Research Article
Connected Autonomous Vehicle Platoon Control Through Multi-agent Deep Reinforcement Learning
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@INPROCEEDINGS{10.1007/978-3-030-93479-8_16, author={Guangfei Xu and Bing Chen and Guangxian Li and Xiangkun He}, title={Connected Autonomous Vehicle Platoon Control Through Multi-agent Deep Reinforcement Learning}, proceedings={Broadband Communications, Networks, and Systems. 12th EAI International Conference, BROADNETS 2021, Virtual Event, October 28--29, 2021, Proceedings}, proceedings_a={BROADNETS}, year={2022}, month={1}, keywords={Intelligent transportation system Connected autonomous vehicle Multi-objective platoon control Multi-agent deep reinforcement learning}, doi={10.1007/978-3-030-93479-8_16} }- Guangfei Xu
Bing Chen
Guangxian Li
Xiangkun He
Year: 2022
Connected Autonomous Vehicle Platoon Control Through Multi-agent Deep Reinforcement Learning
BROADNETS
Springer
DOI: 10.1007/978-3-030-93479-8_16
Abstract
The rise of the artificial intelligence (AI) brings golden opportunity to accelerate the development of the intelligent transportation system (ITS). The platoon control of connected autonomous vehicle (CAV) as the key technology exhibits superior for improving traffic system. However, there still exist some challenges in multi-objective platoon control and multi-agent interaction. Therefore, this paper proposed a connected autonomous vehicle latoon control approach with multi-agent deep reinforcement learning (MADRL). Finally, the results in stochastic mixed traffic flow based on SUMO (simulation of urban mobility) platform demonstrate that the proposed method is feasible, effective and advanced.
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