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.

中文翻译：

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基于凸可行集的模型预测控制的分布式运动协调

由于它们的高计算复杂性和潜在的死锁，在现实世界中的多主体系统中基于优化的运动协调方法的实施仍然具有挑战性。本文提出了一种基于凸可行集（CFS）算法的分布式模型预测控制（MPC）方法，用于自动驾驶中的多车辆运动协调。通过使用CFS凸显避免碰撞约束，可以实时计算无碰撞轨迹。我们分析了潜在的死锁，并表明可以通过更改车辆的所需速度来解决死锁。MPC结构可确保我们的算法对低级跟踪错误具有鲁棒性。提议的分布式方法已在多种具有挑战性的多车环境中进行了测试，包括非结构化道路，十字路口，人行横道，排的形成，合并和超车场景。数值结果及与其他方法（包括集中式MPC和速度互易障碍物）的比较表明，该方法计算效率高，鲁棒性好，并且避免了死锁。