Looking at Drivers and Passengers to Inform Automated Driver State Monitoring of In and Out of the Loop

  • Christopher D. D. CabrallEmail author
  • Veronika Petrovych
  • Riender Happee
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 597)


The “drivenger” aim of the current study was to investigate attentional differentiation of drivers (who are in control) from passengers (who have no control) to inform future driver-in-the-loop monitoring/detection systems and facilitate multiple levels of manual/automated driving. Eye-tracking glasses were worn simultaneously by the driver and front seat passenger on 32 on road trips. Halfway en-route, the passenger was tasked with pretending with their eyes to be driving. Converging with a recent and independent drivenger study, our results found differences of higher probabilities of small saccades and significantly shorter blinks from our drivers and pseudo-drivers. Additionally, a new measure of eye eccentricity differentiated between driver/passenger roles. While naturalistic attentional manipulations may not be appropriately safe/available with actual automated vehicles, future studies might aim to further use the eye behavior of passengers to refine robust measures of driver (in)attention with increasing reductions in measurement intrusiveness and data filtering/processing overhead requirements.


Human-systems integration Driver state monitoring Eye-tracking Passenger Driver Drivenger Automated driving 



The authors are supported by and performed the current work under the Marie Curie ITN: HF Auto, Human Factors of Automated Driving (PITN-GA-2013605817), We would like to thank Dr. Magnus Hjalmdahl for inspiration towards simultaneous eye tracking of driver vs. passenger while driving across Sweden between VTI office while playing with one of the eye tracking glasses used in the present study. Additionally, we are also indebted to Peter van Leeuwen for his insights into the potential value of driver visual eccentricity behavior, specifically regarding both time and distances away from center taken jointly together.


  1. 1.
    Kyriakidis, M., de Winter, J.C.F., Stanton, N., Bellet, T., van Arem, B., Brookhuis, K., et al.: A human factors perspective on automated driving. Theor. Issues Ergon. Sci. (2017)Google Scholar
  2. 2.
    Kyriakidis, M., Happee, R., de Winter, J.C.F.: Public opinion on automated driving: results of an international questionnaire among 5000 respondents. Transp. Res. F 32, 127–140 (2015)CrossRefGoogle Scholar
  3. 3.
    SAE: taxonomy and definitions for terms related to on-road motor vehicle automated driving systems. On-road automated vehicle standards committee, SAE International (2014).
  4. 4.
    Smith, B.W.: Human error as cause of vehicle crashes. Center for Internet and Society at Stanford Law School (2013).
  5. 5.
    Baxter, G., Rooksby, J., Wang, Y., Khajeh-Hosseini, A.: The ironies of automation … still going strong at 30? In: Proceedings of the ECCE Conference, Edinburgh (2012)Google Scholar
  6. 6.
    Parasuraman, R., Sheridan, T.B., Wickens, C.D.: A model for types and levels of human interaction with automation. Trans. Syst. Man Cybern. Syst. Hum. 30, 286–297 (2012)CrossRefGoogle Scholar
  7. 7.
    Hergeth, S., Lorenz, L., Vilmek, R., Krems, J.: Keep your scanners peeled: gaze behavior as a measure of automation trust during highly automated driving. Hum. Factors 58(3), 509–519 (2016)CrossRefGoogle Scholar
  8. 8.
    Mackworth, N.: The breakdown of vigilance during prolonged visual search. Q. J. Exp. Psychol. 1(1), 6–21 (1948)CrossRefGoogle Scholar
  9. 9.
    Cabrall, C.D.D., Happee, R., de Winter, J.C.F.: From Mackworth’s clock to the open road: a literature review on driver vigilance task operationalization. Transp. Res. F 40, 169–189 (2016)CrossRefGoogle Scholar
  10. 10.
    Caird, J.K., Horrey, W.: Twelve practical and useful questions about driving simulation. In: Fisher, D.L., et al. (eds.) Handbook of Driving Simulation for Engineering, Medicine, and Psychology, pp. 5.1–5.16 (2011). Chap. 5Google Scholar
  11. 11.
    Chapman, P., Underwood, G.: Visual search of dynamic scenes: event types and the role of experience in viewing driving situations. In: Underwood, G. (ed.) Eye Guidances in Reading and Scene Perception, pp. 369–393. Elsevier, Oxford (1998)CrossRefGoogle Scholar
  12. 12.
    Sheridan, T.B.: Recollections on Presence beginnings, some challenges for augmented and virtual reality. Presence 25(1), 75–77 (2016)CrossRefGoogle Scholar
  13. 13.
    Regan, M.A, Williamson, A., Grzebieta, R., Tao, L.: Naturalistic driving studies: literature review and planning for the australian naturalistic driving study. Australian College of Road Safety (ACRS) (2012).
  14. 14.
    Baltodano, S., Sibi, S., Martelaro, N., Gowda, N., Ju, W.: The RRADS platform: a real road autonomous driving simulator. In: Proceedings of the 7th International Conference on Automotive User Interfaces and Vehicular Applications, pp. 281–288 (2015)Google Scholar
  15. 15.
    Takeda, Y., Sato, T., Kimura, K., Komine, H., Akamatsu, M., Sato, J.: Electrophysiological evaluation of attention in drivers and passengers: toward an understating of drivers’ attentional state in autonomous vehicles. Transp. Res. F 42, 140–150 (2016)CrossRefGoogle Scholar
  16. 16.
    Wang, Y., Reimer, B., Dobres, J., Mehler, B.: The sensitivity of different methodologies for characterizing driver’s gaze concentration under increase cognitive demand. Transp. Res. F 26, 227–237 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Christopher D. D. Cabrall
    • 1
    Email author
  • Veronika Petrovych
    • 2
  • Riender Happee
    • 1
  1. 1.Intelligent Vehicles, Department of Cognitive RoboticsDelft University of TechnologyDelftThe Netherlands
  2. 2.Driver and Vehicle, Department of Computer and Information Science (IDA), Swedish National Road and Transport Research Institute (VTI)Linkoping UniversityLinkopingSweden

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