# An Online Learning Framework for Energy-Efficient Navigation of Electric Vehicles

arXiv: https://arxiv.org/abs/2003.01416 · HTML: https://mlsanity.com/paper/2003.01416/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2020.

## Built on by (most cited first, 8 of 8)

- [Online Learning of Network Bottlenecks via Minimax Paths](https://mlsanity.com/paper/2109.08467/) (2021)
- [Online Learning of Energy Consumption for Navigation of Electric Vehicles](https://mlsanity.com/paper/2111.02314/) (2021)
- [A Contextual Combinatorial Semi-Bandit Approach to Network Bottleneck Identification](https://mlsanity.com/paper/2206.08144/) (2022)
- [Autonomous Drug Design with Multi-Armed Bandits](https://mlsanity.com/paper/2207.01393/) (2022)
- [A Unified Framework for Online Trip Destination Prediction](https://mlsanity.com/paper/2101.04520/) (2021)
- [Passive and Active Learning of Driver Behavior from Electric Vehicles](https://mlsanity.com/paper/2203.02179/) (2022)
- [A Combinatorial Semi-Bandit Approach to Charging Station Selection for Electric Vehicles](https://mlsanity.com/paper/2301.07156/) (2023)
- [Prediction of Time and Distance of Trips Using Explainable Attention-based LSTMs](https://mlsanity.com/paper/2303.15087/) (2023)

## Abstract (arXiv)

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.