Obstacle Stop Planner

Overview

obstacle_stop_planner has following modules

• Obstacle Stop Planner
  • inserts a stop point in trajectory when there is a static point cloud on the trajectory.
• Slow Down Planner
  • inserts a deceleration section in trajectory when there is a point cloud near the trajectory.
• Adaptive Cruise Controller (ACC)
  • embeds target velocity in trajectory when there is a dynamic point cloud on the trajectory.

Input topics

Name	Type	Description
~/input/pointcloud	sensor_msgs::PointCloud2	obstacle pointcloud
~/input/trajectory	autoware_auto_planning_msgs::Trajectory	trajectory
~/input/vector_map	autoware_auto_mapping_msgs::HADMapBin	vector map
~/input/odometry	nav_msgs::Odometry	vehicle velocity
~/input/dynamic_objects	autoware_auto_perception_msgs::PredictedObjects	dynamic objects
~/input/expand_stop_range	tier4_planning_msgs::msg::ExpandStopRange	expand stop range

Output topics

Name	Type	Description
~output/trajectory	autoware_auto_planning_msgs::Trajectory	trajectory to be followed
~output/stop_reasons	tier4_planning_msgs::StopReasonArray	reasons that cause the vehicle to stop

Common Parameter

Parameter	Type	Description
enable_slow_down	bool	enable slow down planner [-]
max_velocity	double	max velocity [m/s]
hunting_threshold	double	even if the obstacle disappears, the stop judgment continues for hunting_threshold [s]

Obstacle Stop Planner

Role

This module inserts the stop point before the obstacle with margin. In nominal case, the margin is the sum of baselink_to_front and max_longitudinal_margin. The baselink_to_front means the distance between baselink(center of rear-wheel axis) and front of the car. The detection area is generated along the processed trajectory as following figure. (This module cut off the input trajectory behind the ego position and decimates the trajectory points for reducing computational costs.)

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If another stop point has already been inserted by other modules within max_longitudinal_margin, the margin is the sum of baselink_to_front and min_longitudinal_margin. This feature exists to avoid stopping unnaturally position. (For example, the ego stops unnaturally far away from stop line of crosswalk that pedestrians cross to without this feature.)

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The module searches the obstacle pointcloud within detection area. When the pointcloud is found, Adaptive Cruise Controller modules starts to work. only when Adaptive Cruise Controller modules does not insert target velocity, the stop point is inserted to the trajectory. The stop point means the point with 0 velocity.

Restart prevention

If it needs X meters (e.g. 0.5 meters) to stop once the vehicle starts moving due to the poor vehicle control performance, the vehicle goes over the stopping position that should be strictly observed when the vehicle starts to moving in order to approach the near stop point (e.g. 0.3 meters away).

This module has parameter hold_stop_margin_distance in order to prevent from these redundant restart. If the vehicle is stopped within hold_stop_margin_distance meters from stop point of the module, the module judges that the vehicle has already stopped for the module’s stop point and plans to keep stopping current position even if the vehicle is stopped due to other factors.

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Parameters

Stop position

Parameter	Type	Description
max_longitudinal_margin	double	margin between obstacle and the ego’s front [m]
min_longitudinal_margin	double	if any obstacle exists within max_longitudinal_margin, this module set margin as the value of stop margin to min_longitudinal_margin [m]
hold_stop_margin_distance	double	parameter for restart prevention (See above section) [m]

Obstacle detection area

Parameter	Type	Description
lateral_margin	double	lateral margin from the vehicle footprint for collision obstacle detection area [m]
step_length	double	step length for pointcloud search range [m]
extend_distance	double	extend trajectory to consider after goal obstacle in the extend_distance [m]

Flowchart

@startuml
title insertStopPoint
start


:callback trajectory;

:trim trajectory from self-pose;

:decimate trajectory;

:generate detection area;

if ( search obstacle pointcloud in detection area?) then (yes(find))
else (no(not find))
  stop
endif

if ( insert target velocity by adaptive cruise module?) then (yes)
  stop
else (no)
endif

:insert stop point;


stop
@enduml

Slow Down Planner

Role

This module inserts the slow down section before the obstacle with forward margin and backward margin. The forward margin is the sum of baselink_to_front and longitudinal_forward_margin, and the backward margin is the sum of baselink_to_front and longitudinal_backward_margin. The ego keeps slow down velocity in slow down section. The velocity is calculated the following equation.

$v_{target} = v_{min} + \frac{l_{ld} - l_{vw}/2}{l_{margin}} (v_{max} - v_{min} )$

• $v_{target}$ : slow down target velocity [m/s]
• $v_{min}$ : min_slow_down_velocity [m/s]
• $v_{max}$ : max_slow_down_velocity [m/s]
• $l_{ld}$ : lateral deviation between the obstacle and the ego footprint [m]
• $l_{margin}$ : lateral_margin [m]
• $l_{vw}$ : width of the ego footprint [m]

The above equation means that the smaller the lateral deviation of the pointcloud, the lower the velocity of the slow down section.

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Parameters

Slow down section

Parameter	Type	Description
longitudinal_forward_margin	double	margin between obstacle and the ego’s front [m]
longitudinal_backward_margin	double	margin between obstacle and the ego’s rear [m]

Obstacle detection area

Parameter	Type	Description
lateral_margin	double	lateral margin from the vehicle footprint for slow down obstacle detection area [m]

Slow down target velocity

Parameter	Type	Description
max_slow_down_velocity	double	max slow down velocity [m/s]
min_slow_down_velocity	double	min slow down velocity [m/s]

Flowchart

@startuml
title insertDecelerationPoint
start


:callback trajectory;

:trim trajectory from self-pose;

:decimate trajectory;

:generate detection area;

if ( search obstacle pointcloud in detection area?) then (yes(find))
else (no(not find))
  stop
endif

:insert deceleration point;


stop
@enduml

Adaptive Cruise Controller

Role

Adaptive Cruise Controller module embeds maximum velocity in trajectory when there is a dynamic point cloud on the trajectory. The value of maximum velocity depends on the own velocity, the velocity of the point cloud ( = velocity of the front car), and the distance to the point cloud (= distance to the front car).

Parameter	Type	Description
adaptive_cruise_control.use_object_to_estimate_vel	bool	use dynamic objects for estimating object velocity or not
adaptive_cruise_control.use_pcl_to_estimate_vel	bool	use raw pointclouds for estimating object velocity or not
adaptive_cruise_control.consider_obj_velocity	bool	consider forward vehicle velocity to calculate target velocity in adaptive cruise or not
adaptive_cruise_control.obstacle_velocity_thresh_to_start_acc	double	start adaptive cruise control when the velocity of the forward obstacle exceeds this value [m/s]
adaptive_cruise_control.obstacle_velocity_thresh_to_stop_acc	double	stop acc when the velocity of the forward obstacle falls below this value [m/s]
adaptive_cruise_control.emergency_stop_acceleration	double	supposed minimum acceleration (deceleration) in emergency stop [m/ss]
adaptive_cruise_control.emergency_stop_idling_time	double	supposed idling time to start emergency stop [s]
adaptive_cruise_control.min_dist_stop	double	minimum distance of emergency stop [m]
adaptive_cruise_control.obstacle_emergency_stop_acceleration	double	supposed minimum acceleration (deceleration) in emergency stop [m/ss]
adaptive_cruise_control.max_standard_acceleration	double	supposed maximum acceleration in active cruise control [m/ss]
adaptive_cruise_control.min_standard_acceleration	double	supposed minimum acceleration (deceleration) in active cruise control [m/ss]
adaptive_cruise_control.standard_idling_time	double	supposed idling time to react object in active cruise control [s]
adaptive_cruise_control.min_dist_standard	double	minimum distance in active cruise control [m]
adaptive_cruise_control.obstacle_min_standard_acceleration	double	supposed minimum acceleration of forward obstacle [m/ss]
adaptive_cruise_control.margin_rate_to_change_vel	double	rate of margin distance to insert target velocity [-]
adaptive_cruise_control.use_time_compensation_to_calc_distance	bool	use time-compensation to calculate distance to forward vehicle
adaptive_cruise_control.p_coefficient_positive	double	coefficient P in PID control (used when target dist -current_dist >=0) [-]
adaptive_cruise_control.p_coefficient_negative	double	coefficient P in PID control (used when target dist -current_dist <0) [-]
adaptive_cruise_control.d_coefficient_positive	double	coefficient D in PID control (used when delta_dist >=0) [-]
adaptive_cruise_control.d_coefficient_negative	double	coefficient D in PID control (used when delta_dist <0) [-]
adaptive_cruise_control.object_polygon_length_margin	double	The distance to extend the polygon length the object in pointcloud-object matching [m]
adaptive_cruise_control.object_polygon_width_margin	double	The distance to extend the polygon width the object in pointcloud-object matching [m]
adaptive_cruise_control.valid_estimated_vel_diff_time	double	Maximum time difference treated as continuous points in speed estimation using a point cloud [s]
adaptive_cruise_control.valid_vel_que_time	double	Time width of information used for speed estimation in speed estimation using a point cloud [s]
adaptive_cruise_control.valid_estimated_vel_max	double	Maximum value of valid speed estimation results in speed estimation using a point cloud [m/s]
adaptive_cruise_control.valid_estimated_vel_min	double	Minimum value of valid speed estimation results in speed estimation using a point cloud [m/s]
adaptive_cruise_control.thresh_vel_to_stop	double	Embed a stop line if the maximum speed calculated by ACC is lower than this speed [m/s]
adaptive_cruise_control.lowpass_gain_of_upper_velocity	double	Lowpass-gain of target velocity
adaptive_cruise_control.use_rough_velocity_estimation:	bool	Use rough estimated velocity if the velocity estimation is failed
adaptive_cruise_control.rough_velocity_rate	double	In the rough velocity estimation, the velocity of front car is estimated as self current velocity * this value

Flowchart

@startuml
title insertTargetVelocity()
start

:get target vehicle point (*1) in detection area ;

partition Estimate-Target-Velocity {

if (Is there a DynamicObject on the target vehicle point?) then (yes)
:use the DynamicObject velocity\nas a target vehicle point velocity (*2);
else (no)

  if (The target vehicle point is found in the previous step?) then (yes)
  else (no (estimation failed))
  stop
  endif

  :estimate the target vehicle point velocity \nby the travel distance from the previous step;

  if (The estimated velocity is valid?) then (yes)
  else (no (estimation failed))
  stop
  endif

  :use the estimated velocity\nas a target vehicle point velocity (*2);

endif
}

if (Is the target vehicle point velocity fast enough?) then (yes)
else (no)
  stop
endif

if (calculate distance to the pointcloud from self-position?) then (yes)
else (no)
  stop
endif

:calculate target velocity to be inserted in the trajectory;

if (the target velocity is fast enough?) then (yes)
else (no)
  stop
endif

:insert the target velocity;

stop
@enduml

(*1) The target vehicle point is calculated as a closest obstacle PointCloud from ego along the trajectory.

(*2) The sources of velocity estimation can be changed by the following ROS parameters.

• adaptive_cruise_control.use_object_to_estimate_vel
• adaptive_cruise_control.use_pcl_to_estimate_vel

This module works only when the target point is found in the detection area of the Obstacle stop planner module.

The first process of this module is to estimate the velocity of the target vehicle point. The velocity estimation uses the velocity information of dynamic objects or the travel distance of the target vehicle point from the previous step. The dynamic object information is primal, and the travel distance estimation is used as a backup in case of the perception failure. If the target vehicle point is contained in the bounding box of a dynamic object geometrically, the velocity of the dynamic object is used as the target point velocity. Otherwise, the target point velocity is calculated by the travel distance of the target point from the previous step; that is (current_position - previous_position) / dt. Note that this travel distance based estimation fails when the target point is detected in the first time (it mainly happens in the cut-in situation). To improve the stability of the estimation, the median of the calculation result for several steps is used.

If the calculated velocity is within the threshold range, it is used as the target point velocity.

Only when the estimation is succeeded and the estimated velocity exceeds the value of obstacle_stop_velocity_thresh_*, the distance to the pointcloud from self-position is calculated. For prevent chattering in the mode transition, obstacle_velocity_thresh_to_start_acc is used for the threshold to start adaptive cruise, and obstacle_velocity_thresh_to_stop_acc is used for the threshold to stop adaptive cruise. When the calculated distance value exceeds the emergency distance $d_{emergency}$ calculated by emergency_stop parameters, target velocity to insert is calculated.

The emergency distance $d_{emergency}$ is calculated as follows.

$d_{emergency} = d_{margin_{emergency}} + t_{idling_{emergency}} \cdot v_{ego} + (-\frac{v_{ego}^2}{2 \cdot a_{ego_{emergency}}}) - (-\frac{v_{obj}^2}{2 \cdot a_{obj_{emergency}}})$

• $d_{margin_{emergency}}$ is a minimum margin to the obstacle pointcloud. The value of $d_{margin_{emergency}}$ depends on the parameter min_dist_stop
• $t_{idling_{emergency}}$ is a supposed idling time. The value of $t_{idling_{emergency}}$ depends on the parameter emergency_stop_idling_time
• $v_{ego}$ is a current velocity of own vehicle
• $a_{ego_{{emergency}}}$ is a minimum acceleration (maximum deceleration) of own vehicle. The value of $a{ego_{_{emergency}}}$ depends on the parameter emergency_stop_acceleration
• $v_{obj}$ is a current velocity of obstacle pointcloud.
• $a_{obj_{{emergency}}}$ is a supposed minimum acceleration of obstacle pointcloud. The value of $a{obj_{_{emergency}}}$ depends on the parameter obstacle_emergency_stop_acceleration
• *Above $X_{_{emergency}}$ parameters are used only in emergency situation.

The target velocity is determined to keep the distance to the obstacle pointcloud from own vehicle at the standard distance $d_{standard}$ calculated as following. Therefore, if the distance to the obstacle pointcloud is longer than standard distance, The target velocity becomes higher than the current velocity, and vice versa. For keeping the distance, a PID controller is used.

$d_{standard} = d_{margin_{standard}} + t_{idling_{standard}} \cdot v_{ego} + (-\frac{v_{ego}^2}{2 \cdot a_{ego_{standard}}}) - (-\frac{v_{obj}^2}{2 \cdot a_{obj_{standard}}})$

• $d_{margin_{standard}}$ is a minimum margin to the obstacle pointcloud. The value of $d_{margin_{standard}}$ depends on the parameter min_dist_stop
• $t_{idling_{standard}}$ is a supposed idling time. The value of $t_{idling_{standard}}$ depends on the parameter standard_stop_idling_time
• $v_{ego}$ is a current velocity of own vehicle
• $a_{ego_{{standard}}}$ is a minimum acceleration (maximum deceleration) of own vehicle. The value of $a{ego_{_{standard}}}$ depends on the parameter min_standard_acceleration
• $v_{obj}$ is a current velocity of obstacle pointcloud.
• $a_{obj_{{standard}}}$ is a supposed minimum acceleration of obstacle pointcloud. The value of $a{obj_{_{standard}}}$ depends on the parameter obstacle_min_standard_acceleration
• *Above $X_{_{standard}}$ parameters are used only in non-emergency situation.

If the target velocity exceeds the value of thresh_vel_to_stop, the target velocity is embedded in the trajectory.

Known Limits

• It is strongly depends on velocity planning module whether or not it moves according to the target speed embedded by Adaptive Cruise Controller module. If the velocity planning module is updated, please take care of the vehicle’s behavior as much as possible and always be ready for overriding.

• The velocity estimation algorithm in Adaptive Cruise Controller is depend on object tracking module. Please note that if the object-tracking fails or the tracking result is incorrect, it the possibility that the vehicle behaves dangerously.

• It does not work for backward driving, but publishes the path of the input as it is. Please use obstacle_cruise_planner if you want to stop against an obstacle when backward driving.