arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2007.09101eess.SPcs.AIcs.LGcs.RO

Reinforcement Learning-Enabled Decision-Making Strategies for a Vehicle-Cyber-Physical-System in Connected Environment

University of Waterloo(滑铁卢大学);Chongqing University(重庆大学)

Teng Liu, Xiaolin Tang, Jinwei Zhang, Wenbo Li, Zejian Deng, Yalian Yang

英文摘要:

As a typical vehicle-cyber-physical-system (V-CPS), connected automated vehicles attracted more and more attention in recent years. This paper focuses on discussing the decision-making (DM) strategy for autonomous vehicles in a connected environment. First, the highway DM problem is formulated, wherein the vehicles can exchange information via wireless networking. Then, two classical reinforcement learning (RL) algorithms, Q-learning and Dyna, are leveraged to derive the DM strategies in a predefined driving scenario. Finally, the control performance of the derived DM policies in safety and efficiency is analyzed. Furthermore, the inherent differences of the RL algorithms are embodied and discussed in DM strategies.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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