Distributed Motion Coordination Using Convex Feasible Set Based Model Predictive Control
This addresses motion coordination for autonomous vehicles, offering a distributed solution to reduce computational complexity and prevent deadlocks, though it appears incremental as it builds on existing MPC and convexification methods.
The paper tackles the challenge of real-time motion coordination for multi-vehicle autonomous driving by proposing a distributed model predictive control approach using convex feasible sets, which enables collision-free trajectory computation and deadlock resolution, showing computational efficiency and robustness in various scenarios.
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.