Effects of Driver Characteristics and Driver State on Predicting Turning Maneuvers in Urban Areas: Is There a Need for Individualized Parametrization?

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 484)


In future, advanced driver assistance systems (ADAS) may be able to adapt to the needs of the driver, thus reducing the risk of information overload in complex traffic situations. One way of achieving this may include the use of predictive algorithms that anticipate the driver’s intention to perform a certain traffic maneuver based on vehicle data, such as acceleration and deceleration parameters. In order to explore whether the predictive quality of such algorithms may be mitigated by individual driver-specific parameters such as driver characteristics (i.e. emotional driving [ED] and uncritical self-awareness [US]) as well as driver state (specifically stress), an empirical test-track study was conducted with N = 40 participants. The results indicate that maximum longitudinal and lateral acceleration vary significantly depending on driver characteristics. Moreover, analyses of the collected data suggest that incorporating psychological aspects into driver models can promote new insights into driving behavior.


Maneuver prediction Driver intention Driver model Driver characteristics Driver state Driver behavior Driver assistance Urban Intersection 



Research was conducted in the context of the project “Recognition of driver intention—prediction of driver behavior” as part of the German research initiative UR:BAN (http://urban-online.org). UR:BAN is supported by the German Federal Ministry of Economics and Technology on the basis of a decision by the German Bundestag.


  1. 1.
    Bengler, K., Dietmayer, K., Farber, B., Maurer, M., Stiller, C., Winner, H.: Three decades of driver assistance systems: review and future perspectives. IEEE Intell. Transport. Syst. Mag. 6(4), 6–22 (2014)CrossRefGoogle Scholar
  2. 2.
    LSE Cities, LSE Cities Report: July 2012–September 2014. London: LSE Cities, London School of Economics and Political Science (2014)Google Scholar
  3. 3.
    Manstetten, D., Bengler, K., Busch, F., Färber, B., Lehsing, C., Neukum, A., Petermann-Stock, I., Schendzielorz, T.: “UR:BAN MV human factors in traffic”—a German research project to increase safety in urban traffic. In: 20th World Congress on Intelligent Transport Systems (ITS World Congress), pp. 596–605, Tokyo, Japan, 14–18 Oct 2013. Curran, Red Hook, NY (2014)Google Scholar
  4. 4.
    Charlton, S.G.: Restricting intersection visibility to reduce approach speeds. Accid. Anal. Prev. 35(5), 817–823 (2003)CrossRefGoogle Scholar
  5. 5.
    Beggiato, M., Krems, J.F.: The evolution of mental model, trust and acceptance of adaptive cruise control in relation to initial information. Transp. Res. Part F Traffic Psychol. Behav. 18, 47–57 (2013)CrossRefGoogle Scholar
  6. 6.
    Diederichs, F., Pöhler, G.: Driving maneuver prediction based on driver behavior observation. In: 2014 AHFE International, 5th International Conference on Applied Human Factors and Ergonomics, 19–23 July 2014, Kraków, Poland (2014)Google Scholar
  7. 7.
    Statistisches Bundesamt (Destatis): Verkehrsunfälle - Fachserie 8 Reihe 7 – 2014. Wiesbaden (2015)Google Scholar
  8. 8.
    Werneke, J., Vollrath, M.: What does the driver look at? The influence of intersection characteristics on attention allocation and driving behavior. Accid. Anal. Prev. 45, 610–619 (2012)CrossRefGoogle Scholar
  9. 9.
    Graichen, M., Färber, B.: Approaching urban intersections: visibility, glance behavior and potential analysis for predicting turning maneuvers. In: IEEE Intelligent Vehicles Symposium (IV): Gothenburg, Sweden, June 19–22 2016. IEEE (2016)Google Scholar
  10. 10.
    Liebner, M., Klanner, F.: Driver intent inference and risk assessment. In: Winner, H., Hakuli, S., Lotz, F., Singer, C. (eds.) Handbook of Driver Assistance Systems: Basic Information, Components and Systems for Active Safety and Comfort. Springer International Publishing, Cham (2016)Google Scholar
  11. 11.
    Liebner, M., Klanner, F., Baumann, M., Ruhhammer, C., Stiller, C.: Velocity-based driver intent inference at urban intersections in the presence of preceding vehicles. IEEE Intell. Transp. Syst. Mag. 5(2), 10–21 (2013)CrossRefGoogle Scholar
  12. 12.
    OECD/ECMT, Young Drivers: The Road to Safety. OECD Publishing and European Conference of Ministers of Transport, Paris (2006)Google Scholar
  13. 13.
    Ahie, L.M., Charlton, S.G., Starkey, N.J.: The role of preference in speed choice. Transp. Res. Part F Traffic Psychol. Behav. 30, 66–73 (2015)CrossRefGoogle Scholar
  14. 14.
    Schmidt, L., Piringer, A.: Wiener Testsystem: Verkehrsspezifischer Itempool (Kurzbezeichnung VIP). SCHUHFRIED GmbH, Mödling (2012)Google Scholar
  15. 15.
    Arndt, S.: Evaluierung der Akzeptanz von Fahrerassistenzsystemen: Modell zum Kaufverhalten von Endkunden. VS Verlag für Sozialwissenschaften, Wiesbaden (2011)CrossRefGoogle Scholar
  16. 16.
    Gstalter, H., Fastenmeier, W.: Reliability of drivers in urban intersections. Accid. Anal. Prev. 42(1), 225–234 (2010)CrossRefGoogle Scholar
  17. 17.
    Gaab, J.: PASA—primary appraisal secondary appraisal. Verhaltenstherapie 19(2), 114–115 (2009)CrossRefGoogle Scholar
  18. 18.
    Rammstedt, B., John, O.P.: Measuring personality in one minute or less: a 10-item short version of the big five inventory in English and German. J. Res. Pers. 41(1), 203–212 (2007)CrossRefGoogle Scholar
  19. 19.
    Hoyle, R.H., Stephenson, M.T., Palmgreen, P., Lorch, E.P., Donohew, R.: Reliability and validity of a brief measure of sensation seeking. Pers. Individ. Differ. 32(3), 401–414 (2002)CrossRefGoogle Scholar
  20. 20.
    Özkan, T., Lajunen, T.: Multidimensional traffic locus of control scale (T-LOC): factor structure and relationship to risky driving. Pers. Individ. Differ. 38(3), 533–545 (2005)CrossRefGoogle Scholar
  21. 21.
    Pereira, M., Lietz, H., Beggiato, M.: Development of a database for storage and analysis of behavioural data. In: Stevens, A., Krems, J., Brusque, C. (eds.) Driver Adaptation to Information and Assistance Systems, pp. 301–317. The Institution of Engineering and Technology (IET), London (2014)Google Scholar
  22. 22.
    Westland, J.C.: Structural Equation Models. Stud. Syst. Decis. Control 22(5), 152 (2015)MathSciNetGoogle Scholar
  23. 23.
    Field, A.: Discovering statistics using IBM SPSS statistics: and sex and drugs and rock ‘n’ roll, 4th edn. Sage, Los Angeles (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Human Factors InstituteUniversität der Bundeswehr MünchenNeubibergGermany

Personalised recommendations