The ever-increasing heavy traffic congestion potentially impedes the accessibility of emergency vehicles (EVs), resulting in detrimental impacts on critical services and even safety of people's lives. Hence, it is significant to propose an efficient scheduling approach to help EVs arrive faster. Existing vehicle-centric scheduling approaches aim to recommend the optimal paths for EVs based on the current traffic status while the road-centric scheduling approaches aim to improve the traffic condition and assign a higher priority for EVs to pass an intersection. With the intuition that real-time vehicle-road information interaction and strategy coordination can bring more benefits, we propose LEVID, a LEarning-based cooperative VehIcle-roaD scheduling approach including a real-time route planning module and a collaborative traffic signal control module, which interact with each other and make decisions iteratively. The real-time route planning module adapts the artificial potential field method to address the real-time changes of traffic signals and avoid falling into a local optimum. The collaborative traffic signal control module leverages a graph attention reinforcement learning framework to extract the latent features of different intersections and abstract their interplay to learn cooperative policies. Extensive experiments based on multiple real-world datasets show that our approach outperforms the state-of-the-art baselines.
Emergency Lane-Change Simulation: A Behavioral Guidance Approach for Risky Scenario Generation
School of Intelligent Systems Engineering, Sun Yat-sen University（中山大学智能系统工程学院）；Guangdong Provincial Key Laboratory of Intelligent Transportation System, Sun Yat-sen University（广东省智能交通系统重点实验室）；Department of Civil and Environmental Engineering, The Pennsylvania State University（宾夕法尼亚州立大学土木与环境工程系）；School of Traffic Engineering and the Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University（同济大学交通工程学院及交通工程重点实验室）
