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autoware_motion_velocity_boundary_departure_prevention_module package from autoware_universe repo

autoware_agnocast_wrapper autoware_auto_common autoware_boundary_departure_checker autoware_component_interface_specs_universe autoware_component_interface_tools autoware_component_interface_utils autoware_cuda_dependency_meta autoware_fake_test_node autoware_glog_component autoware_goal_distance_calculator autoware_grid_map_utils autoware_path_distance_calculator autoware_polar_grid autoware_time_utils autoware_traffic_light_recognition_marker_publisher autoware_traffic_light_utils autoware_universe_utils tier4_api_utils autoware_autonomous_emergency_braking autoware_collision_detector autoware_control_command_gate autoware_control_performance_analysis autoware_control_validator autoware_external_cmd_selector autoware_joy_controller autoware_lane_departure_checker autoware_mpc_lateral_controller autoware_obstacle_collision_checker autoware_operation_mode_transition_manager autoware_pid_longitudinal_controller autoware_predicted_path_checker autoware_pure_pursuit autoware_shift_decider autoware_smart_mpc_trajectory_follower autoware_stop_mode_operator autoware_trajectory_follower_base autoware_trajectory_follower_node autoware_vehicle_cmd_gate autoware_control_evaluator autoware_kinematic_evaluator autoware_localization_evaluator autoware_perception_online_evaluator autoware_planning_evaluator autoware_scenario_simulator_v2_adapter tier4_autoware_api_launch tier4_control_launch tier4_localization_launch tier4_map_launch tier4_perception_launch tier4_planning_launch tier4_sensing_launch tier4_simulator_launch tier4_system_launch tier4_vehicle_launch autoware_geo_pose_projector autoware_ar_tag_based_localizer autoware_landmark_manager autoware_lidar_marker_localizer autoware_localization_error_monitor autoware_pose2twist autoware_pose_covariance_modifier autoware_pose_estimator_arbiter autoware_pose_instability_detector yabloc_common yabloc_image_processing yabloc_monitor yabloc_particle_filter yabloc_pose_initializer autoware_map_tf_generator autoware_bevfusion autoware_bytetrack autoware_cluster_merger autoware_compare_map_segmentation autoware_crosswalk_traffic_light_estimator autoware_detected_object_feature_remover autoware_detected_object_validation autoware_detection_by_tracker autoware_elevation_map_loader autoware_euclidean_cluster autoware_ground_segmentation autoware_image_projection_based_fusion autoware_lidar_apollo_instance_segmentation autoware_lidar_centerpoint autoware_lidar_transfusion autoware_map_based_prediction autoware_multi_object_tracker autoware_object_merger autoware_object_range_splitter autoware_object_sorter autoware_object_velocity_splitter autoware_occupancy_grid_map_outlier_filter autoware_probabilistic_occupancy_grid_map autoware_radar_fusion_to_detected_object autoware_radar_object_tracker autoware_radar_tracks_msgs_converter autoware_raindrop_cluster_filter autoware_shape_estimation autoware_simpl_prediction autoware_simple_object_merger autoware_tensorrt_bevdet autoware_tensorrt_classifier autoware_tensorrt_common autoware_tensorrt_plugins autoware_tensorrt_yolox autoware_tracking_object_merger autoware_traffic_light_arbiter autoware_traffic_light_category_merger autoware_traffic_light_classifier autoware_traffic_light_fine_detector autoware_traffic_light_map_based_detector autoware_traffic_light_multi_camera_fusion autoware_traffic_light_occlusion_predictor autoware_traffic_light_selector autoware_traffic_light_visualization perception_utils autoware_costmap_generator autoware_diffusion_planner autoware_external_velocity_limit_selector autoware_freespace_planner autoware_freespace_planning_algorithms autoware_hazard_lights_selector autoware_mission_planner_universe autoware_path_optimizer autoware_path_smoother autoware_remaining_distance_time_calculator autoware_rtc_interface autoware_scenario_selector autoware_surround_obstacle_checker autoware_behavior_path_avoidance_by_lane_change_module autoware_behavior_path_bidirectional_traffic_module autoware_behavior_path_dynamic_obstacle_avoidance_module autoware_behavior_path_external_request_lane_change_module autoware_behavior_path_goal_planner_module autoware_behavior_path_lane_change_module autoware_behavior_path_planner autoware_behavior_path_planner_common autoware_behavior_path_sampling_planner_module autoware_behavior_path_side_shift_module autoware_behavior_path_start_planner_module autoware_behavior_path_static_obstacle_avoidance_module autoware_behavior_velocity_blind_spot_module autoware_behavior_velocity_crosswalk_module autoware_behavior_velocity_detection_area_module autoware_behavior_velocity_intersection_module autoware_behavior_velocity_no_drivable_lane_module autoware_behavior_velocity_no_stopping_area_module autoware_behavior_velocity_occlusion_spot_module autoware_behavior_velocity_rtc_interface autoware_behavior_velocity_run_out_module autoware_behavior_velocity_speed_bump_module autoware_behavior_velocity_template_module autoware_behavior_velocity_traffic_light_module autoware_behavior_velocity_virtual_traffic_light_module autoware_behavior_velocity_walkway_module autoware_motion_velocity_boundary_departure_prevention_module autoware_motion_velocity_dynamic_obstacle_stop_module autoware_motion_velocity_obstacle_cruise_module autoware_motion_velocity_obstacle_slow_down_module autoware_motion_velocity_obstacle_velocity_limiter_module autoware_motion_velocity_out_of_lane_module autoware_motion_velocity_road_user_stop_module autoware_motion_velocity_run_out_module autoware_planning_validator autoware_planning_validator_intersection_collision_checker autoware_planning_validator_latency_checker autoware_planning_validator_rear_collision_checker autoware_planning_validator_test_utils autoware_planning_validator_trajectory_checker autoware_bezier_sampler autoware_frenet_planner autoware_path_sampler autoware_sampler_common autoware_cuda_pointcloud_preprocessor autoware_cuda_utils autoware_image_diagnostics autoware_image_transport_decompressor autoware_imu_corrector autoware_pcl_extensions autoware_pointcloud_preprocessor autoware_radar_objects_adapter autoware_radar_scan_to_pointcloud2 autoware_radar_static_pointcloud_filter autoware_radar_threshold_filter autoware_radar_tracks_noise_filter autoware_livox_tag_filter autoware_carla_interface autoware_dummy_perception_publisher autoware_fault_injection autoware_learning_based_vehicle_model autoware_simple_planning_simulator autoware_vehicle_door_simulator tier4_dummy_object_rviz_plugin autoware_bluetooth_monitor autoware_command_mode_decider autoware_command_mode_decider_plugins autoware_command_mode_switcher autoware_command_mode_switcher_plugins autoware_command_mode_types autoware_component_monitor autoware_component_state_monitor autoware_adapi_visualizers autoware_automatic_pose_initializer autoware_default_adapi_universe autoware_diagnostic_graph_aggregator autoware_diagnostic_graph_utils autoware_dummy_diag_publisher autoware_dummy_infrastructure autoware_duplicated_node_checker autoware_hazard_status_converter autoware_mrm_comfortable_stop_operator autoware_mrm_emergency_stop_operator autoware_mrm_handler autoware_pipeline_latency_monitor autoware_processing_time_checker autoware_system_monitor autoware_topic_relay_controller autoware_topic_state_monitor autoware_velodyne_monitor reaction_analyzer autoware_accel_brake_map_calibrator autoware_external_cmd_converter autoware_raw_vehicle_cmd_converter autoware_steer_offset_estimator autoware_bag_time_manager_rviz_plugin autoware_traffic_light_rviz_plugin tier4_adapi_rviz_plugin tier4_camera_view_rviz_plugin tier4_control_mode_rviz_plugin tier4_datetime_rviz_plugin tier4_perception_rviz_plugin tier4_planning_factor_rviz_plugin tier4_state_rviz_plugin tier4_system_rviz_plugin tier4_traffic_light_rviz_plugin tier4_vehicle_rviz_plugin

ROS Distro
github

Package Summary

Tags No category tags.
Version 0.46.0
License Apache License 2.0
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Description
Checkout URI https://github.com/autowarefoundation/autoware_universe.git
VCS Type git
VCS Version main
Last Updated 2025-07-31
Dev Status UNKNOWN
Released UNRELEASED
Tags planner ros calibration self-driving-car autonomous-driving autonomous-vehicles ros2 3d-map autoware
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Package Description

boundary departure prevention feature in motion_velocity_planner

Additional Links

No additional links.

Maintainers

  • Zulfaqar Azmi
  • Alqudah Mohammad
  • Takumi Odashima
  • Maxime Clement

Authors

No additional authors.

Boundary Departure Prevention Module

!!! Warning

The Boundary Departure Prevention Module is experimental. It subscribes to the control module’s predicted path and steering report, creating a circular dependency. This violates Autoware’s design principle of forward-only data flow, where control depends on planning, not the other way round. As a result, this module is not officially supported and will remain unofficial for the foreseeable future.

Role

This module inserts slow down points and publishes error diagnostics when the ego vehicle is near or about to cross an uncrossable boundary, such as a road border.

It also accounts for several types of erroneous behavior that could cause the vehicle to unintentionally cross these boundaries. These behaviors are classified as abnormalities.

Abnormalities

Abnormalities refer to erroneous behaviors at the component level, often caused by noisy or unreliable outputs. In this module, these abnormalities are embedded into the predicted footprints derived from the control module’s predicted path, specifically, the MPC (Model Predictive Control) trajectory. Each point along the MPC path is converted into a footprint, and potential deviations due to abnormal conditions are evaluated.

The module addresses the following types of abnormalities:

1. Normal (No Abnormality)

In typical operation, the MPC trajectory may contain small deviations or noise, especially when the vehicle cannot track the planned path perfectly. These deviations are minor and not necessarily the result of a malfunction, but they are still accounted for to ensure safe boundary handling.

Localization Abnormality

Localization errors can cause the ego vehicle to misjudge its position relative to road boundaries such as curbs or road edges. This can happen due to:

  • Sensor noise or drift, leading to inaccurate pose estimation.
  • Map inaccuracies, where the HD map’s geometry does not precisely align with the real-world boundary.
  • Dynamic uncertainty at higher speeds, where even small errors are magnified due to the vehicle covering more distance in less time, reducing the margin for correction.

These factors can result in the vehicle unintentionally approaching or crossing an uncrossable boundary, even when the planned path appears valid.

How Footprint Expansion Helps with Localization and Map Errors

  • Provides a conservative buffer for localization errors: If the ego vehicle’s estimated position is off due to GPS drift or sensor noise, the expanded footprint increases the likelihood of detecting potential boundary conflicts. While this may lead to conservative (and possibly false) detections, it helps ensure safety by accounting for uncertainty in the vehicle’s true position.
  • Compensates for map inaccuracies: Slight misalignments in Lanelet2 map geometries, such as curb or road edge misplacement, can make it difficult to rely on raw map boundaries. Expanding the footprint ensures that even with these inaccuracies, the system remains cautious and avoids unintentional boundary violations.
Without Abnormality Margins With Localization Abnormality Margin
Footprint without abnormality margin Footprint with localization abnormality margin

By expanding the footprint, the system introduces a safety margin that accounts for minor localization and mapping uncertainties, especially critical at higher speeds.he expanded footprint creates a small buffer or “safety margin,” allowing the vehicle to operate safely despite minor abnormality.

Steering Abnormality

Unexpected steering behavior can cause the vehicle to deviate from its planned trajectory, leading to boundary departure even when planning and localization are functioning correctly. This can occur due to:

  • Actuator faults: such as delayed or stuck steering commands.
  • Software issues: like frozen control outputs or bugs in the steering optimization logic.
  • Unexpected maneuvers: for example, emergency avoidance or unintended sharp turns.

In such cases, the actual motion of the vehicle diverges from the MPC trajectory, increasing the risk of departure.

How steering margin helps with steering abnormality

  • Catches lateral deviations early: If the vehicle drifts due to steering faults, like stuck actuators or sudden command spikes, the expanded margin ahead of the vehicle can detect the deviation before the ego crosses into an unsafe region.
  • Predicts future risk along the path: The margin extends along the forward direction of the predicted path, enabling the system to foresee potential boundary violations caused by small steering errors that compound over time.
Without Abnormality Margins With Steering Abnormality Margin
Footprint without abnormality margin Footprint with localization abnormality margin

This method works even when control outputs are inaccurate. Because the margin is computed using the predicted path, not just the current pose, it accounts for latency, actuator delays, and other uncertainties in vehicle response. This allows the system to trigger early mitigation actions, such as slowing down or stopping, before the situation becomes critical.

Longitudinal Tracking Abnormality

Sometimes, the actual motion of the vehicle along the longitudinal axis does not match the MPC-predicted trajectory. For instance:

The ego vehicle might be ahead or behind the predicted position due to mismatches in acceleration or braking behavior.

This discrepancy becomes more problematic when the vehicle is near an uncrossable boundary, as it reduces the reliability of future footprint predictions.

How Longitudinal Expansion Helps with Tracking Abnormality

  • Accounts for ego being ahead of the predicted pose: During lane changes, avoidance maneuvers, or turns on curved roads, the ego vehicle may move faster than expected or take a slightly different path than predicted. By extending the footprint longitudinally (in the direction of motion), the system accounts for the ego vehicle possibly being ahead of the current MPC trajectory point.
  • Uses speed-scaled margins: The longitudinal margin is scaled based on the current vehicle speed, with an added buffer. At higher speeds, a larger margin is used to reflect the increased risk and reduced reaction time.
  • Captures mismatches during dynamic maneuvers: In situations where heading is changing quickly, like on curved roads or during lateral motion, the ego’s actual position may significantly deviate from the MPC path. The extended footprint covers this discrepancy and helps detect boundary risks even if the predicted path appears safe.
File truncated at 100 lines [see the full file](https://github.com/autowarefoundation/autoware_universe/tree/main/planning/motion_velocity_planner/autoware_motion_velocity_boundary_departure_prevention_module/README.md)
Without Abnormality Margins
CHANGELOG

Changelog for package autoware_motion_velocity_boundary_departure_prevention_module

0.46.0 (2025-06-20)

  • Merge remote-tracking branch 'upstream/main' into tmp/TaikiYamada/bump_version_base

  • feat(motion_velocity_planner): boundary departure prevention module (#10817)

    • feat(motion_velocity_planner): boundary departure prevention module
    • add maintainers
    • Add initial readme
    • Adds abnormalities explanation
    • fix infinite loop error
    • fix goal getter
    • add flowchart
    • add initial image folder
    • Add remaining abnormality footprints images
    • docstring on the boundary departure checker
    • Revert motion_planning.launch.xml to state before 14323161e3
    • Additional docstrings and separating slow down interpolator file
    • Update closest projection
    • jsonschema for parameters
    • fix json schema error
    • fix jsonschema
    • Departure type explanation
    • fix cppcheck failure
    • remove author tag
    • Fix misleading explanation
    • removed unused member variable
    • move boundary departure to experimental namespace

    * update maintainer's email address Co-authored-by: Maxime CLEMENT <<78338830+maxime-clem@users.noreply.github.com>>

    * grammar fix Co-authored-by: Maxime CLEMENT <<78338830+maxime-clem@users.noreply.github.com>>

    • refactoring
    • refactor slow down tuple

    * fix build failure due to clang-tidy ---------Co-authored-by: Maxime CLEMENT <<78338830+maxime-clem@users.noreply.github.com>>

  • Contributors: TaikiYamada4, Zulfaqar Azmi

Launch files

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Messages

No message files found.

Services

No service files found

Plugins

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autoware_motion_velocity_boundary_departure_prevention_module package from autoware_universe repo

autoware_agnocast_wrapper autoware_auto_common autoware_boundary_departure_checker autoware_component_interface_specs_universe autoware_component_interface_tools autoware_component_interface_utils autoware_cuda_dependency_meta autoware_fake_test_node autoware_glog_component autoware_goal_distance_calculator autoware_grid_map_utils autoware_path_distance_calculator autoware_polar_grid autoware_time_utils autoware_traffic_light_recognition_marker_publisher autoware_traffic_light_utils autoware_universe_utils tier4_api_utils autoware_autonomous_emergency_braking autoware_collision_detector autoware_control_command_gate autoware_control_performance_analysis autoware_control_validator autoware_external_cmd_selector autoware_joy_controller autoware_lane_departure_checker autoware_mpc_lateral_controller autoware_obstacle_collision_checker autoware_operation_mode_transition_manager autoware_pid_longitudinal_controller autoware_predicted_path_checker autoware_pure_pursuit autoware_shift_decider autoware_smart_mpc_trajectory_follower autoware_stop_mode_operator autoware_trajectory_follower_base autoware_trajectory_follower_node autoware_vehicle_cmd_gate autoware_control_evaluator autoware_kinematic_evaluator autoware_localization_evaluator autoware_perception_online_evaluator autoware_planning_evaluator autoware_scenario_simulator_v2_adapter tier4_autoware_api_launch tier4_control_launch tier4_localization_launch tier4_map_launch tier4_perception_launch tier4_planning_launch tier4_sensing_launch tier4_simulator_launch tier4_system_launch tier4_vehicle_launch autoware_geo_pose_projector autoware_ar_tag_based_localizer autoware_landmark_manager autoware_lidar_marker_localizer autoware_localization_error_monitor autoware_pose2twist autoware_pose_covariance_modifier autoware_pose_estimator_arbiter autoware_pose_instability_detector yabloc_common yabloc_image_processing yabloc_monitor yabloc_particle_filter yabloc_pose_initializer autoware_map_tf_generator autoware_bevfusion autoware_bytetrack autoware_cluster_merger autoware_compare_map_segmentation autoware_crosswalk_traffic_light_estimator autoware_detected_object_feature_remover autoware_detected_object_validation autoware_detection_by_tracker autoware_elevation_map_loader autoware_euclidean_cluster autoware_ground_segmentation autoware_image_projection_based_fusion autoware_lidar_apollo_instance_segmentation autoware_lidar_centerpoint autoware_lidar_transfusion autoware_map_based_prediction autoware_multi_object_tracker autoware_object_merger autoware_object_range_splitter autoware_object_sorter autoware_object_velocity_splitter autoware_occupancy_grid_map_outlier_filter autoware_probabilistic_occupancy_grid_map autoware_radar_fusion_to_detected_object autoware_radar_object_tracker autoware_radar_tracks_msgs_converter autoware_raindrop_cluster_filter autoware_shape_estimation autoware_simpl_prediction autoware_simple_object_merger autoware_tensorrt_bevdet autoware_tensorrt_classifier autoware_tensorrt_common autoware_tensorrt_plugins autoware_tensorrt_yolox autoware_tracking_object_merger autoware_traffic_light_arbiter autoware_traffic_light_category_merger autoware_traffic_light_classifier autoware_traffic_light_fine_detector autoware_traffic_light_map_based_detector autoware_traffic_light_multi_camera_fusion autoware_traffic_light_occlusion_predictor autoware_traffic_light_selector autoware_traffic_light_visualization perception_utils autoware_costmap_generator autoware_diffusion_planner autoware_external_velocity_limit_selector autoware_freespace_planner autoware_freespace_planning_algorithms autoware_hazard_lights_selector autoware_mission_planner_universe autoware_path_optimizer autoware_path_smoother autoware_remaining_distance_time_calculator autoware_rtc_interface autoware_scenario_selector autoware_surround_obstacle_checker autoware_behavior_path_avoidance_by_lane_change_module autoware_behavior_path_bidirectional_traffic_module autoware_behavior_path_dynamic_obstacle_avoidance_module autoware_behavior_path_external_request_lane_change_module autoware_behavior_path_goal_planner_module autoware_behavior_path_lane_change_module autoware_behavior_path_planner autoware_behavior_path_planner_common autoware_behavior_path_sampling_planner_module autoware_behavior_path_side_shift_module autoware_behavior_path_start_planner_module autoware_behavior_path_static_obstacle_avoidance_module autoware_behavior_velocity_blind_spot_module autoware_behavior_velocity_crosswalk_module autoware_behavior_velocity_detection_area_module autoware_behavior_velocity_intersection_module autoware_behavior_velocity_no_drivable_lane_module autoware_behavior_velocity_no_stopping_area_module autoware_behavior_velocity_occlusion_spot_module autoware_behavior_velocity_rtc_interface autoware_behavior_velocity_run_out_module autoware_behavior_velocity_speed_bump_module autoware_behavior_velocity_template_module autoware_behavior_velocity_traffic_light_module autoware_behavior_velocity_virtual_traffic_light_module autoware_behavior_velocity_walkway_module autoware_motion_velocity_boundary_departure_prevention_module autoware_motion_velocity_dynamic_obstacle_stop_module autoware_motion_velocity_obstacle_cruise_module autoware_motion_velocity_obstacle_slow_down_module autoware_motion_velocity_obstacle_velocity_limiter_module autoware_motion_velocity_out_of_lane_module autoware_motion_velocity_road_user_stop_module autoware_motion_velocity_run_out_module autoware_planning_validator autoware_planning_validator_intersection_collision_checker autoware_planning_validator_latency_checker autoware_planning_validator_rear_collision_checker autoware_planning_validator_test_utils autoware_planning_validator_trajectory_checker autoware_bezier_sampler autoware_frenet_planner autoware_path_sampler autoware_sampler_common autoware_cuda_pointcloud_preprocessor autoware_cuda_utils autoware_image_diagnostics autoware_image_transport_decompressor autoware_imu_corrector autoware_pcl_extensions autoware_pointcloud_preprocessor autoware_radar_objects_adapter autoware_radar_scan_to_pointcloud2 autoware_radar_static_pointcloud_filter autoware_radar_threshold_filter autoware_radar_tracks_noise_filter autoware_livox_tag_filter autoware_carla_interface autoware_dummy_perception_publisher autoware_fault_injection autoware_learning_based_vehicle_model autoware_simple_planning_simulator autoware_vehicle_door_simulator tier4_dummy_object_rviz_plugin autoware_bluetooth_monitor autoware_command_mode_decider autoware_command_mode_decider_plugins autoware_command_mode_switcher autoware_command_mode_switcher_plugins autoware_command_mode_types autoware_component_monitor autoware_component_state_monitor autoware_adapi_visualizers autoware_automatic_pose_initializer autoware_default_adapi_universe autoware_diagnostic_graph_aggregator autoware_diagnostic_graph_utils autoware_dummy_diag_publisher autoware_dummy_infrastructure autoware_duplicated_node_checker autoware_hazard_status_converter autoware_mrm_comfortable_stop_operator autoware_mrm_emergency_stop_operator autoware_mrm_handler autoware_pipeline_latency_monitor autoware_processing_time_checker autoware_system_monitor autoware_topic_relay_controller autoware_topic_state_monitor autoware_velodyne_monitor reaction_analyzer autoware_accel_brake_map_calibrator autoware_external_cmd_converter autoware_raw_vehicle_cmd_converter autoware_steer_offset_estimator autoware_bag_time_manager_rviz_plugin autoware_traffic_light_rviz_plugin tier4_adapi_rviz_plugin tier4_camera_view_rviz_plugin tier4_control_mode_rviz_plugin tier4_datetime_rviz_plugin tier4_perception_rviz_plugin tier4_planning_factor_rviz_plugin tier4_state_rviz_plugin tier4_system_rviz_plugin tier4_traffic_light_rviz_plugin tier4_vehicle_rviz_plugin

ROS Distro
github

Package Summary

Tags No category tags.
Version 0.46.0
License Apache License 2.0
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Description
Checkout URI https://github.com/autowarefoundation/autoware_universe.git
VCS Type git
VCS Version main
Last Updated 2025-07-31
Dev Status UNKNOWN
Released UNRELEASED
Tags planner ros calibration self-driving-car autonomous-driving autonomous-vehicles ros2 3d-map autoware
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Package Description

boundary departure prevention feature in motion_velocity_planner

Additional Links

No additional links.

Maintainers

  • Zulfaqar Azmi
  • Alqudah Mohammad
  • Takumi Odashima
  • Maxime Clement

Authors

No additional authors.

Boundary Departure Prevention Module

!!! Warning

The Boundary Departure Prevention Module is experimental. It subscribes to the control module’s predicted path and steering report, creating a circular dependency. This violates Autoware’s design principle of forward-only data flow, where control depends on planning, not the other way round. As a result, this module is not officially supported and will remain unofficial for the foreseeable future.

Role

This module inserts slow down points and publishes error diagnostics when the ego vehicle is near or about to cross an uncrossable boundary, such as a road border.

It also accounts for several types of erroneous behavior that could cause the vehicle to unintentionally cross these boundaries. These behaviors are classified as abnormalities.

Abnormalities

Abnormalities refer to erroneous behaviors at the component level, often caused by noisy or unreliable outputs. In this module, these abnormalities are embedded into the predicted footprints derived from the control module’s predicted path, specifically, the MPC (Model Predictive Control) trajectory. Each point along the MPC path is converted into a footprint, and potential deviations due to abnormal conditions are evaluated.

The module addresses the following types of abnormalities:

1. Normal (No Abnormality)

In typical operation, the MPC trajectory may contain small deviations or noise, especially when the vehicle cannot track the planned path perfectly. These deviations are minor and not necessarily the result of a malfunction, but they are still accounted for to ensure safe boundary handling.

Localization Abnormality

Localization errors can cause the ego vehicle to misjudge its position relative to road boundaries such as curbs or road edges. This can happen due to:

  • Sensor noise or drift, leading to inaccurate pose estimation.
  • Map inaccuracies, where the HD map’s geometry does not precisely align with the real-world boundary.
  • Dynamic uncertainty at higher speeds, where even small errors are magnified due to the vehicle covering more distance in less time, reducing the margin for correction.

These factors can result in the vehicle unintentionally approaching or crossing an uncrossable boundary, even when the planned path appears valid.

How Footprint Expansion Helps with Localization and Map Errors

  • Provides a conservative buffer for localization errors: If the ego vehicle’s estimated position is off due to GPS drift or sensor noise, the expanded footprint increases the likelihood of detecting potential boundary conflicts. While this may lead to conservative (and possibly false) detections, it helps ensure safety by accounting for uncertainty in the vehicle’s true position.
  • Compensates for map inaccuracies: Slight misalignments in Lanelet2 map geometries, such as curb or road edge misplacement, can make it difficult to rely on raw map boundaries. Expanding the footprint ensures that even with these inaccuracies, the system remains cautious and avoids unintentional boundary violations.
Without Abnormality Margins With Localization Abnormality Margin
Footprint without abnormality margin Footprint with localization abnormality margin

By expanding the footprint, the system introduces a safety margin that accounts for minor localization and mapping uncertainties, especially critical at higher speeds.he expanded footprint creates a small buffer or “safety margin,” allowing the vehicle to operate safely despite minor abnormality.

Steering Abnormality

Unexpected steering behavior can cause the vehicle to deviate from its planned trajectory, leading to boundary departure even when planning and localization are functioning correctly. This can occur due to:

  • Actuator faults: such as delayed or stuck steering commands.
  • Software issues: like frozen control outputs or bugs in the steering optimization logic.
  • Unexpected maneuvers: for example, emergency avoidance or unintended sharp turns.

In such cases, the actual motion of the vehicle diverges from the MPC trajectory, increasing the risk of departure.

How steering margin helps with steering abnormality

  • Catches lateral deviations early: If the vehicle drifts due to steering faults, like stuck actuators or sudden command spikes, the expanded margin ahead of the vehicle can detect the deviation before the ego crosses into an unsafe region.
  • Predicts future risk along the path: The margin extends along the forward direction of the predicted path, enabling the system to foresee potential boundary violations caused by small steering errors that compound over time.
Without Abnormality Margins With Steering Abnormality Margin
Footprint without abnormality margin Footprint with localization abnormality margin

This method works even when control outputs are inaccurate. Because the margin is computed using the predicted path, not just the current pose, it accounts for latency, actuator delays, and other uncertainties in vehicle response. This allows the system to trigger early mitigation actions, such as slowing down or stopping, before the situation becomes critical.

Longitudinal Tracking Abnormality

Sometimes, the actual motion of the vehicle along the longitudinal axis does not match the MPC-predicted trajectory. For instance:

The ego vehicle might be ahead or behind the predicted position due to mismatches in acceleration or braking behavior.

This discrepancy becomes more problematic when the vehicle is near an uncrossable boundary, as it reduces the reliability of future footprint predictions.

How Longitudinal Expansion Helps with Tracking Abnormality

  • Accounts for ego being ahead of the predicted pose: During lane changes, avoidance maneuvers, or turns on curved roads, the ego vehicle may move faster than expected or take a slightly different path than predicted. By extending the footprint longitudinally (in the direction of motion), the system accounts for the ego vehicle possibly being ahead of the current MPC trajectory point.
  • Uses speed-scaled margins: The longitudinal margin is scaled based on the current vehicle speed, with an added buffer. At higher speeds, a larger margin is used to reflect the increased risk and reduced reaction time.
  • Captures mismatches during dynamic maneuvers: In situations where heading is changing quickly, like on curved roads or during lateral motion, the ego’s actual position may significantly deviate from the MPC path. The extended footprint covers this discrepancy and helps detect boundary risks even if the predicted path appears safe.
File truncated at 100 lines [see the full file](https://github.com/autowarefoundation/autoware_universe/tree/main/planning/motion_velocity_planner/autoware_motion_velocity_boundary_departure_prevention_module/README.md)
Without Abnormality Margins
CHANGELOG

Changelog for package autoware_motion_velocity_boundary_departure_prevention_module

0.46.0 (2025-06-20)

  • Merge remote-tracking branch 'upstream/main' into tmp/TaikiYamada/bump_version_base

  • feat(motion_velocity_planner): boundary departure prevention module (#10817)

    • feat(motion_velocity_planner): boundary departure prevention module
    • add maintainers
    • Add initial readme
    • Adds abnormalities explanation
    • fix infinite loop error
    • fix goal getter
    • add flowchart
    • add initial image folder
    • Add remaining abnormality footprints images
    • docstring on the boundary departure checker
    • Revert motion_planning.launch.xml to state before 14323161e3
    • Additional docstrings and separating slow down interpolator file
    • Update closest projection
    • jsonschema for parameters
    • fix json schema error
    • fix jsonschema
    • Departure type explanation
    • fix cppcheck failure
    • remove author tag
    • Fix misleading explanation
    • removed unused member variable
    • move boundary departure to experimental namespace

    * update maintainer's email address Co-authored-by: Maxime CLEMENT <<78338830+maxime-clem@users.noreply.github.com>>

    * grammar fix Co-authored-by: Maxime CLEMENT <<78338830+maxime-clem@users.noreply.github.com>>

    • refactoring
    • refactor slow down tuple

    * fix build failure due to clang-tidy ---------Co-authored-by: Maxime CLEMENT <<78338830+maxime-clem@users.noreply.github.com>>

  • Contributors: TaikiYamada4, Zulfaqar Azmi

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

Recent questions tagged autoware_motion_velocity_boundary_departure_prevention_module at Robotics Stack Exchange

No version for distro kilted showing github. Known supported distros are highlighted in the buttons above.
Package symbol

autoware_motion_velocity_boundary_departure_prevention_module package from autoware_universe repo

autoware_agnocast_wrapper autoware_auto_common autoware_boundary_departure_checker autoware_component_interface_specs_universe autoware_component_interface_tools autoware_component_interface_utils autoware_cuda_dependency_meta autoware_fake_test_node autoware_glog_component autoware_goal_distance_calculator autoware_grid_map_utils autoware_path_distance_calculator autoware_polar_grid autoware_time_utils autoware_traffic_light_recognition_marker_publisher autoware_traffic_light_utils autoware_universe_utils tier4_api_utils autoware_autonomous_emergency_braking autoware_collision_detector autoware_control_command_gate autoware_control_performance_analysis autoware_control_validator autoware_external_cmd_selector autoware_joy_controller autoware_lane_departure_checker autoware_mpc_lateral_controller autoware_obstacle_collision_checker autoware_operation_mode_transition_manager autoware_pid_longitudinal_controller autoware_predicted_path_checker autoware_pure_pursuit autoware_shift_decider autoware_smart_mpc_trajectory_follower autoware_stop_mode_operator autoware_trajectory_follower_base autoware_trajectory_follower_node autoware_vehicle_cmd_gate autoware_control_evaluator autoware_kinematic_evaluator autoware_localization_evaluator autoware_perception_online_evaluator autoware_planning_evaluator autoware_scenario_simulator_v2_adapter tier4_autoware_api_launch tier4_control_launch tier4_localization_launch tier4_map_launch tier4_perception_launch tier4_planning_launch tier4_sensing_launch tier4_simulator_launch tier4_system_launch tier4_vehicle_launch autoware_geo_pose_projector autoware_ar_tag_based_localizer autoware_landmark_manager autoware_lidar_marker_localizer autoware_localization_error_monitor autoware_pose2twist autoware_pose_covariance_modifier autoware_pose_estimator_arbiter autoware_pose_instability_detector yabloc_common yabloc_image_processing yabloc_monitor yabloc_particle_filter yabloc_pose_initializer autoware_map_tf_generator autoware_bevfusion autoware_bytetrack autoware_cluster_merger autoware_compare_map_segmentation autoware_crosswalk_traffic_light_estimator autoware_detected_object_feature_remover autoware_detected_object_validation autoware_detection_by_tracker autoware_elevation_map_loader autoware_euclidean_cluster autoware_ground_segmentation autoware_image_projection_based_fusion autoware_lidar_apollo_instance_segmentation autoware_lidar_centerpoint autoware_lidar_transfusion autoware_map_based_prediction autoware_multi_object_tracker autoware_object_merger autoware_object_range_splitter autoware_object_sorter autoware_object_velocity_splitter autoware_occupancy_grid_map_outlier_filter autoware_probabilistic_occupancy_grid_map autoware_radar_fusion_to_detected_object autoware_radar_object_tracker autoware_radar_tracks_msgs_converter autoware_raindrop_cluster_filter autoware_shape_estimation autoware_simpl_prediction autoware_simple_object_merger autoware_tensorrt_bevdet autoware_tensorrt_classifier autoware_tensorrt_common autoware_tensorrt_plugins autoware_tensorrt_yolox autoware_tracking_object_merger autoware_traffic_light_arbiter autoware_traffic_light_category_merger autoware_traffic_light_classifier autoware_traffic_light_fine_detector autoware_traffic_light_map_based_detector autoware_traffic_light_multi_camera_fusion autoware_traffic_light_occlusion_predictor autoware_traffic_light_selector autoware_traffic_light_visualization perception_utils autoware_costmap_generator autoware_diffusion_planner autoware_external_velocity_limit_selector autoware_freespace_planner autoware_freespace_planning_algorithms autoware_hazard_lights_selector autoware_mission_planner_universe autoware_path_optimizer autoware_path_smoother autoware_remaining_distance_time_calculator autoware_rtc_interface autoware_scenario_selector autoware_surround_obstacle_checker autoware_behavior_path_avoidance_by_lane_change_module autoware_behavior_path_bidirectional_traffic_module autoware_behavior_path_dynamic_obstacle_avoidance_module autoware_behavior_path_external_request_lane_change_module autoware_behavior_path_goal_planner_module autoware_behavior_path_lane_change_module autoware_behavior_path_planner autoware_behavior_path_planner_common autoware_behavior_path_sampling_planner_module autoware_behavior_path_side_shift_module autoware_behavior_path_start_planner_module autoware_behavior_path_static_obstacle_avoidance_module autoware_behavior_velocity_blind_spot_module autoware_behavior_velocity_crosswalk_module autoware_behavior_velocity_detection_area_module autoware_behavior_velocity_intersection_module autoware_behavior_velocity_no_drivable_lane_module autoware_behavior_velocity_no_stopping_area_module autoware_behavior_velocity_occlusion_spot_module autoware_behavior_velocity_rtc_interface autoware_behavior_velocity_run_out_module autoware_behavior_velocity_speed_bump_module autoware_behavior_velocity_template_module autoware_behavior_velocity_traffic_light_module autoware_behavior_velocity_virtual_traffic_light_module autoware_behavior_velocity_walkway_module autoware_motion_velocity_boundary_departure_prevention_module autoware_motion_velocity_dynamic_obstacle_stop_module autoware_motion_velocity_obstacle_cruise_module autoware_motion_velocity_obstacle_slow_down_module autoware_motion_velocity_obstacle_velocity_limiter_module autoware_motion_velocity_out_of_lane_module autoware_motion_velocity_road_user_stop_module autoware_motion_velocity_run_out_module autoware_planning_validator autoware_planning_validator_intersection_collision_checker autoware_planning_validator_latency_checker autoware_planning_validator_rear_collision_checker autoware_planning_validator_test_utils autoware_planning_validator_trajectory_checker autoware_bezier_sampler autoware_frenet_planner autoware_path_sampler autoware_sampler_common autoware_cuda_pointcloud_preprocessor autoware_cuda_utils autoware_image_diagnostics autoware_image_transport_decompressor autoware_imu_corrector autoware_pcl_extensions autoware_pointcloud_preprocessor autoware_radar_objects_adapter autoware_radar_scan_to_pointcloud2 autoware_radar_static_pointcloud_filter autoware_radar_threshold_filter autoware_radar_tracks_noise_filter autoware_livox_tag_filter autoware_carla_interface autoware_dummy_perception_publisher autoware_fault_injection autoware_learning_based_vehicle_model autoware_simple_planning_simulator autoware_vehicle_door_simulator tier4_dummy_object_rviz_plugin autoware_bluetooth_monitor autoware_command_mode_decider autoware_command_mode_decider_plugins autoware_command_mode_switcher autoware_command_mode_switcher_plugins autoware_command_mode_types autoware_component_monitor autoware_component_state_monitor autoware_adapi_visualizers autoware_automatic_pose_initializer autoware_default_adapi_universe autoware_diagnostic_graph_aggregator autoware_diagnostic_graph_utils autoware_dummy_diag_publisher autoware_dummy_infrastructure autoware_duplicated_node_checker autoware_hazard_status_converter autoware_mrm_comfortable_stop_operator autoware_mrm_emergency_stop_operator autoware_mrm_handler autoware_pipeline_latency_monitor autoware_processing_time_checker autoware_system_monitor autoware_topic_relay_controller autoware_topic_state_monitor autoware_velodyne_monitor reaction_analyzer autoware_accel_brake_map_calibrator autoware_external_cmd_converter autoware_raw_vehicle_cmd_converter autoware_steer_offset_estimator autoware_bag_time_manager_rviz_plugin autoware_traffic_light_rviz_plugin tier4_adapi_rviz_plugin tier4_camera_view_rviz_plugin tier4_control_mode_rviz_plugin tier4_datetime_rviz_plugin tier4_perception_rviz_plugin tier4_planning_factor_rviz_plugin tier4_state_rviz_plugin tier4_system_rviz_plugin tier4_traffic_light_rviz_plugin tier4_vehicle_rviz_plugin

ROS Distro
github

Package Summary

Tags No category tags.
Version 0.46.0
License Apache License 2.0
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Description
Checkout URI https://github.com/autowarefoundation/autoware_universe.git
VCS Type git
VCS Version main
Last Updated 2025-07-31
Dev Status UNKNOWN
Released UNRELEASED
Tags planner ros calibration self-driving-car autonomous-driving autonomous-vehicles ros2 3d-map autoware
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Package Description

boundary departure prevention feature in motion_velocity_planner

Additional Links

No additional links.

Maintainers

  • Zulfaqar Azmi
  • Alqudah Mohammad
  • Takumi Odashima
  • Maxime Clement

Authors

No additional authors.

Boundary Departure Prevention Module

!!! Warning

The Boundary Departure Prevention Module is experimental. It subscribes to the control module’s predicted path and steering report, creating a circular dependency. This violates Autoware’s design principle of forward-only data flow, where control depends on planning, not the other way round. As a result, this module is not officially supported and will remain unofficial for the foreseeable future.

Role

This module inserts slow down points and publishes error diagnostics when the ego vehicle is near or about to cross an uncrossable boundary, such as a road border.

It also accounts for several types of erroneous behavior that could cause the vehicle to unintentionally cross these boundaries. These behaviors are classified as abnormalities.

Abnormalities

Abnormalities refer to erroneous behaviors at the component level, often caused by noisy or unreliable outputs. In this module, these abnormalities are embedded into the predicted footprints derived from the control module’s predicted path, specifically, the MPC (Model Predictive Control) trajectory. Each point along the MPC path is converted into a footprint, and potential deviations due to abnormal conditions are evaluated.

The module addresses the following types of abnormalities:

1. Normal (No Abnormality)

In typical operation, the MPC trajectory may contain small deviations or noise, especially when the vehicle cannot track the planned path perfectly. These deviations are minor and not necessarily the result of a malfunction, but they are still accounted for to ensure safe boundary handling.

Localization Abnormality

Localization errors can cause the ego vehicle to misjudge its position relative to road boundaries such as curbs or road edges. This can happen due to:

  • Sensor noise or drift, leading to inaccurate pose estimation.
  • Map inaccuracies, where the HD map’s geometry does not precisely align with the real-world boundary.
  • Dynamic uncertainty at higher speeds, where even small errors are magnified due to the vehicle covering more distance in less time, reducing the margin for correction.

These factors can result in the vehicle unintentionally approaching or crossing an uncrossable boundary, even when the planned path appears valid.

How Footprint Expansion Helps with Localization and Map Errors

  • Provides a conservative buffer for localization errors: If the ego vehicle’s estimated position is off due to GPS drift or sensor noise, the expanded footprint increases the likelihood of detecting potential boundary conflicts. While this may lead to conservative (and possibly false) detections, it helps ensure safety by accounting for uncertainty in the vehicle’s true position.
  • Compensates for map inaccuracies: Slight misalignments in Lanelet2 map geometries, such as curb or road edge misplacement, can make it difficult to rely on raw map boundaries. Expanding the footprint ensures that even with these inaccuracies, the system remains cautious and avoids unintentional boundary violations.
Without Abnormality Margins With Localization Abnormality Margin
Footprint without abnormality margin Footprint with localization abnormality margin

By expanding the footprint, the system introduces a safety margin that accounts for minor localization and mapping uncertainties, especially critical at higher speeds.he expanded footprint creates a small buffer or “safety margin,” allowing the vehicle to operate safely despite minor abnormality.

Steering Abnormality

Unexpected steering behavior can cause the vehicle to deviate from its planned trajectory, leading to boundary departure even when planning and localization are functioning correctly. This can occur due to:

  • Actuator faults: such as delayed or stuck steering commands.
  • Software issues: like frozen control outputs or bugs in the steering optimization logic.
  • Unexpected maneuvers: for example, emergency avoidance or unintended sharp turns.

In such cases, the actual motion of the vehicle diverges from the MPC trajectory, increasing the risk of departure.

How steering margin helps with steering abnormality

  • Catches lateral deviations early: If the vehicle drifts due to steering faults, like stuck actuators or sudden command spikes, the expanded margin ahead of the vehicle can detect the deviation before the ego crosses into an unsafe region.
  • Predicts future risk along the path: The margin extends along the forward direction of the predicted path, enabling the system to foresee potential boundary violations caused by small steering errors that compound over time.
Without Abnormality Margins With Steering Abnormality Margin
Footprint without abnormality margin Footprint with localization abnormality margin

This method works even when control outputs are inaccurate. Because the margin is computed using the predicted path, not just the current pose, it accounts for latency, actuator delays, and other uncertainties in vehicle response. This allows the system to trigger early mitigation actions, such as slowing down or stopping, before the situation becomes critical.

Longitudinal Tracking Abnormality

Sometimes, the actual motion of the vehicle along the longitudinal axis does not match the MPC-predicted trajectory. For instance:

The ego vehicle might be ahead or behind the predicted position due to mismatches in acceleration or braking behavior.

This discrepancy becomes more problematic when the vehicle is near an uncrossable boundary, as it reduces the reliability of future footprint predictions.

How Longitudinal Expansion Helps with Tracking Abnormality

  • Accounts for ego being ahead of the predicted pose: During lane changes, avoidance maneuvers, or turns on curved roads, the ego vehicle may move faster than expected or take a slightly different path than predicted. By extending the footprint longitudinally (in the direction of motion), the system accounts for the ego vehicle possibly being ahead of the current MPC trajectory point.
  • Uses speed-scaled margins: The longitudinal margin is scaled based on the current vehicle speed, with an added buffer. At higher speeds, a larger margin is used to reflect the increased risk and reduced reaction time.
  • Captures mismatches during dynamic maneuvers: In situations where heading is changing quickly, like on curved roads or during lateral motion, the ego’s actual position may significantly deviate from the MPC path. The extended footprint covers this discrepancy and helps detect boundary risks even if the predicted path appears safe.
File truncated at 100 lines [see the full file](https://github.com/autowarefoundation/autoware_universe/tree/main/planning/motion_velocity_planner/autoware_motion_velocity_boundary_departure_prevention_module/README.md)
Without Abnormality Margins
CHANGELOG

Changelog for package autoware_motion_velocity_boundary_departure_prevention_module

0.46.0 (2025-06-20)

  • Merge remote-tracking branch 'upstream/main' into tmp/TaikiYamada/bump_version_base

  • feat(motion_velocity_planner): boundary departure prevention module (#10817)

    • feat(motion_velocity_planner): boundary departure prevention module
    • add maintainers
    • Add initial readme
    • Adds abnormalities explanation
    • fix infinite loop error
    • fix goal getter
    • add flowchart
    • add initial image folder
    • Add remaining abnormality footprints images
    • docstring on the boundary departure checker
    • Revert motion_planning.launch.xml to state before 14323161e3
    • Additional docstrings and separating slow down interpolator file
    • Update closest projection
    • jsonschema for parameters
    • fix json schema error
    • fix jsonschema
    • Departure type explanation
    • fix cppcheck failure
    • remove author tag
    • Fix misleading explanation
    • removed unused member variable
    • move boundary departure to experimental namespace

    * update maintainer's email address Co-authored-by: Maxime CLEMENT <<78338830+maxime-clem@users.noreply.github.com>>

    * grammar fix Co-authored-by: Maxime CLEMENT <<78338830+maxime-clem@users.noreply.github.com>>

    • refactoring
    • refactor slow down tuple

    * fix build failure due to clang-tidy ---------Co-authored-by: Maxime CLEMENT <<78338830+maxime-clem@users.noreply.github.com>>

  • Contributors: TaikiYamada4, Zulfaqar Azmi

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

Recent questions tagged autoware_motion_velocity_boundary_departure_prevention_module at Robotics Stack Exchange

No version for distro rolling showing github. Known supported distros are highlighted in the buttons above.
Package symbol

autoware_motion_velocity_boundary_departure_prevention_module package from autoware_universe repo

autoware_agnocast_wrapper autoware_auto_common autoware_boundary_departure_checker autoware_component_interface_specs_universe autoware_component_interface_tools autoware_component_interface_utils autoware_cuda_dependency_meta autoware_fake_test_node autoware_glog_component autoware_goal_distance_calculator autoware_grid_map_utils autoware_path_distance_calculator autoware_polar_grid autoware_time_utils autoware_traffic_light_recognition_marker_publisher autoware_traffic_light_utils autoware_universe_utils tier4_api_utils autoware_autonomous_emergency_braking autoware_collision_detector autoware_control_command_gate autoware_control_performance_analysis autoware_control_validator autoware_external_cmd_selector autoware_joy_controller autoware_lane_departure_checker autoware_mpc_lateral_controller autoware_obstacle_collision_checker autoware_operation_mode_transition_manager autoware_pid_longitudinal_controller autoware_predicted_path_checker autoware_pure_pursuit autoware_shift_decider autoware_smart_mpc_trajectory_follower autoware_stop_mode_operator autoware_trajectory_follower_base autoware_trajectory_follower_node autoware_vehicle_cmd_gate autoware_control_evaluator autoware_kinematic_evaluator autoware_localization_evaluator autoware_perception_online_evaluator autoware_planning_evaluator autoware_scenario_simulator_v2_adapter tier4_autoware_api_launch tier4_control_launch tier4_localization_launch tier4_map_launch tier4_perception_launch tier4_planning_launch tier4_sensing_launch tier4_simulator_launch tier4_system_launch tier4_vehicle_launch autoware_geo_pose_projector autoware_ar_tag_based_localizer autoware_landmark_manager autoware_lidar_marker_localizer autoware_localization_error_monitor autoware_pose2twist autoware_pose_covariance_modifier autoware_pose_estimator_arbiter autoware_pose_instability_detector yabloc_common yabloc_image_processing yabloc_monitor yabloc_particle_filter yabloc_pose_initializer autoware_map_tf_generator autoware_bevfusion autoware_bytetrack autoware_cluster_merger autoware_compare_map_segmentation autoware_crosswalk_traffic_light_estimator autoware_detected_object_feature_remover autoware_detected_object_validation autoware_detection_by_tracker autoware_elevation_map_loader autoware_euclidean_cluster autoware_ground_segmentation autoware_image_projection_based_fusion autoware_lidar_apollo_instance_segmentation autoware_lidar_centerpoint autoware_lidar_transfusion autoware_map_based_prediction autoware_multi_object_tracker autoware_object_merger autoware_object_range_splitter autoware_object_sorter autoware_object_velocity_splitter autoware_occupancy_grid_map_outlier_filter autoware_probabilistic_occupancy_grid_map autoware_radar_fusion_to_detected_object autoware_radar_object_tracker autoware_radar_tracks_msgs_converter autoware_raindrop_cluster_filter autoware_shape_estimation autoware_simpl_prediction autoware_simple_object_merger autoware_tensorrt_bevdet autoware_tensorrt_classifier autoware_tensorrt_common autoware_tensorrt_plugins autoware_tensorrt_yolox autoware_tracking_object_merger autoware_traffic_light_arbiter autoware_traffic_light_category_merger autoware_traffic_light_classifier autoware_traffic_light_fine_detector autoware_traffic_light_map_based_detector autoware_traffic_light_multi_camera_fusion autoware_traffic_light_occlusion_predictor autoware_traffic_light_selector autoware_traffic_light_visualization perception_utils autoware_costmap_generator autoware_diffusion_planner autoware_external_velocity_limit_selector autoware_freespace_planner autoware_freespace_planning_algorithms autoware_hazard_lights_selector autoware_mission_planner_universe autoware_path_optimizer autoware_path_smoother autoware_remaining_distance_time_calculator autoware_rtc_interface autoware_scenario_selector autoware_surround_obstacle_checker autoware_behavior_path_avoidance_by_lane_change_module autoware_behavior_path_bidirectional_traffic_module autoware_behavior_path_dynamic_obstacle_avoidance_module autoware_behavior_path_external_request_lane_change_module autoware_behavior_path_goal_planner_module autoware_behavior_path_lane_change_module autoware_behavior_path_planner autoware_behavior_path_planner_common autoware_behavior_path_sampling_planner_module autoware_behavior_path_side_shift_module autoware_behavior_path_start_planner_module autoware_behavior_path_static_obstacle_avoidance_module autoware_behavior_velocity_blind_spot_module autoware_behavior_velocity_crosswalk_module autoware_behavior_velocity_detection_area_module autoware_behavior_velocity_intersection_module autoware_behavior_velocity_no_drivable_lane_module autoware_behavior_velocity_no_stopping_area_module autoware_behavior_velocity_occlusion_spot_module autoware_behavior_velocity_rtc_interface autoware_behavior_velocity_run_out_module autoware_behavior_velocity_speed_bump_module autoware_behavior_velocity_template_module autoware_behavior_velocity_traffic_light_module autoware_behavior_velocity_virtual_traffic_light_module autoware_behavior_velocity_walkway_module autoware_motion_velocity_boundary_departure_prevention_module autoware_motion_velocity_dynamic_obstacle_stop_module autoware_motion_velocity_obstacle_cruise_module autoware_motion_velocity_obstacle_slow_down_module autoware_motion_velocity_obstacle_velocity_limiter_module autoware_motion_velocity_out_of_lane_module autoware_motion_velocity_road_user_stop_module autoware_motion_velocity_run_out_module autoware_planning_validator autoware_planning_validator_intersection_collision_checker autoware_planning_validator_latency_checker autoware_planning_validator_rear_collision_checker autoware_planning_validator_test_utils autoware_planning_validator_trajectory_checker autoware_bezier_sampler autoware_frenet_planner autoware_path_sampler autoware_sampler_common autoware_cuda_pointcloud_preprocessor autoware_cuda_utils autoware_image_diagnostics autoware_image_transport_decompressor autoware_imu_corrector autoware_pcl_extensions autoware_pointcloud_preprocessor autoware_radar_objects_adapter autoware_radar_scan_to_pointcloud2 autoware_radar_static_pointcloud_filter autoware_radar_threshold_filter autoware_radar_tracks_noise_filter autoware_livox_tag_filter autoware_carla_interface autoware_dummy_perception_publisher autoware_fault_injection autoware_learning_based_vehicle_model autoware_simple_planning_simulator autoware_vehicle_door_simulator tier4_dummy_object_rviz_plugin autoware_bluetooth_monitor autoware_command_mode_decider autoware_command_mode_decider_plugins autoware_command_mode_switcher autoware_command_mode_switcher_plugins autoware_command_mode_types autoware_component_monitor autoware_component_state_monitor autoware_adapi_visualizers autoware_automatic_pose_initializer autoware_default_adapi_universe autoware_diagnostic_graph_aggregator autoware_diagnostic_graph_utils autoware_dummy_diag_publisher autoware_dummy_infrastructure autoware_duplicated_node_checker autoware_hazard_status_converter autoware_mrm_comfortable_stop_operator autoware_mrm_emergency_stop_operator autoware_mrm_handler autoware_pipeline_latency_monitor autoware_processing_time_checker autoware_system_monitor autoware_topic_relay_controller autoware_topic_state_monitor autoware_velodyne_monitor reaction_analyzer autoware_accel_brake_map_calibrator autoware_external_cmd_converter autoware_raw_vehicle_cmd_converter autoware_steer_offset_estimator autoware_bag_time_manager_rviz_plugin autoware_traffic_light_rviz_plugin tier4_adapi_rviz_plugin tier4_camera_view_rviz_plugin tier4_control_mode_rviz_plugin tier4_datetime_rviz_plugin tier4_perception_rviz_plugin tier4_planning_factor_rviz_plugin tier4_state_rviz_plugin tier4_system_rviz_plugin tier4_traffic_light_rviz_plugin tier4_vehicle_rviz_plugin

ROS Distro
github

Package Summary

Tags No category tags.
Version 0.46.0
License Apache License 2.0
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Description
Checkout URI https://github.com/autowarefoundation/autoware_universe.git
VCS Type git
VCS Version main
Last Updated 2025-07-31
Dev Status UNKNOWN
Released UNRELEASED
Tags planner ros calibration self-driving-car autonomous-driving autonomous-vehicles ros2 3d-map autoware
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Package Description

boundary departure prevention feature in motion_velocity_planner

Additional Links

No additional links.

Maintainers

  • Zulfaqar Azmi
  • Alqudah Mohammad
  • Takumi Odashima
  • Maxime Clement

Authors

No additional authors.

Boundary Departure Prevention Module

!!! Warning

The Boundary Departure Prevention Module is experimental. It subscribes to the control module’s predicted path and steering report, creating a circular dependency. This violates Autoware’s design principle of forward-only data flow, where control depends on planning, not the other way round. As a result, this module is not officially supported and will remain unofficial for the foreseeable future.

Role

This module inserts slow down points and publishes error diagnostics when the ego vehicle is near or about to cross an uncrossable boundary, such as a road border.

It also accounts for several types of erroneous behavior that could cause the vehicle to unintentionally cross these boundaries. These behaviors are classified as abnormalities.

Abnormalities

Abnormalities refer to erroneous behaviors at the component level, often caused by noisy or unreliable outputs. In this module, these abnormalities are embedded into the predicted footprints derived from the control module’s predicted path, specifically, the MPC (Model Predictive Control) trajectory. Each point along the MPC path is converted into a footprint, and potential deviations due to abnormal conditions are evaluated.

The module addresses the following types of abnormalities:

1. Normal (No Abnormality)

In typical operation, the MPC trajectory may contain small deviations or noise, especially when the vehicle cannot track the planned path perfectly. These deviations are minor and not necessarily the result of a malfunction, but they are still accounted for to ensure safe boundary handling.

Localization Abnormality

Localization errors can cause the ego vehicle to misjudge its position relative to road boundaries such as curbs or road edges. This can happen due to:

  • Sensor noise or drift, leading to inaccurate pose estimation.
  • Map inaccuracies, where the HD map’s geometry does not precisely align with the real-world boundary.
  • Dynamic uncertainty at higher speeds, where even small errors are magnified due to the vehicle covering more distance in less time, reducing the margin for correction.

These factors can result in the vehicle unintentionally approaching or crossing an uncrossable boundary, even when the planned path appears valid.

How Footprint Expansion Helps with Localization and Map Errors

  • Provides a conservative buffer for localization errors: If the ego vehicle’s estimated position is off due to GPS drift or sensor noise, the expanded footprint increases the likelihood of detecting potential boundary conflicts. While this may lead to conservative (and possibly false) detections, it helps ensure safety by accounting for uncertainty in the vehicle’s true position.
  • Compensates for map inaccuracies: Slight misalignments in Lanelet2 map geometries, such as curb or road edge misplacement, can make it difficult to rely on raw map boundaries. Expanding the footprint ensures that even with these inaccuracies, the system remains cautious and avoids unintentional boundary violations.
Without Abnormality Margins With Localization Abnormality Margin
Footprint without abnormality margin Footprint with localization abnormality margin

By expanding the footprint, the system introduces a safety margin that accounts for minor localization and mapping uncertainties, especially critical at higher speeds.he expanded footprint creates a small buffer or “safety margin,” allowing the vehicle to operate safely despite minor abnormality.

Steering Abnormality

Unexpected steering behavior can cause the vehicle to deviate from its planned trajectory, leading to boundary departure even when planning and localization are functioning correctly. This can occur due to:

  • Actuator faults: such as delayed or stuck steering commands.
  • Software issues: like frozen control outputs or bugs in the steering optimization logic.
  • Unexpected maneuvers: for example, emergency avoidance or unintended sharp turns.

In such cases, the actual motion of the vehicle diverges from the MPC trajectory, increasing the risk of departure.

How steering margin helps with steering abnormality

  • Catches lateral deviations early: If the vehicle drifts due to steering faults, like stuck actuators or sudden command spikes, the expanded margin ahead of the vehicle can detect the deviation before the ego crosses into an unsafe region.
  • Predicts future risk along the path: The margin extends along the forward direction of the predicted path, enabling the system to foresee potential boundary violations caused by small steering errors that compound over time.
Without Abnormality Margins With Steering Abnormality Margin
Footprint without abnormality margin Footprint with localization abnormality margin

This method works even when control outputs are inaccurate. Because the margin is computed using the predicted path, not just the current pose, it accounts for latency, actuator delays, and other uncertainties in vehicle response. This allows the system to trigger early mitigation actions, such as slowing down or stopping, before the situation becomes critical.

Longitudinal Tracking Abnormality

Sometimes, the actual motion of the vehicle along the longitudinal axis does not match the MPC-predicted trajectory. For instance:

The ego vehicle might be ahead or behind the predicted position due to mismatches in acceleration or braking behavior.

This discrepancy becomes more problematic when the vehicle is near an uncrossable boundary, as it reduces the reliability of future footprint predictions.

How Longitudinal Expansion Helps with Tracking Abnormality

  • Accounts for ego being ahead of the predicted pose: During lane changes, avoidance maneuvers, or turns on curved roads, the ego vehicle may move faster than expected or take a slightly different path than predicted. By extending the footprint longitudinally (in the direction of motion), the system accounts for the ego vehicle possibly being ahead of the current MPC trajectory point.
  • Uses speed-scaled margins: The longitudinal margin is scaled based on the current vehicle speed, with an added buffer. At higher speeds, a larger margin is used to reflect the increased risk and reduced reaction time.
  • Captures mismatches during dynamic maneuvers: In situations where heading is changing quickly, like on curved roads or during lateral motion, the ego’s actual position may significantly deviate from the MPC path. The extended footprint covers this discrepancy and helps detect boundary risks even if the predicted path appears safe.
File truncated at 100 lines [see the full file](https://github.com/autowarefoundation/autoware_universe/tree/main/planning/motion_velocity_planner/autoware_motion_velocity_boundary_departure_prevention_module/README.md)
Without Abnormality Margins
CHANGELOG

Changelog for package autoware_motion_velocity_boundary_departure_prevention_module

0.46.0 (2025-06-20)

  • Merge remote-tracking branch 'upstream/main' into tmp/TaikiYamada/bump_version_base

  • feat(motion_velocity_planner): boundary departure prevention module (#10817)

    • feat(motion_velocity_planner): boundary departure prevention module
    • add maintainers
    • Add initial readme
    • Adds abnormalities explanation
    • fix infinite loop error
    • fix goal getter
    • add flowchart
    • add initial image folder
    • Add remaining abnormality footprints images
    • docstring on the boundary departure checker
    • Revert motion_planning.launch.xml to state before 14323161e3
    • Additional docstrings and separating slow down interpolator file
    • Update closest projection
    • jsonschema for parameters
    • fix json schema error
    • fix jsonschema
    • Departure type explanation
    • fix cppcheck failure
    • remove author tag
    • Fix misleading explanation
    • removed unused member variable
    • move boundary departure to experimental namespace

    * update maintainer's email address Co-authored-by: Maxime CLEMENT <<78338830+maxime-clem@users.noreply.github.com>>

    * grammar fix Co-authored-by: Maxime CLEMENT <<78338830+maxime-clem@users.noreply.github.com>>

    • refactoring
    • refactor slow down tuple

    * fix build failure due to clang-tidy ---------Co-authored-by: Maxime CLEMENT <<78338830+maxime-clem@users.noreply.github.com>>

  • Contributors: TaikiYamada4, Zulfaqar Azmi

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

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Package symbol

autoware_motion_velocity_boundary_departure_prevention_module package from autoware_universe repo

autoware_agnocast_wrapper autoware_auto_common autoware_boundary_departure_checker autoware_component_interface_specs_universe autoware_component_interface_tools autoware_component_interface_utils autoware_cuda_dependency_meta autoware_fake_test_node autoware_glog_component autoware_goal_distance_calculator autoware_grid_map_utils autoware_path_distance_calculator autoware_polar_grid autoware_time_utils autoware_traffic_light_recognition_marker_publisher autoware_traffic_light_utils autoware_universe_utils tier4_api_utils autoware_autonomous_emergency_braking autoware_collision_detector autoware_control_command_gate autoware_control_performance_analysis autoware_control_validator autoware_external_cmd_selector autoware_joy_controller autoware_lane_departure_checker autoware_mpc_lateral_controller autoware_obstacle_collision_checker autoware_operation_mode_transition_manager autoware_pid_longitudinal_controller autoware_predicted_path_checker autoware_pure_pursuit autoware_shift_decider autoware_smart_mpc_trajectory_follower autoware_stop_mode_operator autoware_trajectory_follower_base autoware_trajectory_follower_node autoware_vehicle_cmd_gate autoware_control_evaluator autoware_kinematic_evaluator autoware_localization_evaluator autoware_perception_online_evaluator autoware_planning_evaluator autoware_scenario_simulator_v2_adapter tier4_autoware_api_launch tier4_control_launch tier4_localization_launch tier4_map_launch tier4_perception_launch tier4_planning_launch tier4_sensing_launch tier4_simulator_launch tier4_system_launch tier4_vehicle_launch autoware_geo_pose_projector autoware_ar_tag_based_localizer autoware_landmark_manager autoware_lidar_marker_localizer autoware_localization_error_monitor autoware_pose2twist autoware_pose_covariance_modifier autoware_pose_estimator_arbiter autoware_pose_instability_detector yabloc_common yabloc_image_processing yabloc_monitor yabloc_particle_filter yabloc_pose_initializer autoware_map_tf_generator autoware_bevfusion autoware_bytetrack autoware_cluster_merger autoware_compare_map_segmentation autoware_crosswalk_traffic_light_estimator autoware_detected_object_feature_remover autoware_detected_object_validation autoware_detection_by_tracker autoware_elevation_map_loader autoware_euclidean_cluster autoware_ground_segmentation autoware_image_projection_based_fusion autoware_lidar_apollo_instance_segmentation autoware_lidar_centerpoint autoware_lidar_transfusion autoware_map_based_prediction autoware_multi_object_tracker autoware_object_merger autoware_object_range_splitter autoware_object_sorter autoware_object_velocity_splitter autoware_occupancy_grid_map_outlier_filter autoware_probabilistic_occupancy_grid_map autoware_radar_fusion_to_detected_object autoware_radar_object_tracker autoware_radar_tracks_msgs_converter autoware_raindrop_cluster_filter autoware_shape_estimation autoware_simpl_prediction autoware_simple_object_merger autoware_tensorrt_bevdet autoware_tensorrt_classifier autoware_tensorrt_common autoware_tensorrt_plugins autoware_tensorrt_yolox autoware_tracking_object_merger autoware_traffic_light_arbiter autoware_traffic_light_category_merger autoware_traffic_light_classifier autoware_traffic_light_fine_detector autoware_traffic_light_map_based_detector autoware_traffic_light_multi_camera_fusion autoware_traffic_light_occlusion_predictor autoware_traffic_light_selector autoware_traffic_light_visualization perception_utils autoware_costmap_generator autoware_diffusion_planner autoware_external_velocity_limit_selector autoware_freespace_planner autoware_freespace_planning_algorithms autoware_hazard_lights_selector autoware_mission_planner_universe autoware_path_optimizer autoware_path_smoother autoware_remaining_distance_time_calculator autoware_rtc_interface autoware_scenario_selector autoware_surround_obstacle_checker autoware_behavior_path_avoidance_by_lane_change_module autoware_behavior_path_bidirectional_traffic_module autoware_behavior_path_dynamic_obstacle_avoidance_module autoware_behavior_path_external_request_lane_change_module autoware_behavior_path_goal_planner_module autoware_behavior_path_lane_change_module autoware_behavior_path_planner autoware_behavior_path_planner_common autoware_behavior_path_sampling_planner_module autoware_behavior_path_side_shift_module autoware_behavior_path_start_planner_module autoware_behavior_path_static_obstacle_avoidance_module autoware_behavior_velocity_blind_spot_module autoware_behavior_velocity_crosswalk_module autoware_behavior_velocity_detection_area_module autoware_behavior_velocity_intersection_module autoware_behavior_velocity_no_drivable_lane_module autoware_behavior_velocity_no_stopping_area_module autoware_behavior_velocity_occlusion_spot_module autoware_behavior_velocity_rtc_interface autoware_behavior_velocity_run_out_module autoware_behavior_velocity_speed_bump_module autoware_behavior_velocity_template_module autoware_behavior_velocity_traffic_light_module autoware_behavior_velocity_virtual_traffic_light_module autoware_behavior_velocity_walkway_module autoware_motion_velocity_boundary_departure_prevention_module autoware_motion_velocity_dynamic_obstacle_stop_module autoware_motion_velocity_obstacle_cruise_module autoware_motion_velocity_obstacle_slow_down_module autoware_motion_velocity_obstacle_velocity_limiter_module autoware_motion_velocity_out_of_lane_module autoware_motion_velocity_road_user_stop_module autoware_motion_velocity_run_out_module autoware_planning_validator autoware_planning_validator_intersection_collision_checker autoware_planning_validator_latency_checker autoware_planning_validator_rear_collision_checker autoware_planning_validator_test_utils autoware_planning_validator_trajectory_checker autoware_bezier_sampler autoware_frenet_planner autoware_path_sampler autoware_sampler_common autoware_cuda_pointcloud_preprocessor autoware_cuda_utils autoware_image_diagnostics autoware_image_transport_decompressor autoware_imu_corrector autoware_pcl_extensions autoware_pointcloud_preprocessor autoware_radar_objects_adapter autoware_radar_scan_to_pointcloud2 autoware_radar_static_pointcloud_filter autoware_radar_threshold_filter autoware_radar_tracks_noise_filter autoware_livox_tag_filter autoware_carla_interface autoware_dummy_perception_publisher autoware_fault_injection autoware_learning_based_vehicle_model autoware_simple_planning_simulator autoware_vehicle_door_simulator tier4_dummy_object_rviz_plugin autoware_bluetooth_monitor autoware_command_mode_decider autoware_command_mode_decider_plugins autoware_command_mode_switcher autoware_command_mode_switcher_plugins autoware_command_mode_types autoware_component_monitor autoware_component_state_monitor autoware_adapi_visualizers autoware_automatic_pose_initializer autoware_default_adapi_universe autoware_diagnostic_graph_aggregator autoware_diagnostic_graph_utils autoware_dummy_diag_publisher autoware_dummy_infrastructure autoware_duplicated_node_checker autoware_hazard_status_converter autoware_mrm_comfortable_stop_operator autoware_mrm_emergency_stop_operator autoware_mrm_handler autoware_pipeline_latency_monitor autoware_processing_time_checker autoware_system_monitor autoware_topic_relay_controller autoware_topic_state_monitor autoware_velodyne_monitor reaction_analyzer autoware_accel_brake_map_calibrator autoware_external_cmd_converter autoware_raw_vehicle_cmd_converter autoware_steer_offset_estimator autoware_bag_time_manager_rviz_plugin autoware_traffic_light_rviz_plugin tier4_adapi_rviz_plugin tier4_camera_view_rviz_plugin tier4_control_mode_rviz_plugin tier4_datetime_rviz_plugin tier4_perception_rviz_plugin tier4_planning_factor_rviz_plugin tier4_state_rviz_plugin tier4_system_rviz_plugin tier4_traffic_light_rviz_plugin tier4_vehicle_rviz_plugin

ROS Distro
github

Package Summary

Tags No category tags.
Version 0.46.0
License Apache License 2.0
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Description
Checkout URI https://github.com/autowarefoundation/autoware_universe.git
VCS Type git
VCS Version main
Last Updated 2025-07-31
Dev Status UNKNOWN
Released UNRELEASED
Tags planner ros calibration self-driving-car autonomous-driving autonomous-vehicles ros2 3d-map autoware
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Package Description

boundary departure prevention feature in motion_velocity_planner

Additional Links

No additional links.

Maintainers

  • Zulfaqar Azmi
  • Alqudah Mohammad
  • Takumi Odashima
  • Maxime Clement

Authors

No additional authors.

Boundary Departure Prevention Module

!!! Warning

The Boundary Departure Prevention Module is experimental. It subscribes to the control module’s predicted path and steering report, creating a circular dependency. This violates Autoware’s design principle of forward-only data flow, where control depends on planning, not the other way round. As a result, this module is not officially supported and will remain unofficial for the foreseeable future.

Role

This module inserts slow down points and publishes error diagnostics when the ego vehicle is near or about to cross an uncrossable boundary, such as a road border.

It also accounts for several types of erroneous behavior that could cause the vehicle to unintentionally cross these boundaries. These behaviors are classified as abnormalities.

Abnormalities

Abnormalities refer to erroneous behaviors at the component level, often caused by noisy or unreliable outputs. In this module, these abnormalities are embedded into the predicted footprints derived from the control module’s predicted path, specifically, the MPC (Model Predictive Control) trajectory. Each point along the MPC path is converted into a footprint, and potential deviations due to abnormal conditions are evaluated.

The module addresses the following types of abnormalities:

1. Normal (No Abnormality)

In typical operation, the MPC trajectory may contain small deviations or noise, especially when the vehicle cannot track the planned path perfectly. These deviations are minor and not necessarily the result of a malfunction, but they are still accounted for to ensure safe boundary handling.

Localization Abnormality

Localization errors can cause the ego vehicle to misjudge its position relative to road boundaries such as curbs or road edges. This can happen due to:

  • Sensor noise or drift, leading to inaccurate pose estimation.
  • Map inaccuracies, where the HD map’s geometry does not precisely align with the real-world boundary.
  • Dynamic uncertainty at higher speeds, where even small errors are magnified due to the vehicle covering more distance in less time, reducing the margin for correction.

These factors can result in the vehicle unintentionally approaching or crossing an uncrossable boundary, even when the planned path appears valid.

How Footprint Expansion Helps with Localization and Map Errors

  • Provides a conservative buffer for localization errors: If the ego vehicle’s estimated position is off due to GPS drift or sensor noise, the expanded footprint increases the likelihood of detecting potential boundary conflicts. While this may lead to conservative (and possibly false) detections, it helps ensure safety by accounting for uncertainty in the vehicle’s true position.
  • Compensates for map inaccuracies: Slight misalignments in Lanelet2 map geometries, such as curb or road edge misplacement, can make it difficult to rely on raw map boundaries. Expanding the footprint ensures that even with these inaccuracies, the system remains cautious and avoids unintentional boundary violations.
Without Abnormality Margins With Localization Abnormality Margin
Footprint without abnormality margin Footprint with localization abnormality margin

By expanding the footprint, the system introduces a safety margin that accounts for minor localization and mapping uncertainties, especially critical at higher speeds.he expanded footprint creates a small buffer or “safety margin,” allowing the vehicle to operate safely despite minor abnormality.

Steering Abnormality

Unexpected steering behavior can cause the vehicle to deviate from its planned trajectory, leading to boundary departure even when planning and localization are functioning correctly. This can occur due to:

  • Actuator faults: such as delayed or stuck steering commands.
  • Software issues: like frozen control outputs or bugs in the steering optimization logic.
  • Unexpected maneuvers: for example, emergency avoidance or unintended sharp turns.

In such cases, the actual motion of the vehicle diverges from the MPC trajectory, increasing the risk of departure.

How steering margin helps with steering abnormality

  • Catches lateral deviations early: If the vehicle drifts due to steering faults, like stuck actuators or sudden command spikes, the expanded margin ahead of the vehicle can detect the deviation before the ego crosses into an unsafe region.
  • Predicts future risk along the path: The margin extends along the forward direction of the predicted path, enabling the system to foresee potential boundary violations caused by small steering errors that compound over time.
Without Abnormality Margins With Steering Abnormality Margin
Footprint without abnormality margin Footprint with localization abnormality margin

This method works even when control outputs are inaccurate. Because the margin is computed using the predicted path, not just the current pose, it accounts for latency, actuator delays, and other uncertainties in vehicle response. This allows the system to trigger early mitigation actions, such as slowing down or stopping, before the situation becomes critical.

Longitudinal Tracking Abnormality

Sometimes, the actual motion of the vehicle along the longitudinal axis does not match the MPC-predicted trajectory. For instance:

The ego vehicle might be ahead or behind the predicted position due to mismatches in acceleration or braking behavior.

This discrepancy becomes more problematic when the vehicle is near an uncrossable boundary, as it reduces the reliability of future footprint predictions.

How Longitudinal Expansion Helps with Tracking Abnormality

  • Accounts for ego being ahead of the predicted pose: During lane changes, avoidance maneuvers, or turns on curved roads, the ego vehicle may move faster than expected or take a slightly different path than predicted. By extending the footprint longitudinally (in the direction of motion), the system accounts for the ego vehicle possibly being ahead of the current MPC trajectory point.
  • Uses speed-scaled margins: The longitudinal margin is scaled based on the current vehicle speed, with an added buffer. At higher speeds, a larger margin is used to reflect the increased risk and reduced reaction time.
  • Captures mismatches during dynamic maneuvers: In situations where heading is changing quickly, like on curved roads or during lateral motion, the ego’s actual position may significantly deviate from the MPC path. The extended footprint covers this discrepancy and helps detect boundary risks even if the predicted path appears safe.
File truncated at 100 lines [see the full file](https://github.com/autowarefoundation/autoware_universe/tree/main/planning/motion_velocity_planner/autoware_motion_velocity_boundary_departure_prevention_module/README.md)
Without Abnormality Margins
CHANGELOG

Changelog for package autoware_motion_velocity_boundary_departure_prevention_module

0.46.0 (2025-06-20)

  • Merge remote-tracking branch 'upstream/main' into tmp/TaikiYamada/bump_version_base

  • feat(motion_velocity_planner): boundary departure prevention module (#10817)

    • feat(motion_velocity_planner): boundary departure prevention module
    • add maintainers
    • Add initial readme
    • Adds abnormalities explanation
    • fix infinite loop error
    • fix goal getter
    • add flowchart
    • add initial image folder
    • Add remaining abnormality footprints images
    • docstring on the boundary departure checker
    • Revert motion_planning.launch.xml to state before 14323161e3
    • Additional docstrings and separating slow down interpolator file
    • Update closest projection
    • jsonschema for parameters
    • fix json schema error
    • fix jsonschema
    • Departure type explanation
    • fix cppcheck failure
    • remove author tag
    • Fix misleading explanation
    • removed unused member variable
    • move boundary departure to experimental namespace

    * update maintainer's email address Co-authored-by: Maxime CLEMENT <<78338830+maxime-clem@users.noreply.github.com>>

    * grammar fix Co-authored-by: Maxime CLEMENT <<78338830+maxime-clem@users.noreply.github.com>>

    • refactoring
    • refactor slow down tuple

    * fix build failure due to clang-tidy ---------Co-authored-by: Maxime CLEMENT <<78338830+maxime-clem@users.noreply.github.com>>

  • Contributors: TaikiYamada4, Zulfaqar Azmi

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

Recent questions tagged autoware_motion_velocity_boundary_departure_prevention_module at Robotics Stack Exchange

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autoware_motion_velocity_boundary_departure_prevention_module package from autoware_universe repo

autoware_agnocast_wrapper autoware_auto_common autoware_boundary_departure_checker autoware_component_interface_specs_universe autoware_component_interface_tools autoware_component_interface_utils autoware_cuda_dependency_meta autoware_fake_test_node autoware_glog_component autoware_goal_distance_calculator autoware_grid_map_utils autoware_path_distance_calculator autoware_polar_grid autoware_time_utils autoware_traffic_light_recognition_marker_publisher autoware_traffic_light_utils autoware_universe_utils tier4_api_utils autoware_autonomous_emergency_braking autoware_collision_detector autoware_control_command_gate autoware_control_performance_analysis autoware_control_validator autoware_external_cmd_selector autoware_joy_controller autoware_lane_departure_checker autoware_mpc_lateral_controller autoware_obstacle_collision_checker autoware_operation_mode_transition_manager autoware_pid_longitudinal_controller autoware_predicted_path_checker autoware_pure_pursuit autoware_shift_decider autoware_smart_mpc_trajectory_follower autoware_stop_mode_operator autoware_trajectory_follower_base autoware_trajectory_follower_node autoware_vehicle_cmd_gate autoware_control_evaluator autoware_kinematic_evaluator autoware_localization_evaluator autoware_perception_online_evaluator autoware_planning_evaluator autoware_scenario_simulator_v2_adapter tier4_autoware_api_launch tier4_control_launch tier4_localization_launch tier4_map_launch tier4_perception_launch tier4_planning_launch tier4_sensing_launch tier4_simulator_launch tier4_system_launch tier4_vehicle_launch autoware_geo_pose_projector autoware_ar_tag_based_localizer autoware_landmark_manager autoware_lidar_marker_localizer autoware_localization_error_monitor autoware_pose2twist autoware_pose_covariance_modifier autoware_pose_estimator_arbiter autoware_pose_instability_detector yabloc_common yabloc_image_processing yabloc_monitor yabloc_particle_filter yabloc_pose_initializer autoware_map_tf_generator autoware_bevfusion autoware_bytetrack autoware_cluster_merger autoware_compare_map_segmentation autoware_crosswalk_traffic_light_estimator autoware_detected_object_feature_remover autoware_detected_object_validation autoware_detection_by_tracker autoware_elevation_map_loader autoware_euclidean_cluster autoware_ground_segmentation autoware_image_projection_based_fusion autoware_lidar_apollo_instance_segmentation autoware_lidar_centerpoint autoware_lidar_transfusion autoware_map_based_prediction autoware_multi_object_tracker autoware_object_merger autoware_object_range_splitter autoware_object_sorter autoware_object_velocity_splitter autoware_occupancy_grid_map_outlier_filter autoware_probabilistic_occupancy_grid_map autoware_radar_fusion_to_detected_object autoware_radar_object_tracker autoware_radar_tracks_msgs_converter autoware_raindrop_cluster_filter autoware_shape_estimation autoware_simpl_prediction autoware_simple_object_merger autoware_tensorrt_bevdet autoware_tensorrt_classifier autoware_tensorrt_common autoware_tensorrt_plugins autoware_tensorrt_yolox autoware_tracking_object_merger autoware_traffic_light_arbiter autoware_traffic_light_category_merger autoware_traffic_light_classifier autoware_traffic_light_fine_detector autoware_traffic_light_map_based_detector autoware_traffic_light_multi_camera_fusion autoware_traffic_light_occlusion_predictor autoware_traffic_light_selector autoware_traffic_light_visualization perception_utils autoware_costmap_generator autoware_diffusion_planner autoware_external_velocity_limit_selector autoware_freespace_planner autoware_freespace_planning_algorithms autoware_hazard_lights_selector autoware_mission_planner_universe autoware_path_optimizer autoware_path_smoother autoware_remaining_distance_time_calculator autoware_rtc_interface autoware_scenario_selector autoware_surround_obstacle_checker autoware_behavior_path_avoidance_by_lane_change_module autoware_behavior_path_bidirectional_traffic_module autoware_behavior_path_dynamic_obstacle_avoidance_module autoware_behavior_path_external_request_lane_change_module autoware_behavior_path_goal_planner_module autoware_behavior_path_lane_change_module autoware_behavior_path_planner autoware_behavior_path_planner_common autoware_behavior_path_sampling_planner_module autoware_behavior_path_side_shift_module autoware_behavior_path_start_planner_module autoware_behavior_path_static_obstacle_avoidance_module autoware_behavior_velocity_blind_spot_module autoware_behavior_velocity_crosswalk_module autoware_behavior_velocity_detection_area_module autoware_behavior_velocity_intersection_module autoware_behavior_velocity_no_drivable_lane_module autoware_behavior_velocity_no_stopping_area_module autoware_behavior_velocity_occlusion_spot_module autoware_behavior_velocity_rtc_interface autoware_behavior_velocity_run_out_module autoware_behavior_velocity_speed_bump_module autoware_behavior_velocity_template_module autoware_behavior_velocity_traffic_light_module autoware_behavior_velocity_virtual_traffic_light_module autoware_behavior_velocity_walkway_module autoware_motion_velocity_boundary_departure_prevention_module autoware_motion_velocity_dynamic_obstacle_stop_module autoware_motion_velocity_obstacle_cruise_module autoware_motion_velocity_obstacle_slow_down_module autoware_motion_velocity_obstacle_velocity_limiter_module autoware_motion_velocity_out_of_lane_module autoware_motion_velocity_road_user_stop_module autoware_motion_velocity_run_out_module autoware_planning_validator autoware_planning_validator_intersection_collision_checker autoware_planning_validator_latency_checker autoware_planning_validator_rear_collision_checker autoware_planning_validator_test_utils autoware_planning_validator_trajectory_checker autoware_bezier_sampler autoware_frenet_planner autoware_path_sampler autoware_sampler_common autoware_cuda_pointcloud_preprocessor autoware_cuda_utils autoware_image_diagnostics autoware_image_transport_decompressor autoware_imu_corrector autoware_pcl_extensions autoware_pointcloud_preprocessor autoware_radar_objects_adapter autoware_radar_scan_to_pointcloud2 autoware_radar_static_pointcloud_filter autoware_radar_threshold_filter autoware_radar_tracks_noise_filter autoware_livox_tag_filter autoware_carla_interface autoware_dummy_perception_publisher autoware_fault_injection autoware_learning_based_vehicle_model autoware_simple_planning_simulator autoware_vehicle_door_simulator tier4_dummy_object_rviz_plugin autoware_bluetooth_monitor autoware_command_mode_decider autoware_command_mode_decider_plugins autoware_command_mode_switcher autoware_command_mode_switcher_plugins autoware_command_mode_types autoware_component_monitor autoware_component_state_monitor autoware_adapi_visualizers autoware_automatic_pose_initializer autoware_default_adapi_universe autoware_diagnostic_graph_aggregator autoware_diagnostic_graph_utils autoware_dummy_diag_publisher autoware_dummy_infrastructure autoware_duplicated_node_checker autoware_hazard_status_converter autoware_mrm_comfortable_stop_operator autoware_mrm_emergency_stop_operator autoware_mrm_handler autoware_pipeline_latency_monitor autoware_processing_time_checker autoware_system_monitor autoware_topic_relay_controller autoware_topic_state_monitor autoware_velodyne_monitor reaction_analyzer autoware_accel_brake_map_calibrator autoware_external_cmd_converter autoware_raw_vehicle_cmd_converter autoware_steer_offset_estimator autoware_bag_time_manager_rviz_plugin autoware_traffic_light_rviz_plugin tier4_adapi_rviz_plugin tier4_camera_view_rviz_plugin tier4_control_mode_rviz_plugin tier4_datetime_rviz_plugin tier4_perception_rviz_plugin tier4_planning_factor_rviz_plugin tier4_state_rviz_plugin tier4_system_rviz_plugin tier4_traffic_light_rviz_plugin tier4_vehicle_rviz_plugin

ROS Distro
github

Package Summary

Tags No category tags.
Version 0.46.0
License Apache License 2.0
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Description
Checkout URI https://github.com/autowarefoundation/autoware_universe.git
VCS Type git
VCS Version main
Last Updated 2025-07-31
Dev Status UNKNOWN
Released UNRELEASED
Tags planner ros calibration self-driving-car autonomous-driving autonomous-vehicles ros2 3d-map autoware
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Package Description

boundary departure prevention feature in motion_velocity_planner

Additional Links

No additional links.

Maintainers

  • Zulfaqar Azmi
  • Alqudah Mohammad
  • Takumi Odashima
  • Maxime Clement

Authors

No additional authors.

Boundary Departure Prevention Module

!!! Warning

The Boundary Departure Prevention Module is experimental. It subscribes to the control module’s predicted path and steering report, creating a circular dependency. This violates Autoware’s design principle of forward-only data flow, where control depends on planning, not the other way round. As a result, this module is not officially supported and will remain unofficial for the foreseeable future.

Role

This module inserts slow down points and publishes error diagnostics when the ego vehicle is near or about to cross an uncrossable boundary, such as a road border.

It also accounts for several types of erroneous behavior that could cause the vehicle to unintentionally cross these boundaries. These behaviors are classified as abnormalities.

Abnormalities

Abnormalities refer to erroneous behaviors at the component level, often caused by noisy or unreliable outputs. In this module, these abnormalities are embedded into the predicted footprints derived from the control module’s predicted path, specifically, the MPC (Model Predictive Control) trajectory. Each point along the MPC path is converted into a footprint, and potential deviations due to abnormal conditions are evaluated.

The module addresses the following types of abnormalities:

1. Normal (No Abnormality)

In typical operation, the MPC trajectory may contain small deviations or noise, especially when the vehicle cannot track the planned path perfectly. These deviations are minor and not necessarily the result of a malfunction, but they are still accounted for to ensure safe boundary handling.

Localization Abnormality

Localization errors can cause the ego vehicle to misjudge its position relative to road boundaries such as curbs or road edges. This can happen due to:

  • Sensor noise or drift, leading to inaccurate pose estimation.
  • Map inaccuracies, where the HD map’s geometry does not precisely align with the real-world boundary.
  • Dynamic uncertainty at higher speeds, where even small errors are magnified due to the vehicle covering more distance in less time, reducing the margin for correction.

These factors can result in the vehicle unintentionally approaching or crossing an uncrossable boundary, even when the planned path appears valid.

How Footprint Expansion Helps with Localization and Map Errors

  • Provides a conservative buffer for localization errors: If the ego vehicle’s estimated position is off due to GPS drift or sensor noise, the expanded footprint increases the likelihood of detecting potential boundary conflicts. While this may lead to conservative (and possibly false) detections, it helps ensure safety by accounting for uncertainty in the vehicle’s true position.
  • Compensates for map inaccuracies: Slight misalignments in Lanelet2 map geometries, such as curb or road edge misplacement, can make it difficult to rely on raw map boundaries. Expanding the footprint ensures that even with these inaccuracies, the system remains cautious and avoids unintentional boundary violations.
Without Abnormality Margins With Localization Abnormality Margin
Footprint without abnormality margin Footprint with localization abnormality margin

By expanding the footprint, the system introduces a safety margin that accounts for minor localization and mapping uncertainties, especially critical at higher speeds.he expanded footprint creates a small buffer or “safety margin,” allowing the vehicle to operate safely despite minor abnormality.

Steering Abnormality

Unexpected steering behavior can cause the vehicle to deviate from its planned trajectory, leading to boundary departure even when planning and localization are functioning correctly. This can occur due to:

  • Actuator faults: such as delayed or stuck steering commands.
  • Software issues: like frozen control outputs or bugs in the steering optimization logic.
  • Unexpected maneuvers: for example, emergency avoidance or unintended sharp turns.

In such cases, the actual motion of the vehicle diverges from the MPC trajectory, increasing the risk of departure.

How steering margin helps with steering abnormality

  • Catches lateral deviations early: If the vehicle drifts due to steering faults, like stuck actuators or sudden command spikes, the expanded margin ahead of the vehicle can detect the deviation before the ego crosses into an unsafe region.
  • Predicts future risk along the path: The margin extends along the forward direction of the predicted path, enabling the system to foresee potential boundary violations caused by small steering errors that compound over time.
Without Abnormality Margins With Steering Abnormality Margin
Footprint without abnormality margin Footprint with localization abnormality margin

This method works even when control outputs are inaccurate. Because the margin is computed using the predicted path, not just the current pose, it accounts for latency, actuator delays, and other uncertainties in vehicle response. This allows the system to trigger early mitigation actions, such as slowing down or stopping, before the situation becomes critical.

Longitudinal Tracking Abnormality

Sometimes, the actual motion of the vehicle along the longitudinal axis does not match the MPC-predicted trajectory. For instance:

The ego vehicle might be ahead or behind the predicted position due to mismatches in acceleration or braking behavior.

This discrepancy becomes more problematic when the vehicle is near an uncrossable boundary, as it reduces the reliability of future footprint predictions.

How Longitudinal Expansion Helps with Tracking Abnormality

  • Accounts for ego being ahead of the predicted pose: During lane changes, avoidance maneuvers, or turns on curved roads, the ego vehicle may move faster than expected or take a slightly different path than predicted. By extending the footprint longitudinally (in the direction of motion), the system accounts for the ego vehicle possibly being ahead of the current MPC trajectory point.
  • Uses speed-scaled margins: The longitudinal margin is scaled based on the current vehicle speed, with an added buffer. At higher speeds, a larger margin is used to reflect the increased risk and reduced reaction time.
  • Captures mismatches during dynamic maneuvers: In situations where heading is changing quickly, like on curved roads or during lateral motion, the ego’s actual position may significantly deviate from the MPC path. The extended footprint covers this discrepancy and helps detect boundary risks even if the predicted path appears safe.
File truncated at 100 lines [see the full file](https://github.com/autowarefoundation/autoware_universe/tree/main/planning/motion_velocity_planner/autoware_motion_velocity_boundary_departure_prevention_module/README.md)
Without Abnormality Margins
CHANGELOG

Changelog for package autoware_motion_velocity_boundary_departure_prevention_module

0.46.0 (2025-06-20)

  • Merge remote-tracking branch 'upstream/main' into tmp/TaikiYamada/bump_version_base

  • feat(motion_velocity_planner): boundary departure prevention module (#10817)

    • feat(motion_velocity_planner): boundary departure prevention module
    • add maintainers
    • Add initial readme
    • Adds abnormalities explanation
    • fix infinite loop error
    • fix goal getter
    • add flowchart
    • add initial image folder
    • Add remaining abnormality footprints images
    • docstring on the boundary departure checker
    • Revert motion_planning.launch.xml to state before 14323161e3
    • Additional docstrings and separating slow down interpolator file
    • Update closest projection
    • jsonschema for parameters
    • fix json schema error
    • fix jsonschema
    • Departure type explanation
    • fix cppcheck failure
    • remove author tag
    • Fix misleading explanation
    • removed unused member variable
    • move boundary departure to experimental namespace

    * update maintainer's email address Co-authored-by: Maxime CLEMENT <<78338830+maxime-clem@users.noreply.github.com>>

    * grammar fix Co-authored-by: Maxime CLEMENT <<78338830+maxime-clem@users.noreply.github.com>>

    • refactoring
    • refactor slow down tuple

    * fix build failure due to clang-tidy ---------Co-authored-by: Maxime CLEMENT <<78338830+maxime-clem@users.noreply.github.com>>

  • Contributors: TaikiYamada4, Zulfaqar Azmi

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

Recent questions tagged autoware_motion_velocity_boundary_departure_prevention_module at Robotics Stack Exchange

No version for distro iron showing github. Known supported distros are highlighted in the buttons above.
Package symbol

autoware_motion_velocity_boundary_departure_prevention_module package from autoware_universe repo

autoware_agnocast_wrapper autoware_auto_common autoware_boundary_departure_checker autoware_component_interface_specs_universe autoware_component_interface_tools autoware_component_interface_utils autoware_cuda_dependency_meta autoware_fake_test_node autoware_glog_component autoware_goal_distance_calculator autoware_grid_map_utils autoware_path_distance_calculator autoware_polar_grid autoware_time_utils autoware_traffic_light_recognition_marker_publisher autoware_traffic_light_utils autoware_universe_utils tier4_api_utils autoware_autonomous_emergency_braking autoware_collision_detector autoware_control_command_gate autoware_control_performance_analysis autoware_control_validator autoware_external_cmd_selector autoware_joy_controller autoware_lane_departure_checker autoware_mpc_lateral_controller autoware_obstacle_collision_checker autoware_operation_mode_transition_manager autoware_pid_longitudinal_controller autoware_predicted_path_checker autoware_pure_pursuit autoware_shift_decider autoware_smart_mpc_trajectory_follower autoware_stop_mode_operator autoware_trajectory_follower_base autoware_trajectory_follower_node autoware_vehicle_cmd_gate autoware_control_evaluator autoware_kinematic_evaluator autoware_localization_evaluator autoware_perception_online_evaluator autoware_planning_evaluator autoware_scenario_simulator_v2_adapter tier4_autoware_api_launch tier4_control_launch tier4_localization_launch tier4_map_launch tier4_perception_launch tier4_planning_launch tier4_sensing_launch tier4_simulator_launch tier4_system_launch tier4_vehicle_launch autoware_geo_pose_projector autoware_ar_tag_based_localizer autoware_landmark_manager autoware_lidar_marker_localizer autoware_localization_error_monitor autoware_pose2twist autoware_pose_covariance_modifier autoware_pose_estimator_arbiter autoware_pose_instability_detector yabloc_common yabloc_image_processing yabloc_monitor yabloc_particle_filter yabloc_pose_initializer autoware_map_tf_generator autoware_bevfusion autoware_bytetrack autoware_cluster_merger autoware_compare_map_segmentation autoware_crosswalk_traffic_light_estimator autoware_detected_object_feature_remover autoware_detected_object_validation autoware_detection_by_tracker autoware_elevation_map_loader autoware_euclidean_cluster autoware_ground_segmentation autoware_image_projection_based_fusion autoware_lidar_apollo_instance_segmentation autoware_lidar_centerpoint autoware_lidar_transfusion autoware_map_based_prediction autoware_multi_object_tracker autoware_object_merger autoware_object_range_splitter autoware_object_sorter autoware_object_velocity_splitter autoware_occupancy_grid_map_outlier_filter autoware_probabilistic_occupancy_grid_map autoware_radar_fusion_to_detected_object autoware_radar_object_tracker autoware_radar_tracks_msgs_converter autoware_raindrop_cluster_filter autoware_shape_estimation autoware_simpl_prediction autoware_simple_object_merger autoware_tensorrt_bevdet autoware_tensorrt_classifier autoware_tensorrt_common autoware_tensorrt_plugins autoware_tensorrt_yolox autoware_tracking_object_merger autoware_traffic_light_arbiter autoware_traffic_light_category_merger autoware_traffic_light_classifier autoware_traffic_light_fine_detector autoware_traffic_light_map_based_detector autoware_traffic_light_multi_camera_fusion autoware_traffic_light_occlusion_predictor autoware_traffic_light_selector autoware_traffic_light_visualization perception_utils autoware_costmap_generator autoware_diffusion_planner autoware_external_velocity_limit_selector autoware_freespace_planner autoware_freespace_planning_algorithms autoware_hazard_lights_selector autoware_mission_planner_universe autoware_path_optimizer autoware_path_smoother autoware_remaining_distance_time_calculator autoware_rtc_interface autoware_scenario_selector autoware_surround_obstacle_checker autoware_behavior_path_avoidance_by_lane_change_module autoware_behavior_path_bidirectional_traffic_module autoware_behavior_path_dynamic_obstacle_avoidance_module autoware_behavior_path_external_request_lane_change_module autoware_behavior_path_goal_planner_module autoware_behavior_path_lane_change_module autoware_behavior_path_planner autoware_behavior_path_planner_common autoware_behavior_path_sampling_planner_module autoware_behavior_path_side_shift_module autoware_behavior_path_start_planner_module autoware_behavior_path_static_obstacle_avoidance_module autoware_behavior_velocity_blind_spot_module autoware_behavior_velocity_crosswalk_module autoware_behavior_velocity_detection_area_module autoware_behavior_velocity_intersection_module autoware_behavior_velocity_no_drivable_lane_module autoware_behavior_velocity_no_stopping_area_module autoware_behavior_velocity_occlusion_spot_module autoware_behavior_velocity_rtc_interface autoware_behavior_velocity_run_out_module autoware_behavior_velocity_speed_bump_module autoware_behavior_velocity_template_module autoware_behavior_velocity_traffic_light_module autoware_behavior_velocity_virtual_traffic_light_module autoware_behavior_velocity_walkway_module autoware_motion_velocity_boundary_departure_prevention_module autoware_motion_velocity_dynamic_obstacle_stop_module autoware_motion_velocity_obstacle_cruise_module autoware_motion_velocity_obstacle_slow_down_module autoware_motion_velocity_obstacle_velocity_limiter_module autoware_motion_velocity_out_of_lane_module autoware_motion_velocity_road_user_stop_module autoware_motion_velocity_run_out_module autoware_planning_validator autoware_planning_validator_intersection_collision_checker autoware_planning_validator_latency_checker autoware_planning_validator_rear_collision_checker autoware_planning_validator_test_utils autoware_planning_validator_trajectory_checker autoware_bezier_sampler autoware_frenet_planner autoware_path_sampler autoware_sampler_common autoware_cuda_pointcloud_preprocessor autoware_cuda_utils autoware_image_diagnostics autoware_image_transport_decompressor autoware_imu_corrector autoware_pcl_extensions autoware_pointcloud_preprocessor autoware_radar_objects_adapter autoware_radar_scan_to_pointcloud2 autoware_radar_static_pointcloud_filter autoware_radar_threshold_filter autoware_radar_tracks_noise_filter autoware_livox_tag_filter autoware_carla_interface autoware_dummy_perception_publisher autoware_fault_injection autoware_learning_based_vehicle_model autoware_simple_planning_simulator autoware_vehicle_door_simulator tier4_dummy_object_rviz_plugin autoware_bluetooth_monitor autoware_command_mode_decider autoware_command_mode_decider_plugins autoware_command_mode_switcher autoware_command_mode_switcher_plugins autoware_command_mode_types autoware_component_monitor autoware_component_state_monitor autoware_adapi_visualizers autoware_automatic_pose_initializer autoware_default_adapi_universe autoware_diagnostic_graph_aggregator autoware_diagnostic_graph_utils autoware_dummy_diag_publisher autoware_dummy_infrastructure autoware_duplicated_node_checker autoware_hazard_status_converter autoware_mrm_comfortable_stop_operator autoware_mrm_emergency_stop_operator autoware_mrm_handler autoware_pipeline_latency_monitor autoware_processing_time_checker autoware_system_monitor autoware_topic_relay_controller autoware_topic_state_monitor autoware_velodyne_monitor reaction_analyzer autoware_accel_brake_map_calibrator autoware_external_cmd_converter autoware_raw_vehicle_cmd_converter autoware_steer_offset_estimator autoware_bag_time_manager_rviz_plugin autoware_traffic_light_rviz_plugin tier4_adapi_rviz_plugin tier4_camera_view_rviz_plugin tier4_control_mode_rviz_plugin tier4_datetime_rviz_plugin tier4_perception_rviz_plugin tier4_planning_factor_rviz_plugin tier4_state_rviz_plugin tier4_system_rviz_plugin tier4_traffic_light_rviz_plugin tier4_vehicle_rviz_plugin

ROS Distro
github

Package Summary

Tags No category tags.
Version 0.46.0
License Apache License 2.0
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Description
Checkout URI https://github.com/autowarefoundation/autoware_universe.git
VCS Type git
VCS Version main
Last Updated 2025-07-31
Dev Status UNKNOWN
Released UNRELEASED
Tags planner ros calibration self-driving-car autonomous-driving autonomous-vehicles ros2 3d-map autoware
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Package Description

boundary departure prevention feature in motion_velocity_planner

Additional Links

No additional links.

Maintainers

  • Zulfaqar Azmi
  • Alqudah Mohammad
  • Takumi Odashima
  • Maxime Clement

Authors

No additional authors.

Boundary Departure Prevention Module

!!! Warning

The Boundary Departure Prevention Module is experimental. It subscribes to the control module’s predicted path and steering report, creating a circular dependency. This violates Autoware’s design principle of forward-only data flow, where control depends on planning, not the other way round. As a result, this module is not officially supported and will remain unofficial for the foreseeable future.

Role

This module inserts slow down points and publishes error diagnostics when the ego vehicle is near or about to cross an uncrossable boundary, such as a road border.

It also accounts for several types of erroneous behavior that could cause the vehicle to unintentionally cross these boundaries. These behaviors are classified as abnormalities.

Abnormalities

Abnormalities refer to erroneous behaviors at the component level, often caused by noisy or unreliable outputs. In this module, these abnormalities are embedded into the predicted footprints derived from the control module’s predicted path, specifically, the MPC (Model Predictive Control) trajectory. Each point along the MPC path is converted into a footprint, and potential deviations due to abnormal conditions are evaluated.

The module addresses the following types of abnormalities:

1. Normal (No Abnormality)

In typical operation, the MPC trajectory may contain small deviations or noise, especially when the vehicle cannot track the planned path perfectly. These deviations are minor and not necessarily the result of a malfunction, but they are still accounted for to ensure safe boundary handling.

Localization Abnormality

Localization errors can cause the ego vehicle to misjudge its position relative to road boundaries such as curbs or road edges. This can happen due to:

  • Sensor noise or drift, leading to inaccurate pose estimation.
  • Map inaccuracies, where the HD map’s geometry does not precisely align with the real-world boundary.
  • Dynamic uncertainty at higher speeds, where even small errors are magnified due to the vehicle covering more distance in less time, reducing the margin for correction.

These factors can result in the vehicle unintentionally approaching or crossing an uncrossable boundary, even when the planned path appears valid.

How Footprint Expansion Helps with Localization and Map Errors

  • Provides a conservative buffer for localization errors: If the ego vehicle’s estimated position is off due to GPS drift or sensor noise, the expanded footprint increases the likelihood of detecting potential boundary conflicts. While this may lead to conservative (and possibly false) detections, it helps ensure safety by accounting for uncertainty in the vehicle’s true position.
  • Compensates for map inaccuracies: Slight misalignments in Lanelet2 map geometries, such as curb or road edge misplacement, can make it difficult to rely on raw map boundaries. Expanding the footprint ensures that even with these inaccuracies, the system remains cautious and avoids unintentional boundary violations.
Without Abnormality Margins With Localization Abnormality Margin
Footprint without abnormality margin Footprint with localization abnormality margin

By expanding the footprint, the system introduces a safety margin that accounts for minor localization and mapping uncertainties, especially critical at higher speeds.he expanded footprint creates a small buffer or “safety margin,” allowing the vehicle to operate safely despite minor abnormality.

Steering Abnormality

Unexpected steering behavior can cause the vehicle to deviate from its planned trajectory, leading to boundary departure even when planning and localization are functioning correctly. This can occur due to:

  • Actuator faults: such as delayed or stuck steering commands.
  • Software issues: like frozen control outputs or bugs in the steering optimization logic.
  • Unexpected maneuvers: for example, emergency avoidance or unintended sharp turns.

In such cases, the actual motion of the vehicle diverges from the MPC trajectory, increasing the risk of departure.

How steering margin helps with steering abnormality

  • Catches lateral deviations early: If the vehicle drifts due to steering faults, like stuck actuators or sudden command spikes, the expanded margin ahead of the vehicle can detect the deviation before the ego crosses into an unsafe region.
  • Predicts future risk along the path: The margin extends along the forward direction of the predicted path, enabling the system to foresee potential boundary violations caused by small steering errors that compound over time.
Without Abnormality Margins With Steering Abnormality Margin
Footprint without abnormality margin Footprint with localization abnormality margin

This method works even when control outputs are inaccurate. Because the margin is computed using the predicted path, not just the current pose, it accounts for latency, actuator delays, and other uncertainties in vehicle response. This allows the system to trigger early mitigation actions, such as slowing down or stopping, before the situation becomes critical.

Longitudinal Tracking Abnormality

Sometimes, the actual motion of the vehicle along the longitudinal axis does not match the MPC-predicted trajectory. For instance:

The ego vehicle might be ahead or behind the predicted position due to mismatches in acceleration or braking behavior.

This discrepancy becomes more problematic when the vehicle is near an uncrossable boundary, as it reduces the reliability of future footprint predictions.

How Longitudinal Expansion Helps with Tracking Abnormality

  • Accounts for ego being ahead of the predicted pose: During lane changes, avoidance maneuvers, or turns on curved roads, the ego vehicle may move faster than expected or take a slightly different path than predicted. By extending the footprint longitudinally (in the direction of motion), the system accounts for the ego vehicle possibly being ahead of the current MPC trajectory point.
  • Uses speed-scaled margins: The longitudinal margin is scaled based on the current vehicle speed, with an added buffer. At higher speeds, a larger margin is used to reflect the increased risk and reduced reaction time.
  • Captures mismatches during dynamic maneuvers: In situations where heading is changing quickly, like on curved roads or during lateral motion, the ego’s actual position may significantly deviate from the MPC path. The extended footprint covers this discrepancy and helps detect boundary risks even if the predicted path appears safe.
File truncated at 100 lines [see the full file](https://github.com/autowarefoundation/autoware_universe/tree/main/planning/motion_velocity_planner/autoware_motion_velocity_boundary_departure_prevention_module/README.md)
Without Abnormality Margins
CHANGELOG

Changelog for package autoware_motion_velocity_boundary_departure_prevention_module

0.46.0 (2025-06-20)

  • Merge remote-tracking branch 'upstream/main' into tmp/TaikiYamada/bump_version_base

  • feat(motion_velocity_planner): boundary departure prevention module (#10817)

    • feat(motion_velocity_planner): boundary departure prevention module
    • add maintainers
    • Add initial readme
    • Adds abnormalities explanation
    • fix infinite loop error
    • fix goal getter
    • add flowchart
    • add initial image folder
    • Add remaining abnormality footprints images
    • docstring on the boundary departure checker
    • Revert motion_planning.launch.xml to state before 14323161e3
    • Additional docstrings and separating slow down interpolator file
    • Update closest projection
    • jsonschema for parameters
    • fix json schema error
    • fix jsonschema
    • Departure type explanation
    • fix cppcheck failure
    • remove author tag
    • Fix misleading explanation
    • removed unused member variable
    • move boundary departure to experimental namespace

    * update maintainer's email address Co-authored-by: Maxime CLEMENT <<78338830+maxime-clem@users.noreply.github.com>>

    * grammar fix Co-authored-by: Maxime CLEMENT <<78338830+maxime-clem@users.noreply.github.com>>

    • refactoring
    • refactor slow down tuple

    * fix build failure due to clang-tidy ---------Co-authored-by: Maxime CLEMENT <<78338830+maxime-clem@users.noreply.github.com>>

  • Contributors: TaikiYamada4, Zulfaqar Azmi

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

Recent questions tagged autoware_motion_velocity_boundary_departure_prevention_module at Robotics Stack Exchange

No version for distro melodic showing github. Known supported distros are highlighted in the buttons above.
Package symbol

autoware_motion_velocity_boundary_departure_prevention_module package from autoware_universe repo

autoware_agnocast_wrapper autoware_auto_common autoware_boundary_departure_checker autoware_component_interface_specs_universe autoware_component_interface_tools autoware_component_interface_utils autoware_cuda_dependency_meta autoware_fake_test_node autoware_glog_component autoware_goal_distance_calculator autoware_grid_map_utils autoware_path_distance_calculator autoware_polar_grid autoware_time_utils autoware_traffic_light_recognition_marker_publisher autoware_traffic_light_utils autoware_universe_utils tier4_api_utils autoware_autonomous_emergency_braking autoware_collision_detector autoware_control_command_gate autoware_control_performance_analysis autoware_control_validator autoware_external_cmd_selector autoware_joy_controller autoware_lane_departure_checker autoware_mpc_lateral_controller autoware_obstacle_collision_checker autoware_operation_mode_transition_manager autoware_pid_longitudinal_controller autoware_predicted_path_checker autoware_pure_pursuit autoware_shift_decider autoware_smart_mpc_trajectory_follower autoware_stop_mode_operator autoware_trajectory_follower_base autoware_trajectory_follower_node autoware_vehicle_cmd_gate autoware_control_evaluator autoware_kinematic_evaluator autoware_localization_evaluator autoware_perception_online_evaluator autoware_planning_evaluator autoware_scenario_simulator_v2_adapter tier4_autoware_api_launch tier4_control_launch tier4_localization_launch tier4_map_launch tier4_perception_launch tier4_planning_launch tier4_sensing_launch tier4_simulator_launch tier4_system_launch tier4_vehicle_launch autoware_geo_pose_projector autoware_ar_tag_based_localizer autoware_landmark_manager autoware_lidar_marker_localizer autoware_localization_error_monitor autoware_pose2twist autoware_pose_covariance_modifier autoware_pose_estimator_arbiter autoware_pose_instability_detector yabloc_common yabloc_image_processing yabloc_monitor yabloc_particle_filter yabloc_pose_initializer autoware_map_tf_generator autoware_bevfusion autoware_bytetrack autoware_cluster_merger autoware_compare_map_segmentation autoware_crosswalk_traffic_light_estimator autoware_detected_object_feature_remover autoware_detected_object_validation autoware_detection_by_tracker autoware_elevation_map_loader autoware_euclidean_cluster autoware_ground_segmentation autoware_image_projection_based_fusion autoware_lidar_apollo_instance_segmentation autoware_lidar_centerpoint autoware_lidar_transfusion autoware_map_based_prediction autoware_multi_object_tracker autoware_object_merger autoware_object_range_splitter autoware_object_sorter autoware_object_velocity_splitter autoware_occupancy_grid_map_outlier_filter autoware_probabilistic_occupancy_grid_map autoware_radar_fusion_to_detected_object autoware_radar_object_tracker autoware_radar_tracks_msgs_converter autoware_raindrop_cluster_filter autoware_shape_estimation autoware_simpl_prediction autoware_simple_object_merger autoware_tensorrt_bevdet autoware_tensorrt_classifier autoware_tensorrt_common autoware_tensorrt_plugins autoware_tensorrt_yolox autoware_tracking_object_merger autoware_traffic_light_arbiter autoware_traffic_light_category_merger autoware_traffic_light_classifier autoware_traffic_light_fine_detector autoware_traffic_light_map_based_detector autoware_traffic_light_multi_camera_fusion autoware_traffic_light_occlusion_predictor autoware_traffic_light_selector autoware_traffic_light_visualization perception_utils autoware_costmap_generator autoware_diffusion_planner autoware_external_velocity_limit_selector autoware_freespace_planner autoware_freespace_planning_algorithms autoware_hazard_lights_selector autoware_mission_planner_universe autoware_path_optimizer autoware_path_smoother autoware_remaining_distance_time_calculator autoware_rtc_interface autoware_scenario_selector autoware_surround_obstacle_checker autoware_behavior_path_avoidance_by_lane_change_module autoware_behavior_path_bidirectional_traffic_module autoware_behavior_path_dynamic_obstacle_avoidance_module autoware_behavior_path_external_request_lane_change_module autoware_behavior_path_goal_planner_module autoware_behavior_path_lane_change_module autoware_behavior_path_planner autoware_behavior_path_planner_common autoware_behavior_path_sampling_planner_module autoware_behavior_path_side_shift_module autoware_behavior_path_start_planner_module autoware_behavior_path_static_obstacle_avoidance_module autoware_behavior_velocity_blind_spot_module autoware_behavior_velocity_crosswalk_module autoware_behavior_velocity_detection_area_module autoware_behavior_velocity_intersection_module autoware_behavior_velocity_no_drivable_lane_module autoware_behavior_velocity_no_stopping_area_module autoware_behavior_velocity_occlusion_spot_module autoware_behavior_velocity_rtc_interface autoware_behavior_velocity_run_out_module autoware_behavior_velocity_speed_bump_module autoware_behavior_velocity_template_module autoware_behavior_velocity_traffic_light_module autoware_behavior_velocity_virtual_traffic_light_module autoware_behavior_velocity_walkway_module autoware_motion_velocity_boundary_departure_prevention_module autoware_motion_velocity_dynamic_obstacle_stop_module autoware_motion_velocity_obstacle_cruise_module autoware_motion_velocity_obstacle_slow_down_module autoware_motion_velocity_obstacle_velocity_limiter_module autoware_motion_velocity_out_of_lane_module autoware_motion_velocity_road_user_stop_module autoware_motion_velocity_run_out_module autoware_planning_validator autoware_planning_validator_intersection_collision_checker autoware_planning_validator_latency_checker autoware_planning_validator_rear_collision_checker autoware_planning_validator_test_utils autoware_planning_validator_trajectory_checker autoware_bezier_sampler autoware_frenet_planner autoware_path_sampler autoware_sampler_common autoware_cuda_pointcloud_preprocessor autoware_cuda_utils autoware_image_diagnostics autoware_image_transport_decompressor autoware_imu_corrector autoware_pcl_extensions autoware_pointcloud_preprocessor autoware_radar_objects_adapter autoware_radar_scan_to_pointcloud2 autoware_radar_static_pointcloud_filter autoware_radar_threshold_filter autoware_radar_tracks_noise_filter autoware_livox_tag_filter autoware_carla_interface autoware_dummy_perception_publisher autoware_fault_injection autoware_learning_based_vehicle_model autoware_simple_planning_simulator autoware_vehicle_door_simulator tier4_dummy_object_rviz_plugin autoware_bluetooth_monitor autoware_command_mode_decider autoware_command_mode_decider_plugins autoware_command_mode_switcher autoware_command_mode_switcher_plugins autoware_command_mode_types autoware_component_monitor autoware_component_state_monitor autoware_adapi_visualizers autoware_automatic_pose_initializer autoware_default_adapi_universe autoware_diagnostic_graph_aggregator autoware_diagnostic_graph_utils autoware_dummy_diag_publisher autoware_dummy_infrastructure autoware_duplicated_node_checker autoware_hazard_status_converter autoware_mrm_comfortable_stop_operator autoware_mrm_emergency_stop_operator autoware_mrm_handler autoware_pipeline_latency_monitor autoware_processing_time_checker autoware_system_monitor autoware_topic_relay_controller autoware_topic_state_monitor autoware_velodyne_monitor reaction_analyzer autoware_accel_brake_map_calibrator autoware_external_cmd_converter autoware_raw_vehicle_cmd_converter autoware_steer_offset_estimator autoware_bag_time_manager_rviz_plugin autoware_traffic_light_rviz_plugin tier4_adapi_rviz_plugin tier4_camera_view_rviz_plugin tier4_control_mode_rviz_plugin tier4_datetime_rviz_plugin tier4_perception_rviz_plugin tier4_planning_factor_rviz_plugin tier4_state_rviz_plugin tier4_system_rviz_plugin tier4_traffic_light_rviz_plugin tier4_vehicle_rviz_plugin

ROS Distro
github

Package Summary

Tags No category tags.
Version 0.46.0
License Apache License 2.0
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Description
Checkout URI https://github.com/autowarefoundation/autoware_universe.git
VCS Type git
VCS Version main
Last Updated 2025-07-31
Dev Status UNKNOWN
Released UNRELEASED
Tags planner ros calibration self-driving-car autonomous-driving autonomous-vehicles ros2 3d-map autoware
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Package Description

boundary departure prevention feature in motion_velocity_planner

Additional Links

No additional links.

Maintainers

  • Zulfaqar Azmi
  • Alqudah Mohammad
  • Takumi Odashima
  • Maxime Clement

Authors

No additional authors.

Boundary Departure Prevention Module

!!! Warning

The Boundary Departure Prevention Module is experimental. It subscribes to the control module’s predicted path and steering report, creating a circular dependency. This violates Autoware’s design principle of forward-only data flow, where control depends on planning, not the other way round. As a result, this module is not officially supported and will remain unofficial for the foreseeable future.

Role

This module inserts slow down points and publishes error diagnostics when the ego vehicle is near or about to cross an uncrossable boundary, such as a road border.

It also accounts for several types of erroneous behavior that could cause the vehicle to unintentionally cross these boundaries. These behaviors are classified as abnormalities.

Abnormalities

Abnormalities refer to erroneous behaviors at the component level, often caused by noisy or unreliable outputs. In this module, these abnormalities are embedded into the predicted footprints derived from the control module’s predicted path, specifically, the MPC (Model Predictive Control) trajectory. Each point along the MPC path is converted into a footprint, and potential deviations due to abnormal conditions are evaluated.

The module addresses the following types of abnormalities:

1. Normal (No Abnormality)

In typical operation, the MPC trajectory may contain small deviations or noise, especially when the vehicle cannot track the planned path perfectly. These deviations are minor and not necessarily the result of a malfunction, but they are still accounted for to ensure safe boundary handling.

Localization Abnormality

Localization errors can cause the ego vehicle to misjudge its position relative to road boundaries such as curbs or road edges. This can happen due to:

  • Sensor noise or drift, leading to inaccurate pose estimation.
  • Map inaccuracies, where the HD map’s geometry does not precisely align with the real-world boundary.
  • Dynamic uncertainty at higher speeds, where even small errors are magnified due to the vehicle covering more distance in less time, reducing the margin for correction.

These factors can result in the vehicle unintentionally approaching or crossing an uncrossable boundary, even when the planned path appears valid.

How Footprint Expansion Helps with Localization and Map Errors

  • Provides a conservative buffer for localization errors: If the ego vehicle’s estimated position is off due to GPS drift or sensor noise, the expanded footprint increases the likelihood of detecting potential boundary conflicts. While this may lead to conservative (and possibly false) detections, it helps ensure safety by accounting for uncertainty in the vehicle’s true position.
  • Compensates for map inaccuracies: Slight misalignments in Lanelet2 map geometries, such as curb or road edge misplacement, can make it difficult to rely on raw map boundaries. Expanding the footprint ensures that even with these inaccuracies, the system remains cautious and avoids unintentional boundary violations.
Without Abnormality Margins With Localization Abnormality Margin
Footprint without abnormality margin Footprint with localization abnormality margin

By expanding the footprint, the system introduces a safety margin that accounts for minor localization and mapping uncertainties, especially critical at higher speeds.he expanded footprint creates a small buffer or “safety margin,” allowing the vehicle to operate safely despite minor abnormality.

Steering Abnormality

Unexpected steering behavior can cause the vehicle to deviate from its planned trajectory, leading to boundary departure even when planning and localization are functioning correctly. This can occur due to:

  • Actuator faults: such as delayed or stuck steering commands.
  • Software issues: like frozen control outputs or bugs in the steering optimization logic.
  • Unexpected maneuvers: for example, emergency avoidance or unintended sharp turns.

In such cases, the actual motion of the vehicle diverges from the MPC trajectory, increasing the risk of departure.

How steering margin helps with steering abnormality

  • Catches lateral deviations early: If the vehicle drifts due to steering faults, like stuck actuators or sudden command spikes, the expanded margin ahead of the vehicle can detect the deviation before the ego crosses into an unsafe region.
  • Predicts future risk along the path: The margin extends along the forward direction of the predicted path, enabling the system to foresee potential boundary violations caused by small steering errors that compound over time.
Without Abnormality Margins With Steering Abnormality Margin
Footprint without abnormality margin Footprint with localization abnormality margin

This method works even when control outputs are inaccurate. Because the margin is computed using the predicted path, not just the current pose, it accounts for latency, actuator delays, and other uncertainties in vehicle response. This allows the system to trigger early mitigation actions, such as slowing down or stopping, before the situation becomes critical.

Longitudinal Tracking Abnormality

Sometimes, the actual motion of the vehicle along the longitudinal axis does not match the MPC-predicted trajectory. For instance:

The ego vehicle might be ahead or behind the predicted position due to mismatches in acceleration or braking behavior.

This discrepancy becomes more problematic when the vehicle is near an uncrossable boundary, as it reduces the reliability of future footprint predictions.

How Longitudinal Expansion Helps with Tracking Abnormality

  • Accounts for ego being ahead of the predicted pose: During lane changes, avoidance maneuvers, or turns on curved roads, the ego vehicle may move faster than expected or take a slightly different path than predicted. By extending the footprint longitudinally (in the direction of motion), the system accounts for the ego vehicle possibly being ahead of the current MPC trajectory point.
  • Uses speed-scaled margins: The longitudinal margin is scaled based on the current vehicle speed, with an added buffer. At higher speeds, a larger margin is used to reflect the increased risk and reduced reaction time.
  • Captures mismatches during dynamic maneuvers: In situations where heading is changing quickly, like on curved roads or during lateral motion, the ego’s actual position may significantly deviate from the MPC path. The extended footprint covers this discrepancy and helps detect boundary risks even if the predicted path appears safe.
File truncated at 100 lines [see the full file](https://github.com/autowarefoundation/autoware_universe/tree/main/planning/motion_velocity_planner/autoware_motion_velocity_boundary_departure_prevention_module/README.md)
Without Abnormality Margins
CHANGELOG

Changelog for package autoware_motion_velocity_boundary_departure_prevention_module

0.46.0 (2025-06-20)

  • Merge remote-tracking branch 'upstream/main' into tmp/TaikiYamada/bump_version_base

  • feat(motion_velocity_planner): boundary departure prevention module (#10817)

    • feat(motion_velocity_planner): boundary departure prevention module
    • add maintainers
    • Add initial readme
    • Adds abnormalities explanation
    • fix infinite loop error
    • fix goal getter
    • add flowchart
    • add initial image folder
    • Add remaining abnormality footprints images
    • docstring on the boundary departure checker
    • Revert motion_planning.launch.xml to state before 14323161e3
    • Additional docstrings and separating slow down interpolator file
    • Update closest projection
    • jsonschema for parameters
    • fix json schema error
    • fix jsonschema
    • Departure type explanation
    • fix cppcheck failure
    • remove author tag
    • Fix misleading explanation
    • removed unused member variable
    • move boundary departure to experimental namespace

    * update maintainer's email address Co-authored-by: Maxime CLEMENT <<78338830+maxime-clem@users.noreply.github.com>>

    * grammar fix Co-authored-by: Maxime CLEMENT <<78338830+maxime-clem@users.noreply.github.com>>

    • refactoring
    • refactor slow down tuple

    * fix build failure due to clang-tidy ---------Co-authored-by: Maxime CLEMENT <<78338830+maxime-clem@users.noreply.github.com>>

  • Contributors: TaikiYamada4, Zulfaqar Azmi

Launch files

No launch files found

Messages

No message files found.

Services

No service files found

Plugins

No plugins found.

Recent questions tagged autoware_motion_velocity_boundary_departure_prevention_module at Robotics Stack Exchange

No version for distro noetic showing github. Known supported distros are highlighted in the buttons above.
Package symbol

autoware_motion_velocity_boundary_departure_prevention_module package from autoware_universe repo

autoware_agnocast_wrapper autoware_auto_common autoware_boundary_departure_checker autoware_component_interface_specs_universe autoware_component_interface_tools autoware_component_interface_utils autoware_cuda_dependency_meta autoware_fake_test_node autoware_glog_component autoware_goal_distance_calculator autoware_grid_map_utils autoware_path_distance_calculator autoware_polar_grid autoware_time_utils autoware_traffic_light_recognition_marker_publisher autoware_traffic_light_utils autoware_universe_utils tier4_api_utils autoware_autonomous_emergency_braking autoware_collision_detector autoware_control_command_gate autoware_control_performance_analysis autoware_control_validator autoware_external_cmd_selector autoware_joy_controller autoware_lane_departure_checker autoware_mpc_lateral_controller autoware_obstacle_collision_checker autoware_operation_mode_transition_manager autoware_pid_longitudinal_controller autoware_predicted_path_checker autoware_pure_pursuit autoware_shift_decider autoware_smart_mpc_trajectory_follower autoware_stop_mode_operator autoware_trajectory_follower_base autoware_trajectory_follower_node autoware_vehicle_cmd_gate autoware_control_evaluator autoware_kinematic_evaluator autoware_localization_evaluator autoware_perception_online_evaluator autoware_planning_evaluator autoware_scenario_simulator_v2_adapter tier4_autoware_api_launch tier4_control_launch tier4_localization_launch tier4_map_launch tier4_perception_launch tier4_planning_launch tier4_sensing_launch tier4_simulator_launch tier4_system_launch tier4_vehicle_launch autoware_geo_pose_projector autoware_ar_tag_based_localizer autoware_landmark_manager autoware_lidar_marker_localizer autoware_localization_error_monitor autoware_pose2twist autoware_pose_covariance_modifier autoware_pose_estimator_arbiter autoware_pose_instability_detector yabloc_common yabloc_image_processing yabloc_monitor yabloc_particle_filter yabloc_pose_initializer autoware_map_tf_generator autoware_bevfusion autoware_bytetrack autoware_cluster_merger autoware_compare_map_segmentation autoware_crosswalk_traffic_light_estimator autoware_detected_object_feature_remover autoware_detected_object_validation autoware_detection_by_tracker autoware_elevation_map_loader autoware_euclidean_cluster autoware_ground_segmentation autoware_image_projection_based_fusion autoware_lidar_apollo_instance_segmentation autoware_lidar_centerpoint autoware_lidar_transfusion autoware_map_based_prediction autoware_multi_object_tracker autoware_object_merger autoware_object_range_splitter autoware_object_sorter autoware_object_velocity_splitter autoware_occupancy_grid_map_outlier_filter autoware_probabilistic_occupancy_grid_map autoware_radar_fusion_to_detected_object autoware_radar_object_tracker autoware_radar_tracks_msgs_converter autoware_raindrop_cluster_filter autoware_shape_estimation autoware_simpl_prediction autoware_simple_object_merger autoware_tensorrt_bevdet autoware_tensorrt_classifier autoware_tensorrt_common autoware_tensorrt_plugins autoware_tensorrt_yolox autoware_tracking_object_merger autoware_traffic_light_arbiter autoware_traffic_light_category_merger autoware_traffic_light_classifier autoware_traffic_light_fine_detector autoware_traffic_light_map_based_detector autoware_traffic_light_multi_camera_fusion autoware_traffic_light_occlusion_predictor autoware_traffic_light_selector autoware_traffic_light_visualization perception_utils autoware_costmap_generator autoware_diffusion_planner autoware_external_velocity_limit_selector autoware_freespace_planner autoware_freespace_planning_algorithms autoware_hazard_lights_selector autoware_mission_planner_universe autoware_path_optimizer autoware_path_smoother autoware_remaining_distance_time_calculator autoware_rtc_interface autoware_scenario_selector autoware_surround_obstacle_checker autoware_behavior_path_avoidance_by_lane_change_module autoware_behavior_path_bidirectional_traffic_module autoware_behavior_path_dynamic_obstacle_avoidance_module autoware_behavior_path_external_request_lane_change_module autoware_behavior_path_goal_planner_module autoware_behavior_path_lane_change_module autoware_behavior_path_planner autoware_behavior_path_planner_common autoware_behavior_path_sampling_planner_module autoware_behavior_path_side_shift_module autoware_behavior_path_start_planner_module autoware_behavior_path_static_obstacle_avoidance_module autoware_behavior_velocity_blind_spot_module autoware_behavior_velocity_crosswalk_module autoware_behavior_velocity_detection_area_module autoware_behavior_velocity_intersection_module autoware_behavior_velocity_no_drivable_lane_module autoware_behavior_velocity_no_stopping_area_module autoware_behavior_velocity_occlusion_spot_module autoware_behavior_velocity_rtc_interface autoware_behavior_velocity_run_out_module autoware_behavior_velocity_speed_bump_module autoware_behavior_velocity_template_module autoware_behavior_velocity_traffic_light_module autoware_behavior_velocity_virtual_traffic_light_module autoware_behavior_velocity_walkway_module autoware_motion_velocity_boundary_departure_prevention_module autoware_motion_velocity_dynamic_obstacle_stop_module autoware_motion_velocity_obstacle_cruise_module autoware_motion_velocity_obstacle_slow_down_module autoware_motion_velocity_obstacle_velocity_limiter_module autoware_motion_velocity_out_of_lane_module autoware_motion_velocity_road_user_stop_module autoware_motion_velocity_run_out_module autoware_planning_validator autoware_planning_validator_intersection_collision_checker autoware_planning_validator_latency_checker autoware_planning_validator_rear_collision_checker autoware_planning_validator_test_utils autoware_planning_validator_trajectory_checker autoware_bezier_sampler autoware_frenet_planner autoware_path_sampler autoware_sampler_common autoware_cuda_pointcloud_preprocessor autoware_cuda_utils autoware_image_diagnostics autoware_image_transport_decompressor autoware_imu_corrector autoware_pcl_extensions autoware_pointcloud_preprocessor autoware_radar_objects_adapter autoware_radar_scan_to_pointcloud2 autoware_radar_static_pointcloud_filter autoware_radar_threshold_filter autoware_radar_tracks_noise_filter autoware_livox_tag_filter autoware_carla_interface autoware_dummy_perception_publisher autoware_fault_injection autoware_learning_based_vehicle_model autoware_simple_planning_simulator autoware_vehicle_door_simulator tier4_dummy_object_rviz_plugin autoware_bluetooth_monitor autoware_command_mode_decider autoware_command_mode_decider_plugins autoware_command_mode_switcher autoware_command_mode_switcher_plugins autoware_command_mode_types autoware_component_monitor autoware_component_state_monitor autoware_adapi_visualizers autoware_automatic_pose_initializer autoware_default_adapi_universe autoware_diagnostic_graph_aggregator autoware_diagnostic_graph_utils autoware_dummy_diag_publisher autoware_dummy_infrastructure autoware_duplicated_node_checker autoware_hazard_status_converter autoware_mrm_comfortable_stop_operator autoware_mrm_emergency_stop_operator autoware_mrm_handler autoware_pipeline_latency_monitor autoware_processing_time_checker autoware_system_monitor autoware_topic_relay_controller autoware_topic_state_monitor autoware_velodyne_monitor reaction_analyzer autoware_accel_brake_map_calibrator autoware_external_cmd_converter autoware_raw_vehicle_cmd_converter autoware_steer_offset_estimator autoware_bag_time_manager_rviz_plugin autoware_traffic_light_rviz_plugin tier4_adapi_rviz_plugin tier4_camera_view_rviz_plugin tier4_control_mode_rviz_plugin tier4_datetime_rviz_plugin tier4_perception_rviz_plugin tier4_planning_factor_rviz_plugin tier4_state_rviz_plugin tier4_system_rviz_plugin tier4_traffic_light_rviz_plugin tier4_vehicle_rviz_plugin

ROS Distro
github

Package Summary

Tags No category tags.
Version 0.46.0
License Apache License 2.0
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Description
Checkout URI https://github.com/autowarefoundation/autoware_universe.git
VCS Type git
VCS Version main
Last Updated 2025-07-31
Dev Status UNKNOWN
Released UNRELEASED
Tags planner ros calibration self-driving-car autonomous-driving autonomous-vehicles ros2 3d-map autoware
Contributing Help Wanted (-)
Good First Issues (-)
Pull Requests to Review (-)

Package Description

boundary departure prevention feature in motion_velocity_planner

Additional Links

No additional links.

Maintainers

  • Zulfaqar Azmi
  • Alqudah Mohammad
  • Takumi Odashima
  • Maxime Clement

Authors

No additional authors.

Boundary Departure Prevention Module

!!! Warning

The Boundary Departure Prevention Module is experimental. It subscribes to the control module’s predicted path and steering report, creating a circular dependency. This violates Autoware’s design principle of forward-only data flow, where control depends on planning, not the other way round. As a result, this module is not officially supported and will remain unofficial for the foreseeable future.

Role

This module inserts slow down points and publishes error diagnostics when the ego vehicle is near or about to cross an uncrossable boundary, such as a road border.

It also accounts for several types of erroneous behavior that could cause the vehicle to unintentionally cross these boundaries. These behaviors are classified as abnormalities.

Abnormalities

Abnormalities refer to erroneous behaviors at the component level, often caused by noisy or unreliable outputs. In this module, these abnormalities are embedded into the predicted footprints derived from the control module’s predicted path, specifically, the MPC (Model Predictive Control) trajectory. Each point along the MPC path is converted into a footprint, and potential deviations due to abnormal conditions are evaluated.

The module addresses the following types of abnormalities:

1. Normal (No Abnormality)

In typical operation, the MPC trajectory may contain small deviations or noise, especially when the vehicle cannot track the planned path perfectly. These deviations are minor and not necessarily the result of a malfunction, but they are still accounted for to ensure safe boundary handling.

Localization Abnormality

Localization errors can cause the ego vehicle to misjudge its position relative to road boundaries such as curbs or road edges. This can happen due to:

  • Sensor noise or drift, leading to inaccurate pose estimation.
  • Map inaccuracies, where the HD map’s geometry does not precisely align with the real-world boundary.
  • Dynamic uncertainty at higher speeds, where even small errors are magnified due to the vehicle covering more distance in less time, reducing the margin for correction.

These factors can result in the vehicle unintentionally approaching or crossing an uncrossable boundary, even when the planned path appears valid.

How Footprint Expansion Helps with Localization and Map Errors

  • Provides a conservative buffer for localization errors: If the ego vehicle’s estimated position is off due to GPS drift or sensor noise, the expanded footprint increases the likelihood of detecting potential boundary conflicts. While this may lead to conservative (and possibly false) detections, it helps ensure safety by accounting for uncertainty in the vehicle’s true position.
  • Compensates for map inaccuracies: Slight misalignments in Lanelet2 map geometries, such as curb or road edge misplacement, can make it difficult to rely on raw map boundaries. Expanding the footprint ensures that even with these inaccuracies, the system remains cautious and avoids unintentional boundary violations.
Without Abnormality Margins With Localization Abnormality Margin
Footprint without abnormality margin Footprint with localization abnormality margin

By expanding the footprint, the system introduces a safety margin that accounts for minor localization and mapping uncertainties, especially critical at higher speeds.he expanded footprint creates a small buffer or “safety margin,” allowing the vehicle to operate safely despite minor abnormality.

Steering Abnormality

Unexpected steering behavior can cause the vehicle to deviate from its planned trajectory, leading to boundary departure even when planning and localization are functioning correctly. This can occur due to:

  • Actuator faults: such as delayed or stuck steering commands.
  • Software issues: like frozen control outputs or bugs in the steering optimization logic.
  • Unexpected maneuvers: for example, emergency avoidance or unintended sharp turns.

In such cases, the actual motion of the vehicle diverges from the MPC trajectory, increasing the risk of departure.

How steering margin helps with steering abnormality

  • Catches lateral deviations early: If the vehicle drifts due to steering faults, like stuck actuators or sudden command spikes, the expanded margin ahead of the vehicle can detect the deviation before the ego crosses into an unsafe region.
  • Predicts future risk along the path: The margin extends along the forward direction of the predicted path, enabling the system to foresee potential boundary violations caused by small steering errors that compound over time.
Without Abnormality Margins With Steering Abnormality Margin
Footprint without abnormality margin Footprint with localization abnormality margin

This method works even when control outputs are inaccurate. Because the margin is computed using the predicted path, not just the current pose, it accounts for latency, actuator delays, and other uncertainties in vehicle response. This allows the system to trigger early mitigation actions, such as slowing down or stopping, before the situation becomes critical.

Longitudinal Tracking Abnormality

Sometimes, the actual motion of the vehicle along the longitudinal axis does not match the MPC-predicted trajectory. For instance:

The ego vehicle might be ahead or behind the predicted position due to mismatches in acceleration or braking behavior.

This discrepancy becomes more problematic when the vehicle is near an uncrossable boundary, as it reduces the reliability of future footprint predictions.

How Longitudinal Expansion Helps with Tracking Abnormality

  • Accounts for ego being ahead of the predicted pose: During lane changes, avoidance maneuvers, or turns on curved roads, the ego vehicle may move faster than expected or take a slightly different path than predicted. By extending the footprint longitudinally (in the direction of motion), the system accounts for the ego vehicle possibly being ahead of the current MPC trajectory point.
  • Uses speed-scaled margins: The longitudinal margin is scaled based on the current vehicle speed, with an added buffer. At higher speeds, a larger margin is used to reflect the increased risk and reduced reaction time.
  • Captures mismatches during dynamic maneuvers: In situations where heading is changing quickly, like on curved roads or during lateral motion, the ego’s actual position may significantly deviate from the MPC path. The extended footprint covers this discrepancy and helps detect boundary risks even if the predicted path appears safe.
File truncated at 100 lines [see the full file](https://github.com/autowarefoundation/autoware_universe/tree/main/planning/motion_velocity_planner/autoware_motion_velocity_boundary_departure_prevention_module/README.md)
Without Abnormality Margins
CHANGELOG

Changelog for package autoware_motion_velocity_boundary_departure_prevention_module

0.46.0 (2025-06-20)

  • Merge remote-tracking branch 'upstream/main' into tmp/TaikiYamada/bump_version_base

  • feat(motion_velocity_planner): boundary departure prevention module (#10817)

    • feat(motion_velocity_planner): boundary departure prevention module
    • add maintainers
    • Add initial readme
    • Adds abnormalities explanation
    • fix infinite loop error
    • fix goal getter
    • add flowchart
    • add initial image folder
    • Add remaining abnormality footprints images
    • docstring on the boundary departure checker
    • Revert motion_planning.launch.xml to state before 14323161e3
    • Additional docstrings and separating slow down interpolator file
    • Update closest projection
    • jsonschema for parameters
    • fix json schema error
    • fix jsonschema
    • Departure type explanation
    • fix cppcheck failure
    • remove author tag
    • Fix misleading explanation
    • removed unused member variable
    • move boundary departure to experimental namespace

    * update maintainer's email address Co-authored-by: Maxime CLEMENT <<78338830+maxime-clem@users.noreply.github.com>>

    * grammar fix Co-authored-by: Maxime CLEMENT <<78338830+maxime-clem@users.noreply.github.com>>

    • refactoring
    • refactor slow down tuple

    * fix build failure due to clang-tidy ---------Co-authored-by: Maxime CLEMENT <<78338830+maxime-clem@users.noreply.github.com>>

  • Contributors: TaikiYamada4, Zulfaqar Azmi

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