Participant Perception and HMI Preferences During Simulated AV Disengagements

  • Syeda RizviEmail author
  • Francesca Favaro
  • Sumaid Mahmood
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 962)


This study examined drivers’ responses to simulated autonomous technology failures in semi-autonomous vehicles. A population of 40 individuals was tested, considering the following independent variables: age of the driver, speed at disengagement, and time at which the disengagement occurred. Participants received auditory and visual warning at the time of disengagement and were asked to regain control of the vehicle while maneuvering within a S-curve turn. Participants’ perception associated to the estimation of success of the control takeover, estimation of test duration, and estimation of the speed of travel showed poor accuracy. Speed recollection accuracy was lower for older participants, while younger participants showed overconfidence in the assessment of the quality of their control takeover. The employed human-machine interface highlighted concerns on the use of central console displays. Trust in the technology and nervousness to the possibility of a disengagement showed higher levels of anxiety for high speeds.


Human factors Human machine interfaces Driving simulations Age-related issues Trust in automation Physiological measures 



The authors would like to acknowledge the help from Mr. Sky Eurich, Mrs. Nazanin Nader, and Mrs. Shivangi Agarwal for the data collection process. The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. The report is funded by a grant from the U.S. Department of Transportation’s University Transportation Centers Program (69A3551747127) managed by the Mineta Transportation Institute of San Jose. The U.S. Government assumes no liability for the contents or use thereof.


  1. 1.
    Bazilinskyy, P., De Winter, J.C.F.: Analyzing crowdsourced ratings of speech-based take-over requests for automated driving. Appl. Ergon. 64, 56–64 (2017)CrossRefGoogle Scholar
  2. 2.
    California Department of Motor Vehicles (CA DMV).: Article 3.7 – Autonomous Vehicles. Title 13, Division 1, par. 227.32, Autonomous Vehicles Order to Adopt (2018)Google Scholar
  3. 3.
    Favaro, F.M., Seewald, P., Scholtes, M., Eurich, S.: Quality of control takeover following disengagements in semi-autonomous vehicles. Submitted to J. of Transp. Res., November (2018)Google Scholar
  4. 4.
    Mok, B.K.J., Johns, M., Lee, K.J., Ive, H.P., Miller, D., Ju, W.: Timing of unstructured transitions of control in automated driving. In: Intelligent Vehicles Symposium (IV), pp. 1167–1172. IEEE (2015)Google Scholar
  5. 5.
    Society of Automotive Engineers – SAE.: On-Road Automated Vehicle Standards committee. Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems (2014)Google Scholar
  6. 6.
    US Department of Transportation, Federal Highway Administration (FHWA).: Licensed Drivers in the US Demographics for 2016 (2016)Google Scholar
  7. 7.
    Zhu, W., Zeng, N., Wang, N.: Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS implementations. NESUG proceedings: health care and life sciences, Baltimore, Maryland, 19, 67 (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Syeda Rizvi
    • 1
    Email author
  • Francesca Favaro
    • 1
    • 2
  • Sumaid Mahmood
    • 1
  1. 1.RiSAS Research CenterSan Jose State UniversitySan JoseUSA
  2. 2.Department of Aviation and TechnologySan Jose State UniversitySan JoseUSA

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