Abstract
This work concerns the design and development of a driving simulation system, which exhibits intelligent driving behaviour at the tactical level, as part of a traffic simulation environment. Our tactical driving system using genetic algorithms, named GA-INTACT, accounts for the subject vehicle and other vehicles positions and 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. Simulation results demonstrated that the adoption of the Genetic Algorithms approach for obtaining near-optimum driving solutions eliminates the need for learning driving patterns, and allows the efficient handling of the complex nature of tactical driving modelling problem. The role of the driving behaviour in influencing the outcome of the driver’s decision is emphasised, an aspect that was not treated sufficiently in previous tactical driving simulation approaches.
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References
Arain, M.A., et al.: Action planning for the collision avoiding system using neural networks. In: Proc Intelligent Vehicles Sym., pp. 119–124 (1993)
Baluja, S., Sukthankar, R.: Prototyping intelligent vehicle modules using evolutionary algorithms. In: Evolutionary Algorithms in Engineering Applications. Springer, Heidelberg (1998)
Campell, N.W., et al.: Autonomous road vehicle navigation. Engineering Applications of Artificial Intelligence 7(2), 177–190 (1990)
Ehlet, P.A.M., Rothkrantz, L.J.M.: Microscopic traffic simulation with reactive driving agents. In: Proc. 4th IEEE Intelligent Transportation Systems Conf., USA, pp. 860–865 (2001)
Foresti, G., et al.: A distributed approach to 3D scene recognition. IEEE Trans. Vehicular Technology. 43(2), 389–406 (1994)
Goldberg, D.E.: Genetic algorithms in search, optimisation, and machine learning. Addison-Wesley, Reading (1989)
Liatsis, P., Tawfik, H.M.: Two dimensional road shape optimisation using genetic algorithms. Mathematics and Computers in simulation 5, 19–31 (1999)
Leutzbach, W.: Introduction to the theory of traffic flow. Springer, Berlin (1988)
Lyons, G., Hunt, J.: Traffic models - a role for neural networks. In: Proc. of neural networks and combinatorial traffic models, pp. 71–79 (1993)
Reece, D.A., Shafer, S.A.: A computational model of driving for autonomous vehicles. Transportation research. 27A(1), 23–50 (1993)
Sukthankar, R., Baluja, S., Hancock, J.: Multiple adaptive agents for tactical driving. International Journal of Artificial Intelligence 9(1) (1998)
Tawfik, H.M.: A graphical simulation environment for modelling of road and traffic scenarios. PhD Thesis. Control Systems Centre, UMIST, UK (2000)
Tribe, R.T., et al.: Intelligent driver support. In: 2nd World Congress on Intelligent Transport Systems, Yokohama, Japan (1995)
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© 2006 Springer-Verlag Berlin Heidelberg
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Tawfik, H., Liatsis, P. (2006). Modelling Tactical Driving Manoeuvres with GA-INTACT. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science – ICCS 2006. ICCS 2006. Lecture Notes in Computer Science, vol 3993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11758532_100
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DOI: https://doi.org/10.1007/11758532_100
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