Hope for the Best, Prepare for the Worst: Occlusion-Aware Contingency Planning for Autonomous Vehicles
2026-07-03 • Robotics
Robotics
AI summaryⓘ
The authors developed a new method to help self-driving cars safely navigate city streets where other vehicles or pedestrians might be hidden from view. Their system predicts where these hidden people or cars could be and plans routes that avoid crashes even if something suddenly appears. By using a special type of planning that considers what the car might or might not see in the future, their approach is safer without being overly cautious. They tested it in a simulated situation with blind spots and showed it works well to prevent accidents.
autonomous vehiclesocclusionstrajectory planningcollision avoidancemotion plannerreachability analysisurban environmenthidden traffic participantstree-based plannersimulation
Authors
Truls Nyberg, Anna Gautier, Jana Tumova
Abstract
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.