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

arXiv: https://arxiv.org/abs/2409.08980 · HTML: https://mlsanity.com/paper/2409.08980/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 114 references that are in the map.
- First posted on arXiv 13 Sep 2024.

## Builds on (most cited first, 13 of 13)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Human-Centered Explainable AI (XAI): From Algorithms to User Experiences](https://mlsanity.com/paper/2110.10790/) (2021)
- [Designing for Responsible Trust in AI Systems: A Communication Perspective](https://mlsanity.com/paper/2204.13828/) (2022)
- [Modeling Dispositional and Initial learned Trust in Automated Vehicles with Predictability and Explainability](https://mlsanity.com/paper/2012.13603/) (2020)
- [MoodCapture: Depression Detection Using In-the-Wild Smartphone Images](https://mlsanity.com/paper/2402.16182/) (2024)
- [A Classification Model for Sensing Human Trust in Machines Using EEG and GSR](https://mlsanity.com/paper/1803.09861/) (2018)
- [Real-time Trust Prediction in Conditionally Automated Driving Using Physiological Measures](https://mlsanity.com/paper/2212.00607/) (2022)
- [Explainable AI And Visual Reasoning: Insights From Radiology](https://mlsanity.com/paper/2304.03318/) (2023)
- [Effects of Multimodal Explanations for Autonomous Driving on Driving Performance, Cognitive Load, Expertise, Confidence, and Trust](https://mlsanity.com/paper/2401.04206/) (2024)
- [What Did My Car Say? Impact of Autonomous Vehicle Explanation Errors and Driving Context On Comfort, Reliance, Satisfaction, and Driving Confidence](https://mlsanity.com/paper/2409.05731/) (2024)
- [Developing Situational Awareness for Joint Action with Autonomous Vehicles](https://mlsanity.com/paper/2404.11800/) (2024)
- [WARNING This Contains Misinformation: The Effect of Cognitive Factors, Beliefs, and Personality on Misinformation Warning Tag Attitudes](https://mlsanity.com/paper/2407.02710/) (2024)

## Built on by (most cited first, 2 of 2)

- [SocialLM: Social Signal Processing of Patient-Provider Communication using LLMs and Contextual Aggregation](https://mlsanity.com/paper/2505.04152/) (2025)
- [Artificial Intelligence for Modeling and Simulation of Mixed Automated and Human Traffic](https://mlsanity.com/paper/2604.12857/) (2026)

## Abstract (arXiv)

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.