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Autonomous vehicles interacting with other traffic participants heavily relyon the perception and prediction of other agents' behaviors to plan safetrajectories. However, as occlusions limit the vehicle's perception ability,reasoning about potential hazards beyond the field of view is one of the mostchallenging issues in developing autonomous driving systems. This paperintroduces a novel analytical approach that poses safe trajectory planningunder occlusions as a hybrid zero-sum dynamic game between the autonomousvehicle (evader) and an initially hidden traffic participant (pursuer). Due toocclusions, the pursuer's state is initially unknown to the evader and maylater be discovered by the vehicle's sensors. The analysis yields optimalstrategies for both players as well as the set of initial conditions from whichthe autonomous vehicle is guaranteed to avoid collisions. We leverage thistheoretical result to develop a novel trajectory planning framework forautonomous driving that provides worst-case safety guarantees while minimizingconservativeness by accounting for the vehicle's ability to actively avoidother road users as soon as they are detected in future observations. Ourframework is agnostic to the driving environment and suitable for variousmotion planners. We demonstrate our algorithm on challenging urban and highwaydriving scenarios using the open-source CARLA simulator.

Safe Occlusion-aware Autonomous Driving via Game-Theoretic Active Perception