The Impact of Different Human-Machine Interface Feedback Modalities on Older Participants’ User Experience of CAVs in a Simulator Environment

  • Iveta EimontaiteEmail author
  • Alexandra Voinescu
  • Chris Alford
  • Praminda Caleb-Solly
  • Phillip Morgan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 964)


Rapidly developing Autonomous Vehicle (AV) technology has potential to provide solutions to some of the aging population challenges, such as social isolation resulting from an inability to be independently mobile. However for AVs success, users’ acceptance is essential. Fifteen participants (M 70 years) participated in an autonomous driving simulator trial with voice-based CAV status feedback in a decision-making scenario – whether to pick up a friend on the way. The within-subject conditions/journeys were: Audio feedback (Audio)/Pick-Up; Audio/No-Pick-Up; No-Audio/Pick-Up. Additionally, the effect of feedback during different external journey conditions was also considered, resulting in two between-subjects conditions – day and night travel. Participants physiological, cognitive and affective measures show greater situational awareness and workload ratings in the No-Audio/Pick-Up condition with increased Post-trial trust rating and overall higher positive affect. These results indicate that the greatest concentration was required in the no-sound condition, suggesting that sound/multimodal feedback improved ease of operation and journey experience.


Connected Autonomous Vehicles Human-machine interaction Feedback modalities Older participants Hear rate Trust Task load 


  1. 1.
    Jee, C., Mercer, C.: Driverless car news: the great driverless car race: where will the UK place? (2017)
  2. 2.
    SAE International: U.S. Department of transportation’s new policy on automated vehicles adopts SAE International’s levels of automation for defining driving automation in on-road motor vehicles (2016)Google Scholar
  3. 3.
    Musselwhite, C., Haddad, H.: Mobility, accessibility and quality of later life. Qual. Ageing Older Adults 11, 25–37 (2010)CrossRefGoogle Scholar
  4. 4.
    Abraham, H., Lee, C., Brady, S., Mehler, B., Reimer, B., Coughlin, J..: Autonomous vehicles and alternatives to driving: trust, preferences, and effects of age. Presented at the Transportation Research Board 96th Annual Meeting, Washington DC, United States (2017)Google Scholar
  5. 5.
    Moreno-Jiménez, B., Rodríguez-Carvajal, R., Garrosa Hernández, E., Morante Benadero, M.A., et al.: Terminal versus non-terminal care in physician burnout: the role of decision-making processes and attitudes to death. Salud Ment. 31, 93–101 (2008)Google Scholar
  6. 6.
    Mills, M.E., Sullivan, K.: The importance of information giving for patients newly diagnosed with cancer: a review of the literature. J. Clin. Nurs. 8, 631–642 (1999)CrossRefGoogle Scholar
  7. 7.
    Ussher, J., Kirsten, L., Butow, P., Sandoval, M.: What do cancer support groups provide which other supportive relationships do not? The experience of peer support groups for people with cancer. Soc. Sci. Med. 62, 2565–2576 (2006)CrossRefGoogle Scholar
  8. 8.
    Lautizi, M., Laschinger, H.K.S., Ravazzolo, S.: Workplace empowerment, job satisfaction and job stress among Italian mental health nurses: an exploratory study. J. Nurs. Manag. 17, 446–452 (2009)CrossRefGoogle Scholar
  9. 9.
    Ozer, E.M., Bandura, A.: Mechanisms governing empowerment effects: a self-efficacy analysis. J. Pers. Soc. Psychol. 58, 472 (1990)CrossRefGoogle Scholar
  10. 10.
    Pearson, L.C., Moomaw, W.: The relationship between teacher autonomy and stress, work satisfaction, empowerment, and professionalism. Educ. Res. Q. 29, 37 (2005)Google Scholar
  11. 11.
    Morgan, P., Caleb-Solly, P., Voinescu, A., Williams, C.: Literature review: human-machine interface. Project report, UWE Bristol, 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 (2018)Google Scholar
  13. 13.
    Gable, T.M., Walker, B.N., Gable, T.: Georgia tech simulator sickness screening protocol, 16 (2013)Google Scholar
  14. 14.
    Watson, D., Anna, L., Tellegen, A.: Development and validation of brief measures of positive and negative affect: the PANAS Scales. J. Pers. Soc. Psychol. 54, 1063–1070 (1988)CrossRefGoogle Scholar
  15. 15.
    Hart, S.G., Staveland, L.E.: Development of NASA-TLX (task load index): results of empirical and theoretical research. In: Advances in Psychology, pp. 139–183. Elsevier (1988)Google Scholar
  16. 16.
    Buysse, D.J., Reynolds III, C.F., Monk, T., Berman, S.R., Kupfer, D.J.: The Pittsburgh sleep quality index: a new instrument for psychiatric practice and research. Psychiatry Res. 28, 193–213 (1989)CrossRefGoogle Scholar
  17. 17.
    Taylor, R.M., Selcon, S.J.: Cognitive quality and situational awareness with advanced aircraft attitude displays. In: Proceedings of the Human Factors Society Annual Meeting, vol. 34, pp. 26–30 (1990)CrossRefGoogle Scholar
  18. 18.
    Gold, C., Körber, M., Hohenberger, C., Lechner, D., Bengler, K.: Trust in automation – before and after the experience of take-over scenarios in a highly automated vehicle. Procedia Manuf. 3, 3025–3032 (2015)CrossRefGoogle Scholar
  19. 19.
    Ben-Shakhar, G.: Standardization within individuals: a simple method to neutralize individual differences in skin conductance. Psychophysiology 22, 292–299 (1985)CrossRefGoogle Scholar
  20. 20.
    Bechara, A., Damasio, H., Tranel, D., Damasio, A.R.: The Iowa Gambling Task and the somatic marker hypothesis: some questions and answers. Trends Cogn. Sci. 9, 159–162 (2005)CrossRefGoogle Scholar
  21. 21.
    Ben-Shakhar, G., Bornstein, G., Hopfensitz, A., Van Winden, F.: Reciprocity and emotions: arousal, self-reports, and expectations. CESifo Working Paper 1298, pp. 1–16 (2004)Google Scholar
  22. 22.
    Bartneck, C., Suzuki, T., Kanda, T., Nomura, T.: The influence of people’s culture and prior experiences with Aibo on their attitude towards robots. AI Soc. 21, 217–230 (2006)CrossRefGoogle Scholar
  23. 23.
    Stafford, R.Q., Broadbent, E., Jayawardena, C., Unger, U., Kuo, I.H., Igic, A., Wong, R., Kerse, N., Watson, C., MacDonald, B.A.: Improved robot attitudes and emotions at a retirement home after meeting a robot. In: 2010 IEEE RO-MAN, pp. 82–87. IEEE (2010)Google Scholar
  24. 24.
    Nomura, T., Shintani, T., Fujii, K., Hokabe, K.: Experimental investigation of relationships between anxiety, negative attitudes, and allowable distance of robots. In: Proceedings of the 2nd IASTED International Conference on Human Computer Interaction, Chamonix, France, pp. 13–18. ACTA Press (2007)Google Scholar
  25. 25.
    McLucas, A.C.: Decision Making: Risk Management, Systems Thinking and Situation Awareness. Argos Press P/L, Canberra (2003)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Iveta Eimontaite
    • 1
    Email author
  • Alexandra Voinescu
    • 2
  • Chris Alford
    • 1
  • Praminda Caleb-Solly
    • 3
  • Phillip Morgan
    • 4
  1. 1.Faculty of Health and Applied SciencesUniversity of the West of EnglandBristolUK
  2. 2.Department of PsychologyUniversity of BathBathUK
  3. 3.Bristol Robotics Laboratory and Institute of Bio-Sensing TechnologiesUniversity of the West of EnglandBristolUK
  4. 4.School of PsychologyCardiff UniversityCardiffUK

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