Evolving neural networks through augmenting topologies
Evolutionary Computation
Generating large-scale neural networks through discovering geometric regularities
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A multiobjective optimization algorithm for discovering driving strategies
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Visual perception of obstacles and vehicles for platooning
IEEE Transactions on Intelligent Transportation Systems
The Impact of Cooperative Adaptive Cruise Control on Traffic-Flow Characteristics
IEEE Transactions on Intelligent Transportation Systems
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Discovering driving strategies with a multiobjective optimization algorithm
Applied Soft Computing
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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.