Advertisement

Further Steps towards Driver Modeling According to the Bayesian Programming Approach

  • Claus Möbus
  • Mark Eilers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5620)

Abstract

The Human Centered Design (HCD) of Partial Autonomous Driver Assistance Systems (PADAS) requires Digital Human Models (DHMs) of human control strategies for simulating traffic scenarios. We describe first results to model lateral and longitudinal control behavior of drivers with simple dynamic Bayesian sensory-motor models according to the Bayesian Programming (BP) approach: Bayesian Autonomous Driver (BAD) models. BAD models are learnt from multivariate time series of driving episodes generated by single or groups of users. The variables of the time series describe phenomena and processes of perception, cognition, and action control of drivers. BAD models reconstruct the joint probability distribution (JPD) of those variables by a composition of conditional probability distributions (CPDs). The real-time control of virtual vehicles is achieved by inferring the appropriate actions under the evidence of sensory percepts with the help of the reconstructed JPD.

Keywords

digital human response models driver models Bayesian autonomous driver models learning of human control strategies probabilistic Bayesian lateral and longitudinal control graphical modeling human behavior learning and transfer Bayesian Programming 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Anderson, J.R.: Learning and Memory. John Wiley, Chichester (2002)Google Scholar
  2. 2.
    Salvucci, D.D., Gray, R.: A Two-Point Visual Control Model of Steering. Perception 33, 1233–1248 (2004)CrossRefGoogle Scholar
  3. 3.
    Salvucci, D.D.: Integrated Models of Driver Behavior. In: Gray, W.D. (ed.) Integrated models of cognitive systems, pp. 356–367. Oxford University Press, New York (2007)CrossRefGoogle Scholar
  4. 4.
    Jürgensohn, T.: Control Theory Models of the Driver. In: Cacciabue (ed.), pp. 277–292 (2007)Google Scholar
  5. 5.
    Weir, D.H., Chao, K.C.: Review of Control Theory Models for Directional and Speed Control. In: Cacciabue, P.C., pp. 293–311 (2007)Google Scholar
  6. 6.
    Möbus, C., Hübner, S., Garbe, H.: Driver Modelling: Two-Point-or Inverted Gaze-Beam-Steering. In: Rötting, M., Wozny, G., Klostermann, A., Huss, J. (Hrsgb) Prospektive Gestaltung von Mensch-Technik-Interaktion, 7. Berliner Werkstatt Mensch-Maschine-Systeme, Berlin, Fortschritt-Berichte VDI-Reihe 22 (Nr. 25), pp. 483–488. VDI Verlag, Düsseldorf (2007)Google Scholar
  7. 7.
    Möbus, C., Eilers, M.: First Steps Towards Driver Modeling According to the Bayesian Programming Approach, Symposium Cognitive Modeling. In: Urbas, L., Goschke, T., Velichkovsky, B. (eds.) KogWis, vol. 6, p. 59. Christoph Hille, Dresden (2008)Google Scholar
  8. 8.
    Möbus, C., Eilers, M., Garbe, H., Zilinski, M.: Probabilistic, and Empirical Grounded Modeling of Agents in Partial Cooperative (Traffic) Scenarios. In: Conference Proceedings, HCI 2009, Digital Human Modeling. LNCS (LNAI). Springer, San Diego (2009)Google Scholar
  9. 9.
    Cacciabue, P.C. (ed.): Modelling Driver Behaviour in Automotive Environments. Springer, London (2007)Google Scholar
  10. 10.
    Chater, N., Oxford, M. (eds.): The Probabilistic Mind: Prospects for Bayesian Cognitive Science. Oxford University Press, Oxford (2008)Google Scholar
  11. 11.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press, Cambridge (2005)zbMATHGoogle Scholar
  12. 12.
    Anderson, J.R., Fincham, J.M., Qin, Y., Stocco, A.: A Central circuit of the mind. Trends in Cognitive Science 12(4), 136–143 (2008)CrossRefGoogle Scholar
  13. 13.
    Bessiere, P., Laugier, C., Siegwart, R. (eds.): Probabilistic Reasoning and Decision Making in Sensory-Motor Systems. Springer, Berlin (2008)zbMATHGoogle Scholar
  14. 14.
    Xu, Y., Lee, K.K.C.: Human Behavior Learning and Transfer. CRC Press, Boca Raton (2006)Google Scholar
  15. 15.
    Möbus, C., Hübner, S., Garbe, H.: Driver Modelling: Two-Point- or Inverted Gaze-Beam-Steering. In: Rötting, M., Wozny, G., Klostermann, A., Huss, J. (eds.) Prospektive Gestaltung von Mensch-Technik-Interaktion, Fortschritt-Berichte VDI-Reihe 22 (Nr. 25), pp. 483–488. VDI Verlag, Düsseldorf (2007)Google Scholar
  16. 16.
    Hamker, F.H.: RBF learning in a non-stationary environment: the stability-plasticity dilemma. In: Howlett, R.J., Jain, L.C. (eds.) Radial Basis Function networks 1: Recent Developments in Theory and Applications; Studies in fuzziness and soft computing, ch. 9, vol. 66, pp. 219–251. Physica Verlag, Heidelberg (2001)Google Scholar
  17. 17.
    Lebeltel, O., Bessiere, P., Diard, J., Mazer, E.: Bayesian Robot Programming. Autonomous Robots 16, 49–79 (2004)CrossRefGoogle Scholar
  18. 18.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Mateo (1988)zbMATHGoogle Scholar
  19. 19.
    Pearl, J.: Causality: Models, Reasoning and Interference, 2nd edn. Cambridge University Press, Cambridge (2009)CrossRefzbMATHGoogle Scholar
  20. 20.
    Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search, 2nd edn. MIT Press, Cambridge (2001)zbMATHGoogle Scholar
  21. 21.
    Neapolitan, R.E.: Learning Bayesian Networks. Prentice Hall, Upper Saddle River (2004)Google Scholar
  22. 22.
    Jensen, F.V., Nielsen, T.D.: Bayesian Networks and Decision Graphs, 2nd edn. Springer, Heidelberg (2007)CrossRefzbMATHGoogle Scholar
  23. 23.
    Bessiere, P.: Survey: Probabilistic Methodology and Techniques for Artifact Conception and Development, Repport de Recherche, No. 4730, INRIA (2003)Google Scholar
  24. 24.
    Meila, M., Jordan, M.I.: Learning Fine Motion by Markov Mixtures of Experts, MIT, AI Memo No. 1567 (1995)Google Scholar
  25. 25.
    Horrey, W.J., Wickens, C.D., Consalus, K.P.: Modeling Driver’s Visual Attention Allocation While Interacting With In-Vehicle Technologies. J. Exp. Psych. 12, 67–78 (2006)Google Scholar
  26. 26.
    TORCS, http://torcs.sourceforge.net/ (visited 18.10, 2008)

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Claus Möbus
    • 1
  • Mark Eilers
    • 1
  1. 1.University of Oldenburg / OFFISGermany

Personalised recommendations