用图神经网络让车辆自动为救护车开道，提速超四成。
Hierarchical GNN-Based Multi-Agent Learning for Dynamic Queue-Jump Lane and Emergency Vehicle Corridor Formation
- 分层设计：高层规划全局策略，底层控制执行路径
- 仿真中救护车通行时间减少28.3%，比无协同快44.6%
- 可适应不同车数，碰撞率仅0.3%，背景交通效率仍达81%
紧急车辆需快速通过拥堵路段，现有策略难以适应动态交通。本文提出一种基于分层图神经网络（GNN）的多智能体强化学习框架，协调联网车辆形成应急通道。系统采用高层规划器制定全局策略，低层控制器执行轨迹，利用图注意力网络实现可扩展性。通过多智能体近端策略优化（MAPPO）训练，在仿真中相比基线降低28.3%的紧急车辆通行时间，比非协同交通快44.6%。系统碰撞率接近零（0.3%），同时保持81%的背景交通效率。消融实验与泛化测试验证了框架在多样场景下的鲁棒性。结果表明，结合GNN与分层学习对智能交通系统具有显著效能。
原文摘要 · Abstract (English)
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.
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