论文标题
在信号交叉点上的电动连接车辆的生态驾驶：一种参数化的增强学习方法
Eco-driving for Electric Connected Vehicles at Signalized Intersections: A Parameterized Reinforcement Learning approach
论文作者
论文摘要
本文提出了基于增强学习（RL）的电动连接车辆（CVS）的生态驾驶框架，以提高信号交叉点的车辆能效。通过整合基于型号的汽车的政策，改变车道的政策和RL政策，以确保简历的安全操作来指定车辆代理。随后，制定了马尔可夫决策过程（MDP），这使车辆能够执行纵向控制和横向决策，从而共同优化了交叉口附近CVS的CAR跟踪和改变车道的行为。然后，将混合动作空间参数化为层次结构，从而在动态的交通环境中使用二维运动模式训练代理。最后，我们所提出的方法从基于单车的透视图和基于流的透视图中在Sumo软件中进行了评估。结果表明，我们的策略可以通过学习适当的动作方案而不会中断其他人类驱动的车辆（HDV）来大大减少能源消耗。
This paper proposes an eco-driving framework for electric connected vehicles (CVs) based on reinforcement learning (RL) to improve vehicle energy efficiency at signalized intersections. The vehicle agent is specified by integrating the model-based car-following policy, lane-changing policy, and the RL policy, to ensure safe operation of a CV. Subsequently, a Markov Decision Process (MDP) is formulated, which enables the vehicle to perform longitudinal control and lateral decisions, jointly optimizing the car-following and lane-changing behaviors of the CVs in the vicinity of intersections. Then, the hybrid action space is parameterized as a hierarchical structure and thereby trains the agents with two-dimensional motion patterns in a dynamic traffic environment. Finally, our proposed methods are evaluated in SUMO software from both a single-vehicle-based perspective and a flow-based perspective. The results show that our strategy can significantly reduce energy consumption by learning proper action schemes without any interruption of other human-driven vehicles (HDVs).
