Multimodal Physiological Detection of Passive Fatigue in SAE Level 3 Automated Driving Using Eye-Movement and ECG Features

In SAE Level 3 automated driving, drivers are required to supervise vehicle operation and respond to overtaking requests. Owing to task monotony and insufficient workload, drivers are prone to passive fatigue, which may impair vigilance and safety. This study investigated passive fatigue development during automated driving and proposed a multimodal detection method. Thirty licensed participants completed one automated driving task and one manual driving task in a driving simulator. Eye-movement and ECG/heart rate variability indicators were synchronously collected, and fatigue states were assessed using the Karolinska Sleepiness Scale. Results show that passive fatigue during automated driving developed differently from active fatigue during manual driving. Based on PERCLOS, pupil diameter, pupil diameter variation, SDNN, and LF/HF, an SVM-based passive fatigue detection model was developed to classify alert and passive fatigue states. Across repeated subject-wise validation, the model achieved an accuracy of 89.19%, sensitivity of 91.83%, specificity of 86.54%, balanced accuracy of 89.19%, F1 score of 89.48%, and precision of 87.28%, outperforming the model trained on manual driving active fatigue data. These findings demonstrate the need for scenario-specific driver-state monitoring models in automated driving systems and provide an applied physiological sensing approach for passive fatigue detection and warning design.