A multi-objective approach to evolving platooning strategies in intelligent transportation systems
conference paper
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.
TNO Identifier
478830
Publisher
ACM
Source title
15th Genetic and Evolutionary Computation Conference, GECCO 2013, 6-10 July 2013, Amsterdam, Netherlands
Place of publication
New York, NY
Pages
1397-1404
Files
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