标题： 基于数字孪生的智能交叉口协同自动驾驶：一种多智能体强化学习方法 显示英文标题

标题： Digital Twin-based Cooperative Autonomous Driving in Smart Intersections: A Multi-Agent Reinforcement Learning Approach

摘要： 无信号交叉口由于复杂的交通流和盲区而带来安全和效率的挑战。 在本文中，提出了一种基于数字孪生（DT）的合作驾驶系统，采用路边单元（RSU）中心架构，以提高无信号交叉口的安全性和效率。 该系统利用全面的鸟瞰图（BEV）感知来消除盲区，并采用结合离线预训练与在线微调的混合强化学习（RL）框架。 具体而言，驾驶策略首先使用保守Q学习（CQL）结合行为克隆（BC）在真实数据集上进行训练，然后通过多智能体近端策略优化（MAPPO）结合自注意力机制进行微调，以处理动态多智能体协调问题。 RSU通过车对基础设施（V2I）通信实现实时指令。 实验结果表明，所提出的方法在协调最多三辆联网自动驾驶汽车（CAVs）时，故障率低于0.03%，显著优于传统方法。 此外，该系统在推理时间低于40毫秒的情况下表现出次线性的计算扩展性。 此外，它在各种无信号交叉口场景中表现出强大的泛化能力，表明其在实际应用中的可行性和部署准备情况。 显示英文摘要

摘要： Unsignalized intersections pose safety and efficiency challenges due to complex traffic flows and blind spots. In this paper, a digital twin (DT)-based cooperative driving system with roadside unit (RSU)-centric architecture is proposed for enhancing safety and efficiency at unsignalized intersections. The system leverages comprehensive bird-eye-view (BEV) perception to eliminate blind spots and employs a hybrid reinforcement learning (RL) framework combining offline pre-training with online fine-tuning. Specifically, driving policies are initially trained using conservative Q-learning (CQL) with behavior cloning (BC) on real datasets, then fine-tuned using multi-agent proximal policy optimization (MAPPO) with self-attention mechanisms to handle dynamic multi-agent coordination. The RSU implements real-time commands via vehicle-to-infrastructure (V2I) communications. Experimental results show that the proposed method yields failure rates below 0.03\% coordinating up to three connected autonomous vehicles (CAVs), significantly outperforming traditional methods. In addition, the system exhibits sub-linear computational scaling with inference times under 40 ms. Furthermore, it demonstrates robust generalization across diverse unsignalized intersection scenarios, indicating its practicality and readiness for real-world deployment.