Index

Intersection

Role

The intersection module is responsible for safely going through urban intersections by:

1. checking collisions with upcoming vehicles
2. recognizing the occluded area in the intersection
3. reacting to arrow signals of associated traffic lights

The module is designed to be agnostic to left-hand/right-hand traffic rules and works on crossroads, T-shape junctions, etc.

Activation condition

This module is activated when the path contains the lanes with turn_direction tag. More precisely, if the lane_ids of the path contains the ids of those lanes, corresponding instances of intersection module are activated on each lanes respectively.

Requirements/Limitations

• The HDMap needs to have the information of turn_direction tag (which should be one of straight, left, right) for all the lanes in intersections and right_of_way tag for specific lanes (refer to RightOfWay section for more details). See lanelet2_extension document for more detail.
• WIP(perception requirements/limitations)
• WIP(sensor visibility requirements/limitations)

Attention area

The attention area in the intersection are defined as the set of lanes that are conflicting with ego vehicle's path and their preceding lanes up to common.attention_area_length meters. RightOfWay tag is used to rule out the lanes that each lane has priority given the traffic light relation and turn_direction priority(yield lane).

Intersection Area, which is supposed to be defined on the HDMap, is an area converting the entire intersection.

Right Of Way

Following table shows an example of how to assign right_of_way tag and set yield_lanes to each lane in intersections.

turn direction / traffic light	w/ traffic light	w/o traffic light
straight	Highest priority of all	Priority over left/right lanes of the same group
left(Left hand traffic)	Priority over the other group and right lanes of the same group	Priority over right lanes of the same group
right(Left hand traffic)	Priority only over the other group	priority only over the other group
left(Right hand traffic)	Priority only over the other group	Priority only over the other group
right(Right hand traffic)	Priority over the other group and left lanes of the same group	priority over left lanes of the same group

This setting gives the following attention_area configurations.

Target objects

For stuck vehicle detection and collision detection, this module checks car, bus, truck, trailer, motor cycle, and bicycle type objects.

Objects that satisfy all of the following conditions are considered as target objects (possible collision objects):

• The center of mass of the object is within a certain distance from the attention lane (threshold = common.attention_area_margin) .
  • (Optional condition) The center of gravity is in the intersection area.
    • To deal with objects that is in the area not covered by the lanelets in the intersection.
• The posture of object is the same direction as the attention lane (threshold = common.attention_area_angle_threshold).
• Not being in the adjacent lanes of the ego vehicle.

Stuck Vehicle Detection

If there is any object on the path in inside the intersection and at the exit of the intersection (up to stuck_vehicle_detect_dist) lane and its velocity is less than a threshold (stuck_vehicle.stuck_vehicle_vel_thr), the object is regarded as a stuck vehicle. If stuck vehicles exist, this module inserts a stopline a certain distance (=stop_line_margin) before the overlapped region with other lanes. The stuck vehicle detection area is generated based on the vehicle path, so the stuck vehicle stopline is not inserted if the upstream module generated avoidance path

Collision detection

The following process is performed for the targets objects to determine whether the ego vehicle can pass the intersection safely. If it is judged that the ego vehicle cannot pass through the intersection with enough margin, this module inserts a stopline on the path.

1. calculate the time interval that the ego vehicle is in the intersection. This time is set as \(t_s\) ~ \(t_e\)
2. extract the predicted path of the target object whose confidence is greater than collision_detection.min_predicted_path_confidence.
3. detect collision between the extracted predicted path and ego's predicted path in the following process.
   1. obtain the passing area of the ego vehicle \(A_{ego}\) in \(t_s\) ~ \(t_e\).
   2. calculate the passing area of the target object \(A_{target}\) at \(t_s\) - collision_detection.collision_start_margin_time ~ \(t_e\) + collision_detection.collision_end_margin_time for each predicted path (*1).
   3. check if \(A_{ego}\) and \(A_{target}\) polygons are overlapped (has collision).
4. when a collision is detected, the module inserts a stopline.
5. If ego is over the pass_judge_line, collision checking is not processed to avoid sudden braking and/or unnecessary stop in the middle of the intersection.

The parameters collision_detection.collision_start_margin_time and collision_detection.collision_end_margin_time can be interpreted as follows:

• If the ego vehicle was to pass through the intersection earlier than the target object, collision would be detected if the time difference between the two was less than collision_detection.collision_start_margin_time.
• If the ego vehicle was to pass through the intersection later than the target object, collision would be detected if the time difference between the two was less than collision_detection.collision_end_margin_time.

If collision is detected, the state transits to "STOP" immediately. On the other hand, the state does not transit to "GO" unless safe judgement continues for a certain period collision_detection.state_transit_margin to prevent the chattering of decisions.

Currently, the intersection module uses motion_velocity_smoother feature to precisely calculate ego vehicle velocity profile along the intersection lane under longitudinal/lateral constraints. If the flag collision_detection.use_upstream_velocity is true, the target velocity profile of the original path is used. Otherwise the target velocity is set to common.intersection_velocity. In the trajectory smoothing process the target velocity at/before ego trajectory points are set to ego current velocity. The smoothed trajectory is then converted to an array of (time, distance) which indicates the arrival time to each trajectory point on the path from current ego position. You can visualize this array by adding the lane id to debug.ttc and running

ros2 run behavior_velocity_intersection_module ttc.py --lane_id <lane_id>

Stop Line Automatic Generation

If a stopline is associated with the intersection lane on the map, that line is used as the stopline for collision detection. Otherwise the path is interpolated at a certain intervals (=common.path_interpolation_ds), and the point which is stop_line_margin meters behind the attention area is defined as the position of the stop line for the vehicle front.

Pass Judge Line

To avoid sudden braking, if deceleration and jerk more than a threshold (behavior_velocity_planner.max_accel and behavior_velocity_planner.max_jerk) is required to stop just in front of the attention area, namely the first_attention_stop_line, this module does not insert stopline after it passed the default stop_line position.

The position of the pass judge line depends on the occlusion detection configuration and the existence of the associated traffic light of the intersection lane.

• If occlusion.enable is false, the pass judge line before the first_attention_stop_line by the braking distance \(v_{ego}^{2} / 2a_{max}\).
• If occlusion.enable is true and:
  • if there are associated traffic lights, the pass judge line is at the occlusion_peeking_stop_line in order to continue peeking/collision detection while occlusion is detected.
  • if there are no associated traffic lights and:
    • if occlusion is detected, pass judge line is at the occlusion_wo_tl_pass_judge_line to continue peeking.
    • if occlusion is not detected, pass judge line is at the same place at the case where occlusion.enable is false.

Occlusion detection

If the flag occlusion.enable is true this module checks if there is sufficient field of view (FOV) on the attention area up to occlusion.occlusion_attention_area_length. If FOV is not clear enough the ego vehicle will once stop at the default stop line for occlusion.before_creep_stop_time, and then slowly creep toward occlusion peeking stop line at the speed of occlusion.occlusion_creep_velocity if occlusion.enable_creeping is true. During the creeping if collision is detected this module inserts a stop line instantly, and if the FOV gets sufficiently clear the intersection occlusion wall will disappear. If occlusion is cleared and no collision is detected the ego vehicle will pass the intersection.

The occlusion is detected as the common area of occlusion attention area(which is partially the same as the normal attention area) and the unknown cells of the occupancy grid map. The occupancy grid map is denoised using morphology with the window size of occlusion.denoise_kernel. The occlusion attention area lanes are discretized to line strings and they are used to generate a grid whose each cell represents the distance from the ego path along the lane as shown below.

If the nearest occlusion cell value is below the threshold, occlusion is detected. It is expected that the occlusion gets cleared as the vehicle approaches the occlusion peeking stop line.

In there are no traffic lights associated with the lane, the ego vehicle will make a brief stop at the default stop line and the position where the vehicle heading touches the attention area for the first time(which is denoted as first attention stop line). After stopping at the first attention area stop line this module inserts occlusion.absence_traffic_light.creep_velocity velocity between ego and the position which is occlusion.absence_traffic_light.maximum_peeking_distance ahead of first attention area stop line while occlusion is not cleared. If collision is detected, ego will instantly stop. Once the occlusion is cleared or ego passed occlusion.absence_traffic_light.maximum_peeking_distance this module does not detect collision and occlusion because ego vehicle is already inside the intersection.

Data Structure

IntersectionLanelets

IntersectionStopLines

Each stop lines are generated from interpolated path points to obtain precise positions.

Module Parameters

Parameter	Type	Description
common.attention_area_margin	double	[m] margin for expanding attention area width
common.attention_area_length	double	[m] range for object detection
common.attention_area_angle_threshold	double	[rad] threshold of angle difference between the detected object and lane
common.stop_line_margin	double	[m] margin before stop line
common.intersection_velocity	double	[m/s] velocity profile for pass judge calculation
common.intersection_max_accel	double	[m/s^2] acceleration profile for pass judge calculation
common.stop_overshoot_margin	double	[m] margin for the overshoot from stopline
stuck_vehicle.stuck_vehicle_detect_dist	double	[m] length toward from the exit of intersection for stuck vehicle detection
stuck_vehicle.stuck_vehicle_vel_thr	double	[m/s] velocity threshold for stuck vehicle detection
collision_detection.state_transit_margin_time	double	[m] time margin to change state
collision_detection.min_predicted_path_confidence	double	[-] minimum confidence value of predicted path to use for collision detection
collision_detection.collision_start_margin_time	double	[s] time margin for the beginning of collision with upcoming vehicle
collision_detection.collision_end_margin_time	double	[s] time margin for the finish of collision with upcoming vehicle
collision_detection.keep_detection_vel_thr	double	[m/s] ego velocity threshold for continuing collision detection before pass judge line
occlusion.occlusion_attention_area_length	double	[m] the length of attention are for occlusion detection
occlusion.enable_creeping	bool	[-] flag to insert occlusion_creep_velocity while peeking to intersection occlusion stopline
occlusion.peeking_offset	double	[m] the offset of the front of the vehicle into the attention area for peeking to occlusion
occlusion.min_vehicle_brake_for_rss	double	[m/s] assumed minimum brake of the vehicle running from behind the occlusion
occlusion.max_vehicle_velocity_for_rss	double	[m/s] assumed maximum velocity of the vehicle running from behind the occlusion

For developers only

Parameter	Type	Description
common.path_interpolation_ds	double	[m] path interpolation interval
occlusion.denoise_kernel	double	[m] the window size of morphology process for clearing noisy occlusion

How to turn parameters

WIP

Flowchart

WIP

Merge From Private

Role

When an ego vehicle enters a public road from a private road (e.g. a parking lot), it needs to face and stop before entering the public road to make sure it is safe.

This module is activated when there is an intersection at the private area from which the vehicle enters the public road. The stop line is generated both when the goal is in the intersection lane and when the path goes beyond the intersection lane. The basic behavior is the same as the intersection module, but the ego vehicle must stop once at the stop line.

Activation Timing

This module is activated when the following conditions are met:

• ego-lane has a private tag
• ego-lane has a conflict with other no-private lanelets

Module Parameters

Parameter	Type	Description
merge_from_private_road/stop_duration_sec	double	[m] time margin to change state

Known Issue

If ego vehicle go over the stop line for a certain distance, then ego vehicle will not transit from STOP.