Examining pedestrians’ trust in automated vehicles based on attributes of trust: A qualitative study

Zhou, Siyuan; Sun, Xu; Wang, Qingfeng; Liu, Bingjian; Burnett, Gary

Abstract

Pedestrians’ trust in automated vehicles (AVs) needs to be analyzed and deconstructed to update it from its current broad concept into several lower-level attributes for assessment and measurement. In this study, we have employed virtual reality (VR) and scenario-based interviews to examine the trust of pedestrians toward AVs, based on the attributes of trust and trustworthiness. A hybrid approach of inductive and deductive thematic analysis of the responses of 36 participants was undertaken. Eight such attributes emerged from the analysis, including statistical reliability and dependability, competence, predictability, familiarity, authority/subversion, liberty/oppression, care/harm, and sanctity/degradation. The first four are objective attributes concerning automation trustworthiness and human trust in automation, while the remaining four are subjective attributes, analogous to properties of human morality. The findings of this study provide an empirical grounding for trust theories. Specifically, we have highlighted the importance of subjective qualities in constituting pedestrian-AV trust, including “automation morality” and “care/harm”.

Citation

Zhou, S., Sun, X., Wang, Q., Liu, B., & Burnett, G. (2023). Examining pedestrians’ trust in automated vehicles based on attributes of trust: A qualitative study. Applied Ergonomics, 109, Article 103997. https://doi.org/10.1016/j.apergo.2023.103997

• Journal Article Type: Article
• Acceptance Date: Feb 6, 2023
• Online Publication Date: May 10, 2023
• Publication Date: 2023-05
• Deposit Date: May 4, 2023
• Publicly Available Date: May 11, 2024
• Journal: Applied Ergonomics
• Print ISSN: 0003-6870
• Publisher: Elsevier BV
• Peer Reviewed: Peer Reviewed
• Volume: 109
• Article Number: 103997
• DOI: https://doi.org/10.1016/j.apergo.2023.103997
• Keywords: Automated vehicles; Attributes of trust; Qualitative analysis
• Public URL: https://nottingham-repository.worktribe.com/output/17386271
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0003687023000352?via%3Dihub