Estimating Driver Workload with Systematically Varying Traffic Complexity Using Machine Learning: Experimental Design

  • Udara E. ManawaduEmail author
  • Takahiro Kawano
  • Shingo Murata
  • Mitsuhiro Kamezaki
  • Shigeki Sugano
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 722)


Traffic complexity is one of the factors affecting driver workload. In order to study the relationship between traffic complexity levels and workload, a designed experiment is required, especially to vary traffic flow parameters systematically in a simulated environment. This paper describes the experimental design of a simulator study for developing a computational model to estimate the behavior of driver workload based on traffic complexity. Driving simulators allow creating and testing different traffic scenarios and manipulating independent variables to improve the quality of data, as compared to real world experiments. Physiological responses such as heart rate, skin conductance, and pupil size have been found to be related to workload. By adapting a data-driven method, we integrated electrocardiography sensors, electro-dermal activity sensors, and eye-tracker to acquire driver physiological signals and gaze information. Preliminary results show a positive correlation between traffic complexity levels and corresponding physiological responses, performance, and subjective measures.


Driving simulation Driver workload Intelligent vehicles 


  1. 1.
    Brookhuis, K.A., de Waard, D.: Monitoring drivers’ mental workload in driving simulators using physiological measures. Accid. Anal. Prev. 42, 898–903 (2010)CrossRefGoogle Scholar
  2. 2.
    Bendat, J.S., Piersol, A.G.: Random Data: Analysis and Measurement Procedures, vol. 729. Wiley (2011)Google Scholar
  3. 3.
    de Waard, D.: The measurement of drivers’ mental workload. Ph.D. thesis, University of Groningen, Traffic Research Centre, Haren, The Netherlands (1996) Google Scholar
  4. 4.
    Jahn, G., Oehme, A., Krems, J.F., Gelau, C.: Peripheral detection as a workload measure in driving: effects of traffic complexity and route guidance system use in a driving study. Transp. Res. Part F Traffic Psychol. Behav. 8, 255–275 (2005)CrossRefGoogle Scholar
  5. 5.
    Teh, E., Jamson, S., Carsten, O., Jamson, H.: Temporal fluctuations in driving demand: the effect of traffic complexity on subjective measures of workload and driving performance. Transp. Res. Part F Traffic Psychol. Behav. 22, 207–217 (2014)CrossRefGoogle Scholar
  6. 6.
    Faure, V., Lobjois, R., Benguigui, N.: The effects of driving environment complexity and dual tasking on drivers’ mental workload and eye blink behavior. Transp. Res. Part F Traffic Psychol. Behav. 40, 78–90 (2016)CrossRefGoogle Scholar
  7. 7.
    Piechulla, W., Mayser, C., Gehrke, H., König, W.: Reducing drivers’ mental workload by means of an adaptive man–machine interface. Transp. Res. Part F Traffic Psychol. Behav. 6, 233–248 (2003)CrossRefGoogle Scholar
  8. 8.
    Solovey, E.T., Zec, M., Abdon, E., Perez, G., Reimer, B., Mehler, B.: Classifying driver workload using physiological and driving performance data: two field studies. In: Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing System, pp. 4057–4066 (2014) Google Scholar
  9. 9.
    Zhang, Y., Kaber, D.B., Rogers, M., Liang, Y., Gangakhedkar, S.: The effects of visual and cognitive distractions on operational and tactical driving behaviors. Hum. Factors J. Hum. Factors Ergon. Soc. 56(3), 592–604 (2013)CrossRefGoogle Scholar
  10. 10.
    Liang, Y., Reyes, M.L., Lee, J.D.: Real-time detection of driver cognitive distraction using support vector machines. IEEE Trans. Intell. Transp. Syst. 8, 340–350 (2007)CrossRefGoogle Scholar
  11. 11.
    Liao, Y., Li, S.E., Wang, W., Wang, Y., Li, G., Cheng, B.: Detection of driver cognitive distraction: a comparison study of stop-controlled intersection and speed-limited highway. IEEE Trans. Intell. Transp. Syst. 17(6), 1628–1637 (2016)CrossRefGoogle Scholar
  12. 12.
    Lapedes, A., Farber, R.: Nonlinear signal processing using neural networks: prediction and system modelling. In: IEEE International Conference on Neural Networks (1987)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Udara E. Manawadu
    • 1
    Email author
  • Takahiro Kawano
    • 1
  • Shingo Murata
    • 1
  • Mitsuhiro Kamezaki
    • 1
  • Shigeki Sugano
    • 1
  1. 1.Waseda UniversityTokyoJapan

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