Takeover Time Prediction for Conditionally Automated Driving Vehicles: Considering Mixed Traffic Flow Environment

The length of time for drivers to take over conditionally automated driving vehicles (CADV) is often influenced by numerous factors, especially the mixed traffic flow environment. To analyze the influencing factors of driver’s takeover time in the mixed traffic flow environment, we study a real-vehicle takeover experiment with the CADV and radar video integrated machine provided by Jiangsu University. DeepGBM algorithm and Shapley additive explanation (SHAP) are utilized to predict and analyze the takeover time based on the data obtained from the real-vehicle experiment. The results show that the DeepGBM algorithm performs a better accuracy in predicting the takeover time compared with the other algorithms. Moreover, the predicting effect of DeepGBM in the scene of average takeover time is better than the shorter and longer ones. On the other hand, when taking over CADV in the intersection area, the driver pays more attention to the vehicle’s speed. While in the non-intersection area, the driver is more attentive to the longitudinal distance difference with the vehicle ahead. This study further found that CADVs are subject to significant lateral interference for the takeover process in intersection areas, significantly impacting the driver’s takeover time and vehicle safety. This study can provide a theoretical reference for automated driving companies to design takeover times.