  The implementation of optimization-based motion coordination approaches in
real world multi-agent systems remains challenging due to their high
computational complexity and potential deadlocks. This paper presents a
distributed model predictive control (MPC) approach based on convex feasible
set (CFS) algorithm for multi-vehicle motion coordination in autonomous
driving. By using CFS to convexify the collision avoidance constraints,
collision-free trajectories can be computed in real time. We analyze the
potential deadlocks and show that a deadlock can be resolved by changing
vehicles' desired speeds. The MPC structure ensures that our algorithm is
robust to low-level tracking errors. The proposed distributed method has been
tested in multiple challenging multi-vehicle environments, including
unstructured road, intersection, crossing, platoon formation, merging, and
overtaking scenarios. The numerical results and comparison with other
approaches (including a centralized MPC and reciprocal velocity obstacles) show
that the proposed method is computationally efficient and robust, and avoids
deadlocks.
