A Multimodal Systematic Review of Drivers’ Fatigue Detection Methodologies, Datasets, and Models

Driver fatigue continues to be a leading contributor to global road accidents, posing significant risks particularly in long-distance, night-time, and commercial driving contexts. Recent advances in artificial intelligence (AI), sensor fusion, and signal processing have led to the development of automated fatigue detection systems capable of providing early warnings to prevent collisions. However, the field remains fragmented in terms of data modalities, modeling techniques, evaluation standards, and deployment readiness. This systematic review synthesizes findings from 78 peer-reviewed studies published between 2014 and 2025, critically analyzing the use of behavioral, physiological, vehicular, and multimodal approaches for fatigue detection. The review identifies a clear methodological shift from handcrafted features and traditional machine learning toward deep learning models, particularly CNNs and hybrid CNN-LSTM frameworks, enhancing performance in visual and EEG-based fatigue detection. Despite high accuracies reported under controlled conditions (often exceeding 95%), only a minority of models were validated in real-world settings or evaluated for real-time, in-vehicle deployability. Explainability and personalization, essential for trust and usability in human-centered AI, were substantially underrepresented which featured in only 19.2% and 12.8% of the studies respectively. Moreover, the widespread use of small sample sizes, unimodal data, and non-standardized metrics impedes reproducibility and comparative analysis. Nonetheless, emerging trends such as federated learning, edge deployment, and privacy-preserving AI indicate a growing emphasis on ethical, scalable, and context-aware systems. This review concludes by offering a roadmap for future research: emphasizing multimodal integration, standardized evaluation, on-road validation, and transparent model architectures, to bridge the gap between experimental prototypes and real-world fatigue monitoring systems.