Planning Evaluator

Purpose

This package provides nodes that generate various metrics to evaluate the quality of planning.

Metrics can be published in real time and saved to a JSON file when the node is shut down:

• metrics_for_publish:
  • Metrics listed in metrics_for_publish are calculated and published to the topic.

• metrics_for_output:
  • Metrics listed in metrics_for_output are saved to a JSON file when the node shuts down (if output_metrics is set to true).
  • These metrics include statistics derived from metrics_for_publish and additional information such as parameters and descriptions.

Metrics

All possible metrics are defined in the Metric enumeration located in:

• include/autoware/planning_evaluator/metrics/metric.hpp
• include/autoware/planning_evaluator/metrics/output_metric.hpp

These files also provide string conversions and human-readable descriptions for use in output files.

Metric Classifications

By Data Type

1. Statistics-based Metrics:

   • Calculated using autoware_utils::Accumulator, which tracks minimum, maximum, mean, and count values.
   • Sub-metrics: /mean, /min, /max, and /count.

2. Value-based Metrics:

   • Metrics with a single value.
   • Sub-metrics: /value.
   • Some metrics with older implementations use the statistics-based format of /mean, /min, /max, but all values are the same.

3. Count-based Metrics:

   • Count occurrences of specific events over a time period.
   • Sub-metrics: /count, or /count_in_duration.

By Purpose

1. Trajectory Metrics
2. Trajectory Deviation Metrics
3. Trajectory Stability Metrics
4. Trajectory Obstacle Metrics
5. Modified Goal Metrics
6. Planning Factor Metrics
7. Steering Metrics
8. Blinker Metrics
9. Other Information

Detailed Metrics

Trajectory Metrics

Evaluates the current trajectory T(0) itself. Metrics are calculated and published when a trajectory is received.

Implemented metrics

• curvature: Statistics of curvature at each trajectory point.
  • Sub-metrics to publish: /mean, /min, /max.
  • Sub-metrics to output: The same as above but take the published data as data point instead of each trajectory point.

• point_interval: Statistics of distances between consecutive trajectory points.
  • Sub-metrics to publish: /mean, /min, /max.
  • Sub-metrics to output: The same as above but take the published data as data point instead of each trajectory point.

• relative_angle: Statistics of angles between consecutive trajectory points.
  • Parameters: trajectory.min_point_dist_m (minimum distance between points).
  • Sub-metrics to publish: /mean, /min, /max.
  • Sub-metrics to output: The same as above but take the published data as data point instead of each trajectory point.

• resampled_relative_angle: Similar to relative_angle, but considers a point at a fixed distance (e.g., half the vehicle length) for angle calculation from the current point as the next point to calculate the relative angle, instead of using the immediately adjacent point.

• length: Total trajectory length.
  • Sub-metrics: Value-based metric, but using the statistics-based format of /mean, /min, /max with the same value.
  • Sub-metrics to output: /mean, /min, /max for the published data.

• duration: Expected driving time to travel the trajectory.
  • Sub-metrics: Value-based metric, but using the statistics-based format of /mean, /min, /max with the same value.
  • Sub-metrics to output: /mean, /min, /max for the published data.

• velocity: Statistics of velocity at each trajectory point.
  • Sub-metrics to publish: /mean, /min, /max.
  • Sub-metrics to output: The same as above but take the published data as data point instead of each trajectory point.

• acceleration: Statistics of acceleration at each trajectory point.
  • Sub-metrics to publish: /mean, /min, /max.
  • Sub-metrics to output: The same as above but take the published data as data point instead of each trajectory point.

• jerk: Statistics of jerk at each trajectory point.
  • Sub-metrics to publish: /mean, /min, /max.
  • Sub-metrics to output: The same as above but take the published data as data point instead of each trajectory point.

Trajectory Deviation Metrics

Evaluates the trajectory deviation by comparing the trajectory T(0) and the reference trajectory.

Metrics are calculated and publish only when the node receives a trajectory.

The following information are used to calculate metrics:

• the trajectory T(0).
• the reference trajectory assumed to be used as the reference to plan T(0).

Implemented metrics

• lateral_deviation: Statistics of the lateral deviation between trajectory points and the closest reference trajectory points.
  • Sub-metrics to publish: /mean, /min, /max.
  • Sub-metrics to output: The same as above but take the published data as data point instead of each trajectory point.

• yaw_deviation: Statistics of the yaw deviation between trajectory points and the closest reference trajectory points.
  • Sub-metrics to publish: /mean, /min, /max.
  • Sub-metrics to output: The same as above but take the published data as data point instead of each trajectory point.

• velocity_deviation: Statistics of the velocity deviation between trajectory points and the closest reference trajectory points.
  • Sub-metrics to publish: /mean, /min, /max.
  • Sub-metrics to output: The same as above but take the published data as data point instead of each trajectory point.

Trajectory Stability Metrics

Evaluates the trajectory stability by comparing the trajectory T(0) and previous trajectory T(-1).

Metrics are calculated and publish only when the node receives a trajectory.

The following information are used to calculate metrics:

• the trajectory T(0) itself.
• the previous trajectory T(-1).
• the current ego odometry.

Implemented metrics

stability: Statistics of the lateral deviation between T(0) and T(-1) within a lookahead duration and distance.

• Parameters: trajectory.lookahead.max_time_s, trajectory.lookahead.max_dist_m.
• Sub-metrics to publish: /mean, /min, /max.
• Sub-metrics to output: The same as above but take the published data as data point instead of each trajectory point.

• stability_frechet: Frechet distance between T(0) and T(-1) within a lookahead duration and distance.
  • Parameters: Same as stability.
  • Sub-metrics to publish: /mean, /min, /max.
  • Sub-metrics to output: The same as above but take the published data as data point instead of each trajectory point.

• lateral_trajectory_displacement_local: Absolute lateral displacement between T(0) and T(-1) at the ego position.
  • Sub-metrics to publish: Value-based metric, but using the statistics-based format.
  • Sub-metrics to output: /mean, /min, /max for the published data.

• lateral_trajectory_displacement_lookahead: Statistics of absolute lateral displacement between T(0) and T(-1) within a lookahead duration.
• Parameters: trajectory.evaluation_time_s.
• Sub-metrics to publish: /mean, /min, /max.
• Sub-metrics to output: The same as above but take the published data as data point instead of each trajectory point.

Trajectory Obstacle Metrics

Evaluate the safety of T(0) with respect to obstacles.

Metrics are calculated and published only when the node receives a trajectory and perception results.

The following information are used to calculate metrics:

• the trajectory T(0).
• the set of objects in the environment.

By default, metrics are published for each object individually using the object's UUID as the identifier, along with the worst value across all objects. You can set obstacle.worst_only to true to publish only the worst value.

Implemented metrics

• obstacle_distance: Distance from the object's center point to the ego's future trajectory (closest point).
  • Parameters: None.
  • Sub-metrics to publish (per object): /{object_uuid} or /worst (worst value across all objects).
  • Sub-metrics to output: /mean, /min, /max for the published data.

• obstacle_ttc: Time To Collision (TTC) with the target object. Considers predicted paths of objects and ego trajectory.
  • TTC is calculated for objects that will actually collide with ego (overlap at the same time).
  • Parameters:
    • obstacle.collision_thr_m: distance margin to consider a collision occurs between object footprints and ego trajectory footprints.
    • obstacle.use_ego_traj_vel: if true, use planned trajectory velocity; otherwise use current ego velocity.
    • obstacle.stop_velocity_mps: velocity threshold to consider the object or ego as static.
    • obstacle.min_time_interval_s: minimum time interval for ego trajectory resampling.
    • obstacle.min_spatial_interval_m: minimum spatial interval for ego trajectory resampling.
  • Sub-metrics to publish (per object): /{object_uuid} or /worst (worst value across all objects).
  • Sub-metrics to output: /mean, /min, /max for the published data.

• obstacle_pet: Post Encroachment Time (PET) with the target object.
  • PET is the time difference between the target object leaving the overlap region and ego entering it.
  • Only calculated for objects whose future trajectory overlaps with ego's but do not collide at the same time.
  • An object cannot have both TTC and PET simultaneously (if TTC exists, PET is zero).
  • Parameters: Same as obstacle_ttc.
  • Sub-metrics to publish (per object): /{object_uuid} or /worst (worst value across all objects).
  • Sub-metrics to output: /mean, /min, /max for the published data.

• obstacle_drac: Deceleration Rate to Avoid Collision (DRAC) with the target object.
  • The minimum deceleration required to avoid collision with the target object.
  • Published alongside TTC for potential collision targets.
  • Parameters: Same as obstacle_ttc.
  • Sub-metrics to publish (per object): /{object_uuid} or /worst (worst value across all objects).
  • Sub-metrics to output: /mean, /min, /max for the published data.

Modified Goal Metrics

Evaluate deviations between the modified goal and the ego position.

Metrics are calculated and publish only when the node receives a modified goal message.

Implemented metrics

• modified_goal_longitudinal_deviation: Statistics of the longitudinal deviation between the modified goal and the current ego position.
  • Sub-metrics to publish: Value-based metric, but using the statics-based format.
  • Sub-metrics to output: /mean, /min, /max for the published data.

• modified_goal_lateral_deviation: Statistics of the lateral deviation between the modified goal and the current ego position.
  • Sub-metrics to publish: Value-based metric, but using the statics-based format.
  • Sub-metrics to output: /mean, /min, /max for the published data.

• modified_goal_yaw_deviation: Statistics of the yaw deviation between the modified goal and the current ego position.
  • Sub-metrics to publish: Value-based metric, but using the statics-based format.
  • Sub-metrics to output: /mean, /min, /max for the published data.

Planning Factor Metrics

Evaluates the behavior of each planning module by checking planning factors.

Metrics are calculated and publish only when the node receives that planning factors published by each module.

The modules listed in the module_list in the parameter file are evaluated.

Implemented metrics

• stop_decision: Evaluate stop decisions for each module.
  • Parameters:
    • stop_decision.time_count_threshold_s: time threshold to count a stop decision as a new one.
    • stop_decision.dist_count_threshold_m: distance threshold to count a stop decision as a new one.
    • stop_decision.topic_prefix: topic prefix for planning factors
    • stop_decision.module_list: list of modules' names to check, the {topic_prefix}/{module_name} should be a valid topic.
  • Sub-metrics to publish:
    • /{module_name}/keep_duration(value-based): the time current stop decision keeps.
    • /{module_name}/distance_to_stop(value-based): the distance to the stop line.
    • /{module_name}/count(count-based): the stop decision index, in other words, the number of stop decisions made by the module.
  • Sub-metrics to output:
    • /{module_name}/keep_duration/mean, /{module_name}/keep_duration/min, /{module_name}/keep_duration/max: the statistics of the published keep_duration.
    • /{module_name}/count: the total number of stop decisions.

• abnormal_stop_decision: Evaluate abnormal stop decisions for each module.
  • A stop decision is considered as abnormal if the ego cannot stop with the current velocity and the maximum deceleration limit.
  • Parameters:
    • stop_decision.abnormal_deceleration_threshold_mps2: maximum deceleration limit to consider the stop decision as abnormal.
    • Other parameters are shared with stop_decision.
  • Sub-metrics to publish: The same as stop_decision.
  • Sub-metrics to output: The same as stop_decision.

Blinker Metrics

Evaluates the blinker status of the vehicle.

Metrics are calculated and publish only when the node receives a turn indicators report message.

Implemented metrics

• blinker_change_count: Count blinker status changes.
  • A change is counted when the blinker status transitions from off/left to right, or from off/right to left.
• Parameters:
  • blinker_change_count.window_duration_s: duration to count the changes for publishing.
• Sub-metrics to publish: /count_in_duration.
• Sub-metrics to output:
  • /count_in_duration/min, /count_in_duration/max, /count_in_duration/mean: the statistics of the published keep_duration.
  • /count: total number of changes.

Steering Metrics

Evaluates the steering status of the vehicle.

Metrics are calculated and publish only when the node receives a steering report message.

Implemented metrics

• steer_change_count: Count steering rate changes.
  • A change is counted when the steering rate changes from positive/0 to negative or from negative/0 to positive.
  • Parameters:
    • steer_change_count.window_duration_s: duration to count the changes for publishing.
    • steer_change_count.steer_rate_margin: margin to consider the steer rate as 0.
  • Sub-metrics to publish: /count_in_duration.
  • Sub-metrics to output:
    • /count_in_duration/min, /count_in_duration/max, /count_in_duration/mean: the statistics of the published keep_duration.
    • /count: total number of changes.

Other Information

Additional useful information related to planning:

Implemented metrics

• kinematic_state: Current kinematic state of the vehicle
  • Sub-metrics to publish:
    • /velocity: current ego velocity.
    • /acceleration: current ego acceleration.
    • /jerk: current ego jerk.

• ego_lane_info: Lanelet information.
  • Sub-metrics to publish:
    • /lanelet_id: ID of the lanelet where the ego is located.
    • /s: Arc length of ego position in the lanelet.
    • /t: Lateral offset of ego position in the lanelet.

Inputs / Outputs

Inputs

Name	Type	Description
~/input/trajectory	autoware_planning_msgs::msg::Trajectory	Main trajectory to evaluate
~/input/reference_trajectory	autoware_planning_msgs::msg::Trajectory	Reference trajectory to use for deviation metrics
~/input/objects	autoware_perception_msgs::msg::PredictedObjects	Obstacles
~/input/modified_goal	autoware_planning_msgs::msg::PoseWithUuidStamped	Modified goal
~/input/odometry	nav_msgs::msg::Odometry	Current odometry of the vehicle
~/input/route	autoware_planning_msgs::msg::LaneletRoute	Route information
~/input/vector_map	autoware_map_msgs::msg::LaneletMapBin	Vector map information
~/input/acceleration	geometry_msgs::msg::AccelWithCovarianceStamped	Current acceleration of the vehicle
~/input/steering_status	autoware_vehicle_msgs::msg::SteeringReport	Current steering of the vehicle
~/input/turn_indicators_status	autoware_vehicle_msgs::msg::TurnIndicatorsReport	Current blinker status of the vehicle
{topic_prefix}/{module_name}	autoware_internal_planning_msgs::msg::PlanningFactorArray	Planning factors of each module to evaluate

Outputs

Each publishing-based metric is published on the same topic.

Name	Type	Description
~/metrics	tier4_metric_msgs::msg::MetricArray	MetricArray with all published metrics
~/debug/processing_time_ms	autoware_internal_debug_msgs::msg::Float64Stamped	Node processing time in milliseconds

• If output_metrics = true, the evaluation node writes the output-based metrics measured during its lifetime to <ros2_logging_directory>/autoware_metrics/<node_name>-<time_stamp>.json when shut down.

Parameters

Name	Type	Description	Default	Range
ego_frame	string	reference frame of ego	base_link	N/A
metrics_for_publish	array	metrics to collect and publish	['curvature', 'point_interval', 'relative_angle', 'resampled_relative_angle', 'length', 'duration', 'velocity', 'acceleration', 'jerk', 'lateral_deviation', 'yaw_deviation', 'velocity_deviation', 'lateral_trajectory_displacement_local', 'lateral_trajectory_displacement_global', 'stability', 'stability_frechet', 'obstacle_distance', 'obstacle_ttc', 'obstacle_pet', 'obstacle_drac', 'modified_goal_longitudinal_deviation', 'modified_goal_lateral_deviation', 'modified_goal_yaw_deviation', 'stop_decision', 'abnormal_stop_decision', 'blinker_change_count', 'steer_change_count']	N/A
metrics_for_output	array	metrics to output to json file when the node is terminated	['curvature', 'point_interval', 'relative_angle', 'resampled_relative_angle', 'length', 'duration', 'velocity', 'acceleration', 'jerk', 'lateral_deviation', 'yaw_deviation', 'velocity_deviation', 'lateral_trajectory_displacement_local', 'lateral_trajectory_displacement_global', 'stability', 'stability_frechet', 'obstacle_distance', 'obstacle_ttc', 'obstacle_pet', 'obstacle_drac', 'modified_goal_longitudinal_deviation', 'modified_goal_lateral_deviation', 'modified_goal_yaw_deviation', 'stop_decision', 'abnormal_stop_decision', 'blinker_change_count', 'steer_change_count']	N/A
trajectory.min_point_dist_m	float	minimum distance between two successive points to use for angle calculation	0.1	N/A
lookahead.max_dist_m	float	maximum distance from ego along the trajectory to use for calculation	5.0	N/A
lookahead.max_time_s	float	maximum time ahead of ego along the trajectory to use for calculation	3.0	N/A
obstacle.collision_thr_m	float	distance threshold to consider a collision/overlapping occurs between object footprints and ego trajectory footprints	0.0	N/A
obstacle.worst_only	boolean	if true, only the worst obstacle metric value is published; if false, metrics are published for each object	False	N/A
obstacle.use_ego_traj_vel	boolean	if true, the planned trajectory velocity is used; if false, current ego velocity is used	False	N/A
obstacle.stop_velocity_mps	float	[m/s] velocity threshold to consider the object or ego is static, used to speed up calculation	0.2777	N/A
obstacle.min_time_interval_s	float	[s] minimum time interval between two successive points for ego trajectory resampling to speed up calculation	0.05	N/A
obstacle.min_spatial_interval_m	float	[m] minimum spatial interval between two successive points for ego trajectory resampling to speed up calculation	0.1	N/A
stop_decision.topic_prefix	string	prefix of the topic to subscribe to for planning factors	/planning/planning_factors/	N/A
stop_decision.time_count_threshold_s	float	time [s] threshold to count a stop as a new one	60.0	N/A
stop_decision.dist_count_threshold_m	float	distance [m] threshold to count a stop as a new one	5.0	N/A
stop_decision.abnormal_deceleration_threshold_mps2	float	abnormal deceleration threshold [m/s^2] to count a stop as abnormal	2.0	N/A
stop_decision.module_list	array	list of modules to be checked for stop decision, the topic_prefix/module topic should be available and publish the autoware_internal_planning_msgs::PlanningFactorArray message	['avoidance_by_lane_change', 'behavior_path_planner', 'blind_spot', 'crosswalk', 'detection_area', 'dynamic_obstacle_avoidance', 'dynamic_obstacle_stop', 'goal_planner', 'intersection', 'lane_change_left', 'lane_change_right', 'motion_velocity_planner', 'merge_from_private', 'no_drivable_lane', 'no_stopping_area', 'obstacle_cruise', 'obstacle_slow_down', 'obstacle_stop', 'occlusion_spot', 'out_of_lane', 'run_out', 'side_shift', 'start_planner', 'static_obstacle_avoidance', 'stop_line', 'surround_obstacle_checker', 'traffic_light', 'virtual_traffic_light', 'walkway']	N/A
blinker_change_count.window_duration_s	float	window duration [s] to count blinker changes for publishing	5.0	N/A
steer_change_count.window_duration_s	float	window duration [s] to count steer changes for publishing	5.0	N/A
steer_change_count.steer_rate_margin	float	margin of steer_rate [rad/s] around 0 to count as steer change	0.1	N/A

Assumptions / Known limits

There is a strong assumption that when receiving a trajectory T(0), it has been generated using the last received reference trajectory and objects. This can be wrong if a new reference trajectory or objects are published while T(0) is being calculated.

Precision is currently limited by the resolution of the trajectories. It is possible to interpolate the trajectory and reference trajectory to increase precision but would make computation significantly more expensive.

Future extensions / Unimplemented parts

• Use Route or Path messages as reference trajectory.
• The collision assessment metrics (done in another node https://tier4.atlassian.net/browse/AJD-263).
• motion_evaluator_node.
  • Node which constructs a trajectory over time from the real motion of ego.
  • Only a proof of concept is currently implemented.
• Take into account the shape, not only the centroid of the object for the obstacle metrics.