An online learning framework for energy-efficient navigation of electric vehicles
Paper i proceeding, 2020

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.


Niklas Åkerblom

Chalmers, Data- och informationsteknik, Data Science

Volvo Cars

Yuxin Chen

University of Chicago

Morteza Haghir Chehreghani

Chalmers, Data- och informationsteknik, Data Science

IJCAI International Joint Conference on Artificial Intelligence

10450823 (ISSN)

Vol. 2021-january 2051-2057

29th International Joint Conference on Artificial Intelligence, IJCAI 2020
Yokohama, Japan,

EENE: Energieffektiv Navigering för Elfordon

FFI - Fordonsstrategisk forskning och innovation (2018-01937), 2019-01-01 -- 2022-12-31.


Övrig annan teknik


Robotteknik och automation



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