Multi-Modal Driver Drowsiness Detection in ADAS via Attention-Guided Siamese Network with Temporal Modeling

Driver drowsiness detection plays a critical role in improving road safety, as drowsiness substantially increases the likelihood of traffic accidents. In this study, we propose a novel multi-modal framework within Advanced Driver Assistance Systems (ADAS) that leverages an Attention-Guided Siamese Network coupled with temporal modeling to accurately capture both spatial and temporal patterns of driver fatigue. The Siamese network processes paired facial images, enabling the extraction of discriminative features that highlight subtle changes in driver state. The attention mechanism is explicitly applied to the spatial feature maps within each branch of the Siamese network, allowing the model to focus selectively on key facial regions—such as eyes and mouth—that are most indicative of drowsiness, while also weighting complementary sensor modalities dynamically. Temporal modeling is incorporated through a sequential module (e.g., LSTM or temporal convolution) that analyzes the extracted features over time, capturing gradual and evolving signs of drowsiness that static frame-based methods often overlook. Extensive evaluations on benchmark datasets (YawDD, NTHUDDD) and a novel real-world driving dataset demonstrate superior accuracy exceeding 98.8%, along with strong cross-subject generalization. Ablation studies confirm the critical contributions of the attention mechanism in improving feature discrimination, and the temporal modeling module in enhancing sensitivity to progressive drowsiness. The proposed method surpasses traditional approaches in temporal awareness, data efficiency, and resilience to inter-subject and environmental variations, offering a robust and interpretable solution for real-time driver drowsiness monitoring in intelligent vehicles.