2022/06/28 by Abdul Rahman Kreidieh, Zhe Fu, Kreidieh, Abdul Rahman +3
Computer Science · Engineering · #Age of Information Optimization #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Robotics (cs.RO) #Systems and Control (eess.SY) #Traffic Prediction and Management Techniques #Traffic control and management #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2208.12534
openalex publication_date 2022/06/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
The rise of vehicle automation has generated significant interest in the potential role of future automated vehicles (AVs). In particular, in highly dense traffic settings, AVs are expected to serve as congestion-dampeners, mitigating the presence of instabilities that arise from various sources. However, in many applications, such maneuvers rely heavily on non-local sensing or coordination by interacting AVs, thereby rendering their adaptation to real-world settings a particularly difficult challenge. To address this challenge, this paper examines the role of imitation learning in bridging the gap between such control strategies and realistic limitations in communication and sensing. Treating one such controller as an "expert", we demonstrate that imitation learning can succeed in deriving policies that, if adopted by 5% of vehicles, may boost the energy-efficiency of networks with varying traffic conditions by 15% using only local observations. Results and code are available online at https://sites.google.com/view/il-traffic/home.