in urban environments with unpredictable elements. -Local planning includes **trajectory tracking** using Pure Pursuit and Stanley controllers to maintain a steady path. -- **PAF23 Enhancements**: Improved **collision avoidance** algorithms and added real-time updates to the trajectory based on sensor data. The local planner now adapts quickly to lane-change requests or route deviations due to traffic, creating a seamless flow between global and local path planning. - -## **[State Machine for Decision-Making](./paf22/planning/state_machine_design.md)** - -- This modular state machine handles various driving behaviors, including **lane changes**, **intersections**, and **traffic light responses**. -- **Core State Machines**: The **driving state machine** manages normal vehicle navigation, controlling target speed and ensuring lane compliance. The **lane-change state machine** makes safe decisions based on lane availability and traffic, -while the **intersection state machine** manages vehicle approach, stop, and turn behaviors at intersections. - -- **PAF22 Contributions**: Developed the base decision-making states, enabling lane following, simple lane-change maneuvers, and basic intersection handling. -- **PAF23 Contributions**: Significantly enhanced the state machine by adding specialized states for complex maneuvers, like responding to oncoming traffic at intersections, merging onto highways, and making priority-based decisions at roundabouts. -The **intersection state machine** now incorporates detailed behaviors for handling left turns, straight passes, and right turns, considering pedestrian zones and cross-traffic. **PAF23** also introduced an adaptive lane-change state, -which calculates safety based on vehicle speed, distance to adjacent vehicles, and road type. - -## **[OpenDrive Integration and Navigation Data](./paf22/planning/OpenDrive.md)** - -- **OpenDrive** files provide a structured road network description, detailing lanes, road segments, intersections, and traffic signals. The navigation data is published in CARLA as ROS topics containing GPS/world coordinates and route instructions. -- **PAF22 Setup**: Established OpenDrive as the core format for map data, integrating it with the CARLA simulator. Initial work involved parsing road and lane data to create accurate trajectories. -- **PAF23 Enhancements**: Improved the parsing of OpenDrive files, focusing on high-level map data relevant to the vehicle's route, such as signal placements and lane restrictions. This project also optimized the navigation data integration with ROS, -ensuring the vehicle receives consistent updates on its location relative to the route, intersections, and nearby obstacles. -- **Navigation Data Structure**: The system uses navigation data points, including **GPS coordinates, world coordinates, and high-level route instructions** (e.g., turn left, change lanes) to guide the vehicle. Each point is matched with a **road option command**, -instructing the vehicle on how to proceed at specific waypoints. From 8bb463dec5c521879ef9a0a75e538c98dda8102d Mon Sep 17 00:00:00 2001 From: victor-42 Date: Thu, 31 Oct 2024 23:21:24 +0100 Subject: [PATCH 23/57] Added new diagram of current planning architecture to Readme. Changed confusing naming scheme in Readme. 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(preplanning_trajectory.py)](./Preplanning.md) -Preplanning is very close to the global plan. The challenge of the preplanning focuses on creating a trajectory out of +This module focuses on creating a trajectory out of an OpenDrive map (ASAM OpenDrive). As input it receives an xodr file (OpenDrive format) and the target points from the leaderboard with the belonging actions. For example action number 3 means, drive through the intersection. -### [Global plan](./Global_Planner.md) +### [Global Planning (PrePlanner)](./Global_Planner.md) The global planner is responsible for collecting and preparing all data from the leaderboard and other intern components that is needed for the preplanning component. @@ -28,7 +30,9 @@ decision tree, which is easy to adapt and to expand. ### [Local Planning](./Local_Planning.md) -The Local Planning component is responsible for evaluating short term decisions in the local environment of the ego vehicle. It containes components responsible for detecting collisions and reacting e. g. lowering speed. -The local planning also executes behaviors e. g. changes the trajectory for an overtake. +This module includes the Nodes: ACC, CollisionCheck, MotionPlanner + +The Local Planning package is responsible for evaluating short term decisions in the local environment of the ego vehicle. 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