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A Validated Failure Behavior Model for Driver Models to Test Automated Driving Functions

  • Bernd HuberEmail author
  • Christoph Sippl
  • Reinhard German
  • Anatoli Djanatliev
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1018)

Abstract

This contribution proposes a failure behavior model for driver models, which is validated by findings from accident research. Our concept is based on the five-step-method which is used in accident research. Based on this concept, we present a prototypical implementation of an information processing failure and validate the implemented failure model on the basis of a real traffic accident. In conclusion, we discuss and interpret the validation results.

Keywords

Human modeling Failure behavior modeling Simulation 

References

  1. 1.
    McKinsey & Company: Automotive revolution – perspective towards 2030: how the convergence of disruptive technology-driven trends could transform the auto industry. Report, Advanced Industries (2016)Google Scholar
  2. 2.
    Singh, S.: Critical reasons for crashes investigated in the national motor vehicle crash causation survey. Report, Traffic Safety Facts – Crash (2015)Google Scholar
  3. 3.
    Winner, H., Wachenfeld, W., Junietz, P.: Validation and introduction of automated driving. In: Winner, H., Prokop, G., Maurer, M. (eds.) Automotive Systems Engineering II, pp. 177–196. Springer, Cham (2018)CrossRefGoogle Scholar
  4. 4.
    Ulbrich, S., Schuldt, F., Homeier, K., Steinhoff, M., Menzel, T., Krause, J., Maurer, M.: Testing and validating tactical lane change behavior planning for automated driving. In: Watzenig, D., Horn, M. (eds.) Automated Driving, pp. 451–471. Springer, Cham (2017)Google Scholar
  5. 5.
    Schuldt, F., Reschka, A., Maurer, M.: A method for an efficient, systematic test case generation for advanced driver assistance systems in virtual environments. In: Winner, H., Prokop, G., Mauerer, M. (eds.) Automotive Systems Engineering II, pp. 147–175. Springer, Cham (2017)Google Scholar
  6. 6.
    Bagschick, G., Menzel, T., Maurer, M.: Ontology based scene creation for the development of automated vehicles. In: 2018 IEEE Intelligent Vehicles Symposium (IV), pp. 1813–1820 (2018)Google Scholar
  7. 7.
    Lotz, F.G.O.: Eine Referenzarchitektur für die assistierte und automatisierte Fahrzeugführung mit Fahrereinbindung. Doctoral dissertation (2017)Google Scholar
  8. 8.
    Markkula, G., Benderius, O., Wahde, M.: Comparing and validating models of driver steering behaviour in collision avoidance and vehicle stabilization. In: 52th Vehicle systems dynamics, pp. 1658–1680 (2014)CrossRefGoogle Scholar
  9. 9.
    Weber, S., Ernstberger, A., Donner, E., Kiss, M.: Learning from accidents: using technical and subjective information to identify accident mechanisms and to develop driver assistance systems. In: Dorn, L., Sullman, M. (eds.) Driver Behaviour and Training, vol. VI, pp. 223–230. Ashgate, Surrey (2013)Google Scholar
  10. 10.
    Michon, J.A.: A critical view of driver behavior models: what do we know, what should we do? In: Human Behavior and Traffic Safety, pp. 485—524. Plenum Press, New York (1985)CrossRefGoogle Scholar
  11. 11.
  12. 12.
    Gesamtverband der Deutschen Versicherungswirtschaft e. V.: Unfalltypen-Katalog. https://udv.de/sites/default/files/tx_udvpublications/unfalltypen-katalog_udv_web_2.pdf

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Bernd Huber
    • 1
    Email author
  • Christoph Sippl
    • 1
  • Reinhard German
    • 2
  • Anatoli Djanatliev
    • 2
  1. 1.Simulation Electric/ElectronicAUDI AGIngolstadtGermany
  2. 2.Computer Networks and Communication SystemsFriedrich-Alexander Universität ErlangenErlangenGermany

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