Hostname: page-component-89b8bd64d-72crv Total loading time: 0 Render date: 2026-05-08T11:41:59.242Z Has data issue: false hasContentIssue false
  • English
  • Français

DESIGNING FOR CALIBRATED TRUST: EXPLORING THE CHALLENGES IN CALIBRATING TRUST BETWEEN USERS AND AUTONOMOUS VEHICLES

Published online by Cambridge University Press:  27 July 2021

David Callisto Valentine ,
Iskander Smit  and
Euiyoung Kim
David Callisto Valentine*
Affiliation:
Delft University of Technology
Iskander Smit
Affiliation:
Delft University of Technology
Euiyoung Kim
Affiliation:
Delft University of Technology
*
Valentine, David Callisto, Delft University of Technology, Industrial Design Engineering, Netherlands, The, david.cal.valentine@gmail.com

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

Trust is an important factor in building acceptance of autonomous vehicles within our society, but the complex nature of trust makes it challenging to design for an appropriate level of trust. This can lead to instances of mistrust and/or distrust between users and AV’s. Designing for calibrated trust is a possible option to address this challenge. Existing research on designing for calibrated trust focuses on the human machine interaction (HMI), while from literature we infer that trust creation beings much before the first interaction between a user and an AV. The goal of our research is to broaden the scope of calibrated trust, by exploring the pre-use phase and understand the challenges faced in calibration of trust. Within our study 16 mobility experts were interviewed and a thematic analysis of the interviews was conducted. The analysis revealed the lack of clear communication between stakeholders, a solutionism approach towards designing and lack of transparency in design as the prominent challenges. Building on the research insights, we briefly introduce the Calibrated Trust Toolkit as our design solution, and conclude by proposing a sweet spot for achieving calibration of trust between users and autonomous vehicles.

Keywords

CommunicationDesign to XArtificial intelligenceCalibrated TrustAI Solutionism

Information

Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 1143 - 1152
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2021. Published by Cambridge University Press

References

Abraham, H., Seppelt, B., Mehler, B. and Reimer, B. (2017), “What's in a Name”, Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, ACM, New York, NY, USA, pp. 226–234.CrossRefGoogle Scholar
Adnan, N., Md Nordin, S., bin Bahruddin, M.A. and Ali, M. (2018), “How trust can drive forward the user acceptance to the technology? In-vehicle technology for autonomous vehicle”, Transportation Research Part A: Policy and Practice, Vol. 118, pp. 819–836.Google Scholar
Ajzen, I. and Fishbein, M. (1980), “Understanding attitudes and predicting social behaviour”, Englewood Cliffs.Google Scholar
Atiyeh, C. (2018), “Don't Expect a Self-Driving Ferrari”, Car and Drive, available at: https://www.caranddriver.com/news/a23362999/ferrari-no-to-autonomous-cars/ (accessed 7 October 2020).Google Scholar
Boelhouwer, A., van den Beukel, A.P., van der Voort, M.C., Verwey, W.B. and Martens, M.H. (2020), “Supporting drivers of partially automated cars through an adaptive digital in-car tutor”, Information (Switzerland), available at:https://doi.org/10.3390/INFO11040185.CrossRefGoogle Scholar
Campbell, M., Egerstedt, M., How, J.P. and Murray, R.M. (2010), “Autonomous driving in urban environments: Approaches, lessons and challenges”, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, available at:https://doi.org/10.1098/rsta.2010.0110.CrossRefGoogle Scholar
Casner, S.M., Hutchins, E.L. and Norman, D. (2016), “The challenges of partially automated driving”, Communications of the ACM, available at:https://doi.org/10.1145/2830565.CrossRefGoogle Scholar
Collingwood, L. (2017), “Privacy implications and liability issues of autonomous vehicles”, Information and Communications Technology Law, available at:https://doi.org/10.1080/13600834.2017.1269871.CrossRefGoogle Scholar
Ekman, F., Johansson, M. and Sochor, J. (2017), “Creating Appropriate Trust for Autonomous Vehicle Systems: A Framework for Human-Machine Interaction Design”, 95th Annual Meeting of the Transportation Research Board.CrossRefGoogle Scholar
Ekman, F., Johansson, M. and Sochor, J. (2018), “Creating Appropriate Trust in Automated Vehicle Systems: A Framework for HMI Design”, IEEE Transactions on Human-Machine Systems, Institute of Electrical and Electronics Engineers Inc., Vol. 48 No. 1, pp. 95–101.Google Scholar
Endsley, M.R. (2019), “Situation awareness in future autonomous vehicles: Beware of the unexpected”, Advances in Intelligent Systems and Computing, available at:https://doi.org/10.1007/978-3-319-96071-5_32.CrossRefGoogle Scholar
Etzioni, A. (2019), “Cyber Trust”, Journal of Business Ethics, Springer Netherlands, Vol. 156 No. 1, pp. 1–13.CrossRefGoogle Scholar
Fagnant, D.J. and Kockelman, K. (2015), “Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations”, Transportation Research Part A: Policy and Practice, Elsevier Ltd, Vol. 77, pp. 167–181.Google Scholar
Fallon, C.K., Murphy, A.K.G., Zimmerman, L. and Mueller, S.T. (2010), “The calibration of trust in an automated system: A sensemaking process”, 2010 International Symposium on Collaborative Technologies and Systems, CTS 2010, No. October 2016, pp. 390–395.CrossRefGoogle Scholar
Fraedrich, E. and Lenz, B. (2014), “Automated driving: Individual and societal aspects”, Transportation Research Record, available at:https://doi.org/10.3141/2416-08.CrossRefGoogle Scholar
Friedman, B. and Hendry, D.G. (2019), Value Sensitive Design, Value Sensitive Design, The MIT Press, available at:https://doi.org/10.7551/mitpress/7585.001.0001.CrossRefGoogle Scholar
Hagendorff, T. and Wezel, K. (2020), “15 challenges for AI: or what AI (currently) can't do”, AI & SOCIETY, Vol. 35 No. 2, pp. 355–365.CrossRefGoogle Scholar
Hoff, K.A. and Bashir, M. (2015), “Trust in automation: Integrating empirical evidence on factors that influence trust”, Human Factors, Vol. 57 No. 3, pp. 407–434.CrossRefGoogle ScholarPubMed
Hoffman, R.R. (2017), “A taxonomy of emergent trusting in the human-machine relationship”, Cognitive Systems Engineering: The Future for a Changing World, pp. 137–164.CrossRefGoogle Scholar
Kaelbling, L.P., Littman, M.L. and Moore, A.W. (1996), “Reinforcement Learning: A Survey”, Transportation Research Board Annual Meeting, available at: http://arxiv.org/abs/cs/9605103.Google Scholar
Lee, J.D. and Kolodge, K. (2020), “Exploring Trust in Self-Driving Vehicles Through Text Analysis”, Human Factors: The Journal of the Human Factors and Ergonomics Society, Vol. 62 No. 2, pp. 260–277.CrossRefGoogle ScholarPubMed
Lee, J.D. and See, K.A. (2004), “Trust in automation: Designing for appropriate reliance”, Human Factors, Vol. 46 No. 1, pp. 50–80.CrossRefGoogle ScholarPubMed
Lin, R., Ma, L. and Zhang, W. (2018), “An interview study exploring Tesla drivers’ behavioural adaptation”, Applied Ergonomics, Elsevier, Vol. 72 No. December, pp. 37–47.CrossRefGoogle Scholar
Lyons, J.B. and Guznov, S.Y. (2019), “Individual differences in human–machine trust: A multi-study look at the perfect automation schema”, Theoretical Issues in Ergonomics Science, available at:https://doi.org/10.1080/1463922X.2018.1491071.CrossRefGoogle Scholar
Ma, K. (2019), “Artificial unintelligence: How computers misunderstand the world”, The Information Society, pp. 1–3.CrossRefGoogle Scholar
Maguire, M. and Delahunt, B. (2017), “Doing a Thematic Analysis: A Practical, Step-by-Step Guide for Learning and Teaching Scholars. All Ireland Journal of Teaching and Learning in Higher Education”, All Ireland Journal of Teaching and Learning in Higher Education n (AISHE-J).Google Scholar
Miles, M.B., Huberman, A.M. and Saldana, J. (2014), Qualitative Data Analysis: A Methods Sourcebook. Third Edition, The SAGE Handbook of Applied Social Research Methods.Google Scholar
Mirnig, A.G., Wintersberger, P., Sutter, C. and Ziegler, J. (2016), “A framework for analyzing and calibrating trust in automated vehicles”, AutomotiveUI 2016 - 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Adjunct Proceedings, pp. 33–38.CrossRefGoogle Scholar
Nees, M.A. (2018), “Drivers’ perceptions of functionality implied by terms used to describe automation in vehicles”, Proceedings of the Human Factors and Ergonomics Society, Vol. 3, pp. 1893–1897.CrossRefGoogle Scholar
Okamura, K. and Yamada, S. (2020), “Adaptive trust calibration for human-AI collaboration”, PLoS ONE, Vol. 15 No. 2, pp. 1–20.CrossRefGoogle ScholarPubMed
Ong, A., Troncoso, J., Yeung, A., Kim, E. and Agogino, A.M. (2019), “Towards Flexible Ridesharing Experiences: Human-Centered Design of Segmented Shared Spaces”, Communications in Computer and Information Science, available at:https://doi.org/10.1007/978-3-030-23525-3_50.CrossRefGoogle Scholar
Patton, M.Q. (2002), Qualitative research and evaluation methods. Thousand Oaks, Cal.: Sage Publications.Google Scholar
Rasouli, A. and Tsotsos, J.K. (2020), “Autonomous vehicles that interact with pedestrians: A survey of theory and practice”, IEEE Transactions on Intelligent Transportation Systems, IEEE, Vol. 21 No. 3, pp. 900–918.CrossRefGoogle Scholar
Ritchie, J., Lewis, J., Nicholls, C.M. and Ormston, R. (2013), “The foundations of qualitative research. Qualitative research practice: A guide for social science students and researchers”, Qualitative Research Practice: A Guide for Social Science Students and Researchers.Google Scholar
Schoettle, B. and Sivak, M. (2014), “A survey of public opinion about connected vehicles in the U.S., the U.K., and Australia”, 2014 International Conference on Connected Vehicles and Expo, ICCVE 2014 - Proceedings, available at:https://doi.org/10.1109/ICCVE.2014.7297637.CrossRefGoogle Scholar
Shladover, S.E. and Nowakowski, C. (2019), “Regulatory challenges for road vehicle automation: Lessons from the California experience”, Transportation Research Part A: Policy and Practice, available at:https://doi.org/10.1016/j.tra.2017.10.006.CrossRefGoogle Scholar
Silberg, G., Manassa, M., Everhart, K., Subramanian, D., Corley, M., Fraser, H., Sinha, V., et al. (2013), “Self-Driving Cars: Are we Ready?”, Kpmg Llp.Google Scholar
Ulrich, K.T. and Eppinger, S.D. (2012), Product Design and Development: Fifth Edition, McGraw-Hill.Google Scholar
De Visser, E.J., Cohen, M., Freedy, A. and Parasuraman, R. (2014), “A design methodology for trust cue calibration in cognitive agents”, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 8525 LNCS No. PART 1, pp. 251–262.Google Scholar
Woisetschläger, D.M. (2016), “Consumer perceptions of automated driving technologies: An examination of use cases and branding strategies”, Autonomous Driving: Technical, Legal and Social Aspects, available at:https://doi.org/10.1007/978-3-662-48847-8_32.CrossRefGoogle Scholar
Yang, J.H., Han, J. and Park, J.M. (2017), “Toward defining driving automation from a human-centered perspective”, Conference on Human Factors in Computing Systems - Proceedings, Vol. Part F1276, pp. 2248–2254.CrossRefGoogle Scholar