Advertisement

Alert! Automated Vehicle (AV) System Failure – Drivers’ Reactions to a Sudden, Total Automation Disengagement

Conference paper
  • 927 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1212)

Abstract

Despite the “driverless” term, drivers of automated vehicles still constitute an integral part of the human-machine team that will be driving the future. This study emphasized drivers by evaluating the attentiveness, stress levels, and reactions of 67 participants, ages 18–65+ years, during a sudden, total disengagement of automation in a driving simulator-based rural freeway setting. Attentiveness was characterized by a significant increase in gaze fixation and a significant decrease in fatigue, yet stress levels did not appear to significantly change. Regardless of age, gender, or level of non-driving related task involvement, participants reacted to the failure first by steering, requiring 12.30 s (50th percentile) to 29.26 s (90th percentile), followed by speed control after 18.26 s (50th percentile) to 40.86 s (90th percentile). These findings highlight the need for addressing the potentially dangerous implications of automation failure.

Keywords

Automated Vehicle (AV) failure Driver reactions 

References

  1. 1.
    Nilsson, J. et al.: Driver performance in the presence of adaptive cruise control related failures: implications for safety analysis and fault tolerance. In: 43rd Annual IEEE/IFIP Conference on Dependable Systems and Networks Workshop (DSN-W) (2013)Google Scholar
  2. 2.
    Strand, N., et al.: Semi-automated versus highly automated driving in critical situations caused by automation failures. Transp. Res. Part F 27, 218–228 (2014)CrossRefGoogle Scholar
  3. 3.
    Lv, C., et al.: Analysis of autopilot disengagements occurring during autonomous vehicle testing. IEEE/CAA J. Automatica Sinica 5, 58–68 (2018)CrossRefGoogle Scholar
  4. 4.
    Merat, N., et al.: Highly automated driving, secondary task performance, and driver state. Hum. Factors 54(5), 762–771 (2012)CrossRefGoogle Scholar
  5. 5.
    Kim, J., et al.: Take-over performance analysis depending on the drivers’ non-driving secondary tasks in automated vehicles. In: 2018 International Conference on Information and Communication Technology Convergence (ICTC), pp. 1364–1366 (2018)Google Scholar
  6. 6.
    Vogelpohl, T., et al.: Transitioning to manual driving requires additional time after automation deactivation. Transp. Res. Part F Traffic Psychol. Behav. 55, 464–482 (2018)CrossRefGoogle Scholar
  7. 7.
    Greenlee, E., DeLucia, P., Newton, D.: Driver vigilance in automated vehicles: hazard detection failures are a matter of time. Hum. Factors 60(4), 465–476 (2018)CrossRefGoogle Scholar
  8. 8.
    Jamson, A., et al.: Behavioural changes in drivers experiencing highly-automated vehicle control in varying traffic conditions. Transp. Res. Part C 30, 116–125 (2013)CrossRefGoogle Scholar
  9. 9.
    Naujoks, F., et al.: From partial and high automation to manual driving: relationship between non-driving related tasks, drowsiness and take-over performance. Accident Anal. Prev. 121, 28–42 (2018)CrossRefGoogle Scholar
  10. 10.
    Korber, M., et al.: The influence of age on the take-over of vehicle control in highly automated driving. Transp. Res. Part F Traffic Psychol. Behav. 39, 19–32 (2016)CrossRefGoogle Scholar
  11. 11.
    Feldhutter, A., et al.: How the duration of automated driving influences take-over performance and gaze behavior. In: Advances in Ergonomic Design of Systems, Products, and Processes (2017)Google Scholar
  12. 12.
    Favaro, F., Eurich, S., Nader, N.: Autonomous vehicles’ disengagements: trends, triggers, and regulatory limitations. Accident Anal. Prev. 110, 136–148 (2018)CrossRefGoogle Scholar
  13. 13.
    Merat, N., et al.: Transition to manual: driver behavior when resuming control from a highly automated system. Transp. Res. Part F 27, 274–282 (2014)CrossRefGoogle Scholar
  14. 14.
    Mok, B., et al.: Emergency, automation off: unstructured transition timing for distracted drivers of automated vehicles. In: 2015 IEEE International Conference on Intelligent Transportation Systems, pp. 2458–2464 (2015)Google Scholar
  15. 15.
    Kuehn, M., Vogelpohl, T., Vollrath, M.: Takeover times in highly automated driving. In: 25th International Technical Conference on the Enhanced Safety of Vehicles (ESV) National Highway Transportation Safety Administration (2017)Google Scholar
  16. 16.
    Morgan, P., et al.: Manual takeover and handover of a simulated fully autonomous vehicle within urban and extra-urban settings. In: Advances in Human Aspects of Transportation: Advances in Intelligent Systems and Computing. AHFE (2017)Google Scholar
  17. 17.
    Dixit, V., Chand, S., Nair, D.: Autonomous vehicles: disengagements, actions, and reaction times. PLoS One 11(12), e0168054 (2016)CrossRefGoogle Scholar
  18. 18.
    Hergeth, S., Lorenz, L., Krems, J.: Prior familiarization with takeover requests affects drivers’ takeover performance and automation trust. Hum. Factors 59(3), 457–470 (2017)CrossRefGoogle Scholar
  19. 19.
    Funkhouser, K., Drews, F.: Reaction times with switching from autonomous to manual driving control: a pilot investigation. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, pp. 1854–1858 (2016)Google Scholar
  20. 20.
  21. 21.
    Nowakowski, C., et al.: Cooperative adaptive cruise control: driver acceptance of following gap settings less than one second. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, pp. 2033–2037 (2010)Google Scholar
  22. 22.
    El-Dabaja, S.: Drivers of “driverless” vehicles: a human factors study of connected and automated vehicles. PhD Dissertation, College of Engineering and Technology, Ohio University, Athens (2020)Google Scholar
  23. 23.
    Rumschlag, G., et al.: The effects of texting on driving performance in a driving simulator: the influence of driving age. Accident Anal. Prev. 74, 145–149 (2015)CrossRefGoogle Scholar
  24. 24.
    Woods, D., et al.: Age-related slowing of response selection and production in a visual choice reaction time task. Front. Hum. Neurosci. 9, 193 (2015)Google Scholar
  25. 25.
    Salvia, E., et al.: Effects of age and task load on drivers’ response accuracy and reaction time when responding to traffic lights. Front. Aging Neurosci. 8, 169 (2016)CrossRefGoogle Scholar
  26. 26.
    Takoa, G.: Brake reaction times of unalerted drivers. ITE J. 59(3), 19–21 (1989)Google Scholar
  27. 27.
    Koppa, R.: Human factors. In: Gartner, N.. Messer, C., Rathi, A. (eds.) Traffic Flow Theory Transportation Research Board Monograph. National Research Council, Washington (2003)Google Scholar
  28. 28.
    Setti, J., Rakha, H., El-Shawarby, I.: Analysis of brake perception-reaction times on high-speed signalized intersection approaches. In: IEEE Conference on Intelligent Transportation Systems. IEEE (2006)Google Scholar
  29. 29.
    Shanker, R.: Lognormal distribution and its applications in biological and medical sciences. In: 4th International Conference and Exhibition on Biometrics and Biostatistics, OMICS International Conference, San Antonio (2015)Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of Civil Engineering, Russ College of Engineering and TechnologyOhio UniversityAthensUSA

Personalised recommendations