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Towards a Real-Time Driver Workload Estimator: An On-the-Road Study

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

Abstract

Driver distraction is a leading cause of crashes. The introduction of in-vehicle technology in the last decades has added support to the driving task. However, in-vehicle technologies and handheld electronic devices may also be a threat to driver safety due to information overload and distraction. Adaptive in-vehicle information systems may be a solution to this problem. Adaptive systems could aid the driver in obtaining information from the device (by reducing information density) or prevent distraction by not presenting or delaying information when the driver’s workload is high. In this paper, we describe an on-the-road evaluation of a real-time driver workload estimator that makes use of geo-specific information. The results demonstrate the relative validity of our experimental methods and show the potential for using location-based adaptive in-vehicle systems.

Keywords

  • Driver distraction
  • Adaptive in-vehicle information (systems)
  • Driver workload estimation

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References

  1. Dingus, T.A., Guo, F., Lee, S., Antin, J.F., Perez, M., Buchanan-King, M., Hankey, J.: Driver crash risk factors and prevalence evaluation using naturalistic driving data. Proc. Natl. Acad. Sci. 113, 2636–2641 (2016)

    CrossRef  Google Scholar 

  2. Klauer, S.G., Dingus, T.A., Neale, V.L., Sudweeks, J.D., Ramsey, D.J.: The impact of driver inattention on near-crash/crash risk: an analysis using the 100-car naturalistic driving. Technical report, Virginia Tech Transportation Institute (2006)

    Google Scholar 

  3. Lee, J.D.: Technology and teen drivers. J. Saf. Res. 38, 203–213 (2007)

    CrossRef  Google Scholar 

  4. Verwey, W.B.: On-line driver workload estimation. Effects of road situation and age on secondary task measures. Ergonomics 43, 187–209 (2000)

    CrossRef  Google Scholar 

  5. Kountouriotis, G.K., Merat, N.: Leading to distraction: Driver distraction, lead car, and road environment. Accid. Anal. Prev. 89, 22–30 (2016)

    CrossRef  Google Scholar 

  6. Dingus, T.A., Klauer, S.G., Neale, V.L., Petersen, A., Lee, S.E., Sudweeks, J., Perez, M.A., Hankey, J., Ramsey, D., Gupta, S., Bucher, C., Doerzaph, Z.R., Jermeland, J., Knipling, R.R.: The 100-car naturalistic driving study, phase II—results of the 100-car field experiment. Technical report, Virginia Tech Transportation Institute (2006)

    CrossRef  Google Scholar 

  7. Engström, J., Johansson, E., Östlund, J.: Effects of visual and cognitive load in real and simulated motorway driving. Transp. Res. F Traffic Psychol. Behav. 8, 97–120 (2005)

    CrossRef  Google Scholar 

  8. Santos, J., Merat, N., Mouta, S., Brookhuis, K., De Waard, D.: The interaction between driving and in-vehicle information systems: comparison of results from laboratory, simulator and real-world studies. Transp. Res. F Traffic Psychol. Behav. 8, 135–146 (2005)

    CrossRef  Google Scholar 

  9. Hibberd, D.L., Jamsom, S.L., Carsten, O.M.J.: Mitigating the effects of in-vehicle distractions through the use of the psychological refractory period paradigm. Accid. Anal. Prev. 50, 1096–1103 (2013)

    CrossRef  Google Scholar 

  10. Green, P.: Crashes induced by driver information systems and what can be done to reduce them. In: Conference Proceedings Society of Automotive Engineers, Warrendale, PA (2000)

    Google Scholar 

  11. Hancock, P.A., Verwey, W.B.: Fatigue, workload and adaptive driver systems. Accid. Anal. Prev. 29, 495–506 (1997)

    CrossRef  Google Scholar 

  12. Piersma, E.H.: Real time modelling of user workload. In: Quéinnee, Y., Daniellou, F. (eds.) Designing for Everyone, pp. 1547–1549. Taylor & Francis, London (1991)

    Google Scholar 

  13. Zhang, Y., Owechko, Y., Zhang, J.: Driver cognitive workload estimation: a data-driven perspective. IEEE Intelligent Transportation Systems, Washington D.C., USA (2004)

    Google Scholar 

  14. Verwey, W.B.: How can we prevent overload of the driver? In: Parkes A.M., Franzen S. (eds.) Driving Future Vehicles, pp. 235–244. Taylor & Francis, London (1993)

    Google Scholar 

  15. Piechulla, W., Mayser, C., Gehrke, H., König, W.: Reducing drivers’ mental workload by means of an adaptive man-machine interface. Transp. Res. F 6, 233–248 (2003)

    CrossRef  Google Scholar 

  16. Brookhuis, K.A., De Waard, D.: Monitoring drivers’ mental workload in driving simulators using physiological measures. Accid. Anal. Prev. 42, 898–903 (2010)

    CrossRef  Google Scholar 

  17. Recarte, M.A., Perez, E., Conchillo, A., Nunes, L.M.: Mental workload and visual impairment: differences between pupil, blink, and subjective rating. Span. J. Psychol. 11, 374–385 (2008)

    Google Scholar 

  18. May, J.G., Kennedy, R.S., Williams, M.C., Dunlap, W.P., Brannan, J.R.: Eye movement indices of mental workload. Acta Psychol. 75, 75–89 (1990)

    CrossRef  Google Scholar 

  19. He, J., McCarley, J.S.: Effects of cognitive distraction on lane-keeping performance loss or improvement? In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, pp. 1894–1898. Sage Publications (2011)

    Google Scholar 

  20. Pauzie, A.: A method to assess the driver mental workload: the driving activity load index (DALI). Intell. Transport Syst. 2, 315–322 (2008)

    CrossRef  Google Scholar 

  21. ISO 8596:2009: Ophthalmic optics—visual acuity testing—standard optotype and its presentation. Geneva, Switzerland (2009)

    Google Scholar 

  22. Zijlstra, F.R.H., Van Doorn, L.: Construction of a scale to measure perceived effort. Department of Philosophy and Social Sciences, Delft University of Technology, Delft, Netherlands (1985)

    Google Scholar 

  23. Van Leeuwen, P.M., Gómez i Subils, C., Jimenez, A.R., Happee, R., De Winter, J.C.F.: Effects of visual fidelity on curve negotiation, gaze behaviour and simulator discomfort. Ergonomics. 58, 1347–1364 (2015)

    Google Scholar 

  24. Rendon-Velez, E., Van Leeuwen, P.M., Happee, R., Horváth, I., Van der Vegte, W.F., De Winter, J.C.F.: The effects of time pressure on driver performance and physiological activity: a driving simulator study. Transp. Res. F: traffic psychology and behaviour 24, 169–182 (2016)

    Google Scholar 

  25. Beatty, J.: Task-evoked pupillary responses, processing load, and the structure of processing resources. Psychol. Bull. 91, 276–292 (1982)

    CrossRef  Google Scholar 

  26. Van Leeuwen, P.M., Happee, R., De Winter, J.C.F.: Changes of driving performance and gaze behavior of novice drivers during a 30-min simulator-based training. Procedia Manufact. 3, 3325–3332 (2015)

    CrossRef  Google Scholar 

  27. Bartmann, A., Spijkers, W., Hess, M.: Street environment, driving speed and field of vision. In: Gale A.G., Brown I.D. (eds.) Vision in Vehicles III, pp. 381–389 (1991)

    Google Scholar 

  28. Reimer, B., Mehler, B.: The impact of cognitive workload on physiological arousal in young adult drivers: a field study and simulation validation. Ergonomics 54, 932–942 (2011)

    CrossRef  Google Scholar 

  29. Mehler, B., Reimer, B., Coughlin, J., Dusek, J.: Impact of incremental increases in cognitive workload on physiological arousal and performance in young adult drivers. Transp. Res. Rec. J. Transp. Res. Board 2138, 6–12 (2009)

    CrossRef  Google Scholar 

  30. Bernardi, L., Wdowczyk-Szulc, J., Valenti, C., Castoldi, S., Passino, C., Spadacini, G., Sleight, P.: Effects of controlled breathing, mental activity and mental stress with or without verbalization on heart rate variability. J. Am. Coll. Cardiol. 35, 1462–1469 (2000)

    CrossRef  Google Scholar 

  31. Watson, A.B., Yellott, J.I.: A unified formula for light-adapted pupil size. J. Vision 12, 1–16 (2012)

    Google Scholar 

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Acknowledgments

We thank Menno Merts (vehicle and test equipment preparation), Arjan Stuiver, Dick Lenior (test set up/development), and Henny Wilke (test leader) for their support in the research project. The research was supported by the Netherlands Organisation for Scientific Research (NWO) of the Ministry of Education, Culture and Science through the RAAK-PRO project: “ADVICE: Advanced Driver Vehicle Interface in a Complex Environment”. RAAK-PRO focusses on the enhancement of applied scientific research by Universities of Applied Sciences, in cooperation with the industry.

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van Leeuwen, P. et al. (2017). Towards a Real-Time Driver Workload Estimator: An On-the-Road Study. In: Stanton, N., Landry, S., Di Bucchianico, G., Vallicelli, A. (eds) Advances in Human Aspects of Transportation. Advances in Intelligent Systems and Computing, vol 484. Springer, Cham. https://doi.org/10.1007/978-3-319-41682-3_94

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  • DOI: https://doi.org/10.1007/978-3-319-41682-3_94

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