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

Risk and benefit perceptions matter most: The study of 1,457 young adults found that how people perceive the risks and benefits of autonomous vehicles—not their general personality traits—is the strongest predictor of whether they'll trust AVs. This suggests that building trust requires addressing specific concerns about safety and usefulness rather than assuming certain personality types will naturally trust the technology.

Institutional trust is critical: Trust in the organizations developing and deploying AVs significantly influences whether individuals will trust the vehicles themselves. This highlights the importance of transparency, accountability, and demonstrated competence from companies and regulators in building public confidence.

Mental models shape trust decisions: People's existing understanding of how AVs work and what they can do influences trust more than expected. This suggests that education and clear communication about AV capabilities and limitations could be an effective strategy for responsible deployment.

Personality traits are surprisingly weak predictors: Contrary to common assumptions, psychosocial factors like risk-aversion or technology comfort had minimal impact on AV trust. This finding challenges the notion that certain demographic groups are inherently more or less trustworthy of automation, emphasizing instead the importance of tailored, evidence-based communication for diverse audiences.

Prior experience and usability matter: Hands-on experience with AVs and perceptions of how easy they are to use were among the top predictors, suggesting that carefully designed pilot programs and user-friendly interfaces could help build justified trust in the technology.