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/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.

汽车行业的技术进步使自动驾驶越来越接近道路使用。但是，影响公众对自动驾驶汽车（AV）接受程度的最重要因素之一是公众对自动驾驶汽车的信任。许多因素都会影响人们的信任，包括对风险和收益的感知，对AV的了解以及对AV的了解。这项研究旨在通过对1175名参与者进行的一项调查研究，利用这些因素来预测人们对AV的性格和初始学习信任。对于每个参与者，从调查问题中提取了23个特征，以捕获他/她的知识，感知，经验，行为评估以及对AV的感觉。然后将这些功能用作训练eXtreme Gradient Boosting（XGBoost）模型以预测对AV信任的输入。在SHapley Additive ExPlanations（SHAP）的帮助下，我们能够解释XGBoost的信任预测，从而进一步提高XGBoost模型的可解释性。与传统的回归模型和黑匣子机器学习模型相比，我们的研究结果表明，该方法在同时提供对AV信任的高水平可解释性和可预测性方面非常有效。