Can User-Paced, Menu-free Spoken Language Interfaces Improve Dual Task Handling While Driving?

  • Alexander Eriksson
  • Anders Lindström
  • Albert Seward
  • Alexander Seward
  • Katja Kircher
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8511)


The use of speech-based interaction over traditional means of interaction in secondary tasks may increase safety in demanding environments with high requirements on operator attention. Speech interfaces have suffered from issues similar to those of visual displays, as they often rely on a complex menu structure that corresponds to that of visual systems. Recent advances in speech technology allow the use of natural language, eliminating the need for menu structures and offering a tighter coupling between the intention to act and the completion of the action. Modern speech technology may not only make already existing types of interaction safer, but also opens up for new applications, which may enhance safety. One such application is a speech-based hazard reporting system. A small fixed-base simulator study showed that drivers adapt the timing of the hazard reports to the situation at hand, such that an increase in reported workload was avoided.


speech-based interface natural language compensatory behaviour hazard reporting human factors VUI strategic driving behaviour simulated driving IVIS 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Maciej, J., Vollrath, M.: Comparison of manual vs. speech-based interaction with in-vehicle information systems. Accident Analysis and Prevention 41, 924–930 (2009)CrossRefGoogle Scholar
  2. 2.
    Wickens, C.D.: Situation awareness and workload in aviation. Current Directions in Psychological Science 11, 128–133 (2002)CrossRefGoogle Scholar
  3. 3.
    Salvucci, D.D., Taatgen, N.A.: Threaded cognition: an integrated theory of concurrent multitasking. Psychological Review 115, 101–130 (2008)CrossRefGoogle Scholar
  4. 4.
    Wu, X., Li, Z.: Secondary task method for workload measurement in alarm monitoring and identification tasks. In: Rau, P.L.P. (ed.) HCII 2013 and CCD 2013, Part I. LNCS, vol. 8023, pp. 346–354. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  5. 5.
    Grant, R.C., Carswell, C.M., Lio, C.H., Seales, W.B.: Measuring surgeons’ mental workload with a time-based secondary task. Ergonomics in Design: The Quarterly of Human Factors Applications 21, 7–11 (2013)CrossRefGoogle Scholar
  6. 6.
    Alm, H., Nilsson, L.: The effects of a mobile telephone task on driver behavior in a car following situation. Accident Analysis and Prevention 27, 707–715 (1995)CrossRefGoogle Scholar
  7. 7.
    Beede, K.E., Kass, S.J.: Engrossed in conversation: The impact of cell phones on simulated driving performance. Accident Analysis and Prevention 38, 415–421 (2006)CrossRefGoogle Scholar
  8. 8.
    Horrey, W.J., Wickens, C.D.: Examining the impact of cell phone conversations on driving using meta-analytic techniques. Human Factors 48, 196–205 (2006)CrossRefGoogle Scholar
  9. 9.
    Caird, J.K., Willness, C.R., Steel, P., Scialfa, C.: A meta-analysis of the effects of cell phones on driver performance. Accident Analysis and Prevention 40, 1282–1293 (2008)CrossRefGoogle Scholar
  10. 10.
    Sivak, M.: The information that drivers use: Is it indeed 90% visual? Perception 25, 1081–1089 (1996)CrossRefGoogle Scholar
  11. 11.
    Fitch, G.M., Hanowski, R.J.: The risk of a safety-critical event associated with mobile device use as a function of driving task demands. In: Second Conference on Driver Distraction and Inattention, Gothenburg, Sweden (2011)Google Scholar
  12. 12.
    Klauer, S.G., Guo, F., Simons-Morton, B.G., Ouimet, M.C., Lee, S.E., Dingus, T.A.: Distracted driving and risk of road crashes among novice and experienced Drivers. New England Journal of Medicine 370, 54–59 (2014)CrossRefGoogle Scholar
  13. 13.
    Cooper, J.M., Vladisavljevic, I., Medeiros-Ward, N., Martin, P.T., Strayer, D.L.: An investigation of driver distraction near the tipping point of traffic flow stability. Human Factors 51, 261–268 (2009)CrossRefGoogle Scholar
  14. 14.
    O’Brien, N.P., Goodwin, A.H., Foss, R.D.: Talking and texting among teenage drivers: A glass half empty or half full? Traffic Injury Prevention 11, 549–554 (2010)CrossRefGoogle Scholar
  15. 15.
    Esbjörnsson, M., Juhlin, O., Weilenmann, A.: Drivers using mobile phones in traffic: An ethnographic study of interactional adaptation. International Journal of Human-Computer Interaction 22, 37–58 (2007)CrossRefGoogle Scholar
  16. 16.
    Lindström, A., Villing, J., Larsson, S., Seward, A., Åberg, N., Holtelius, C.: The effect of cognitive load on disfluencies during in-vehicle spoken dialogue. Interspeech, Brisbane, Australia (2008)Google Scholar
  17. 17.
    Wickens, C.D.: Processing resources in attention. In: Parasuraman, R., Davies, D.R. (eds.) Varieties of Attention, pp. 63–102. Academic Press, New York (1984)Google Scholar
  18. 18.
    Wickens, C.D.: Multiple resources and mental workload. Human Factors 50, 449–455 (2008)CrossRefGoogle Scholar
  19. 19.
    Derrick, W.L.: Dimensions of operator workload. Human Factors: The Journal of the Human Factors and Ergonomics Society 30, 95–110 (1988)Google Scholar
  20. 20.
    Levy, J., Pashler, H., Boer, E.: Central interference in driving: is there any stopping the psychological refractory period? Psychological Science 17, 228–235 (2006)CrossRefGoogle Scholar
  21. 21.
    NHTSA: Visual-manual NHTSA driver distraction guidelines for in-vehicle electronic devices. Docket NHTSA-2010-0053 (2012)Google Scholar
  22. 22.
    Yager, C.: An evaluation of the effectiveness of voice-to-text programs at reducing incidences of distracted driving. Texas A&M Transportation Institute, The Texas A&M University System, College Station, Texas 77843-3135, Technical Report SWUTC/13/600451-00011-1 (2013)Google Scholar
  23. 23.
    Green, P.A.: Driver interface/HMI standards to minimize driver distraction/ overload. UMTRI, SAE Paper 2008-21-2002 (2008)Google Scholar
  24. 24.
    McCracken, J.H., Aldrich, T.B.: Analyses of selected LHX mission functions: Implications for operator workload and system automation goals. Anacapa Sciences Inc., Research note ASI-479-024-84B (1984)Google Scholar
  25. 25.
    Kun, A.L., Paek, T., Zeljko, M.: The effect of speech interface accuracy on driving performance. Interspeech, Antwerp, Belgium (2007)Google Scholar
  26. 26.
    Green, P.A.: Crashes induced by driver information systems and what can be done to reduce them. In: Conference of the Society of Automotive Engineers (SAE), Warrendale, PA, USA (1999)Google Scholar
  27. 27.
    Shioya, M., Nishimoto, T., Takahashi, J., Daigo, H.: A study of dialogue management principles corresponding to the driver’s workload. In: Abut, H., Hansen, J.L., Takeda, K. (eds.) Advances for In-Vehicle and Mobile Systems, pp. 251–265. Springer, US (2007)CrossRefGoogle Scholar
  28. 28.
    Villing, J., Larsson, S.: Speech, buttons or both? A comparative study of an in-car dialogue system. In: Third International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Salzburg, Austria (2011)Google Scholar
  29. 29.
    Dahlbäck, N., Jönsson, A., Ahrenberg, L.: Wizard of Oz studies — why and how. Knowledge-Based Systems 6, 258–266 (1993)CrossRefGoogle Scholar
  30. 30.
    Byers, J.C., Bittner, A.C., Hill, S.G.: Traditional and raw task load index (TLX) correlations: Are paired comparisons necessary? In: International Industrial Ergonomics and Safety Conference, Cincinnati, Ohio (1989)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Alexander Eriksson
    • 1
    • 3
  • Anders Lindström
    • 2
  • Albert Seward
    • 2
  • Alexander Seward
    • 2
  • Katja Kircher
    • 3
  1. 1.Department of Computer and Information ScienceLinköping UniversityLinköpingSweden
  2. 2.Veridict ABStockholmSweden
  3. 3.Swedish National Road and Transport Research InstituteLinköpingSweden

Personalised recommendations