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走廊。该框架采用高层规划器制定全局策略，低层控制器执行轨迹规划，并利用图注意力网络实现可变智能体数量的扩展性。通过多智能体近端策略优化（MAPPO）训练，该系统在仿真中将应急车辆通行时间较基准方法减少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验证了该框架在不同场景下的鲁棒性。这些结果证明了将图神经网络与分层学习相结合在智能交通系统中的有效性。