map_based_prediction

Role

map_based_prediction is a module to predict the future paths of other vehicles and pedestrians according to the shape of the map and the surrounding environment.

Inner-workings / Algorithms

1. Get lanelet path The first step is to get the lanelet of the current position of the car. After that, we obtain several trajectories based on the map.

2. Lane Change Detection After finding the current lanelet from the current position of the obstacle, our algorithm try to detect the lane change maneuver from the past positions of the obstacle. Our method uses the deviation between the obstacle's current position and its position one second ago and current position to determine if it is about to change lanes. The parameters used for the lane change decision are obtained by analyzing the data obtained from the experiment. We already confirmed that these parameters give the least number of false positives.

3. Confidence calculation We use the following metric to compute the distance to a certain lane.

   d = x^T P x
   

   where x=[lateral_dist, yaw_diff] and P are covariance matrices. Therefore confidence values can be computed as

   confidence = 1/d
   

   Finally, we normalize the confidence value to make it as probability value. Note that the standard deviation of the lateral distance and yaw difference is given by the user.

4. Drawing predicted trajectories From the current position and reference trajectories that we get in the step1, we create predicted trajectories by using Quintic polynomial. Note that, since this algorithm consider lateral and longitudinal motions separately, it sometimes generates dynamically-infeasible trajectories when the vehicle travels at a low speed. To deal with this problem, we only make straight line predictions when the vehicle speed is lower than a certain value (which is given as a parameter).

Inputs / Outputs

Input

Name	Type	Description
~/perception/object_recognition/tracking/objects	autoware_auto_perception_msgs::msg::TrackedObjects	tracking objects without predicted path.
~/vector_map	autoware_auto_mapping_msgs::msg::HADMapBin	binary data of Lanelet2 Map.

Output

Name	Type	Description
~/objects	autoware_auto_perception_msgs::msg::PredictedObjects	tracking objects with predicted path.
~/objects_path_markers	visualization_msgs::msg::MarkerArray	marker for visualization.

Parameters

Parameter	Type	Description
prediction_time_horizon	double	predict time duration for predicted path [s]
prediction_sampling_delta_time	double	sampling time for points in predicted path [s]
min_velocity_for_map_based_prediction	double	apply map-based prediction to the objects with higher velocity than this value
dist_threshold_for_searching_lanelet	double	The threshold of the angle used when searching for the lane to which the object belongs [rad]
delta_yaw_threshold_for_searching_lanelet	double	The threshold of the distance used when searching for the lane to which the object belongs [m]
sigma_lateral_offset	double	Standard deviation for lateral position of objects [m]
sigma_yaw_angle	double	Standard deviation yaw angle of objects [rad]
history_time_length	double	Time span of object information used for prediction [s]
dist_ratio_threshold_to_left_bound	double	Conditions for using lane change detection of objects. Distance to the left bound of lanelet.
dist_ratio_threshold_to_right_bound	double	Conditions for using lane change detection of objects. Distance to the right bound of lanelet.
diff_dist_threshold_to_left_bound	double	Conditions for using lane change detection of objects. Differential value of horizontal position of objects.
diff_dist_threshold_to_right_bound	double	Conditions for using lane change detection of objects. Differential value of horizontal position of objects.

Assumptions / Known limits

map_based_prediction can only predict future trajectories for cars, tracks and buses.

Reference

1. M. Werling, J. Ziegler, S. Kammel, and S. Thrun, “Optimal trajectory generation for dynamic street scenario in a frenet frame,” IEEE International Conference on Robotics and Automation, Anchorage, Alaska, USA, May 2010.
2. A. Houenou, P. Bonnifait, V. Cherfaoui, and Wen Yao, “Vehicle trajectory prediction based on motion model and maneuver recognition,” in 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, nov 2013, pp. 4363–4369.