Emergency vehicle signal priority control method for arterial intersections in an intelligent connected environment

Engineering notes

Simulation experiments based on the Zhengzhou Longhu Autonomous Driving Test Zone show that STG-MAPPO achieves better EV progression efficiency, lower general-traffic delay, and improved safety indicators than fixed-time control, actuated control, rule-based EV priority, DDQN, IPPO, and MAPPO.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Emergency vehicle (EV) progression at consecutive arterial intersections in an intelligent connected environment is often disrupted by upstream queues, downstream spillback, and mismatches in multi-intersection signal coordination. To address this problem, this study proposes a spatiotemporal graph multi-agent proximal policy optimization (STG-MAPPO) method for EV signal priority control. The proposed method models consecutive signalized intersections as cooperative agents, incorporates EV arrival prediction, target-lane yielding capacity, and downstream blockage risk into the state representation, and uses a spatiotemporal graph encoder to capture both topological coupling and traffic-state propagation among intersections. Heterogeneous yielding responses of connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) are explicitly considered in the target-lane yielding model. Simulation experiments based on the Zhengzhou Longhu Autonomous Driving Test Zone show that STG-MAPPO achieves better EV progression efficiency, lower general-traffic delay, and improved safety indicators than fixed-time control, actuated control, rule-based EV priority, DDQN, IPPO, and MAPPO. Under the specified SUMO-based single-EV simulation conditions, the proposed method reduces EV travel time, EV delay, and EV stop frequency by 22.28%, 51.02%, and 60.00%, respectively, compared with DDQN, and by 8.54%, 22.58%, and 33.33%, respectively, compared with MAPPO.

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