Multimodal Fusion for High-Accuracy Fatigue Driving Detection

Abstract

Driving fatigue is a serious global problem that accounts for an extremely high percentage of road traffic accidents, and therefore advanced detection techniques are urgently needed. The article provides a comprehensive overview of the application of multimodal emotion recognition techniques for fatigue driving detection. The article systematically reviews and analyses various types of existing studies that fused visual cues (e.g., facial expressions, yawning) with physiological signals (e.g., EEG, ECG) to improve detection accuracy and robustness. The analysis shows that the multimodal fusion framework can effectively compensate for the limitations of unimodal approaches (e.g., sensitivity to environmental changes). In addition, the article identifies key challenges that hinder the practical implementation of the technology, including data scarcity, insufficient generalization capabilities, real-time processing limitations, and privacy issues. Finally, the article explores promising future research directions, such as lightweight model design, cross-domain adaptation, and dynamic mode selection. This review aims to provide a valuable reference for those engaged in the research and practice of intelligent driver assistance systems.