Real-time Cooperative Motion Planning using Efficient Model Predictive Contouring Control

Jan-Hendrik Pauls, Mario Boxheimer, Christoph Stiller

Abstract: Currently, there is a gap in motion planning approaches. On the one hand, there are optimization-based motion planning techniques which can guarantee safety and feasibility, but are either slow and cooperative or fast and uncooperative. On the other hand, there are learned approaches that are fast and cooperative, but cannot give these desirable guarantees.We propose to combine model predictive contouring control (MPCC) with sophisticated collision avoidance formulations to bridge this gap. By optimizing the total utility of all traffic participants, a cooperative, safe, and feasible trajectory can be planned in real time.Examination of various collision avoidance constraints allows to obtain considerate trajectories while preserving real-time capabilities. A novel inter-stage constraint formulation allows to introduce time-based distance measures in time-discretized MPC formulations.We evaluate the resulting motion planner in various scenarios, comparing two state-of-the-art solvers.