Evolution and Adaptation of an Agent Driving a Scale Model of a Car with Obstacle Avoidance Capabilities

  • Ivan Tanev
  • Michal Joachimczak
  • Katsunori Shimohara
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4095)


We present an approach for evolutionary design of the driving style of an agent, remotely operating a scale model of a car running in a fastest possible way. The agent perceives the environment from a video camera and conveys its actions to the car via standard radio control transmitter. In order to cope with the video feed latency we propose an anticipatory modeling in which the agent considers its current actions based on the anticipated intrinsic (rather than currently available, outdated) state of the car and its surrounding. The driving style is first evolved offline on a software model of the car and then adapted online to the real world. An online evolutionary adaptation of the offline-obtained best styles to the needs to avoid a small obstacle results in lap times that are virtually the same as the best lap times achieved on the same track without obstacles. Presented work is a step towards the automated design of the control software of remotely operated vehicles capable to find an optimal solution to various tasks in different environmental situations. The results, also, can be seen as an attempt to explore the feasibility of developing a framework of adaptive racing games in which the human competes against a computer with matching capabilities, both operating physical, scale models of cars.


Scale Model Obstacle Avoidance Fitness Landscape Rear Wheel Driving Style 
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  1. 1.
    Abbott, E., Powell, D.: Land-vehicle Navigation Using GPS. The Proceedings of the IEEE 87(1), 145–162 (1999)CrossRefGoogle Scholar
  2. 2.
    Frere, P.: Sports Cars and Competition Driving. Bentley Publishing (1992)Google Scholar
  3. 3.
    Funge, J.D.: Artificial Intelligence for Computer Games. Peters Corp. (2004)Google Scholar
  4. 4.
    Goldberg, D.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)MATHGoogle Scholar
  5. 5.
    IBM Corporation: Deep Blue (1997), URL:
  6. 6.
    Jacobi, N.: Minimal Simulations for Evolutionary Robotics, Ph.D. thesis, School of Cognitive and Computing Sciences, Sussex University (1998)Google Scholar
  7. 7.
    Kirschner, M.W., Gerhart, J.: The Plausibility of Life. Resolving Darwin’s Dilemma, p. 336. Yale University Press, New Haven (2005)Google Scholar
  8. 8.
    Meeden, L., Kumar, D.: Trends in Evolutionary Robotics. In: Jain, L.C., Fukuda, T. (eds.) Soft Computing for Intelligent Robotic Systems, pp. 215–233. Physica-Verlag, New York (1998)Google Scholar
  9. 9.
    Miller, B.L., Goldberg, D.E.: Genetic Algorithms, Tournament Selection, And The Ef-fects Of Noise. Complex System 9(3), 193–212 (1995)MathSciNetGoogle Scholar
  10. 10.
    Robocup (2005), URL:
  11. 11.
    Rosen, R.: Anticipatory Systems. Pergamon Press, Oxford (1985)Google Scholar
  12. 12.
    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
  13. 13.
    Tanev, I., Joachimczak, M., Hemmi, H., Shimohara, K.: Evolution of the Driving Styles of Anticipatory Agent Remotely Operating a Scaled Model of Racing Car. In: Proceedings of the 2005 IEEE Congress on Evolutionary Computation (CEC 2005), Edinburgh, UK, September 2-5, pp. 1891–1898 (2005)Google Scholar
  14. 14.
    Wloch, K., Bentley, P.J.: Optimising the performance of a formula one car using a genetic algorithm. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 702–711. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ivan Tanev
    • 1
  • Michal Joachimczak
    • 2
  • Katsunori Shimohara
    • 1
  1. 1.Department of Information Systems DesignDoshisha UniversityKyotanabeJapan
  2. 2.Department of Genetics and Marine BiotechnologyInstitute of Oceanology, Polish Academy of SciencesSopotPoland

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