Multi-agent Reinforcement Learning for Ecological Car-Following Control in Mixed Traffic
Apr 1, 2024·,,,,,,·
0 min read
Qun Wang
Fei Ju
Huaiyu Wang
Yahui Qian
Meixin Zhu
Weichao Zhuang
Liangmo Wang
Abstract
We propose an ecological car-following control strategy based on multi-agent reinforcement learning (MARL) for connected electrified vehicles in mixed traffic flows. Each CAV agent learns a cooperative policy that balances tracking performance, safety, and energy economy, while adapting to the behavior of human-driven preceding and following vehicles. The proposed method consistently reduces energy consumption over single-agent baselines and demonstrates strong generalization to unseen traffic scenarios.
Type
Publication
IEEE Transactions on Transportation Electrification
Authors
Postdoctoral Fellow
Qun Wang is a Postdoctoral Fellow at the AIMS Lab, Hong Kong Polytechnic
University, working with Assistant Professor Hailong Huang. His research
focuses on energy-saving optimization and safety-guaranteed control of
autonomous electrified vehicles, with emphasis on reinforcement
learning for eco-driving, human driving behavior modeling, and
multi-agent car-following control. He has published over 10 papers in
leading journals including IEEE Transactions on ITS, TTE, TCST and Energy.