  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.
