A multiobjective optimization algorithm for discovering driving strategies

Dovgan, Erik; Gams, Matjaž; Filipič, Bogdan

doi:10.1145/2001858.2002083

Cited by 3 publications

(1 citation statement)

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“…The multi-objective driving strategies domain has been investigated by Dovgan et al [13], [14]. They use the NSGA-II multi-objective evolutionary algorithm to determine driving strategies that optimise travel time and fuel economy.…”

Section: Related Workmentioning

confidence: 99%

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

Willigen

Haasdijk

Kester

2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

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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 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.

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Section: Related Workmentioning

confidence: 99%

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

Willigen

Haasdijk

Kester

2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

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Evolving intelligent vehicle control using multi-objective NEAT

Willigen

Haasdijk

Kester

2013

2013 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS)

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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 algorithm based on NEAT and SPEA2 that evolves controllers for such intelligent vehicles. The algorithm yields a set of solutions that embody their own prioritisation of various user requirements such as speed, comfort or fuel economy. This contrasts with most current research into such controllers, where the user preferences are summarised in a single number that the controller development process should optimise. Having multiple prioritisations of preferences would, however, allow the user to select desired vehicle behaviour in real time, for instance fast driving if she's in a hurry or economical driving in more relaxed circumstances. Preliminary results of our experiments show that evolved controllers substantially outperform the human behavioural model. We show that it is possible to evolve a set of vehicle controllers that correspond to different prioritisations of user preferences, giving the driver, on the road, the power to decide which preferences to emphasise.

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