Predicting Trust In Autonomous Vehicles: Modeling Young Adult Psychosocial Traits, Risk-Benefit Attitudes, And Driving Factors With Machine Learning

要旨

Low trust remains a significant barrier to Autonomous Vehicle (AV) adoption. To design trustworthy AVs, we need to better understand the individual traits, attitudes, and experiences that impact people's trust judgements. We use machine learning to understand the most important factors that contribute to young adult trust based on a comprehensive set of personal factors gathered via survey (n = 1457). Factors ranged from psychosocial and cognitive attributes to driving style, experiences, and perceived AV risks and benefits. Using the explainable AI technique SHAP, we found that perceptions of AV risks and benefits, attitudes toward feasibility and usability, institutional trust, prior experience, and a person's mental model are the most important predictors. Surprisingly, psychosocial and many technology- and driving-specific factors were not strong predictors. Results highlight the importance of individual differences for designing trustworthy AVs for diverse groups and lead to key implications for future design and research.

著者
Robert A. Kaufman
University of California, San Diego, La Jolla, California, United States
Emi Lee
University of California, San Diego, La Jolla, California, United States
Manas Satish Bedmutha
UC San Diego, La Jolla, California, United States
David Kirsh
University of California, San Diego, San Diego, California, United States
Nadir Weibel
UC San Diego, La Jolla, California, United States
DOI

10.1145/3706598.3713188

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713188

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Autonomus Vehicle

Annex Hall F204
7 件の発表
2025-05-01 18:00:00
2025-05-01 19:30:00
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