@inproceedings{0e67439398484142817cc148c9256714,
title = "Study on Crossing Behavior Decision-making Model of Unmanned Vehicles",
abstract = "Unmanned vehicles will play an extremely important role in the future development of intelligent transportation system. The research background and significance of unmanned vehicles were introduced and the research status of decision-making of unmanned vehicles at home and abroad was summarized in this paper. And the rule-based behavior decision-making methods and machine learning-based behavior decision-making methods were summarized. To solve the problem of unmanned vehicles crossing at the intersection of city, considering the safety and efficiency of the crossing process, the method of finding the optimal traversing strategy based on the reinforcement learning algorithm was proposed. Finally, the effectiveness of the algorithm was verified by the intersection crossing case. The results show that compared with Q-Learning algorithm, the NQL algorithm proposed in this paper needs fewer training samples and shorter training time when it converges.",
keywords = "Decision-making, NQL, Reinforcement learning, Unmanned vehicles, Urban intersection",
author = "Wei Chen and Mingming Du and Gemeng Liu and Xuemei Chen",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 31st Chinese Control and Decision Conference, CCDC 2019 ; Conference date: 03-06-2019 Through 05-06-2019",
year = "2019",
month = jun,
doi = "10.1109/CCDC.2019.8832569",
language = "English",
series = "Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4142--4147",
booktitle = "Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019",
address = "United States",
}
