From 7442caf26e504c33aa61a2c21a1774329df91d1e Mon Sep 17 00:00:00 2001 From: Sebastian Seitz Date: Thu, 31 Oct 2024 18:13:39 +0100 Subject: [PATCH 1/5] summarized previous research #396 --- doc/research/paf24/old_research_overview.md | 56 +++++++++++++++++++++ 1 file changed, 56 insertions(+) create mode 100644 doc/research/paf24/old_research_overview.md diff --git a/doc/research/paf24/old_research_overview.md b/doc/research/paf24/old_research_overview.md new file mode 100644 index 00000000..6e388ee4 --- /dev/null +++ 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 2/5] 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 3/5] 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 852ad10dd52f9ad5a48be8f319cf9e35697bcec8 Mon Sep 17 00:00:00 2001 From: Sebastian Seitz Date: Thu, 31 Oct 2024 21:14:22 +0100 Subject: [PATCH 4/5] 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 ccbad810690d4472ea6aed3bfc93f42acc982ea0 Mon Sep 17 00:00:00 2001 From: Sebastian Seitz Date: Fri, 1 Nov 2024 21:39:35 +0100 Subject: [PATCH 5/5] 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