A Multi-Objective Approach to Evolving Platooning Strategies in Intelligent Transportation Systems

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.