Occlusion-Aware Path Planning for Collision Avoidance: Leveraging Potential Field Method with Responsibility-Sensitive Safety

Collision avoidance (CA) has always been the foremost task for autonomous vehicles (AVs) under safety criteria. And path planning is directly responsible for generating a safe path to accomplish CA while satisfying other commands. Due to the real-time computation and simple structure, the potential field (PF) has emerged as one of the mainstream path-planning algorithms. However, the current PF is primarily simulated in ideal CA scenarios, assuming complete obstacle information while disregarding occlusion issues where obstacles can be partially or entirely hidden from the AV's sensors. During the occlusion period, the occluded obstacles do not possess a PF. Once the occlusion is over, these obstacles can generate an instantaneous virtual force that impacts the ego vehicle. Therefore, we propose an occlusion-aware path planning (OAPP) with the responsibility-sensitive safety (RSS)-based PF to tackle the occlusion problem for non-connected AVs. We first categorize the detected and occluded obstacles, and then we proceed to the RSS violation check. Finally, we can generate different virtual forces from the PF for occluded and non-occluded obstacles. We compare the proposed OAPP method with other PF -based path planning methods via MATLAB/Simulink. The simulation results indicate that the proposed method can eliminate instantaneous lateral oscillation or sway and produce a smoother path than conventional PF methods.

I. Introduction

Annually, approximately 1.3 million individuals tragically lose their lives due to road traffic crashes, while an additional 20 to 50 million people sustain non-fatal injuries, often resulting in long-term disabilities [1]. Hence, autonomous vehicles (AVs) have been proposed to mitigate traffic injuries, leading to a safer driving environment. Collision avoidance (CA) is currently one of the most challenging tasks faced by AVs, specifically associated with the AV system's planning layer. Path planning in AVs involves the task of identifying an optimal and collision-free route that allows the vehicle to navigate through traffic while ensuring safety, comfort, and efficiency. Therefore, many excellent algorithms have been presented to complete the path-planning task [2], [3], including the rapidly-exploring random tree (RRT), A-star (A*), dynamic window approach (DWA), potential field (PF), etc.