Fast, comfortable or economical: Evolving platooning strategies with many objectives

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 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. In this article, we test our multi-objective approach on 6 objectives. Our method outperforms a widely used human behavioural model on many of the objectives. Some performance is lost when we introduce additional objectives, but these losses are small and therefore acceptable. 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, although we do see that the more objectives are added to the system, the less intuitive the prioritisation of the different objectives becomes.