提出并行更新MCTS方法，提升多车协同决策效率与安全性。
A Value Based Parallel Update MCTS Method for Multi-Agent Cooperative Decision Making of Connected and Automated Vehicles
- 基于并行动作分析，动态排除危险行为以加速搜索
- 在随机生成交通流中表现优于前沿强化学习算法
- 适合智能网联车协同控制场景，提升通行效率与安全
针对智能网联汽车（CAVs）多车协同驾驶中的横向与纵向联合决策问题，本文提出一种适用于有限时域和时间折扣设定的多智能体马尔可夫博弈的蒙特卡洛树搜索（MCTS）并行更新方法。通过分析部分稳态交通流中多车联合动作空间内的并行行为，该方法能快速排除潜在危险动作，从而在不牺牲搜索广度的前提下提升搜索深度。在大量随机生成的交通流场景下进行测试，实验结果表明，该算法具备良好鲁棒性，性能优于当前最优的强化学习算法和启发式方法。采用该算法的车辆驾驶策略表现出超越人类驾驶员的合理性，在协调区域具有更高的交通效率与安全性。
原文摘要 · Abstract (English)
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.
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