论文标题
建模自适应排和基于预订的自主交叉控制：一种深入的增强学习方法
Modeling Adaptive Platoon and Reservation Based Autonomous Intersection Control: A Deep Reinforcement Learning Approach
论文作者
论文摘要
作为减少行进延迟并提高能源效率的策略，在非信号交叉点上连接和自动驾驶汽车（CAVS）的排在学术界越来越流行。但是，很少有研究试图建模最佳排大小与交叉路口周围的交通状况之间的关系。为此，这项研究提出了一个基于自主排的基于自主的交叉控制模型，该模型由深钢筋学习（DRL）技术提供支持。该模型框架具有以下两个级别：第一级采用了与非冲突的车道选择机制集成的基于第一服务（FCFS）的策略，以确定车辆的通过优先级；第二层采用深Q网络算法来根据交叉路口的实时交通状况来识别最佳排尺寸。在交通微模拟器上进行测试时，我们提出的模型与最先进的方法相比，在旅行效率和燃料保护方面表现出卓越的性能。
As a strategy to reduce travel delay and enhance energy efficiency, platooning of connected and autonomous vehicles (CAVs) at non-signalized intersections has become increasingly popular in academia. However, few studies have attempted to model the relation between the optimal platoon size and the traffic conditions around the intersection. To this end, this study proposes an adaptive platoon based autonomous intersection control model powered by deep reinforcement learning (DRL) technique. The model framework has following two levels: the first level adopts a First Come First Serve (FCFS) reservation based policy integrated with a nonconflicting lane selection mechanism to determine vehicles' passing priority; and the second level applies a deep Q-network algorithm to identify the optimal platoon size based on the real-time traffic condition of an intersection. When being tested on a traffic micro-simulator, our proposed model exhibits superior performances on travel efficiency and fuel conservation as compared to the state-of-the-art methods.
