A Takeover Risk Assessment Approach Based on an Improved ANP-XGBoost Algorithm for Human–Machine Driven Vehicles

This study evaluates the risk level of human-machine collaborative driving takeover in a highway environment under the interaction of non-driving related tasks and takeover request prompt scenarios. Using a driving simulator, a
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factor analysis examined non-driving tasks and takeover prompts. Takeover impact and risk indicator features were extracted, with risk indicators weighted using an improved ANP method. On this basis, the XGBoost algorithm was employed to identify crucial variables that reflect the level of takeover risk and to construct a driver takeover risk assessment model. The evaluation results indicate that the risk indicator feature with the greatest influence on the takeover risk level was the minimum TTC, which had the highest correlation (r=−0.81); the scene factor in the takeover influence feature had the highest correlation with the takeover risk level (r=−0.78), which had the greatest influence on driver takeover safety; Although non-driving related tasks exhibited a weak correlation with takeover reaction time, steering reaction time, and minimum TTC, the effect was minor. The XGBoost algorithm-based risk assessment model demonstrated superior performance over LightGBM and SVM, with 87.1% accuracy. Overall, this study highlights the significant influence of takeover scenes and minimum TTC on collaborative driving risk, enabling accurate risk modeling.