A POMDP Maneuver Planner For Occlusions in Urban Scenarios

Behavior planning in urban environments must consider the various existing uncertainties in an explicit way. This work proposes a behavior planner, based on a POMDP formulation, that explicitly considers possibly occluded vehicles. The future field of view of the autonomous car is predicted over the whole planning horizon. Both, occlusions which are generated by static as well as generated by dynamic objects are hereby considered. We use Monte Carlo sampling to generate possible future episodes that are used to derive an optimized policy. The sampled episodes consider the uncertain behavior of the known traffic participants as well as the existence probability of so-called phantom vehicles in occluded areas. By representing all possible, occluded vehicle configurations by its reachable set instead of single particles, a very efficient representation is found. Therefore, we ensure to consider all possible configurations which may drive out of the occluded area in our optimized policy. We propose a generic formulation of the POMDP problem that can be applied to various scenarios for urban driving. Its performance is demonstrated by using simulation scenarios at intersections including multiple vehicles and occlusions caused by static and dynamic objects. It is shown, that the autonomous vehicle approaches occluded areas by far less conservative than a baseline strategy which considers only the current field of view (fov). This is because various, future scenarios are already considered in the policy. In fact, we show that our planner is able to drive nearly the same trajectories as an omniscient planner would.