Occlusion-Aware Planning for Autonomous Driving With Vehicle-to-Everything Communication

Navigating safely through occlusion scenarios remains challenging for Autonomous Vehicles (AVs) due to onboard sensors with obstructed Fields of View (FoVs). Integrating Vehicle-to-Everything (V2X) communication with AVs is beneficial since it provides information beyond the onboard sensors' FoVs. To achieve safe driving behaviors in occlusion scenarios, we present a Partially Observable Markov Decision Process (POMDP) behavior planner enhanced with V2X communication. Our approach leverages the perception data from onboard sensors and V2X communications independently, eliminating the need for fusing them. The planner first employs onboard sensors to identify the occlusion areas. Then, it generates phantom road users within those areas to represent and consider the collision risk of potentially occluded real road users. Following this, we introduce a V2X communication module to provide the most promising detection result in the occluded area, taking factors like observation area coverage, communication latency, and sensor reliability into account. The detection result is subsequently applied to enhance presence and movement estimations for the phantom road users. Lastly, the detected real objects and phantom road users are integrated into the state space of a POMDP planner to provide safe driving policies. Various qualitative and quantitative evaluations demonstrate that our approach delivers safer, more efficient, and more comfortable driving policies in challenging occlusion scenarios when compared to the baseline method, which uses only onboard sensors, and the method that fuses onboard and V2X perceptions.