  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.
