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Technological advances in the automotive industry are bringing automateddriving closer to road use. However, one of the most important factorsaffecting public acceptance of automated vehicles (AVs) is the public's trustin AVs. Many factors can influence people's trust, including perception ofrisks and benefits, feelings, and knowledge of AVs. This study aims to usethese factors to predict people's dispositional and initial learned trust inAVs using a survey study conducted with 1175 participants. For eachparticipant, 23 features were extracted from the survey questions to capturehis or her knowledge, perception, experience, behavioral assessment, andfeelings about AVs. These features were then used as input to train an eXtremeGradient Boosting (XGBoost) model to predict trust in AVs. With the help ofSHapley Additive exPlanations (SHAP), we were able to interpret the trustpredictions of XGBoost to further improve the explainability of the XGBoostmodel. Compared to traditional regression models and black-box machine learningmodels, our findings show that this approach was powerful in providing a highlevel of explainability and predictability of trust in AVs, simultaneously.

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