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]
`chattering_threshold`	double	even if the obstacle disappears, the stop judgment continues for chattering_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.)

example — parameters for obstacle stop planner

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.)

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.

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#

uml diagram

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.

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#

uml diagram

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#

uml diagram

(*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.

adaptive_cruise

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.