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Social Acceptability of Autonomous Vehicles: Unveiling Correlation of Passenger Trust and Emotional Response

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HCI in Mobility, Transport, and Automotive Systems (HCII 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13335))

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Abstract

Social acceptability of fully autonomous systems, such as self-driving cars (SDC), is a prominent challenge that academic communities as well as industries are now facing. Despite advances being made in the technical abilities of SDCs, recent studies indicate that people are negatively predisposed toward utilizing SDCs. To bridge the gap between consumer skepticism and adoption of SDCs, research is needed to better understand the evolution of trust between humans and growing autonomous technologies. In this paper, the question of mainstream acceptance and requisite trust is scrutinized through integration of virtual reality (VR) SDC simulator, an electroencephalographic (EEG) recorder, and a new approach for real-time trust measurement between passengers and SDCs. An experiment on fifty (50) subjects was conducted where participants were exposed to driving scenarios designed to induce positive and negative emotional responses, as sub-dimensions of trust. Emotions were picked up by EEG signals from a certain area of the brain, and simultaneously, trust was measured based on a 5-point Likert scale. The results of our experiment unveiled that there is a direct correlation between passengers’ real-time trust in SDCs and their emotional responses. In other words, the trust level and trust rebuild after faulty behaviors depend on the driving style as well as reaction of the SDC to passengers’ emotions. Our results therefore illustrate that trust in SDCs, and accordingly, social acceptability can be achieved if SDCs become responsive to emotional responses, e.g., by selecting proper operation modes.

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Notes

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    atomicmotionsystems.com.

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    talonsimulations.com/clients.html.

  4. 4.

    IRBNET ID #: 1187756-1.

References

  1. Abd, M.A., Gonzalez, I., Ades, C., Nojoumian, M., Engeberg, E.D.: Simulated robotic device malfunctions resembling malicious cyberattacks impact human perception of trust, satisfaction, and frustration. Int. J. Adv. Robot. Syst. (IJARS) 16(5), 1–16 (2019)

    Google Scholar 

  2. Abd, M.A., Gonzalez, I., Nojoumian, M., Engeberg, E.D.: Trust, satisfaction and frustration measurements during human-robot interaction. In: 30th Florida Conference on Recent Advances in Robotics (FCRAR), pp. 89–93 (2017)

    Google Scholar 

  3. Beer, J., Fisk, A.D., Rogers, W.A.: Toward a framework for levels of robot autonomy in human-robot interaction. J. Hum.-Rob. Interact. 3(2), 74 (2014)

    Article  Google Scholar 

  4. Choi, J.K., Ji, Y.G.: Investigating the importance of trust on adopting an autonomous vehicle. Int. J. Hum.-Comput. Interact. 31(10), 692–702 (2015)

    Article  Google Scholar 

  5. Craig, J., Nojoumian, M.: Should self-driving cars mimic human driving behaviors? In: Krömker, H. (ed.) HCII 2021. LNCS, vol. 12791, pp. 213–225. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-78358-7_14

    Chapter  Google Scholar 

  6. Haak, M., Bos, S., Panic, S., Rothkrantz, L.: Detecting stress using eye blinks and brain activity from EEG signals. In: Proceeding of the 1st Driver Car Interaction and Interface (DCII 2008), pp. 35–60 (2009)

    Google Scholar 

  7. Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y., De Visser, E.J., Parasuraman, R.: A meta-analysis of factors affecting trust in human-robot interaction. J. Hum. Factors Ergon. Soc. 53(5), 517–527 (2011)

    Article  Google Scholar 

  8. Jun, G., Smitha, K.G.: EEG based stress level identification. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 003270–003274, October 2016

    Google Scholar 

  9. Koo, J., Kwac, J., Ju, W., Steinert, M., Leifer, L., Nass, C.: Why did my car just do that? Explaining semi-autonomous driving actions to improve driver understanding, trust, and performance. Int. J. Interact. Design Manuf. (IJIDeM) 9(4), 269–275 (2015)

    Article  Google Scholar 

  10. Kosfeld, M., Heinrichs, M., Zak, P.J., Fischbacher, U., Fehr, E.: Oxytocin increases trust in humans. Nature 435(7042), 673–676 (2005)

    Article  Google Scholar 

  11. Kyriakidis, M., Happee, R., De Winter, J.: Public opinion on automated driving: results of an international questionnaire among 5000 respondents. Transp. Res. F: Traffic Psychol. Behav. 32, 127–140 (2015)

    Article  Google Scholar 

  12. Lin, Y.P., et al.: EEG-based emotion recognition in music listening. IEEE Trans. Biomed. Eng. 57(7), 1798–1806 (2010)

    Article  Google Scholar 

  13. Lotte, F., Congedo, M., LĂ©cuyer, A., Lamarche, F., Arnaldi, B.: A review of classification algorithms for EEG-based brain-computer interfaces. J. Neural Eng. 4(2), R1 (2007)

    Article  Google Scholar 

  14. Harrison McKnight, D., Chervany, N.L.: Trust and distrust definitions: one bite at a time. In: Falcone, R., Singh, M., Tan, Y.-H. (eds.) Trust in Cyber-societies. LNCS (LNAI), vol. 2246, pp. 27–54. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45547-7_3

    Chapter  MATH  Google Scholar 

  15. Nie, D., Wang, X.W., Shi, L.C., Lu, B.L.: EEG-based emotion recognition during watching movies. In: 5th International IEEE/EMBS Conference on Neural Engineering, pp. 667–670 (2011)

    Google Scholar 

  16. Nojoumian, M.: Trust, influence and reputation management based on human reasoning. In: 4th AAAI Workshop on Incentives and Trust in E-Communities (WIT-EC), pp. 21–24 (2015)

    Google Scholar 

  17. Nojoumian, M.: Rational trust modeling. In: Bushnell, L., Poovendran, R., Başar, T. (eds.) GameSec 2018. LNCS, vol. 11199, pp. 418–431. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01554-1_24

    Chapter  Google Scholar 

  18. Nojoumian, M.: Adaptive mood control in semi or fully autonomous vehicles. US Patent 10,981,563 (2021)

    Google Scholar 

  19. Nojoumian, M.: Adaptive driving mode in semi or fully autonomous vehicles. US Patent 11,221,623 (2022)

    Google Scholar 

  20. Nojoumian, M., Lethbridge, T.C.: A new approach for the trust calculation in social networks. In: Filipe, J., Obaidat, M.S. (eds.) ICETE 2006. CCIS, vol. 9, pp. 64–77. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-70760-8_6

    Chapter  Google Scholar 

  21. Park, C., Shahrdar, S., Nojoumian, M.: EEG-based classification of emotional state using an autonomous vehicle simulator. In: 10th Sensor Array and Multichannel Signal Processing Workshop (SAM), pp. 297–300. IEEE (2018)

    Google Scholar 

  22. Putman, P., van Peer, J., Maimari, I., van der Werff, S.: EEG theta/beta ratio in relation to fear-modulated response-inhibition, attentional control, and affective traits. Biol. Psychol. 83(2), 73–78 (2010)

    Article  Google Scholar 

  23. Shahrdar, S., Menezes, L., Nojoumian, M.: A survey on trust in autonomous systems. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) SAI 2018. AISC, vol. 857, pp. 368–386. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-01177-2_27

    Chapter  Google Scholar 

  24. Shahrdar, S., Park, C., Nojoumian, M.: Human trust measurement using an immersive virtual reality autonomous vehicle simulator. In: 2nd AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), pp. 515–520. ACM (2019)

    Google Scholar 

  25. Tong, J., et al.: EEG-based emotion recognition using nonlinear feature. In: IEEE 8th International Conference on Awareness Science and Technology (iCAST), pp. 55–59 (2017)

    Google Scholar 

  26. Wang, X.W., Nie, D., Lu, B.L.: Emotional state classification from EEG data using machine learning approach. Neurocomputing 129, 94–106 (2014)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, 33431, USA

    Corey Park & Mehrdad Nojoumian

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  1. Corey Park
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  2. Mehrdad Nojoumian
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Correspondence to Mehrdad Nojoumian .

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  1. Technische Universität Ilmenau, Ilmenau, Germany

    Heidi Krömker

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Park, C., Nojoumian, M. (2022). Social Acceptability of Autonomous Vehicles: Unveiling Correlation of Passenger Trust and Emotional Response. In: Krömker, H. (eds) HCI in Mobility, Transport, and Automotive Systems. HCII 2022. Lecture Notes in Computer Science, vol 13335. Springer, Cham. https://doi.org/10.1007/978-3-031-04987-3_27

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  • DOI: https://doi.org/10.1007/978-3-031-04987-3_27

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-04987-3

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