Emergency vehicles require rapid passage through congested traffic, yet existing strategies fail to adapt to dynamic conditions. We propose a novel hierarchical graph neural network (GNN)-based multi-agent reinforcement learning framework to coordinate connected vehicles for emergency corridor formation. Our approach uses a high-level planner for global strategy and low-level controllers for trajectory execution, utilizing graph attention networks to scale with variable agent counts. Trained via Multi-Agent Proximal Policy Optimization (MAPPO), the system reduces emergency vehicle travel time by 28.3% compared to baselines and 44.6% compared to uncoordinated traffic in simulations. The design achieves near-zero collision rates (0.3%) while maintaining 81% of background traffic efficiency. Ablation and generalization studies confirm the framework's robustness across diverse scenarios. These results demonstrate the effectiveness of combining GNNs with hierarchical learning for intelligent transportation systems.

翻译：应急车辆需在拥堵交通中快速通行，然而现有策略难以适应动态条件。本文提出一种新颖的基于分层图神经网络（GNN）的多智能体强化学习框架，通过协调网联车辆形成应急通道。该方法采用高层规划器制定全局策略，并利用低层控制器执行轨迹规划，借助图注意力网络实现可变智能体数量的可扩展性。通过多智能体近端策略优化（MAPPO）训练，该系统在仿真中使应急车辆行程时间较基线方法减少28.3%，较无协调交通减少44.6%。该设计在保持背景交通81%通行效率的同时，实现了接近零的碰撞率（0.3%）。消融实验与泛化研究证实了该框架在多场景下的鲁棒性。这些结果表明，将GNN与分层学习相结合可为智能交通系统提供有效解决方案。