TY - CHAP
T1 - A Multi-Objective Approach to Evolving Platooning Strategies in Intelligent Transportation Systems
AU - van Willigen, W.H.
AU - Haasdijk, E.W.
AU - Kester, L.J.H.M.
PY - 2013
Y1 - 2013
N2 - 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 high-level controllers for such intelligent vehicles. The algorithm yields a set of solutions that each embody their own prioritisation 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.
AB - 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 high-level controllers for such intelligent vehicles. The algorithm yields a set of solutions that each embody their own prioritisation 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.
U2 - 10.1145/2463372.2463534
DO - 10.1145/2463372.2463534
M3 - Chapter
SN - 9781450319638
T3 - Gecco'13: Proceedings of the 2013 Genetic and Evolutionary Computation Conference
SP - 1397
EP - 1404
BT - Gecco'13: Proceedings of the 2013 Genetic and Evolutionary Computation Conference
T2 - Genetic and Evolutionary Computation Conference (GECCO)
Y2 - 6 July 2013 through 10 July 2013
ER -