Spatiotemporal motion planning with combinatorial reasoning for autonomous driving

Motion planning for urban environments with numerous moving agents can be viewed as a combinatorial problem. With passing an obstacle before, after, right or left, there are multiple options an autonomous vehicle could choose to execute. These combinatorial aspects need to be taken into account in the planning framework. We address this problem by proposing a novel planning approach that combines trajectory planning and maneuver reasoning. We define a classification for dynamic obstacles along a reference curve that allows us to extract tactical decision sequences. We separate longitudinal and lateral movement to speed up the optimization-based trajectory planning. To map the set of obtained trajectories to maneuver variants, we define a semantic language to describe them. This allows us to choose an optimal trajectory while also ensuring maneuver consistency over time. We demonstrate the capabilities of our approach for a scenario that is still widely considered to be challenging.