Advertisement

The Impact of a Biological Driver State Monitoring System on Visual Attention During Partially Automated Driving

  • Alice StephensonEmail author
  • Iveta Eimontaite
  • Praminda Caleb-Solly
  • Chris Alford
Conference paper
  • 17 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1212)

Abstract

As the shift from manual to automated driving occurs, the driver will be required to take a supervisory role in monitoring the driving environment and system parameters. Driver State Monitoring Systems (DSMS) have been proposed to evaluate the state of the driver and provide support for driver engagement. However, it is not clear how a DSMS may impact attentional mechanisms. Nineteen young adults (mean ± SD age = 19.58 ± 0.94 years) experienced a simulated semi-autonomous driving journey. Participants’ visual attention via eye tracking fixation and visit metrics were compared before, during, and after two distinct notifications designed to enhance driver engagement. The first notification displayed biofeedback changes in physiological state; the second notification provided speed limit changes. Results revealed participants spent longer attending to the outside driving environment during biofeedback. The results suggest the potential for feedback based on relevant physiological parameters to enhance global visual processing strategies during semi-autonomous driving.

Keywords

Semi-autonomous vehicle Notifications Biofeedback Visual attention 

References

  1. 1.
    Kircher, K., Ahlstrom, C.: Predicting visual distraction using driving performance data. Ann. Adv. Automot. Med. 54, 333–342 (2010)Google Scholar
  2. 2.
    Klauer, S., Dingus, T., Neale, V., Sudweeks, J., Ramsey, D.: The Impact of Driver Inattention on Near-Crash/Crash Risk: An Analysis Using the 100-Car Naturalistic Driving Study Data [NHTSA Report No. DOT HS 810 594]. Virginia Tech Transportation Institute, Blacksburg (2006)Google Scholar
  3. 3.
    SAE International: U.S. Department of transportation’s new policy on automated vehicles adopts SAE International levels of automation for defining driving automation in on-road motor vehicles (2018)Google Scholar
  4. 4.
    Braunagel, E., Kasneci, W., Stolzmann, W.: Driver-activity recognition in the context of conditionally autonomous driving. In: IEEE Conference on Intelligent Transportation Systems, pp. 1652–1657. IEEE Computer Society, Washington DC (2015)Google Scholar
  5. 5.
    Parasuraman, R., Manzey, D.H.: Complacency and bias in human use of automation: an attentional integration. Hum. Factors 52, 381–410 (2010)CrossRefGoogle Scholar
  6. 6.
    Hergeth, S., Lorenz, L., Vilimek, R., Krems, J.F.: Keep your scanners peeled: gaze behavior as a measure of automation trust during highly automated driving. Hum. Factors 58, 509–519 (2016)CrossRefGoogle Scholar
  7. 7.
    Jipp, M.: Reaction times to consecutive automation failures: a function of working memory and sustained attention. Hum. Factors 58, 1248–1261 (2016)CrossRefGoogle Scholar
  8. 8.
    Khan, M.Q., Lee, S.: A comprehensive survey of driving monitoring and assistance systems. Sensors 19, 2574 (2019)CrossRefGoogle Scholar
  9. 9.
    Lee, J.D.: Driving Safety. Rev. Hum. Factors Ergon. 1, 172–218 (2005)CrossRefGoogle Scholar
  10. 10.
    Pacheco-Unguetti, A.P., Acosta, A., Callejas, A., Lupiáñez, J.: Attention and anxiety: different attentional functioning under state and trait anxiety. Psychol. Sci. 21, 298–304 (2010)CrossRefGoogle Scholar
  11. 11.
    Morgan, P., Caleb-Solly, P., Voinescu, A., Williams, C.: Literature review: human-machine interface. Proj. rep. UWE Bristol (2016)Google Scholar
  12. 12.
    Morgan, P.L., Voinescu, A., Williams, C., Caleb-Solly, P., Alford, C., Shergold, I., Parkhurst, G., Pipe, A.: An emerging framework to inform effective design of human-machine interfaces for older adults using connected autonomous vehicles. In: Stanton, N.A. (ed.) Advances in Human Aspects of Transportation, pp. 325–334. Springer International Publishing (2018)Google Scholar
  13. 13.
    Eimontaite, I., Voinescu, A., Alford, C., Caleb-Solly, P., Morgan, P.: The impact of different human-machine interface feedback modalities on older participants’ user experience of CAVs in a simulator environment. In: Stanton, N.A. (ed.) Advances in Human Factors of Transportation, pp. 120–132. Springer International Publishing (2019)Google Scholar
  14. 14.
    Shiferaw, B., Downey, L., Crewther, D.: A review of gaze entropy as a measure of visual scanning efficiency. Neurosci. Biobehav. Rev. 96, 353–366 (2019)CrossRefGoogle Scholar
  15. 15.
    Posner, M.I.: Orienting of attention. Q. J. Exp. Psychol. 32, 3–25 (1980)CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Alice Stephenson
    • 1
    Email author
  • Iveta Eimontaite
    • 2
  • Praminda Caleb-Solly
    • 1
  • Chris Alford
    • 1
  1. 1.University of the West of EnglandBristolUK
  2. 2.Cranfield UniversityBedfordUK

Personalised recommendations