期刊
IET INTELLIGENT TRANSPORT SYSTEMS
卷 14, 期 5, 页码 331-337出版社
WILEY
DOI: 10.1049/iet-its.2019.0457
关键词
learning (artificial intelligence); decision making; road vehicles; mobile robots; control engineering computing; traffic engineering computing; autonomous driving; connected vehicles; environmental information; OpenAI reinforcement learning framework; V2X autonomous vehicles; decision-making method
资金
- National Natural Science Foundation of China [61502246, U1804161]
- Nanjing University of Posts and Telecommunications Science Foundation [NY215019]
- Science and Technology Innovation Planning Project of Ministry of Education of China
- National Key Research and Development Program of China [2017YFB0102603]
- NVIDIA DGX-Station/DRIVE PX 2 Program
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.
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