Proceedings of the third international conference on Genetic algorithms
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
The development of a road traffic simulation system in broad areas
Selected papers of the MSSA/IMACS 10th biennial conference on Modelling and simulation
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Two-dimensional road shape optimisation using genetic algorithms
Mathematics and Computers in Simulation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms and Simulated Annealing
Genetic Algorithms and Simulated Annealing
Multiple Adaptive Agents for Tactical Driving
Applied Intelligence
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
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This paper discusses a driving simulation framework, which focuses on the decision-making role performed at the tactical driving level. Our tactical driving technique using Genetic Algorithms (GAs), named GA-INTACT, accounts for the positions of a 'subject' vehicle and those of other vehicles and their speed parameters in the surrounding traffic condition, and selects favourable speed change and lane transition actions for the 'subject' vehicle, according to safety, speed and driving behaviour criteria. The use of GAs for obtaining near-optimum driving solutions eliminates the need for learning driving patterns, and handles the complex nature of modelling tactical driving. In particular, the proposed framework addresses the role of driving behaviour in influencing drivers' tactical decisions – a matter that is not treated systematically and sufficiently by other driving simulation methods.