Abstract

Autonomous driving systems relieve drivers from continuous vehicle operation and constant monitoring, allowing them to engage in non-driving-related tasks (NDRTs). However, immersion in such tasks can impair drivers’ perception of both the takeover situation and the surrounding environment. To quantitatively assess drivers’ risk perception capability during takeover, a driving simulation platform was used to design autonomous takeover scenarios involving three types of NDRTs, three takeover request times (TOR), and two obstacle avoidance conditions. A total of forty participants were recruited to complete the driving experiment. Drivers’ eye movement data were collected, and visual metrics—including fixation, saccade, and pupil diameter—were extracted by defining areas of interest (AOIs). A subjective risk perception scale was developed and administered to measure drivers’ subjective evaluations. Together with takeover reaction time, the K-means clustering method was applied to classify drivers’ risk perception levels into three categories: high, medium, and low. The LightGBM algorithm was selected to construct a baseline classification model for assessing drivers’ risk perception levels. Subsequently, the Whale Optimization Algorithm (WOA) was employed to optimize the hyperparameters of LightGBM, resulting in the WOA-LightGBM model. This optimized model demonstrated improved recall, accuracy, precision, and F1-score, reaching 0.9210, 0.9253, 0.9261, and 0.9201, respectively. Furthermore, SHapley Additive exPlanations (SHAP) analysis was conducted to quantify the contribution of eye movement indicators to risk perception assessment. The results revealed that saccade duration in the NDRT areas significantly reduced drivers’ risk perception levels (SHAP value = −0.71), whereas increased saccade duration in the forward road area effectively restored drivers’ risk perception capability (SHAP value = 0.71). In addition, higher risk perception levels were found to enhance drivers’ takeover performance in terms of vehicle control. These findings provide valuable insights for the management of NDRTs and the optimization of autonomous vehicle takeover systems.

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