An Interpretable Framework for Quantifying and Predicting Driver's Situation Awareness at the Moment of Takeover Request in Conditionally Automated Driving

Abstract

The driver’s situation awareness (SA) at the moment of a takeover request (TOR) is essential for safe control transition in conditionally automated driving. To address limitations in existing methods for SA quantification and prediction, this study proposes an interpretable framework based on the analysis of SA’s formation process and critical role during takeovers. First, an eXtreme Gradient Boosting (XGBoost) model was constructed to predict takeover performance (TOP). SHapley Additive exPlanations (SHAP) was employed to provide local interpretability and identify the contribution of readily measurable SA Level 1 to TOP, based on which SA Level 2 and 3 were operationalized, thereby enabling the continuous quantification of overall SA. Subsequently, another XGBoost model was developed to predict the quantified SA, and SHAP’s global interpretability was used to optimize model performance and analyze the key features’ main and interaction effects on SA. A takeover simulation experiment was conducted to construct the necessary dataset. Results indicated that the quantified SA was able to match the reasonable distribution patterns of SA across different takeover results. The XGBoost model outperformed four comparison models in SA prediction. In addition, the key features’ main and interaction effects on SA were broadly supported by existing studies, confirming the rationality and effectiveness of the SA prediction model. This research provides support for driver state monitoring and the formulation of takeover strategies in conditionally automated driving.