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Driving policies of V2X autonomous vehicles based on reinforcement learning methods
IET Intelligent Transport Systems ( IF 2.7 ) Pub Date : 2020-04-30 , DOI: 10.1049/iet-its.2019.0457
Zhenyu Wu 1 , Kai Qiu 1 , Hongbo Gao 2
Affiliation  

Autonomous driving has been achieving great progress since last several years. However, the autonomous vehicles always ignore the important traffic information on the road because of the uncertainties of driving environment and the limitations of onboard sensors. This might cause serious safety problem in autonomous driving. This study argues that the connected vehicles could share much more environmental information with each other. Therefore, a decision-making method based on reinforcement learning is proposed for V2X autonomous vehicles. First, the V2X autonomous driving architecture with three subsystems is designed. By V2V communication, an autonomous vehicle could obtain much more environmental information. Second, a reinforcement learning based model is applied to learn from the V2V observation data. A simulation environment is setup based on OpenAI reinforcement learning framework. The experimental results demonstrate the effectiveness of the V2X in autonomous driving.

中文翻译:

基于强化学习方法的V2X自动驾驶汽车驾驶策略

自最近几年以来,自动驾驶取得了长足的进步。然而,由于驾驶环境的不确定性和车载传感器的局限性,自动驾驶汽车总是忽略重要的道路交通信息。这可能会在自动驾驶中引起严重的安全问题。这项研究认为,联网车辆之间可以共享更多的环境信息。因此,提出了一种基于强化学习的V2X自动驾驶汽车决策方法。首先,设计了具有三个子系统的V2X自动驾驶架构。通过V2V通信,自动驾驶汽车可以获得更多的环境信息。其次,应用基于强化学习的模型从V2V观测数据中学习。基于OpenAI强化学习框架设置了仿真环境。实验结果证明了V2X在自动驾驶中的有效性。
更新日期:2020-04-30
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