Determining Infrastructure- and Traffic Factors that Increase the Perceived Complexity of Driving Situations

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1212)


When designing experimental studies in the driving domain, an important decision is which driving scenarios to include. It is proposed that HMI need to be adaptive to the complexity of the driving situation, in order to avoid overloading the driver. To further study adaptive HMI a comprehensive list of factors that determine the perceived complexity of a driving situation is required, yet absent. In this, infrastructure- and traffic characteristics that may influence the perceived complexity of a driving situation were collected from literature. Next, four sets of driving scenarios of varying complexities were created and validated in an online survey. The results of this study include: 1) a list of infrastructure- and traffic characteristics that influence the overall complexity of a driving situation, and 2) validated scenarios of varying complexities. These outcomes help researchers and designers in setting up future driving studies.


Automated driving Human-machine interaction Driving situations Complexity 


Funding Statement

This research is supported by the Dutch Domain Applied and Engineering Sciences, which is part of the Netherlands Organisation for Scientific Research (NWO), and which is partly funded by the Ministry of Economic Affairs [project number 14896].


  1. 1.
    Boelhouwer, A., van Dijk, J., Martens, M.H.: Turmoil behind the automated wheel. In: Krömker, H. (ed.) HCI in Mobility, Transport, and Automotive Systems, pp. 3–25. Springer, Cham (2019).
  2. 2.
    Lilis, Y., Zidianakis, E., Partarakis, N., Antona, M., Stephanidis, C.: Personalizing HMI elements in ADAS using ontology meta-models and rule based reasoning. In: International Conference on Universal Access in Human-Computer Interaction, pp. 383–401. Springer, Cham (2017).
  3. 3.
    Mueller, M.: Deficiency drive. Vision Zero International, pp. 44–47 (2014)Google Scholar
  4. 4.
    Kroon, E.C.M., Martens, M.H., Brookhuis, K., Hagenzieker, M., Alferdinck, J.W.A.M., Harms, I., Hof, T.: Human factor guidelines for the design of safe in-car traffic information services (2016)Google Scholar
  5. 5.
    Radlmayr, J., Gold, C., Lorenz, L., Farid, M., Bengler, K.J.: How traffic situations and non-driving related tasks affect the take-over quality in highly automated driving. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, pp. 2063–2067 (2014).
  6. 6.
    Lyu, N., Xie, L., Wu, C., Fu, Q., Deng, C.: Driver’s cognitive workload and driving performance under traffic sign information exposure in complex environments: a case study of the highways in China. Int. J. Environ. Res. Public Health 14, 1–25 (2017). Scholar
  7. 7.
    Gold, C., Körber, M., Lechner, D., Bengler, K.J.: Taking over control from highly automated vehicles in complex traffic situations. Hum. Fact. J. Hum. Fact. Ergon. Soc. 58, 642–652 (2016). Scholar
  8. 8.
    Jamson, A.H., Merat, N., Carsten, O., Lai, F.C.H.: Behavioural changes in drivers experiencing highly-automated vehicle control in varying traffic conditions. Transp. Res. Part C Emerg. Technol. 30, 116–125 (2013). Scholar
  9. 9.
    Fastenmeier, W.: Die Verkehrssituation als Analyseeinheit im Verkehrssystem. In: Fastenmeier, W. (ed.) Autofahrer und Verkehrssituation - Neue Wege zur Bewertung von Sicherheit und Zuverlässigkeit moderner Straßenverkehrssysteme, pp. 27–82. Verlag TÜV Rheinland, Köln (1995)Google Scholar
  10. 10.
    Benda, H.V.: Scaling of the dangerousness of road traffic situations. Part 1: A classification system for road traffic situations from the drivers’ perspective (1977)Google Scholar
  11. 11.
    Hackman, J.R.: Tasks and task performance in research on stress. In: McGrath, J.E. (ed.) Social and Psychological Factors in Stress, pp. 202–237. Rinehart & Winston, New York (1970)Google Scholar
  12. 12.
    Matthews, G., Tsuda, A., Xin, G., Ozeki, Y.: Individual differences in driver stress vulnerability in a Japanese sample. Ergonomics 42, 401–415 (1999). Scholar
  13. 13.
    De Waard, D.: Driving behaviour on a high-accident-rate motorway in the Netherlands. In: Weikert, C., Brookhuis, K.A., Ovidius (eds.) Proceedings of the Europe Chapter of the Human Factors Society Annual Meeting (1991)Google Scholar
  14. 14.
    Zeitlin, L.R.: Estimates of driver mental workload: a long-term field trial of two subsidiary tasks. Hum. Fact. J. Hum. Fact. Ergon. Soc. 37, 611–621 (2006). Scholar
  15. 15.
    Casler, K., Bickel, L., Hackett, E.: Separate but equal? A comparison of participants and data gathered via Amazon’s MTurk, social media, and face-to-face behavioral testing. Comput. Hum. Behav. 29, 2156–2160 (2013). Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of TwenteEnschedeThe Netherlands
  2. 2.Eindhoven University of TechnologyEindhovenThe Netherlands
  3. 3.TNO Traffic and TransportThe HagueThe Netherlands

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