Evolution of General Driving Rules of a Driving Agent

  • Ivan Tanev
  • Hirotaka Yamazaki
  • Tomoyuki Hiroyasu
  • Katsunori Shimohara
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5040)


We present an approach for automated design of the functionary of driving agent, able to operate a software model of fast running car. Our objective is to discover a single driving rule (if existent) that is general enough to be able to adequately control the car in all sections of predefined circuits. In order to evolve an agent with such capabilities, we propose an indirect, generative representation of the driving rules as algebraic functions of the features of the perceived surroundings of the car. These functions, when evaluated for the current surrounding of the car yield concrete values of the main attributes of the driving style (e.g., straight line velocity, turning velocity, etc.), applied by the agent in the currently negotiated section of the circuit. Experimental results verify both the very existence of the general driving rules and the ability of the employed genetic programming framework to automatically discover them. The evolved driving rules offer a favorable generality, in that a single rule can be successfully applied (i) not only for all the sections of a particular circuit, but also (ii) for the sections in several a priori defined circuits featuring different characteristics.


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  1. 1.
    Bentley, R.: Speed Secrets: Professional Race Driving Techniques. Motorbooks International (1998)Google Scholar
  2. 2.
    Fogel, D.B.: Blondie24: Playing at the Edge of AI. Morgan Kaufmann, San Francisco (2001)Google Scholar
  3. 3.
    Frere, P.: Sports Cars and Competition Driving. Bentley Publishing (1992)Google Scholar
  4. 4.
    Funge, J.D.: Artificial Intelligence for Computer Games. Peters Corp. (2004)Google Scholar
  5. 5.
    Gillespie, T.: Fundamentals of Vehicle Dynamics. Society of Automotive Engineers International (1992)Google Scholar
  6. 6.
    Google Maps, Image of the junction near Matsubara city in Osaka Prefecture, Japan,,135.575&spn=0.00354,0.0042
  7. 7.
    IBM Corporation, Deep Blue (1997),
  8. 8.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  9. 9.
  10. 10.
    Suzuki, M., Floreano, D.: Active Vision for Neural Development and Landmark Navigation. In: 50th Anniversary Summit of Artificial Intelligence, pp. 247–248 (2006)Google Scholar
  11. 11.
    Tanev, I., Shimohara, K.: XGP: XML-based Genetic Programming Framework. In: Proceedings of the 34th Symposium of the Society of Instrument and Control Engineers (SICE) on Intelligent Systems, pp. 183–188 (2007)Google Scholar
  12. 12.
    Tanev, I., Joachimczak, M., Shimohara, K.: Evolution and Adaptation of an Agent Driving a Scale Model of a Car with Obstacle Avoidance Capabilities. In: Nolfi, S., Baldassarre, G., Calabretta, R., Hallam, J.C.T., Marocco, D., Meyer, J.-A., Miglino, O., Parisi, D. (eds.) SAB 2006. LNCS (LNAI), vol. 4095, pp. 619–630. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  13. 13.
    Tanev, I., Shimohara, K.: On Human Competitiveness of the Evolved Agent Operating a Scale Model of a Car. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2007), Singapore, September 25-28, 2007, pp. 3646–3653 (2007)Google Scholar
  14. 14.
    Togelius, J., Lucas, S.M.: Evolving Controllers for Simulated Car Racing. In: Proceedings of IEEE Congress on Evolutionary Computations (CEC 2005), Edinburgh, UK, September 2-5, pp. 1906–1913 (2005)Google Scholar
  15. 15.
    Wloch, K., Bentley, P.: Optimizing the Performance of a Formula One Car Using a Genetic Algorithm. In: Proceedings of the 8th International Conference on Parallel Problem Solving from Nature, Birmingham, UK, September 18-22, pp. 702–711 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Ivan Tanev
    • 1
  • Hirotaka Yamazaki
    • 2
  • Tomoyuki Hiroyasu
    • 2
  • Katsunori Shimohara
    • 1
  1. 1.Department of Knowledge Engineering and Computer SciencesDoshisha UniversityKyotoJapan
  2. 2.Department of Information Systems DesignDoshisha UniversityKyotoJapan

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