  Autonomous vehicles interacting with other traffic participants heavily rely
on the perception and prediction of other agents' behaviors to plan safe
trajectories. However, as occlusions limit the vehicle's perception ability,
reasoning about potential hazards beyond the field of view is one of the most
challenging issues in developing autonomous driving systems. This paper
introduces a novel analytical approach that poses safe trajectory planning
under occlusions as a hybrid zero-sum dynamic game between the autonomous
vehicle (evader) and an initially hidden traffic participant (pursuer). Due to
occlusions, the pursuer's state is initially unknown to the evader and may
later be discovered by the vehicle's sensors. The analysis yields optimal
strategies for both players as well as the set of initial conditions from which
the autonomous vehicle is guaranteed to avoid collisions. We leverage this
theoretical result to develop a novel trajectory planning framework for
autonomous driving that provides worst-case safety guarantees while minimizing
conservativeness by accounting for the vehicle's ability to actively avoid
other road users as soon as they are detected in future observations. Our
framework is agnostic to the driving environment and suitable for various
motion planners. We demonstrate our algorithm on challenging urban and highway
driving scenarios using the open-source CARLA simulator.
