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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.

When the stop point that has 0 velocity is inserted, the point is inserted in front of the target point cloud by the distance of baselink to front + stop margin. The baselink to front means the distance between base_link(center of rear-wheel axis) and front of the car. stop margin is determined by the parameters described below.

insert_stop_velocity

When the deceleration section is inserted, the start point of the section is inserted in front of the target point cloud by the distance of baselink to front + slow down forward margin. the end point of the section is inserted behind the target point cloud by the distance of slow down backward margin + baselink to rear. The baselink to rear means the distance between base_link and rear of the car. The velocities of points in the deceleration section are modified to the deceleration velocity. slow down backward margin and slow down forward margin are determined by the parameters described below.

insert_stop_velocity

Input topics#

Name Type Description
~/input/pointcloud sensor_msgs::PointCloud2 obstacle pointcloud
~/input/trajectory autoware_planning_msgs::msg::Trajectory trajectory
~/input/vector_map autoware_lanelet2_msgs::MapBin vector map
~/input/twist geometry_msgs::TwistStamped vehicle velocity
~input/dynamic_objects autoware_perception_msgs::DynamicObjectArray dynamic objects
~/input/expand_stop_range autoware_planning_msgs::msg::ExpandStopRange expand stop range

Output topics#

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

Modules#

Obstacle Stop Planner#

Role#

Obstacle Stop Planner module inserts a stop point in trajectory when there is a static point cloud on the trajectory. This module does not work when Adaptive Cruise Controller works.

Parameter Type Description
stop_planner.stop_margin double stop margin distance from obstacle on the path [m]
stop_planner.min_behavior_stop_margin double stop margin distance when any other stop point is inserted in stop margin [m]
stop_planner.step_length double step length for pointcloud search range [m]
stop_planner.extend_distance double extend trajectory to consider after goal obstacle in the extend_distance
stop_planner.expand_stop_range double margin of vehicle footprint [m]

Flowchart#

uml diagram

First, this module cut off the trajectory behind the car and decimates the points of trajectory for reducing computational costs.

Then, a detection area is generated by the decimated trajectory as following figure. The detection area means the area through which the vehicle-body passes.

vehicle_shape

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.

pointcloud

Slow Down Planner#

Role#

Slow Down Planner module inserts a deceleration point in trajectory when there is a point cloud near the trajectory.

Parameter Type Description
slow_down_planner.slow_down_forward_margin double margin distance from slow down point to vehicle front [m]
slow_down_planner.slow_down_backward_margin double margin distance from slow down point to vehicle rear [m]
slow_down_planner.expand_slow_down_range double offset from vehicle side edge for expanding the search area of the surrounding point cloud [m]
slow_down_planner.max_slow_down_vel double max slow down velocity [m/s]
slow_down_planner.min_slow_down_vel double min slow down velocity [m/s]

Flowchart#

uml diagram

First, this module cut off the trajectory behind the car and decimates the points of trajectory for reducing computational costs. ( This is the same process as that of Obstacle Stop planner module. )

Then, a detection area is generated by the decimated trajectory as following figure. The detection area in this module is the extended area of the detection area used in Obstacle Stop Planner module. The distance to be extended depends on the above parameter expand_slow_down_range.

vehicle_shape_decel

The module searches the obstacle pointcloud within detection area. When the pointcloud is found, the deceleration point is inserted to the trajectory.

pointcloud_decel

The deceleration point means the point with low velocity; the value of the velocity v_{target} is determined as follows.

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

  • v_{min} is minimum target value of Slow Down Planner module. The value of v_{min} depends on the parameter min_slow_down_vel.
  • v_{max} is maximum target value of Slow Down Planner module. The value of v_{max} depends on the parameter max_slow_down_vel.
  • l_{ld} is the lateral deviation of the target pointcloud.
  • l_{vw} is the vehicle width.
  • l_{er} is the expand range of detection area. The value of l_{er} depends on the parameter expand_slow_down_range

The above method means that the smaller the lateral deviation of the pointcloud, the lower the velocity of the deceleration point.

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

This module works only when the obstacle pointcloud is found in the detection area of the Obstacle stop planner module. At first, the velocity of the pointcloud is estimated. The velocity estimation uses the velocity information of dynamic objects, or the distance to the point cloud found in the previous step.

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.