An Online Learning Framework for Energy-Efficient Navigation of Electric Vehicles
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Main track. Pages 2051-2057. https://doi.org/10.24963/ijcai.2020/284
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.
Machine Learning: Online Learning
Multidisciplinary Topics and Applications: Transportation
Machine Learning Applications: Networks