Reinforcement Learning for Eco-Driving in Mixed Traffic
Jan 1, 2024
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1 min read
Developing RL-based control strategies that let connected and automated vehicles save energy while coexisting with heterogeneous human drivers. Our field-data-calibrated framework reduces energy consumption by over 6% without compromising safety or string stability.
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.