  Research indicates that monotonous automated driving increases the incidence
of fatigued driving. Although many prediction models based on advanced machine
learning techniques were proposed to monitor driver fatigue, especially in
manual driving, little is known about how these black-box machine learning
models work. In this paper, we proposed a combination of eXtreme Gradient
Boosting (XGBoost) and SHAP (SHapley Additive exPlanations) to predict driver
fatigue with explanations due to their efficiency and accuracy. First, in order
to obtain the ground truth of driver fatigue, PERCLOS (percentage of eyelid
closure over the pupil over time) between 0 and 100 was used as the response
variable. Second, we built a driver fatigue regression model using both
physiological and behavioral measures with XGBoost and it outperformed other
selected machine learning models with 3.847 root-mean-squared error (RMSE),
1.768 mean absolute error (MAE) and 0.996 adjusted $R^2$. Third, we employed
SHAP to identify the most important predictor variables and uncovered the
black-box XGBoost model by showing the main effects of most important predictor
variables globally and explaining individual predictions locally. Such an
explainable driver fatigue prediction model offered insights into how to
intervene in automated driving when necessary, such as during the takeover
transition period from automated driving to manual driving.
