Advertisement

Game Theory-Based Traffic Modeling for Calibration of Automated Driving Algorithms

  • Nan Li
  • Mengxuan Zhang
  • Yildiray Yildiz
  • Ilya Kolmanovsky
  • Anouck Girard
Chapter
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 476)

Abstract

Automated driving functions need to be validated and calibrated so that a self-driving car can operate safely and efficiently in a traffic environment where interactions between it and other traffic participants constantly occur. In this paper, we describe a traffic simulator capable of representing vehicle interactions in traffic developed based on a game-theoretic traffic model. We demonstrate its functionality for parameter optimization in automated driving algorithms by designing a rule-based highway driving algorithm and calibrating the parameters using the traffic simulator.

Notes

Acknowledgements

Nan Li and Ilya Kolmanovsky acknowledge the support of this research by the National Science Foundation under Award CNS 1544844 to the University of Michigan. Yildiray Yildiz acknowledges the support of this research by the Scientific and Technological Research Council of Turkey under Grant 114E282 to Bilkent University.

References

  1. 1.
    Anderson, J.M., Nidhi, K., Stanley, K.D., Sorensen, P., Samaras, C., Oluwatola, O.A.: Autonomous Vehicle Technology: A Guide for Policymakers. Rand Corporation (2014)Google Scholar
  2. 2.
    Campbell, M., Egerstedt, M., How, J.P., Murray, R.M.: Autonomous driving in urban environments: approaches, lessons and challenges. Philos. Trans. R. Soc. Lond. A Math. Phys. Eng. Sci. 368(1928), 4649–4672 (2010)CrossRefGoogle Scholar
  3. 3.
    Costa-Gomes, M.A., Crawford, V.P.: Cognition and behavior in two-person guessing games: an experimental study. Am. Econ. Rev. 96(5), 1737–1768 (2006)CrossRefGoogle Scholar
  4. 4.
    Costa-Gomes, M.A., Crawford, V.P., Iriberri, N.: Comparing models of strategic thinking in van huyck, battalio, and beil’s coordination games. J. Eur. Econ. Assoc. 7(2–3), 365–376 (2009)CrossRefGoogle Scholar
  5. 5.
    Hedden, T., Zhang, J.: What do you think I think you think? Strategic reasoning in matrix games. Cognition 85(1), 1–36 (2002)CrossRefGoogle Scholar
  6. 6.
    Jaakkola, T., Singh, S.P., Jordan, M.I.: Reinforcement learning algorithm for partially observable markov decision problems. In: Advances in Neural Information Processing Systems, pp. 345–352. Citeseer (1995)Google Scholar
  7. 7.
    Kikuchi, S., Chakroborty, P.: Car-following model based on fuzzy inference system. Transp. Res. Rec. 82–82 (1992)Google Scholar
  8. 8.
    Langari, R.: Autonomous vehicles. In: 2017 American Control Conference (ACC), pp. 4018–4022 (2017).  https://doi.org/10.23919/ACC.2017.7963571
  9. 9.
    Li, N., Oyler, D., Zhang, M., Yildiz, Y., Girard, A., Kolmanovsky, I.: Hierarchical reasoning game theory based approach for evaluation and testing of autonomous vehicle control systems. In: IEEE 55th Conference on Decision and Control (CDC), pp. 727–733. IEEE (2016)Google Scholar
  10. 10.
    Li, N., Oyler, D.W., Zhang, M., Yildiz, Y., Kolmanovsky, I., Girard, A.R.: Game theoretic modeling of driver and vehicle interactions for verification and validation of autonomous vehicle control systems. IEEE Trans. Control Syst. Technol. 99, 1–16 (2017).  https://doi.org/10.1109/TCST.2017.2723574
  11. 11.
    Maurer, M., Gerdes, J.C., Lenz, B., Winner, H.: Autonomous driving: Technical, Legal and Social Aspects. Springer Publishing Company, Incorporated (2016)Google Scholar
  12. 12.
    McDonald, M., Wu, J., Brackstone, M.: Development of a fuzzy logic based microscopic motorway simulation model. In: IEEE Conference on Intelligent Transportation System, pp. 82–87. IEEE (1997)Google Scholar
  13. 13.
    Musavi, N., Onural, D., Gunes, K., Yildiz, Y.: Unmanned aircraft systems airspace integration: a game theoretical framework for concept evaluations. J. Guidance Control Dyn. 40(1), 96–109 (2016)CrossRefGoogle Scholar
  14. 14.
    Oyler, D.W., Yildiz, Y., Girard, A.R., Li, N.I., Kolmanovsky, I.V.: A game theoretical model of traffic with multiple interacting drivers for use in autonomous vehicle development. In: 2016 American Control Conference (ACC), pp. 1705–1710 (2016)Google Scholar
  15. 15.
    Stahl, D.O., Wilson, P.W.: On players models of other players: theory and experimental evidence. Games Econ. Behav. 10(1), 218–254 (1995)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Urmson, C., Anhalt, J., Bagnell, D., Baker, C., Bittner, R., Clark, M., Dolan, J., Duggins, D., Galatali, T., Geyer, C., et al.: Autonomous driving in urban environments: Boss and the urban challenge. J. Field Rob. 25(8), 425–466 (2008)CrossRefGoogle Scholar
  17. 17.
    Yildiz, Y., Agogino, A., Brat, G.: Predicting pilot behavior in medium-scale scenarios using game theory and reinforcement learning. J. Guidance Control Dyn. 37(4), 1335–1343 (2014)CrossRefGoogle Scholar
  18. 18.
    Zhou, J., Schmied, R., Sandalek, A., Kokal, H., del Re, L.: A framework for virtual testing of ADAS. SAE Int. J. Passeng. Cars Electron. Electr. Syst. 9(2016-01-0049), 66–73 (2016)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Nan Li
    • 1
  • Mengxuan Zhang
    • 1
  • Yildiray Yildiz
    • 2
  • Ilya Kolmanovsky
    • 1
  • Anouck Girard
    • 1
  1. 1.Department of Aerospace EngineeringUniversity of MichiganAnn ArborUSA
  2. 2.Department of Mechanical EngineeringBilkent UniversityAnkaraTurkey

Personalised recommendations