Advertisement

Review of Models of Driver Behaviour and Development of a Unified Driver Behaviour Model for Driving in Safety Critical Situations

Conference paper

Abstract

Driver behaviour can be modelled in one of two approaches: ‘Descriptive’ models that describe the driving task in terms of what the driver does, and ‘Functional’ models that attempt to explain why the driver behaves the way he/she does, and how to predict drivers’ performance in demanding and routine situations. Demanding situations elicit peak performance capabilities, and routine situations elicit typical (not necessarily best) behaviour. It seems that the optimal approach might be a hybrid of several types of models, extracting the most useful features of each.

In recent years, a variety of driver support and information management systems have been designed and implemented with the objective of improving safety as well as performance of vehicles. To predict the impact of various assistance systems on driver behaviour predictive models of the interaction of the driver with the vehicle and the environment are necessary. The first step of the ITERATE project is to critically review existing Driver-Vehicle-Environment (DVE) models and identify the most relevant drivers’ parameters and variables that need to be included in such models: (a) in different surface transport modes (this paper deals with road vehicles only, other transport domains are detailed in D1.1 & D1.2 of the ITERATE project), and (b) in different safety critical situations. On the basis of this review, we propose here a Unified Model of Driver behaviour (UMD), that is a hybrid model of the two approaches. The model allows for individual differences on pre-specified dimensions and includes the vehicle and environmental parameters. Within the ITERATE project this model will be used to support safety assessment of innovative technologies (based on the abilities, needs, driving style and capacity of the individual drivers). In this brief paper we describe only the behaviour of a single test driver, while the environment and vehicle are defined as parameters with fixed values (and detailed in D1.2 of the ITERATE project). The selected driver characteristics (and variables used to measure them) are culture (Country), attitudes/personality (Sensation Seeking), experience (Hazard Perception Skills), driver state (Fatigue), and task demand (Subjective workload).

Keywords

Driver behaviour models Culture Attitudes/personality Experience Driver state Task demand 

Notes

Acknowledgment

The research leading to these results was funded by the European Commission Seventh Framework Programme (FP7/2007-2013) under grant agreement no. FP7-SST-2007-RTD-1 Project ITERATE. We thank all the ITERATE partners who contributed significantly to this paper.

References

  1. 1.
    Carsten O (2007) From driver models to modelling the driver: what do we really need to know about the driver? In: Cacciabue PC (ed) Modelling driver behaviour in automotive environments. Springer, London, pp 105–120CrossRefGoogle Scholar
  2. 2.
    European Road Safety Observatory (2006) Fatigue, retrieved July 2009 from www.erso.eu
  3. 3.
    Factor R, Mahalel D, Yair G (2007) The social accident: a theoretical model and a research agenda for studying the influence of social and cultural characteristics on motor vehicle accidents. Acc Anal Prev 39(2007):914–921CrossRefGoogle Scholar
  4. 4.
    Fastenmeier W, Gstalter H (2007) Driving task analysis as a tool in traffic safety research and practice. Saf Sci 45:952–979Google Scholar
  5. 5.
    Fuller R (1984) A conceptualization of driving behavior as threat avoidance. Ergonomics 11:1139–1155CrossRefGoogle Scholar
  6. 6.
    Gibson JJ (1966) The senses considered as perceptual systems. Houghton Mifflin, BostonGoogle Scholar
  7. 7.
    Hollnagel E, Nåbo A, Lau I (2003) A systemic model for Driver-in-Control. In: 2nd international driving symposium on human factors in driver assessment, training, and vehicle design, Park City.Google Scholar
  8. 8.
    Horswill MS, McKenna FP (2004) Drivers’ hazard perception ability: situation awareness on the road. In: Banbury S, Tremblay S (eds) A cognitive approach to situation awareness: Theory and application. Ashgate Publishing, Aldershot, pp 155–175Google Scholar
  9. 9.
    Jonah BA, Thiessen R, Au-Yeung E (2001) Sensation seeking, risky driving and behavioral adaptation. Accid Anal Prev 33:679–684CrossRefGoogle Scholar
  10. 10.
    Keith K, Trentacoste M, Depue L, Granada T, Huckaby E, Ibarguen B, Kantowitz B, Lum W, Wilson T (2005) Roadway human factors and behavioral safety in Europe. Federal Highway Administration, report no. FHWA-PL-05–005 US. Department of Transportation, Washington, DCGoogle Scholar
  11. 11.
    Lee (2008) Fifty years of driving safety research human factors. Hum Factors Ergon Soc 50(3):521–528Google Scholar
  12. 12.
    Lindgren A, Broström R, Chen A, Bengtsson P (2007) Driver attitudes towards Advanced Driver Assistance Systems—cultural differences and similarities. In: de Waard D, Hockey GRJ, Nickel P, Brookhuis KA (eds) Human factors issues in complex system performance. Shaker Publishing, Maastricht, pp 205–215Google Scholar
  13. 13.
    McKenna FP, Crick J (1997) Developments in hazard perception. TRL Report 297. Transport Research Laboratory, CrowthorneGoogle Scholar
  14. 14.
    McLeod R, Walker G, Moray N (2005) Analysing and modelling train driver performance. Appl Ergon 36(6):671–680CrossRefGoogle Scholar
  15. 15.
    McRuer DT, Allen RW, Weir DH, Klein R (1977) New results in driver steering control models. Hum Factors 17:381–397 (as cited by Fastenmeier & Gstalter, 2007)Google Scholar
  16. 16.
    McRuer DT, Weir DH (1969) Theory of manual vehicular control. Ergonomics 12(4):599–633Google Scholar
  17. 17.
    Michon JA (1985) A critical view of driver behaviour models what do we know, what should we do? In Evans L, Schwing R (eds) Human behaviour and traffic safety. Plenum press, New York, pp 485–525.Google Scholar
  18. 18.
    Naatanen R, Summala H (1976) Road user behaviour and traffic accidents. North Holland Publishing Company, New YorkGoogle Scholar
  19. 19.
    Parasuraman R, RILEY V (1997) Humans and automation: use, misuse, disuse, abuse. Hum Factors 39(2):230–253CrossRefGoogle Scholar
  20. 20.
    Ranney TA (1994) Models of driving behavior: a review of their evolution. Accid Anal Prev 26(6), pp 733–750Google Scholar
  21. 21.
    Rasmussen J (1986) Information processing and human-machine interaction: an approach to cognitive engineering. Elsevier Science Inc.Google Scholar
  22. 22.
    Rudin-Brown CM, Noy YI (2002) Investigation of behavioural adaptation to lane departure warning systems. Trans Res Rec 1803:30–37CrossRefGoogle Scholar
  23. 23.
    Sagberg Bjørnskau (2006) Hazard perception & driving experience among novice drivers. Accid Anal Prev 38(2):407–414CrossRefGoogle Scholar
  24. 24.
    Salvucci DD (2006) Modeling driver behavior in a cognitive architecture. Hum Factors 48(2):362–380CrossRefGoogle Scholar
  25. 25.
    SARTRE 3 consortium (2004) European drivers and road risk, Part 1 Report on principle analysesGoogle Scholar
  26. 26.
    Sheridan TB (1970) Big brother as driver: new demands and problems for the man at the wheel. Hum Factors 12:95–101Google Scholar
  27. 27.
    Sheridan TB (2004) Driver distraction from a control theory perspective. Hum Factors 46:587–599CrossRefGoogle Scholar
  28. 28.
    Shinar D (2007) Traffic Safety and Human Behavior. Elsevier, OxfordGoogle Scholar
  29. 29.
    Strahan C, Watson B, Lennonb A (2008) Can organisational safety climate and occupational stress predict work-related driver fatigue? Trans Res Part F 11(2008):418–426Google Scholar
  30. 30.
    Weller G, Schlag B, Gatti G, Jorna R, Leur M.v.d. (2006) Human factors in road design state of the art and empirical evidence Report 8.1 RiPCORD-iSERESTGoogle Scholar
  31. 31.
    Wickens CD (1992) Engineering psychology and human performance, 2nd edn. Harper Collins, New York (as cited by Ranney, 1994)Google Scholar
  32. 32.
    Wilde GJS (1982) The theory of risk homeostasis: Implications for safety and health. Risk Anal 2:209–225CrossRefGoogle Scholar
  33. 33.
    Zuckerman M (1994) Behavioral expressions and biosocial bases of sensation seeking. University of Cambridge Press, CambridgeGoogle Scholar

Copyright information

© Springer-Verlag Italia Srl 2011

Authors and Affiliations

  1. 1.Ben-Gurion University of the NegevBeer ShevaIsrael

Personalised recommendations