Hope for the Best, Prepare for the Worst: Occlusion-Aware Contingency Planning for Autonomous Vehicles

The deployment of autonomous vehicles in urban environments introduces significant safety challenges, particularly in scenarios with occlusions, where critical traffic participants may be hidden from view. Recent accidents involving driverless vehicles highlight the importance of motion planners that explicitly addresses the risks posed by occlusions. In this work, we propose a formal, occlusion-aware trajectory planning framework that guarantees collision avoidance even when there are possible hidden traffic participants. Building on our previous methods that apply reachability analysis to sequentially determine the possible states of hidden traffic participants, we integrate a tree-based motion planner capable of reasoning over future observations and the absence thereof. This approach reduces conservativeness while maintaining safety guarantees. We demonstrate the effectiveness of our framework in a challenging simulated occluded scenario, showing that it pro-actively and efficiently guarantees collision-avoidance.