AIB-MDP: Continuous Probabilistic Motion Planning for Automated Vehicles by Leveraging Action Independent Belief Spaces

While automated research vehicles are already populating the roads, their commercial availability at scale is still to come. Presumably, one of the key challenges is to derive behaviors that are safe and comfortable but at the same time not overcautious, despite considerable uncertainties. These uncertainties stem from imperfect perception, occlusions and limited sensor range, but also from the unknown future behavior of other traffic participants. A holistic uncertainty treatment, for example in a general POMDP formulation, often induces a strong limitation on the action space due to the need for real-time capability. Further, related approaches often do not account for the need for verifiable safety, including traffic rule compliance. The proposed approach is targeted towards scenarios with clear precedence. It is based on an MDP with an action-independent belief (AIB-MDP): We assume that the future belief over the trajectories of other traffic participants is independent of the ego vehicle's behavior. Thus, the future belief can be predicted and simplified in an upstream module, independent of motion planning. This modularization facilitates subsequent ego motion planning in a continuous action space despite the thorough uncertainty consideration. The improved performance compared to state-of-the-art is demonstrated in three example scenarios.