  It is extremely important to ensure a safe takeover transition in
conditionally automated driving. One of the critical factors that quantifies
the safe takeover transition is takeover time. Previous studies identified the
effects of many factors on takeover time, such as takeover lead time,
non-driving tasks, modalities of the takeover requests (TORs), and scenario
urgency. However, there is a lack of research to predict takeover time by
considering these factors all at the same time. Toward this end, we used
eXtreme Gradient Boosting (XGBoost) to predict the takeover time using a
dataset from a meta-analysis study [1]. In addition, we used SHAP (SHapley
Additive exPlanation) to analyze and explain the effects of the predictors on
takeover time. We identified seven most critical predictors that resulted in
the best prediction performance. Their main effects and interaction effects on
takeover time were examined. The results showed that the proposed approach
provided both good performance and explainability. Our findings have
implications on the design of in-vehicle monitoring and alert systems to
facilitate the interaction between the drivers and the automated vehicle.
