DSAI seminar with Niklas Åkerblom

Abstract

Reducing the dependence on fossil fuels in the transport sector is crucial to have a realistic chance of halting climate change. The automotive industry is, therefore, transitioning towards an electrified future at an unprecedented pace. However, in order for electric vehicles to be an attractive alternative to conventional vehicles, some issues, like range anxiety, need to be mitigated. One way to address these problems is by developing more accurate and robust navigation systems for electric vehicles. Furthermore, with highly stochastic and changing traffic conditions, it is useful to continuously update prior knowledge about the traffic environment by gathering data. Passive collection of data related to e.g., energy consumption, travel time or charging power from vehicles in the traffic network might lead to insufficient information gathered in places where there are few vehicles. In this talk, we discuss how to utilize combinatorial semi-bandit methods to adapt the routes presented by the navigation system to adequately explore the road network. We outline a few variants of the problem, as well as ways of addressing them. Finally, we show results from simulation experiments, demonstrating the performance of the methods on multiple problem instances, including long-distance navigation tasks in country-sized road networks.

About the speaker

Niklas Åkerblom is an industrial PhD student at Volvo Car Corporation and the division of Data Science and AI at the department of Computer Science and Engineering, Chalmers University of Technology.

This is a seminar from the DSAI seminars series held every Monday at 14:00 by the Data Science and AI division. The seminars are usually hybrid.