Occlusion-Aware Motion Planning for Autonomous Driving
Denggui Wang, Weiping Fu, Jincao Zhou, Qingyuan Song
Abstract
Motion planning of autonomous vehicles is still challenging in urban road environments with occlusions. In this work, with the view of safety, comfort and efficiency, we present a motion planning framework that enables autonomous vehicles to navigate safely in urban road with occlusions. Our solution mainly includes three parts: local path planning, trajectory planning and speed planning. First, based on the improved Artificial Potential Field to generate the local path, then the optimal trajectory is solved in the S-L coordinate with the local path as the reference line. Finally, the potential risk probability of the occluded area is incorporated into the incomplete information static game framework and implement speed planning based on the game results and the proposed vehicle "safe driving" to complete the collision avoidance between the autonomous vehicle and visible or obscured dynamic traffic participants. Through simulation verification in common traffic scenarios and a comparison with some existing methods, the proposed method is proven to enable autonomous vehicles to navigate safely in traffic scenarios with occlusions and improve the driving efficiency and comfort of autonomous vehicles.