论文标题
v2i基于连接性的动态队列跳跃车道，用于紧急车辆：一种深厚的增强学习方法
V2I Connectivity-Based Dynamic Queue-Jump Lane for Emergency Vehicles: A Deep Reinforcement Learning Approach
论文作者
论文摘要
紧急车辆（EMV）服务是城市的关键功能，由于城市交通拥堵而极具挑战性。 EMV服务延迟背后的主要原因是车辆之间缺乏沟通和合作，阻止EMV。在本文中，我们研究了V2I连接性EMV服务的改进。我们考虑建立基于连接车辆的实时协调的动态队列跳跃车道（DQJL）。我们为DQJL问题开发了一种新颖的马尔可夫决策过程制定，该过程明确解释了驾驶员对接近EMV的反应的不确定性。我们提出了一种基于神经网络的深度增强学习算法，该学习算法有效地计算最佳协调指示。我们还使用城市迁移率模拟（SUMO）验证了微模拟测试床上的方法。验证结果表明，借助我们提出的方法，集中式控制系统比基准系统节省了大约15 \％EMV的时间。
Emergency vehicle (EMV) service is a key function of cities and is exceedingly challenging due to urban traffic congestion. A main reason behind EMV service delay is the lack of communication and cooperation between vehicles blocking EMVs. In this paper, we study the improvement of EMV service under V2I connectivity. We consider the establishment of dynamic queue jump lanes (DQJLs) based on real-time coordination of connected vehicles. We develop a novel Markov decision process formulation for the DQJL problem, which explicitly accounts for the uncertainty of drivers' reaction to approaching EMVs. We propose a deep neural network-based reinforcement learning algorithm that efficiently computes the optimal coordination instructions. We also validate our approach on a micro-simulation testbed using Simulation of Urban Mobility (SUMO). Validation results show that with our proposed methodology, the centralized control system saves approximately 15\% EMV passing time than the benchmark system.
