A multi-objective approach to evolving platooning strategies in intelligent transportation systems

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Abstract

The research in this paper is inspired by a vision of intelligent vehicles that autonomously move along motorways: they join and leave trains of vehicles (platoons), overtake other vehicles, etc. We propose a multi-objective evolutionary algorithm based on NEAT and SPEA2 that evolves highlevel controllers for such intelligent vehicles. The algorithm yields a set of solutions that each embody their own priori-tisation of various user requirements such as speed, comfort or fuel economy. This contrasts with the current practice in researching such controllers, where user preferences are summarised in a single number that the controller development process then optimises. Proof-of-concept experiments show that evolved controllers substantially outperform a widely used human behavioural model. We show that it is possible to evolve a set of vehicle controllers that correspond with different prioritisations of user preferences, giving the driver, on the road, the power to decide which preferences to emphasise. Copyright © 2013 ACM.

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Van Willigen, W., Haasdijk, E., & Kester, L. (2013). A multi-objective approach to evolving platooning strategies in intelligent transportation systems. In GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference (pp. 1397–1404). https://doi.org/10.1145/2463372.2463534

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