A Value Based Parallel Update MCTS Method for Multi-Agent Cooperative Decision Making of Connected and Automated Vehicles
This addresses multi-agent cooperative decision-making for CAVs, offering incremental improvements in traffic flow optimization.
The paper tackled the problem of joint lateral and longitudinal decision-making for connected and automated vehicles (CAVs) in multi-vehicle cooperative driving by proposing a Monte Carlo tree search (MCTS) method with parallel update. The results showed that the algorithm outperformed state-of-the-art reinforcement learning and heuristic methods, with advantages in traffic efficiency and safety, and demonstrated rationality beyond human drivers.
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.