Advertisement

Application of Simulation Based Risk Assessment for Driver Assistance Systems Development

  • Jens Alsen
  • Mirella Cassani
  • Bertram Wortelen
Conference paper

Abstract

This paper proposes the application of a new methodology for an improved human error risk analysis in the current design process of driver assistance systems to a specific case study. The basic ideas of the methodology are: (1) to use well-known and existing techniques; (2) to combine them with a quasi-static approach to account for the variability and dynamicity of Human–Machine Interaction; and (3) to utilise joint cognitive models to evaluate the consequences of the HMI as well as to derive probabilities of human inadequate performances. After a general overview of the risk based design methodology and the description of the driver Model, the proposed case study is developed. Specific attention is given to the application of the driver model within the methodology.

Keywords

Driver model Risk based design Event tree Expanded human performance event tree Human error 

Notes

Acknowledgments

The research leading to these results has received funding from the European Commission Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 218552, Project ISi-PADAS

References

  1. 1.
    Bellet T, et al (2007) Cognitive modelling and computational simulation of drivers mental activities. Cacciabue PC (ed) Modelling driver behaviour in automotive environments, vol 18. Springer, London pp 315–343Google Scholar
  2. 2.
    Boer ER, et al (2005) Driver performance assessment with a car following model. In: Proceeding of the third international driving symposium on human factors in driver assessment, training and vehicle design, pp 433–440Google Scholar
  3. 3.
    Cacciabue PC (2007) (ed) Modelling driver behaviour in automotive environments. 1st edn. Springer, LondonGoogle Scholar
  4. 4.
    Cacciabue PC, Mark Vollrath (2010) The ISi-PADAS project—human modelling and simulation to support human error risk analysis of partially autonomous driver assistance systems (this issue).Google Scholar
  5. 5.
    Horrey WJ, Wickens CD, Consalus KP (2006) Modeling drivers′ visual attention allocation while interacting with in-vehicle technologies. J Exp Psychol: Appl 12(2):67–78CrossRefGoogle Scholar
  6. 6.
    ICAO—International Civil Aviation Organisation (2006) safety management manual doc 9859, AN/460, Montreal, CanadaGoogle Scholar
  7. 7.
    Kaul R, Baumann M, Wortelen B (2010) The influence of predictability and frequency of events on the gaze behaviour while driving (this issue).Google Scholar
  8. 8.
    Salvucci DD (2006) Modeling driver behavior in a cognitive architecture. Hum Factors 48(2):362–380CrossRefGoogle Scholar
  9. 9.
    Swain AD, Guttmann HE (1983) Handbook of human reliability analysis with emphasis on nuclear power plant applications. NUclear REGulatory Commission, NUREG/CR-1278, WashingtonGoogle Scholar
  10. 10.
    Wortelen B, Zilinski M, Baumann M, Muhrer E, Vollrath M, Eilers M, Lüdtke A, Möbus C (2010) Modelling aspects of longitudinal control in an integrated driver model: detection and prediction of forced decisions and visual attention allocation at varying event frequencies. This issueGoogle Scholar

Copyright information

© Springer-Verlag Italia Srl 2011

Authors and Affiliations

  1. 1.OFFIS e.V.Institute for Information TechnologyOldenburgGermany
  2. 2.KITE Solutions s.n.c.Laveno MombelloItaly

Personalised recommendations