A Systematic Survey of Driving Fatigue Monitoring

Zhang, Zhimin; Ning, Huansheng; Zhou, Fang

doi:10.1109/tits.2022.3189346

Cited by 30 publications

(8 citation statements)

References 105 publications

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“…To summarize, the existing fatigue driving detection systems face limitations in equipment deployment, environmental changes, and real-time monitoring. Addressing these challenges represents a crucial research direction for the future development of driving fatigue detection systems [ 35 ]. Consequently, this article will concentrate on resolving the following three problems: The problem of the low fatigue detection accuracy of a single feature.…”

Section: Introductionmentioning

confidence: 99%

Remote Photoplethysmography and Motion Tracking Convolutional Neural Network with Bidirectional Long Short-Term Memory: Non-Invasive Fatigue Detection Method Based on Multi-Modal Fusion

Kong,

Xie,

Niu

et al. 2024

Sensors

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Existing vision-based fatigue detection methods commonly utilize RGB cameras to extract facial and physiological features for monitoring driver fatigue. These features often include single indicators such as eyelid movement, yawning frequency, and heart rate. However, the accuracy of RGB cameras can be affected by factors like varying lighting conditions and motion. To address these challenges, we propose a non-invasive method for multi-modal fusion fatigue detection called RPPMT-CNN-BiLSTM. This method incorporates a feature extraction enhancement module based on the improved Pan–Tompkins algorithm and 1D-MTCNN. This enhances the accuracy of heart rate signal extraction and eyelid features. Furthermore, we use one-dimensional neural networks to construct two models based on heart rate and PERCLOS values, forming a fatigue detection model. To enhance the robustness and accuracy of fatigue detection, the trained model data results are input into the BiLSTM network. This generates a time-fitting relationship between the data extracted from the CNN, allowing for effective dynamic modeling and achieving multi-modal fusion fatigue detection. Numerous experiments validate the effectiveness of the proposed method, achieving an accuracy of 98.2% on the self-made MDAD (Multi-Modal Driver Alertness Dataset). This underscores the feasibility of the algorithm. In comparison with traditional methods, our approach demonstrates higher accuracy and positively contributes to maintaining traffic safety, thereby advancing the field of smart transportation.

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Section: Introductionmentioning

confidence: 99%

Remote Photoplethysmography and Motion Tracking Convolutional Neural Network with Bidirectional Long Short-Term Memory: Non-Invasive Fatigue Detection Method Based on Multi-Modal Fusion

Kong,

Xie,

Niu

et al. 2024

Sensors

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“…Existing vision-based fatigue driving recognition methods can usually be summarized in three steps: face detection, facial feature extraction, and fatigue state decision [ 4 ]. Drivers’ face detection methods include multi-task convolutional neural networks (MTCNNs) [ 5 ], multi-scale feature output, and spatial pyramid pooling [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

FPIRST: Fatigue Driving Recognition Method Based on Feature Parameter Images and a Residual Swin Transformer

Xiao,

Liu,

et al. 2024

Sensors

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Fatigue driving is a serious threat to road safety, which is why accurately identifying fatigue driving behavior and warning drivers in time are of great significance in improving traffic safety. However, accurately recognizing fatigue driving is still challenging due to large intra-class variations in facial expression, continuity of behaviors, and illumination conditions. A fatigue driving recognition method based on feature parameter images and a residual Swin Transformer is proposed in this paper. First, the face region is detected through spatial pyramid pooling and a multi-scale feature output module. Then, a multi-scale facial landmark detector is used to locate 23 key points on the face. The aspect ratios of the eyes and mouth are calculated based on the coordinates of these key points, and a feature parameter matrix for fatigue driving recognition is obtained. Finally, the feature parameter matrix is converted into an image, and the residual Swin Transformer network is presented to recognize fatigue driving. Experimental results on the HNUFD dataset show that the proposed method achieves an accuracy of 96.512%, thus outperforming state-of-the-art methods.

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“…The former judge the fatigue state of drivers according to the subjective survey of drivers and researchers, which is easily affected by the subjective judgment errors of drivers and researchers. Therefore, It is often used as an adjunct to detect driver fatigue [3]. The latter mainly involves feature extraction and analysis of EEG signals, electromyogram, electrocardiogram and electrooculogram, which is generally accepted by researchers and widely used in driver fatigue detection method [4].…”

Section: Introductionmentioning

confidence: 99%

Real-time EEG-based detection of ship driving fatigue using sample entropy

Liu¹,

Li²,

Wang³

2023

Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)

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In our present work, the sample entropy (SampEn) was used to analyze the changes of ship driver driving fatigue state when he drove for a long time from the perspective of human electroencephalogram (EEG). Combining with the eye movement, the law of fatigue change caused by long-time driving of the ship is analyzed. Thus it can conclude that the EEG characteristics, as well as the eye movement, can effectively detect driver's fatigue when he driving.

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A Systematic Survey of Driving Fatigue Monitoring

Cited by 30 publications

References 105 publications

Remote Photoplethysmography and Motion Tracking Convolutional Neural Network with Bidirectional Long Short-Term Memory: Non-Invasive Fatigue Detection Method Based on Multi-Modal Fusion

Remote Photoplethysmography and Motion Tracking Convolutional Neural Network with Bidirectional Long Short-Term Memory: Non-Invasive Fatigue Detection Method Based on Multi-Modal Fusion

FPIRST: Fatigue Driving Recognition Method Based on Feature Parameter Images and a Residual Swin Transformer

Real-time EEG-based detection of ship driving fatigue using sample entropy

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