Predicting Trust In Autonomous Vehicles: Modeling Young Adult Psychosocial Traits, Risk-Benefit Attitudes, And Driving Factors With Machine Learning
This research addresses the barrier to AV adoption by providing insights for designing trustworthy vehicles for diverse groups, though it is incremental as it applies existing methods to new survey data.
The study tackled the problem of low trust in autonomous vehicles by using machine learning to identify key predictors of trust among young adults, finding that perceptions of risks and benefits, attitudes toward feasibility and usability, institutional trust, prior experience, and mental models were the most important factors, with psychosocial and driving-specific factors being less predictive.
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.