Driver Drowsiness State Research Articles

EEG-based TSK fuzzy graph neural network for driver drowsiness estimation

With the development of brain-computer interface (BCI), electroencephalogram (EEG) is considered to be one of the best physiological signals to detect the fatigue state of drivers due to its advantages of extremely high time resolution and low use cost. However, since EEG is sensitive to noise/artifacts, and there is non-stationarity and low signal-to-noise ratio in inter-subject/intra-subject, the accurate estimation of driver drowsiness state remains challenging. Therefore, this paper proposes the EEG-based TSK fuzzy graph neural network (TSKG) to solve the regression problem of driver drowsiness estimation by combining transfer learning (TL), graph convolutional neural network (GCN), information theory, and TSK fuzzy neural network. TSKG extracts the relevant features in EEG through mutual information minimization and regards the fuzzy rules as graph structure data. The extracted relevant features and the original features are used to optimize the fuzzy rules through GCN. TSKG is tested on the fatigue driving dataset, and Root Mean Square Error (RMSE) and Correlation Coefficient (CC) are 0.1681 and 0.7118, respectively. Especially when dealing with difficult samples, TSKG has significantly better generalization performance.

Early Detection of Drowsiness While Driving Using Bio Signals

Recently, driver fatigue is considered worldwide as the main reason of vehicle accidents. However, measuring of the driver fatigue can be directly throughout measuring the driver drowsiness state. This paper is aimed to build a drowsiness detection system. The system applied consists of two part: pre-recorded EOG signals and application of IR sensor on the eyes blinking. The results revealed a good detection of the eyes blinking based on a biological signal from the sensors. Wearable driver drowsiness detection system is a suitable alternative for reducing vehicle accidents.

Isotropic Self-Supervised Learning for Driver Drowsiness Detection With Attention-Based Multimodal Fusion

Driverdrowsiness is an important cause of traffic accidents. Many studies using computer vision techniques to detect driver drowsiness states, such as slow blinking, yawning, and nodding, have demonstrated excellent potential. Although existing studies have made significant progress, the number of samples in the training corpora is small, which makes it difficult for a model to learn effective drowsiness representations from images or videos. To address this issue, we develop an isotropic self-supervised learning (IsoSSL) approach to learn powerful representations of images without relying on human-provided annotations and propose an IsoSSL-MoCo model by combining IsoSSL with momentum contrast (MoCo). To exploit the complementarity of multimodal data, an attention-based multimodal fusion model is also proposed to fuse features from the eye, mouth, and optical flow of the head. Specifically, we first use the IsoSSL-MoCo model to pretrain the image encoders for the three modalities in other datasets. Then, these encoders are fine-tuned and integrated into the proposed fusion model. The feature vectors generated by the image encoders of the three modalities are fed into the recursive layer to extract temporal information. To capture the importance degrees of the effects of temporal features from the three modalities on drowsiness detection, an attention mechanism is introduced to automatically weigh the feature vectors from the recursive layer to improve detection accuracy. Finally, a vector representation is generated by the attention layer and is used to detect driver drowsiness states. Experimental results based on two challenging datasets show that our method outperforms the baseline methods and the latest existing methods.

Automated Drowsiness Detection Through Facial Features Analysis

The lack of concentration, caused by fatigue, is the most factor of the increasing number of accidents. In the last few years, the development of an automatic system which based on facial expression analysis, to controls the driver fatigue and prevents him in advance from accidents, has received a growing interest in all intelligent vehicle systems. In This paper, we propose and compare two methods to detect the driver drowsiness state. These methods extracts geometric features using video to characterize eyes blinking as a nonstationary and nonlinear signal. The first method is based on Cumulative Blink Signal analysis technique”CBS” which locates and analyses the eyes blinking from the obtained nonstationary and nonlinear signal to detect the driver drowsiness state. The second method is based on IFD technic ”Intinsic Functions Decompositionof the nonstationary and nonlinear signal to analyse the nonstationary and nonlinear signal by using the combination between the two methods: Empirical mode decomposition (EMD) and Band Power(BP). For both proposed methods, this analysis is confirmed by the support Vector Machine (SVM) to classify the state ofdriver fatigue. The synthesis results obtained by both methods CBS and IFD are discussed and compared to those of the literature.

Spatio-temporal features for the automatic control of driver drowsiness state and lack of concentration

Driver fatigue is one of the leading causes of road accidents. It affects the mental vigilance of the driver and reduces his personal capacity to drive a vehicle in full safety. These factors increase the risk of human errors which could involve deaths and wounds. Consequently, the development of an automatic system, which controls the driver fatigue and prevents him from accidents in advance, has received a growing interest. In this work, we have proposed a fusion system for drowsiness detection based on blinking measurement and the 3D head pose estimation. We have studied the driver's eye behaviors by analysing a non-stationary and non-linear signal and we estimate the head rotation in the three directions $$Yaw$$Yaw, $$Pitch$$Pitch, and $$Roll$$Roll by exploiting only three interest points of the face. Our suggested system of fusion presents three levels of drowsiness: awake, tired, and very tired. This system is evaluated by both $$DEAP$$DEAP and $$MiraclHB$$MiraclHB databases. The evaluation shows many promising results and shows the effectiveness of the suggested approach.

Driver fatigue detection from electroencephalogram spectrum after electrooculography artefact removal

A driver fatigue monitoring and detection system with high accuracy could be a valuable countermeasure to decrease fatigue-related traffic accidents. This study proposes methods for drowsiness detection based on electroencephalogram (EEG) power spectrum analysis. First, a new algorithm is proposed for independent component analysis with reference (ICA-R) for electrooculography artefacts removal. Comparison is then carried out between the proposed ICA-R algorithm and an adaptive filter. Secondly, 75 EEG spectrum features are extracted from the cleaned EEG. Among all the EEG spectrum-related features, 40 key features are selected by support vector machine recursive feature elimination to improve the performance of the classifier. The validation results show that 86% of the driver's drowsiness states can be accurately detected among drivers, who participate a driving simulator study.

Driver Drowsiness Classification Using Fuzzy Wavelet-Packet-Based Feature-Extraction Algorithm

Driver drowsiness and loss of vigilance are a major cause of road accidents. Monitoring physiological signals while driving provides the possibility of detecting and warning of drowsiness and fatigue. The aim of this paper is to maximize the amount of drowsiness-related information extracted from a set of electroencephalogram (EEG), electrooculogram (EOG), and electrocardiogram (ECG) signals during a simulation driving test. Specifically, we develop an efficient fuzzy mutual-information (MI)- based wavelet packet transform (FMIWPT) feature-extraction method for classifying the driver drowsiness state into one of predefined drowsiness levels. The proposed method estimates the required MI using a novel approach based on fuzzy memberships providing an accurate-information content-estimation measure. The quality of the extracted features was assessed on datasets collected from 31 drivers on a simulation test. The experimental results proved the significance of FMIWPT in extracting features that highly correlate with the different drowsiness levels achieving a classification accuracy of 95%-- 97% on an average across all subjects.