论文标题
电动汽车节能导航的在线学习框架
An Online Learning Framework for Energy-Efficient Navigation of Electric Vehicles
论文作者
论文摘要
节能导航构成电动汽车的重要挑战，因为电池容量有限。我们采用贝叶斯方法来对道路细分市场的能源消耗进行建模，以进行有效的导航。为了学习模型参数，我们开发了一个在线学习框架，并研究了一些探索策略，例如汤普森采样和上限限制。然后，我们将在线学习框架扩展到多代理设置，在该设置中，多辆车可以适应并学习能量模型的参数。我们分析汤普森采样，并在其表现上建立严格的遗憾界限。最后，我们通过在卢森堡Sumo流量数据集上进行的几个现实世界实验演示了我们的方法的性能。
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.
