Index

Run Out

Role

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

Activation Timing

This module is activated if launch_run_out becomes true

Inner-workings / Algorithms

Flow chart

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 autoware velocity smoother module by using current velocity, current acceleration and velocity limits directed by the map and external API.

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_perception_msgs::msg::ObjectClassification>	classifications with probability
shape	autoware_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.

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.

Exclude obstacles by label

This module only acts on target obstacles defined by the target_obstacle_types parameter. If an obstacle of a type not specified by said parameter is detected, it will be ignored by this module.

Exclude obstacles that are already on the ego vehicle's path

In the cases were an obstacle is already on the ego vehicle's path, it cannot "cut in" into the ego's path anymore (which is the situation this module tries to handle) so it might be useful to exclude obstacles already on the vehicle's footprint path. By setting the parameter exclude_obstacles_already_in_path to true, this module will exclude the obstacles that are considered to be already on the ego vehicle's path for more than keep_obstacle_on_path_time_threshold. The module considers the ego vehicle's closest path point to each obstacle's current position, and determines the lateral distance between the obstacle and the right and left sides of the ego vehicle. If the obstacle is located within the left and right extremes of the vehicle for that pose, then it is considered to be inside the ego vehicle's footprint path and it is excluded. The virtual width of the vehicle used to detect if an obstacle is within the path or not, can be adjusted by the ego_footprint_extra_margin parameter.

Exclude obstacles that cross the ego vehicle's "cut line"

This module can exclude obstacles that have predicted paths that will cross the back side of the ego vehicle. It excludes obstacles if their predicted path crosses the ego's "cut line". The "cut line" is a virtual line segment that is perpendicular to the ego vehicle and that passes through the ego's base link.

You can choose whether to use this feature by setting the parameter use_ego_cut_line to true or false. The width of the line can be tuned with the parameter ego_cut_line_length.

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.

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.

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.

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.

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.

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
target_obstacle_types	vector of string	[-] specifies which obstacle types will be considered by the module, if the obstacles classification type is not written here, it will be ignored. candidate: ["PEDESTRIAN", "BICYCLE","MOTORCYCLE"]
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