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.
Learning energy-efficient driving behaviors by imitating experts
08.10.2022
3175298 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Establishing Style-Oriented Driver Models by Imitating Human Driving Behaviors
Online Contents | 2015
|Paper folding driving cuttlefish-imitating underwater robot
Europäisches Patentamt | 2024
|Driving reversing device of manta ray imitating aircraft
Europäisches Patentamt | 2024
|