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The implementation of optimization-based motion coordination approaches inreal world multi-agent systems remains challenging due to their highcomputational complexity and potential deadlocks. This paper presents adistributed model predictive control (MPC) approach based on convex feasibleset (CFS) algorithm for multi-vehicle motion coordination in autonomousdriving. By using CFS to convexify the collision avoidance constraints,collision-free trajectories can be computed in real time. We analyze thepotential deadlocks and show that a deadlock can be resolved by changingvehicles' desired speeds. The MPC structure ensures that our algorithm isrobust to low-level tracking errors. The proposed distributed method has beentested in multiple challenging multi-vehicle environments, includingunstructured road, intersection, crossing, platoon formation, merging, andovertaking scenarios. The numerical results and comparison with otherapproaches (including a centralized MPC and reciprocal velocity obstacles) showthat the proposed method is computationally efficient and robust, and avoidsdeadlocks.

Distributed Motion Coordination Using Convex Feasible Set Based Model Predictive Control