Research progress on driving fatigue mechanism and monitoring-warning technology

JIA Shizhe; WANG Weixi; LU Bo; HE Junni; PEI Xin; SHEN Shifei; WANG Jia

doi:10.16511/j.cnki.qhdxxb.2026.26.021

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Journal of Tsinghua University(Science and Technology) ›› 2026, Vol. 66 ›› Issue (6) : 1199-1211. DOI: 10.16511/j.cnki.qhdxxb.2026.26.021

VEHICLE AND TRAFFIC

Research progress on driving fatigue mechanism and monitoring-warning technology

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[Significance] Driving fatigue is a major contributor to traffic accidents and is characterized by its prevalence, concealment, and associated risks. It impairs drivers' physiological functions and depletes their psychological cognitive resources. Therefore, a comprehensive understanding of its underlying mechanisms, coupled with the development of accurate monitoring, early warning, and intervention technologies, is crucial to enhancing road safety. However, existing reviews often lack a systematic integration of research on fatigue mechanisms and advancements in associated monitoring and intervention technologies. To address this gap, this paper systematically reviews the causal factors and mechanistic hypotheses underlying fatigue and evaluates the current status of monitoring, warning, and intervention technologies. Current research indicates that multisource information fusion, which integrates physiological signals, visual imagery, and driving behavior data, significantly enhances the robustness and accuracy of fatigue detection compared to single-modality approaches. Meanwhile, early-warning and intervention strategies are evolving from passive monitoring toward active prediction, incorporating temporal analysis and contextual awareness. These strategies include the use of in-vehicle visual, auditory, and tactile stimuli; the development of autonomous vehicle takeover systems; and the optimization of road infrastructure design. This review synthesizes recent progress and emerging trends in the research on driving fatigue mechanisms and associated monitoring-warning technologies, and proposes integrated strategies to support effective fatigue management. [Progress] In physiological monitoring, electroencephalography remains the gold standard, with deep learning models such as convolutional neural network (CNN)-attention methods achieving accuracy rates of up to 97.8%. Electrocardiography and heart rate variability measurements are also widely used, but their applicability is limited by the intrusiveness of the sensors. Therefore, current research focuses on noninvasive alternatives such as photoplethysmography and miniaturized devices, although challenges such as motion artifacts and environmental interference persist. Visual monitoring leverages computer vision and deep learning, including CNNs and long short-term memory models, to analyze features such as eye closure percentage, yawn frequency, and head pose, achieving accuracy rates of up to 92.7%. However, its performance is susceptible to lighting conditions, occlusions, and individual differences, necessitating improvements in real-world robustness. Driving behavior monitoring uses data on vehicle dynamics such as steering wheel variability and lane deviation, and machine learning models trained on these data achieve accuracies of up to 91.2%. While this method is privacy-preserving and readily deployable, it suffers from detection latency and is influenced by road conditions and driving habits, which limit its effectiveness for early warning. Multisource data fusion overcomes these limitations by integrating physiological, visual, and behavioral data using architectures such as multicolumn CNNs, achieving superior accuracy. However, high computational demands, data heterogeneity, and model interpretability issues pose challenges for real-time deployment. In terms of warnings and interventions, conventional systems rely on real-time detection to trigger alerts. Current research is shifting focus toward proactive prediction using bio-mathematical models and recurrent neural networks, achieving accuracies of up to 88.2% in forecasting fatigue several minutes in advance. Large language models enable intelligent, adaptive dialogue for graded intervention, supporting integrated “monitoring-assessment-response” frameworks. Autonomous driving technologies, particularly conditional automation, can reduce fatigue by allowing drivers to perform non-driving tasks and by providing emergency vehicle takeover capabilities. Road design and managerial measures complement these technological solutions within a holistic “human-vehicle-road-environment- management” framework. [Conclusions and Prospects] This review outlines the mechanisms underlying fatigue, the associated monitoring technologies, and intervention strategies to address fatigue, emphasizing the key role of multisource data fusion in improving fatigue detection accuracy. The shift from passive warning to proactive intervention, supported by artificial intelligence and autonomous systems, represents a critical technological pathway. However, challenges remain in areas such as the precision of real-time predictions, the comfort of wearable devices, and the computational efficiency of multisource data fusion models. Future research should prioritize dynamic mechanism modeling, cross-scenario adaptive algorithms, and human-machine collaborative intervention to develop more reliable and scalable fatigue mitigation solutions.

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traffic safety / driving fatigue / fatigue monitoring / multi-source information fusion / warning and intervention

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JIA Shizhe, WANG Weixi, LU Bo, HE Junni, PEI Xin, SHEN Shifei, WANG Jia. Research progress on driving fatigue mechanism and monitoring-warning technology[J]. Journal of Tsinghua University(Science and Technology). 2026, 66(6): 1199-1211 https://doi.org/10.16511/j.cnki.qhdxxb.2026.26.021

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