Modeling Driver Lateral Control Behavior for Human--Machine Shared Takeover During Emergency Collision Avoidance Under Dynamic Traffic Environment

Understanding and modeling driver’s lateral control behavior accurately are essential for developing effective human–machine shared takeover (HMST) system during emergency collision avoidance under dynamic traffic environment. However, little attention has been devoted to modeling driver behavior during HMST. Moreover, existing methods mostly focus on the interaction between a single vehicle and the driver, making it difficult to effectively characterize the nonlinear and uncertain behavior of drivers in dynamic traffic environment. Therefore, this study focuses on modeling of driver lateral control behavior during HMST under dynamic traffic environment, and a Transformer-based modeling method with multi-head attention is proposed. To validate the performance of the purposed approach, comparative experiments against traditional modeling approaches are carried out. And twelve drivers are recruited to perform HMST tasks in different emergency collision avoidance scenarios based on a driving simulator. Results of comparative experiments of different driving scenarios demonstrate the superior accuracy of the proposed method. In addition, the shapley (SHAP) additive explanations is employed to enhance the interpretability of the proposed model, thereby providing a reliable foundation for the development of efficient HMST systems.