# Modeling Dispositional and Initial learned Trust in Automated Vehicles with Predictability and Explainability

arXiv: https://arxiv.org/abs/2012.13603 · HTML: https://mlsanity.com/paper/2012.13603/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Dec 2020.

## Built on by (most cited first, 8 of 8)

- [Combat COVID-19 Infodemic Using Explainable Natural Language Processing Models](https://mlsanity.com/paper/2103.00747/) (2021)
- [Towards explainable artificial intelligence (XAI) for early anticipation of traffic accidents](https://mlsanity.com/paper/2108.00273/) (2021)
- [Disengagement Cause-and-Effect Relationships Extraction Using an NLP Pipeline](https://mlsanity.com/paper/2111.03511/) (2021)
- [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)
- [Predicting Trust In Autonomous Vehicles: Modeling Young Adult Psychosocial Traits, Risk-Benefit Attitudes, And Driving Factors With Machine Learning](https://mlsanity.com/paper/2409.08980/) (2024)
- [Predicting Driver Fatigue in Automated Driving with Explainability](https://mlsanity.com/paper/2103.02162/) (2021)
- [Cause-and-Effect Analysis of ADAS: A Comparison Study between Literature Review and Complaint Data](https://mlsanity.com/paper/2208.00249/) (2022)

## Abstract (arXiv)

Technological advances in the automotive industry are bringing automated driving closer to road use. However, one of the most important factors affecting public acceptance of automated vehicles (AVs) is the public's trust in AVs. Many factors can influence people's trust, including perception of risks and benefits, feelings, and knowledge of AVs. This study aims to use these factors to predict people's dispositional and initial learned trust in AVs using a survey study conducted with 1175 participants. For each participant, 23 features were extracted from the survey questions to capture his or her knowledge, perception, experience, behavioral assessment, and feelings about AVs. These features were then used as input to train an eXtreme Gradient Boosting (XGBoost) model to predict trust in AVs. With the help of SHapley Additive exPlanations (SHAP), we were able to interpret the trust predictions of XGBoost to further improve the explainability of the XGBoost model. Compared to traditional regression models and black-box machine learning models, our findings show that this approach was powerful in providing a high level of explainability and predictability of trust in AVs, simultaneously.