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.

翻译：碰撞规避（CA）一直是自动驾驶汽车（AV）在安全准则下的首要任务，而路径规划直接负责生成安全路径以完成CA并满足其他指令需求。由于具备实时计算和结构简单的优势，势场法（PF）已成为主流路径规划算法之一。然而，当前PF主要模拟理想CA场景，假设具备完整障碍物信息，忽视了障碍物可能部分或完全被AV传感器遮挡的遮挡问题。在遮挡期间，被遮挡的障碍物不产生势场；一旦遮挡解除，这些障碍物会瞬间产生虚拟力冲击自车。为此，我们提出基于责任敏感安全（RSS）的遮挡感知路径规划（OAPP），通过改进PF解决非联网AV的遮挡问题。首先对已检测障碍物和被遮挡障碍物进行分类，然后进行RSS违规检查，最终针对被遮挡与未被遮挡障碍物生成不同的PF虚拟力。通过MATLAB/Simulink将所提OAPP方法与其他基于PF的路径规划方法对比，仿真结果表明，该方法能消除瞬时横向振荡或摆动，并生成比传统PF方法更平滑的路径。