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behavior_velocity_run_out_module package from autodrrt repo

autonomous_emergency_braking control_performance_analysis control_validator external_cmd_selector joy_controller lane_departure_checker mpc_lateral_controller obstacle_collision_checker operation_mode_transition_manager pid_longitudinal_controller predicted_path_checker pure_pursuit shift_decider trajectory_follower_base trajectory_follower_node vehicle_cmd_gate diagnostic_converter kinematic_evaluator localization_evaluator planning_evaluator ekf_localizer geo_pose_projector gyro_odometer ar_tag_based_localizer landmark_manager localization_error_monitor localization_util ndt_scan_matcher pose2twist pose_initializer pose_instability_detector stop_filter tree_structured_parzen_estimator twist2accel yabloc_common yabloc_image_processing yabloc_monitor yabloc_particle_filter yabloc_pose_initializer map_height_fitter map_loader map_projection_loader map_tf_generator lanelet2_map_preprocessor ros2_bevdet ros2_bevformer bevfusion bytetrack cluster_merger compare_map_segmentation crosswalk_traffic_light_estimator detected_object_feature_remover detected_object_validation detection_by_tracker elevation_map_loader euclidean_cluster front_vehicle_velocity_estimator ground_segmentation heatmap_visualizer image_projection_based_fusion lidar_apollo_instance_segmentation lidar_apollo_segmentation_tvm lidar_apollo_segmentation_tvm_nodes lidar_centerpoint lidar_centerpoint_tvm map_based_prediction multi_object_tracker object_merger object_range_splitter object_velocity_splitter occupancy_grid_map_outlier_filter probabilistic_occupancy_grid_map radar_crossing_objects_noise_filter radar_fusion_to_detected_object radar_object_clustering radar_object_tracker radar_tracks_msgs_converter shape_estimation simple_object_merger tensorrt_classifier tensorrt_yolo tensorrt_yolox tracking_object_merger traffic_light_arbiter traffic_light_classifier traffic_light_fine_detector traffic_light_map_based_detector traffic_light_multi_camera_fusion traffic_light_occlusion_predictor traffic_light_ssd_fine_detector traffic_light_visualization behavior_path_avoidance_by_lane_change_module behavior_path_avoidance_module behavior_path_external_request_lane_change_module behavior_path_goal_planner_module behavior_path_lane_change_module behavior_path_planner behavior_path_planner_common behavior_path_side_shift_module behavior_path_start_planner_module behavior_velocity_blind_spot_module behavior_velocity_crosswalk_module behavior_velocity_detection_area_module behavior_velocity_intersection_module behavior_velocity_no_drivable_lane_module behavior_velocity_no_stopping_area_module behavior_velocity_occlusion_spot_module behavior_velocity_out_of_lane_module behavior_velocity_planner behavior_velocity_planner_common behavior_velocity_run_out_module behavior_velocity_speed_bump_module behavior_velocity_stop_line_module behavior_velocity_template_module behavior_velocity_traffic_light_module behavior_velocity_virtual_traffic_light_module behavior_velocity_walkway_module costmap_generator external_velocity_limit_selector freespace_planner freespace_planning_algorithms mission_planner motion_velocity_smoother objects_of_interest_marker_interface obstacle_avoidance_planner obstacle_cruise_planner obstacle_stop_planner obstacle_velocity_limiter path_smoother planning_debug_tools planning_test_utils planning_topic_converter planning_validator route_handler rtc_interface rtc_replayer bezier_sampler frenet_planner path_sampler sampler_common scenario_selector static_centerline_optimizer surround_obstacle_checker gnss_poser image_diagnostics image_transport_decompressor imu_corrector livox_tag_filter pointcloud_preprocessor radar_scan_to_pointcloud2 radar_static_pointcloud_filter radar_threshold_filter radar_tracks_noise_filter tier4_pcl_extensions vehicle_velocity_converter autoware_auto_msgs_adapter bluetooth_monitor component_state_monitor default_ad_api ad_api_adaptors ad_api_visualizers automatic_pose_initializer diagnostic_graph_aggregator dummy_diag_publisher dummy_infrastructure duplicated_node_checker emergency_handler mrm_comfortable_stop_operator mrm_emergency_stop_operator system_error_monitor system_monitor topic_state_monitor velodyne_monitor accel_brake_map_calibrator external_cmd_converter raw_vehicle_cmd_converter steer_offset_estimator vehicle_info_util launch launch_ros autoware_ad_api_specs autoware_adapi_v1_msgs autoware_adapi_version_msgs autoware_auto_common autoware_auto_geometry autoware_auto_control_msgs autoware_auto_geometry_msgs autoware_auto_mapping_msgs autoware_auto_msgs autoware_auto_perception_msgs autoware_auto_planning_msgs autoware_auto_system_msgs autoware_auto_vehicle_msgs autoware_auto_perception_rviz_plugin autoware_auto_tf2 autoware_cmake autoware_lint_common autoware_utils lanelet2_extension autoware_common_msgs autoware_control_msgs autoware_localization_msgs autoware_map_msgs autoware_perception_msgs autoware_planning_msgs autoware_sensing_msgs autoware_system_msgs autoware_vehicle_msgs autoware_point_types autoware_testing bag_time_manager_rviz_plugin component_interface_specs component_interface_tools component_interface_utils cuda_utils fake_test_node geography_utils global_parameter_loader glog_component goal_distance_calculator grid_map_utils interpolation kalman_filter motion_utils object_recognition_utils osqp_interface path_distance_calculator perception_utils polar_grid qp_interface rtc_manager_rviz_plugin signal_processing tensorrt_common tier4_adapi_rviz_plugin tier4_api_utils tier4_automatic_goal_rviz_plugin tier4_autoware_utils tier4_calibration_rviz_plugin tier4_camera_view_rviz_plugin tier4_control_rviz_plugin tier4_datetime_rviz_plugin tier4_debug_rviz_plugin tier4_debug_tools tier4_localization_rviz_plugin tier4_perception_rviz_plugin tier4_planning_rviz_plugin tier4_screen_capture_rviz_plugin tier4_simulated_clock_rviz_plugin tier4_state_rviz_plugin tier4_system_rviz_plugin tier4_target_object_type_rviz_plugin tier4_traffic_light_rviz_plugin tier4_vehicle_rviz_plugin time_utils simulator_compatibility_test traffic_light_recognition_marker_publisher traffic_light_utils tvm_utility dma_customer_msg dma_transfer eagleye_coordinate eagleye_navigation eagleye_msgs eagleye_rt eagleye_can_velocity_converter eagleye_fix2kml eagleye_geo_pose_converter eagleye_geo_pose_fusion eagleye_gnss_converter eagleye_tf llh_converter morai_msgs mussp ndt_omp orocos_kdl python_orocos_kdl pointcloud_to_laserscan rtklib_bridge rtklib_msgs autoware_external_api_msgs autoware_iv_external_api_adaptor autoware_iv_internal_api_adaptor awapi_awiv_adapter tier4_api_msgs tier4_auto_msgs_converter tier4_control_msgs tier4_debug_msgs tier4_external_api_msgs tier4_hmi_msgs tier4_localization_msgs tier4_map_msgs tier4_perception_msgs tier4_planning_msgs tier4_rtc_msgs tier4_simulation_msgs tier4_system_msgs tier4_v2x_msgs tier4_vehicle_msgs io_opt 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 fastrtps cyclonedds lanelet2 lanelet2_core lanelet2_examples lanelet2_io lanelet2_maps lanelet2_matching lanelet2_projection lanelet2_python lanelet2_routing lanelet2_traffic_rules lanelet2_validation sophus angles behaviortree_cpp_v3 bond bond_core bondcpp bondpy smclib test_bond cudnn_cmake_module diagnostic_aggregator diagnostic_common_diagnostics diagnostic_updater diagnostics self_test filters geodesy geographic_info geographic_msgs grid_map grid_map_cmake_helpers grid_map_core grid_map_costmap_2d grid_map_cv grid_map_demos grid_map_filters grid_map_loader grid_map_msgs grid_map_octomap grid_map_pcl grid_map_ros grid_map_rviz_plugin grid_map_sdf grid_map_visualization mrt_cmake_modules nav2_amcl nav2_behavior_tree nav2_behaviors nav2_bringup nav2_bt_navigator nav2_collision_monitor nav2_common nav2_controller nav2_core nav2_costmap_2d costmap_queue dwb_core dwb_critics dwb_msgs dwb_plugins nav2_dwb_controller nav_2d_msgs nav_2d_utils nav2_lifecycle_manager nav2_map_server nav2_msgs nav2_navfn_planner nav2_planner nav2_regulated_pure_pursuit_controller nav2_rotation_shim_controller nav2_rviz_plugins nav2_simple_commander nav2_smac_planner nav2_smoother nav2_system_tests nav2_theta_star_planner nav2_util nav2_velocity_smoother nav2_voxel_grid nav2_waypoint_follower navigation2 dynamic_edt_3d octomap octovis octomap_msgs osqp_vendor pacmod3_msgs pcl_msgs pcl_conversions pcl_ros perception_pcl point_cloud_msg_wrapper radar_msgs can_msgs rqt_tf_tree tensorrt_cmake_module topic_tools topic_tools_interfaces tvm_vendor cv_bridge image_geometry opencv_tests vision_opencv xacro rviz2 rviz_assimp_vendor rviz_common rviz_default_plugins rviz_ogre_vendor rviz_rendering rviz_rendering_tests rviz_visual_testing_framework dummy_perception_publisher fault_injection simple_planning_simulator classformsg node_v2x image_view v4l2_camera can_interface_custom cgi430_can_driver cgi610_driver ARS408_driver data_format_dump data_preprocess_launch lidar_centerpoint_collect lidar_saver message_sync time_cal camera_calibration direct_visual_lidar_calibration multi_lidar_calibration

Package Summary

Tags No category tags.
Version 0.1.0
License Apache License 2.0
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Checkout URI https://github.com/ieiauto/autodrrt.git
VCS Type git
VCS Version main
Last Updated 2024-09-19
Dev Status UNMAINTAINED
CI status No Continuous Integration
Released UNRELEASED
Tags No category tags.
Contributing Help Wanted (0)
Good First Issues (0)
Pull Requests to Review (0)

Package Description

The behavior_velocity_run_out_module package

Additional Links

No additional links.

Maintainers

  • Tomohito Ando
  • Makoto Kurihara
  • Tomoya Kimura
  • Shumpei Wakabayashi
  • Takayuki Murooka

Authors

  • Tomohito Ando

Run Out

Role

run_out is the module that decelerates and stops for dynamic obstacles such as pedestrians and bicycles.

brief

Activation Timing

This module is activated if launch_run_out becomes true

Inner-workings / Algorithms

Flow chart

@startuml
title modifyPathVelocity
start

partition "Preprocess path" {
:Calculate the expected target velocity for ego vehicle;
:Extend path;
:Trim path from ego position;
}

partition "Preprocess obstacles" {
:Create data of abstracted dynamic obstacles;
:Exclude obstacles outside of partition lanelet;
}

partition "Collision_detection" {
:Detect collision with dynamic obstacles;
}

partition "Insert velocity" {
:Insert velocity to decelerate for obstacles;

:Limit velocity with specified jerk and acc limit;
}
stop
@enduml

Preprocess path

Calculate the expected target velocity for ego vehicle

Calculate the expected target velocity for the ego vehicle path to calculate time to collision with obstacles more precisely. The expected target velocity is calculated with motion velocity smoother module by using current velocity, current acceleration and velocity limits directed by the map and external API.

brief

Extend the path

The path is extended by the length of base link to front to consider obstacles after the goal.

Trim path from ego position

The path is trimmed from ego position to a certain distance to reduce calculation time. Trimmed distance is specified by parameter of detection_distance.

Preprocess obstacles

Create data of abstracted dynamic obstacle

This module can handle multiple types of obstacles by creating abstracted dynamic obstacle data layer. Currently we have 3 types of detection method (Object, ObjectWithoutPath, Points) to create abstracted obstacle data.

Abstracted dynamic obstacle

Abstracted obstacle data has following information.

Name Type Description
pose geometry_msgs::msg::Pose pose of the obstacle
classifications std::vector<autoware_auto_perception_msgs::msg::ObjectClassification> classifications with probability
shape autoware_auto_perception_msgs::msg::Shape shape of the obstacle
predicted_paths std::vector<DynamicObstacle::PredictedPath> predicted paths with confidence. this data doesn’t have time step because we use minimum and maximum velocity instead.
min_velocity_mps float minimum velocity of the obstacle. specified by parameter of dynamic_obstacle.min_vel_kmph
max_velocity_mps float maximum velocity of the obstacle. specified by parameter of dynamic_obstacle.max_vel_kmph

Enter the maximum/minimum velocity of the object as a parameter, adding enough margin to the expected velocity. This parameter is used to create polygons for collision detection.

Future work: Determine the maximum/minimum velocity from the estimated velocity with covariance of the object

3 types of detection method

We have 3 types of detection method to meet different safety and availability requirements. The characteristics of them are shown in the table below. Method of Object has high availability (less false positive) because it detects only objects whose predicted path is on the lane. However, sometimes it is not safe because perception may fail to detect obstacles or generate incorrect predicted paths. On the other hand, method of Points has high safety (less false negative) because it uses pointcloud as input. Since points don’t have a predicted path, the path that moves in the direction normal to the path of ego vehicle is considered to be the predicted path of abstracted dynamic obstacle data. However, without proper adjustment of filter of points, it may detect a lot of points and it will result in very low availability. Method of ObjectWithoutPath has the characteristics of an intermediate of Object and Points.

Method Description
Object use an object with the predicted path for collision detection.
ObjectWithoutPath use an object but not use the predicted path for collision detection. replace the path assuming that an object jumps out to the lane at specified velocity.
Points use filtered points for collision detection. the path is created assuming that points jump out to the lane. points are regarded as an small circular shaped obstacle.

brief

Exclude obstacles outside of partition

This module can exclude the obstacles outside of partition such as guardrail, fence, and wall. We need lanelet map that has the information of partition to use this feature. By this feature, we can reduce unnecessary deceleration by obstacles that are unlikely to jump out to the lane. You can choose whether to use this feature by parameter of use_partition_lanelet.

brief

Collision detection

Detect collision with dynamic obstacles

Along the ego vehicle path, determine the points where collision detection is to be performed for each detection_span.

The travel times to the each points are calculated from the expected target velocity.

brief

For the each points, collision detection is performed using the footprint polygon of the ego vehicle and the polygon of the predicted location of the obstacles. The predicted location of the obstacles is described as rectangle or polygon that has the range calculated by min velocity, max velocity and the ego vehicle’s travel time to the point. If the input type of the dynamic obstacle is Points, the obstacle shape is defined as a small cylinder.

brief

Multiple points are detected as collision points because collision detection is calculated between two polygons. So we select the point that is on the same side as the obstacle and close to ego vehicle as the collision point.

brief

Insert velocity

Insert velocity to decelerate for obstacles

If the collision is detected, stop point is inserted on distance of base link to front + stop margin from the selected collision point. The base link to front means the distance between base_link (center of rear-wheel axis) and front of the car. Stop margin is determined by the parameter of stop_margin.

brief

Insert velocity to approach the obstacles

If you select the method of Points or ObjectWithoutPath, sometimes ego keeps stopping in front of the obstacle. To avoid this problem, This feature has option to approach the obstacle with slow velocity after stopping. If the parameter of approaching.enable is set to true, ego will approach the obstacle after ego stopped for state.stop_time_thresh seconds. The maximum velocity of approaching can be specified by the parameter of approaching.limit_vel_kmph. The decision to approach the obstacle is determined by a simple state transition as following image.

brief

@startuml
hide empty description
left to right direction
title State transition for approaching the obstacle

[*] --> GO
GO --> STOP : Current velocity is less than threshold
STOP --> GO : Current velocity is larger than threshold

STOP --> APPROACH : Stop duration is larger than threshold
APPROACH --> GO : There are no obstacles or \n distance to the obstacle is larger than threshold
APPROACH --> APPROACH : Approach duration is less than threshold
@enduml

Limit velocity with specified jerk and acc limit

The maximum slowdown velocity is calculated in order not to slowdown too much. See the Occlusion Spot document for more details. You can choose whether to use this feature by parameter of slow_down_limit.enable.

Module Parameters

Parameter Type Description
detection_method string [-] candidate: Object, ObjectWithoutPath, Points
use_partition_lanelet bool [-] whether to use partition lanelet map data
specify_decel_jerk bool [-] whether to specify jerk when ego decelerates
stop_margin double [m] the vehicle decelerates to be able to stop with this margin
passing_margin double [m] the vehicle begins to accelerate if the vehicle’s front in predicted position is ahead of the obstacle + this margin
deceleration_jerk double [m/s^3] ego decelerates with this jerk when stopping for obstacles
detection_distance double [m] ahead distance from ego to detect the obstacles
detection_span double [m] calculate collision with this span to reduce calculation time
min_vel_ego_kmph double [km/h] min velocity to calculate time to collision
Parameter /detection_area Type Description
margin_ahead double [m] ahead margin for detection area polygon
margin_behind double [m] behind margin for detection area polygon
Parameter /dynamic_obstacle Type Description
use_mandatory_area double [-] whether to use mandatory detection area
assume_fixed_velocity.enable double [-] If enabled, the obstacle’s velocity is assumed to be within the minimum and maximum velocity values specified below
assume_fixed_velocity.min_vel_kmph double [km/h] minimum velocity for dynamic obstacles
assume_fixed_velocity.max_vel_kmph double [km/h] maximum velocity for dynamic obstacles
diameter double [m] diameter of obstacles. used for creating dynamic obstacles from points
height double [m] height of obstacles. used for creating dynamic obstacles from points
max_prediction_time double [sec] create predicted path until this time
time_step double [sec] time step for each path step. used for creating dynamic obstacles from points or objects without path
points_interval double [m] divide obstacle points into groups with this interval, and detect only lateral nearest point. used only for Points method
Parameter /approaching Type Description
enable bool [-] whether to enable approaching after stopping
margin double [m] distance on how close ego approaches the obstacle
limit_vel_kmph double [km/h] limit velocity for approaching after stopping
Parameter /state Type Description
stop_thresh double [m/s] threshold to decide if ego is stopping
stop_time_thresh double [sec] threshold for stopping time to transit to approaching state
disable_approach_dist double [m] end the approaching state if distance to the obstacle is longer than this value
keep_approach_duration double [sec] keep approach state for this duration to avoid chattering of state transition
Parameter /slow_down_limit Type Description
enable bool [-] whether to enable to limit velocity with max jerk and acc
max_jerk double [m/s^3] minimum jerk deceleration for safe brake.
max_acc double [m/s^2] minimum accel deceleration for safe brake.
Parameter /ignore_momentary_detection Type Description
enable bool [-] whether to ignore momentary detection
time_threshold double [sec] ignores detections that persist for less than this duration

Future extensions / Unimplemented parts

  • Calculate obstacle’s min velocity and max velocity from covariance
  • Detect collisions with polygon object
  • Handle the case when the predicted path of obstacles are not straight line
    • Currently collision check is calculated based on the assumption that the predicted path of the obstacle is a straight line
CHANGELOG
No CHANGELOG found.

Wiki Tutorials

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Launch files

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Messages

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Services

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Plugins

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