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

2024-09-13

arXiv.org

AI Summary

This study investigates key determinants of young adults’ trust in autonomous vehicles (AVs) to advance societal acceptance and human-AV collaborative design. Method: Leveraging 1,457 survey responses, we trained ensemble learning models—including XGBoost—and employed SHAP (Shapley Additive Explanations) for feature importance analysis and model-agnostic interpretability. Contribution/Results: We identify five primary predictors of AV trust: perceived risk–benefit trade-offs, attitudes toward feasibility, institutional trust, mental models of automation, and social norms—constituting the first empirically validated, multi-dimensional framework for this demographic. Notably, conventional psychological traits and driving habits exhibit negligible predictive power, challenging established theoretical assumptions. Our model achieves state-of-the-art accuracy, providing robust empirical foundations and actionable insights for differentiated, trustworthy human–AV interaction design.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Learning Human Values and Preferences
• Knowledge Representation and Reasoning: Preferences

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

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.

Problem

• Autonomous Vehicles
• Youth Confidence
• Acceptance and Trust

Innovation

• Machine Learning
• Autonomous Vehicles
• Psychological Factors