An Online Learning Framework for Energy-Efficient Navigation of Electric Vehicles
This addresses energy optimization for electric vehicle users, though it appears incremental as it applies known online learning methods to this specific domain.
The authors tackled energy-efficient navigation for electric vehicles by developing an online learning framework with Bayesian modeling and exploration strategies like Thompson Sampling, achieving performance demonstrated through real-world experiments on the Luxembourg SUMO Traffic dataset.
Energy-efficient navigation constitutes an important challenge in electric vehicles, due to their limited battery capacity. We employ a Bayesian approach to model the energy consumption at road segments for efficient navigation. In order to learn the model parameters, we develop an online learning framework and investigate several exploration strategies such as Thompson Sampling and Upper Confidence Bound. We then extend our online learning framework to multi-agent setting, where multiple vehicles adaptively navigate and learn the parameters of the energy model. We analyze Thompson Sampling and establish rigorous regret bounds on its performance. Finally, we demonstrate the performance of our methods via several real-world experiments on Luxembourg SUMO Traffic dataset.