To solve the problem of lateral and logitudinal joint decision-making of multi-vehicle cooperative driving for connected and automated vehicles (CAVs), this paper proposes a Monte Carlo tree search (MCTS) method with parallel update for multi-agent Markov game with limited horizon and time discounted setting. By analyzing the parallel actions in the multi-vehicle joint action space in the partial-steady-state traffic flow, the parallel update method can quickly exclude potential dangerous actions, thereby increasing the search depth without sacrificing the search breadth. The proposed method is tested in a large number of randomly generated traffic flow. The experiment results show that the algorithm has good robustness and better performance than the SOTA reinforcement learning algorithms and heuristic methods. The vehicle driving strategy using the proposed algorithm shows rationality beyond human drivers, and has advantages in traffic efficiency and safety in the coordinating zone.

翻译：为解决网联自动驾驶车辆多车协同驾驶的横向与纵向联合决策问题，本文针对有限时域且带时间折扣因子的多智能体马尔可夫博弈，提出一种采用并行更新的蒙特卡洛树搜索方法。通过分析部分稳态交通流中多车联合动作空间内的并行动作，该并行更新方法能够快速排除潜在的危险动作，从而在不牺牲搜索广度的前提下增加搜索深度。所提方法在大量随机生成的交通流中进行了测试。实验结果表明，该算法具有良好的鲁棒性，且性能优于当前最先进的强化学习算法与启发式方法。采用该算法的车辆驾驶策略展现出超越人类驾驶员的合理性，并在协调区域内具有交通效率与安全性方面的优势。