Combinatorial Semi-Bandit Methods for Navigation of Electric Vehicles

Doctoral thesis, 2024

Climate change is one of the most urgent global challenges humanity is currently facing. As major contributors of greenhouse gas emissions, the transport and automotive sectors have crucial roles to play in solving the problem. To reduce the usage of fossil fuels, electric vehicles need to become more attractive as alternatives to conventional vehicles. Concerns like range anxiety can be mitigated with more accurate navigation systems, especially if such systems are able to sequentially and adaptively collect data to improve their knowledge of the environment.
Hence, this thesis explores a number of different perspectives, settings and methods relating to navigation problems for electric vehicles in uncertain traffic environments. In particular, we focus on a combinatorial multi-armed bandit perspective, since it allows us to adapt and utilize efficient methods for targeted data collection within the navigation setting. Such methods include Bayesian bandit algorithms like Thompson sampling and BayesUCB, which can be used together with prior beliefs informed by domain-specific knowledge to efficiently explore the traffic environment while simultaneously solving the navigation problem.
Throughout the thesis, we apply these kinds of perspectives and methods to various problem settings, including both city-sized and country-sized road networks, relating to online versions of combinatorial optimization problems connected to navigation tasks. Within the appended works, we study the minimization of both expected energy consumption and travel time (including the time required for charging sessions). To show the efficiency of our proposed methods, we perform multiple thorough empirical studies with simulation experiments on realistic problem instances. We also analyze the methods by deriving theoretical upper bounds on their expected regret. With these performance guarantees and results, we aim to demonstrate the utility of the methods for real-world problems and applications.