A Deep Reinforcement Learning Based Car Following Model for Electric Vehicle

• 作者: Bin Ran Huachun Tan Jiankun Peng Yuankai Wu
• 来源期刊: 智能城市应用
• 学科: 工学
• 关键词: AUTONOMOUS electric vehicle car following model DEEP REINFORCEMENT learning TRUST region policy optimization
• 年，卷（期）: 2019,（5）
• 页码范围: 1-8
• 页数: 8页
• 分类号: TP

摘要

Car following (CF) models are an appealing research area because they fundamentally describe longitudinal interactions of vehicles on the road, and contribute significantly to an understanding of traffic flow. There is an emerging trend to use data-driven method to build CF models. One challenge to the data-driven CF models is their capability to achieve optimal longitudinal driven behavior because a lot of bad driving behaviors will be learnt from human drivers by the supervised learning manner. In this study, by utilizing the deep reinforcement learning (DRL) techniques trust region policy optimization (TRPO), a DRL based CF model for electric vehicle (EV) is built. The proposed CF model can learn optimal driving behavior by itself in simulation. The experiments on following standard driving cycle show that the DRL model outperforms the traditional CF model in terms of electricity consumption.