Skip to main content
SpringerLink
Log in

Estimating the Driver’s Workload

Using Smartphone Data to Adapt In-Vehicle Information Systems

  • Conference paper
KI 2013: Advances in Artificial Intelligence (KI 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8077))

Included in the following conference series:

  • 1534 Accesses

Abstract

The use of in-vehicle information systems has increased in the past years. These systems assist the user but can as well cause additional cognitive load. The study presented in this paper was carried out to enable workload estimation in order to adapt information and entertainment systems so that an optimal driver performance and user experience is ensured. For this purpose smartphone sensor data, situational factors and basic user characteristics are taken into account. The study revealed that the driving situation, the gender of the user and the frequency of driving significantly influence the user’s workload. Using only this information and smartphone sensor data the current workload of the driver can be estimated with 86% accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Biermann, A., Eick, E.M., Brünken, R., Debus, G., Leutner, D.: Development and first evaluation of a prediction model for risk of offences and accident involvement among young drivers. Driver Behaviour and Training 2, 169–178 (2005)

    Google Scholar 

  2. Brooke, J.: SUS - A quick and dirty usability scale. Redhatch Consulting, United Kingdom (2011)

    Google Scholar 

  3. Bubb, H.: Fahrerassistenz primär ein beitrag zum komfort oder für die sicherheit? VDI-Berichte, pp. 25–44 (2003)

    Google Scholar 

  4. Cain, B.: A review of the mental workload literature. Tech. rep., DTIC Document (2007)

    Google Scholar 

  5. Cherri, C., Nodari, E., Toffetti, A.: Review of existing tools and methods. Tech. rep., AIDE Deliverable D2.1.1 (2004)

    Google Scholar 

  6. Csikszentmihalyi, M.: FLOW. Das Geheimnis des Glücks. Klett-Cotta, Stuttgart (2008)

    Google Scholar 

  7. Eggemeier, F., Wilson, G., Kramer, A., Damos, D.: Workload assessment in multi-task environments. In: Damos, D. (ed.) Multiple Task Performance, pp. 207–216. Taylor & Francis, London (1991)

    Google Scholar 

  8. Ford, https://media.ford.com/content/fordmedia/fna/us/en/news/2012/06/27/ford-research-developing-intelligent-system-to-help-drivers-mana.html

  9. Fuller, R.: The task-capability interface model of the driving process. Recherche-Transports-Sécurité 66, 47–57 (2000)

    Google Scholar 

  10. Fuller, R.: Towards a general theory of driver behaviour. Accident Analysis & Prevention 37(3), 461–472 (2005)

    Article  Google Scholar 

  11. Gaczek, D.: Entwurf und Regelung eines Verbrauchsassistenten. GRIN Verlag (2009)

    Google Scholar 

  12. Gopher, D., Donchin, E.: Workload - an examination of the concept. In: Boff, K., Kaufman, L., Thomas, J. (eds.) Handbook of Perception and Human Performance. Cognitive Processes and Performance, vol. 2, pp. . 41:1–41:49. Wiley, New York (1986)

    Google Scholar 

  13. Hale, A., Stoop, J., Hommels, J.: Human error models as predictors of accident scenarios for designers in road transport systems. Ergonomics 33(10-11), 1377–1387 (1990)

    Article  Google Scholar 

  14. Hart, S.G., Staveland, L.E.: Development of nasa-tlx (task load index): Results of empirical and theoretical research. Human Mental Workload 1(3), 139–183 (1988)

    Article  Google Scholar 

  15. Jex, H.R.: Measuring mental workload: Problems, progress, and promises. Advances in Psychology 52, 5–39 (1988)

    Article  Google Scholar 

  16. Lysaght, R.J., Hill, S.G., Dick, A., Plamondon, B.D., Linton, P.M.: Operator workload: Comprehensive review and evaluation of operator workload methodologies. Tech. rep., DTIC Document (1989)

    Google Scholar 

  17. Ma, R., Kaber, D.B.: Situation awareness and workload in driving while using adaptive cruise control and a cell phone. International Journal of Industrial Ergonomics 35(10), 939–953 (2005)

    Article  Google Scholar 

  18. Matthews, R., Legg, S., Charlton, S.: The effect of cell phone type on drivers subjective workload during concurrent driving and conversing. Accident Analysis & Prevention 35(4), 451–457 (2003)

    Article  Google Scholar 

  19. Mayser, C., Ebersbach, D., Dietze, M., Lippold, C.: Fahrerassistenzsysteme zur unterstützung der längsregelung im ungebundenen verkehr. In: Conference Aktive Sicherheit durch Fahrerassistenz (2004)

    Google Scholar 

  20. Michon, J.A.: A critical view of driver behavior models: what do we know, what should we do? Springer (1986)

    Google Scholar 

  21. Oron-Gilad, T., Ronen, A., Shinar, D.: Alertness maintaining tasks (amts) while driving. Accident Analysis & Prevention 40(3), 851–860 (2008)

    Article  Google Scholar 

  22. Pauzié, A.: Evaluating driver mental workload using the driving activity load index (dali). In: Proc. of European Conference on Human Interface Design for Intelligent Transport Systems, pp. 67–77 (2008)

    Google Scholar 

  23. Pauzié, A., Manzano, J.: Evaluation of driver mental workload facing new in-vehicle information and communication technology. In: Proceedings of the 20th Enhanced Safety of Vehicles Conference (ESV20), Lyon, France, vol. 10 (2007)

    Google Scholar 

  24. Recarte, M.A., Nunes, L.M.: Mental workload while driving: Effects on visual search, discrimination, and decision making. Journal of Experimental Psychology Applied 9(2), 119–133 (2003)

    Article  Google Scholar 

  25. Schweitzer, J., Green, P.: Task acceptability and workload of driving city streets, rural roads, and expressways: Ratings from video clips (2007)

    Google Scholar 

  26. Taubman-Ben-Ari, O., Mikulincer, M., Gillath, O.: The multidimensional driving style inventory scale construct and validation. Accident Analysis & Prevention 36(3), 323–332 (2004)

    Article  Google Scholar 

  27. Tsimhoni, O., Green, P.: Visual demand of driving and the execution of display-intensive in-vehicle tasks. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 45, pp. 1586–1590. SAGE Publications (2001)

    Google Scholar 

  28. Verwey, W.B.: On-line driver workload estimation. effects of road situation and age on secondary task measures. Ergonomics 43(2), 187–209 (2000)

    Article  Google Scholar 

  29. de Waard, D.: The measurement of drivers’ mental workload. Groningen University, Traffic Research Center (1996)

    Google Scholar 

  30. Wundersitz, L., Burns, N.: Identifying young driver subtypes: relationship to risky driving and crash involvement. Driver Behaviour And Training 2, 155 (2005)

    Google Scholar 

  31. Zeitlin, L.R.: Micromodel for objective estimation of driver mental workload from task data. Transportation Research Record: Journal of the Transportation Research Board 1631(1), 28–34 (1998)

    Article  Google Scholar 

  32. Zhang, Y., Owechko, Y., Zhang, J.: Learning-based driver workload estimation. In: Prokhorov, D. (ed.) Computational Intelligence in Automotive Applications. SCI, vol. 132, pp. 1–17. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  33. Zijstra, C., Doorn, R.V.: The construction of a scale to measure perceived effort. Tech. rep., Department of Philosophy and Social Sciences, Delft University of Technology (1985)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Chair of Information Science, Universität Regensburg, 93040, Regensburg, Germany

    Christina Ohm & Bernd Ludwig

Authors
  1. Christina Ohm
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bernd Ludwig
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Business Informatics I, University of Trier, 54286, Trier, Germany

    Ingo J. Timm

  2. Institute for Web Science and Technologies, University of Koblenz, Universitätsstr. 1, 56070, Koblenz, Germany

    Matthias Thimm

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ohm, C., Ludwig, B. (2013). Estimating the Driver’s Workload. In: Timm, I.J., Thimm, M. (eds) KI 2013: Advances in Artificial Intelligence. KI 2013. Lecture Notes in Computer Science(), vol 8077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40942-4_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-40942-4_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40941-7

  • Online ISBN: 978-3-642-40942-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation