Predicting driver's takeover time based on individual characteristics, external environment, and situation awareness.

• This study constructs a prediction model of driver's takeover time based on individual characteristics, external environment, and situation awareness variables. The performance of the BM + SA model (with situation awareness variables) was better than that of the BM model (without situation awareness variables). This paper has proved that situation awareness is important to takeover time, and the driver's situation awareness should be considered in the study of takeover time. This study can provide support for predicting driver's takeover time. • This study analyzes the main effect of input variables (individual characteristics, external environment, and situation awareness variables) on takeover time and the interactive contribution made by the variables. This study can support analyzing the influence mechanism on takeover time. • This study introduces the Shapely value to explain the XGBoost model, and analyze the contribution of each input variable and their interactive contribution. In addition, the Shapely value explains the variation of individual prediction results. This supports analyzing the contribution of various factors in individual prediction results. The driver's takeover time is crucial to ensure a safe takeover transition in conditional automated driving. The study aimed to construct a prediction model of driver's takeover time based on individual characteristics, external environment, and situation awareness variables. A total of 18 takeover events were designed with scenarios, non-driving-related tasks, takeover request time, and traffic flow as variables. High-fidelity driving simulation experiments were carried out, through which the driver's takeover data was obtained. Fifteen basic factors and three dynamic factors were extracted from individual characteristics, external environment, and situation awareness. In this experiment, these 18 factors were selected as input variables, and XGBoost and Shapely were used as prediction methods. A takeover time prediction model (BM + SA model) was then constructed. Moreover, we analyzed the main effect of input variables on takeover time, and the interactive contribution made by the variables. And in this experiment, the 15 basic factors were selected as input variables, and the basic takeover time prediction model (BM model) was constructed. In addition, this study compared the performance of the two models and analyzed the contribution of input variables to takeover time. The results showed that the goodness of fit of the BM + SA model (Adjusted_ R 2) was 0.7746. The XGBoost model performs better than other models (support vector machine, random forest, CatBoost, and LightBoost models). The relative importance degree of situation awareness variables, individual characteristic variables, and external environment variables to takeover time gradually reduced. Takeover time increased with the scan and gaze durations and decreased with pupil area and self-reported situation awareness scores. There was also an interaction effect between the variables to affect takeover time. Overall, the performance of the BM + SA model was better than that of the BM model. This study can provide support for predicting driver's takeover time and analyzing the mechanism of influence on takeover time. This study can provide support for the development of real-time driver's takeover ability prediction systems and optimization of human–machine interaction design in automated vehicles, as well as for the management department to evaluate and improve the driver's takeover performance in a targeted manner. [ABSTRACT FROM AUTHOR]