Driver Drowsiness Detection Research Articles

Novel Transfer Learning Approach for Driver Drowsiness Detection Using Eye Movement Behavior

Novel Transfer Learning Approach for Driver Drowsiness Detection Using Eye Movement Behavior

Biosignals Monitoring for Driver Drowsiness Detection Using Deep Neural Networks

Biosignals Monitoring for Driver Drowsiness Detection Using Deep Neural Networks

MIDGNWD : Design of an efficient multimodal iterative deep graph network wearable interface for detecting drowsy drivers

Drowsy driving is a significant contributor to road traffic accidents, necessitating the development of robust and efficient detection systems. Existing approaches for detecting drowsy drivers often rely on single modalities such as image-based facial analysis or vehiclebased metrics, which may lack precision and be susceptible to environmental variations. Furthermore, the existing models might be computationally expensive and result in delayed responses. To address these limitations, this paper proposes a novel Multimodal Iterative Deep Graph Network Wearable Interface that leverages a rich set of data including heart rate, respiration, accelerometer movement, location (speed), traffic, and weather data. These data samples are processed through a fusion of different Recurrent Neural Network processes. The Bidirectional Long Short-Term Memory (BiLSTM) which is used to generate augmented features, is fused with Bidirectional Gated Recurrent Unit (BiGRU) which assists in estimating multidomain feature sets. These features are then used to train an efficient Deep Graph Recurrent Network (DGRN), which facilitates the real-time identification of drowsy drivers. The proposed model significantly enhances the precision of drowsy driver detection by 8.5%, accuracy by 9.5%, recall by 8.3%, and Area Under the Curve (AUC) by 4.9%, while also improving specificity by 5.5% and reducing delay by 10.4%. These improvements collectively contribute to a substantial enhancement in road safety, potentially saving lives by preventing accidents due to drowsy driving. The proposed system, with its real-time processing capability and high accuracy, stands as a significant improvement for identifying drowsy drivers, paving the way for safer and smarter transportation systems.

A Multi-Source Fusion Approach for Driver Fatigue Detection Using Physiological Signals and Facial Image

A Multi-Source Fusion Approach for Driver Fatigue Detection Using Physiological Signals and Facial Image

Comprehensive Survey of Driver Drowsiness Systems

Driver monitoring systems have been improved over time as artificial intelligence and computer technology have advanced. Several experimental studies have collected real-world driver drowsiness data and used various artificial intelligence algorithms and feature combinations to dramatically improve the real-time effectiveness of these systems. This study presents an updated assessment of the driver sleepiness detection systems implemented over the last decade. In modern automobiles, assessing the driver's cognitive condition is an important aspect of passenger safety. The term cognitive state refers to a driver's mental and emotional state, which has a substantial impact on their ability to drive safely. Drivers' cognitive states may be altered by factors such as fatigue, distraction, stress, or disability. Intelligent automotive technology may be able to adapt and aid the driver by identifying varied conditions in real-time, reducing the frequency of accidents. The face, being an integral component of the body, communicates a significant quantity of information. The facial expressions, such as blinking and yawning patterns, exhibit changes in a driver when they are experiencing fatigue.

Driver Drowsiness Detection using Evolutionary Machine Learning: A Survey

One of the factors that kills hundreds of people every year is driving accidents caused by drowsy drivers. There are different methods to prevent this type of accidents. Recently Machine Learning (ML) and Deep Learning (DL) have emerged as very effective and valuable approaches for detecting driver drowsiness. Moreover, the optimization of machine learning (ML) and deep learning (DL) models may be achieved through the utilization of evolutionary algorithms (EA). This survey aims to offer an overview of recent studies in driver drowsiness detection-based machine learning and deep learning models that have been improved by EA. This survey divides the approaches for detecting drowsiness into two groups: those that rely on ML, and DL, and those that rely on models-based deep learning and machine learning that are optimized by evolutionary algorithms.

A Connectivity-Aware Graph Neural Network for Real-Time Drowsiness Classification.

Drowsy driving is one of the primary causes of driving fatalities. Electroencephalography (EEG), a method for detecting drowsiness directly from brain activity, has been widely used for detecting driver drowsiness in real-time. Recent studies have revealed the great potential of using brain connectivity graphs constructed based on EEG data for drowsy state predictions. However, traditional brain connectivity networks are irrelevant to the downstream prediction tasks. This article proposes a connectivity-aware graph neural network (CAGNN) using a self-attention mechanism that can generate task-relevant connectivity networks via end-to-end training. Our method achieved an accuracy of 72.6% and outperformed other convolutional neural networks (CNNs) and graph generation methods based on a drowsy driving dataset. In addition, we introduced a squeeze-and-excitation (SE) block to capture important features and demonstrated that the SE attention score can reveal the most important feature band. We compared our generated connectivity graphs in the drowsy and alert states and found drowsiness connectivity patterns, including significantly reduced occipital connectivity and interregional connectivity. Additionally, we performed a post hoc interpretability analysis and found that our method could identify drowsiness features such as alpha spindles. Our code is available online at https://github.com/ALEX95GOGO/CAGNN.

Developing A method to Detect Driver Drowsiness Based on A single EEG Channel and Discriminated Features

Developing A method to Detect Driver Drowsiness Based on A single EEG Channel and Discriminated Features

Developing a method to detect driver drowsiness based on a single EEG channel and discriminated features

Developing a method to detect driver drowsiness based on a single EEG channel and discriminated features

Driving Towards Safety: Examining the Role of Drowsiness Detection Systems in Accident Prevention

Driving Towards Safety: Examining the Role of Drowsiness Detection Systems in Accident Prevention

Road Hypnosis Detection using Lane Detection

The study "Road Hypnosis Detection Using Lane Detection" focuses on the phenomenon known as "road hypnosis" or "highway hypnosis," a mental state in which a person can operate a vehicle for extended distances while reacting to outside events as expected, all without recalling that they did so consciously. Long stretches of time spent driving on smooth, featureless highways can cause this syndrome, which lowers awareness and causes a lack of stimulation. The study suggests a novel approach that makes use of lane identification algorithms to identify road hypnosis. It keeps track of the car's location in the lane and the driver's steering movements using sophisticated image processing and machine learning algorithms. Unusualities in these patterns, which could signal the start of road hypnosis, can be detected by the system. This system attempts to improve road safety and avoid accidents caused by decreased driver alertness due to road hypnosis by giving the driver real-time feedback or initiating automatic safety measures. The suggested approach is thoroughly tested and validated throughout the study in a variety of driving situations and conditions, proving its efficacy and suitability for incorporation into contemporary car safety systems. This study addresses a prevalent but frequently disregarded problem that might result in catastrophic accidents, making a substantial contribution to the field of driver safety systems. It also creates new opportunities for utilizing technology to raise driver awareness and enhance general traffic safety.

Identifying multilayer differential core networks and effective discriminant features for driver fatigue detection

Driver fatigue detection based on EEG has received a lot of attention. In this paper, from the initial brain functional networks of EEG, we first identify multilayer differential core networks based on node and edge discrepancy between different states to capture more discriminant information for the task. Then, the discriminant features are constructed from the resulting multilayer core networks by tensor decomposition and feature selection. The resulting discriminant features contain not only structural information about the network of individual frequency bands, but also additional relational information between the networks of different frequency bands. Our method has the strong ability of information extraction and anti-interference, which can be directly applied to the initial network to identify and utilize weak links with discriminative power. The classification results on the publicly available dataset show that the average accuracy of our method is 9.5% higher than that of the baseline method. When applied to the initial functional network without any pre-processing, the proposed method of this paper can achieve a classification accuracy of 91.14%, which is higher than the results of state-of-the-art related studies on the same dataset. These results indicate that the proposed method is effective and reliable for detecting driver fatigue from EEG.

Monitoring driver drowsiness in partially automated vehicles: Added value from combining postural and physiological indicators

Drowsiness at the wheel is one of the leading causes of road fatalities. Driver monitoring systems (DMS) mainly rely on vehicle-based data and drivers’ facial information to detect drowsiness. However, the introduction of partially autonomous driving will change the way we drive, letting the vehicle manage the driving task while drivers may be free to engage in non-driving tasks. This calls for new ways of detecting drowsiness, and even sleeping, at the wheel. Here, 22 participants drove for 100 min in a static simulator under level-2 automation on a 2 × 2 motorway. Postural (i.e., pressure and movements) and physiological (i.e., cardiac and respiratory) indicators were continuously recorded, while PERCLOS70 was used to classify drowsiness. The results reveal different physiological and postural signatures for the different states of drowsiness defined. While slight drowsiness is mainly associated with a higher heart rate, slower breathing, and an increased number of movements on the seat, being asleep is characterized by a lower heart rate and a slouched position on the seat. This study points to the relevance of using postural indicators in combination with physiological data to detect driver drowsiness. Focusing on the partially automated vehicle, it explores not only resistance to drowsiness but also sleeping at the wheel.

In-vehicle fragrance administration as a countermeasure for driver fatigue

Driver fatigue is a contributing factor in about 10–30% of all fatal crashes. Prevention of fatigue-related crashes relies on robust detection of driver fatigue and application of effective countermeasures. A potential countermeasure is fragrance administration since odors can have alerting effects on humans. The aim here was to investigate if a fragrance incorporating trigeminal components could be used as an in-vehicle countermeasure for driver fatigue.The fragrance was tested in a driving simulator with 21 healthy but sleep-deprived participants. Each participant performed a monotonous driving task twice, once with active fragrance containing a trigeminal component and once with olfactory fragrance, in a cross-over single-blind design. The order of trigeminal/olfactory fragrance was randomized and blinded to the participants. Both fragrances (trigeminal/olfactory) were administered either when the participant fell asleep (defined as eye closure > 3 s) or after approximately 45 min if the participant did not fall asleep.Self-reported sleepiness was assessed using the Karolinska Sleepiness Scale (KSS) every 5 min during driving. Variability in speed and lateral position and line crossing frequency were logged for each drive to measure driving performance. Heart rate measurements (ECG) and eye blinks (EOG) were collected to investigate potential arousing effects of the fragrance and to track objective signs of sleepiness.Mean blink duration, which was used as an objective measure of sleepiness, decreased significantly, after fragrance exposure, as did the frequency of line crossings, but there were no statistically significant differences between the fragrance with trigeminal stimulus and the pure olfactory fragrance.The results are in line with the effects found for other commonly used fatigue countermeasures, like playing loud music. These countermeasures can restore alertness and driving performance for a short while. Whether this is sufficient to support driving performance until the driver can make a safe stop in real traffic remains a topic for future studies.

Driving fatigue detection based on brain source activity and ARMA model.

Fatigue among drivers is a significant issue in society, and according to organizational reports, it substantially contributes to accidents. So accurate fatigue detection in drivers plays a crucial role in reducing the number of people fatalities or injured resulting from accidents. Several methods are proposed for fatigue driver recognition among which electroencephalography (EEG) is one. This paper proposed a method for fatigue recognition by EEG signals with extracted features from source and sensor spaces. The proposed method starts with preprocessing by applying filtering and artifact rejection. Then source localization methods are applied to EEG signals for active source extraction. A multivariate autoregressive (MVAR) model is fitted to selected sources, and a dual Kalman filter is applied to estimate the source activity and their relationships. Then multivariate autoregressive moving average (ARMA) is fitted between EEG and source activity signals. Features are extracted from model parameters, source relationship matrix, and wavelet transform of EEG and source activity signals. The novelty of this approach is the use of ARMA model between source activities (as input) and EEG signals (as output) and feature extraction from source relations. Relevant features are selected using a combination of RelifF and neighborhood component analysis (NCA) methods. Three classifiers, namely k-nearest neighbor (KNN), support vector machine (SVM), and naive Bayesian (NB) classifiers, are employed to classify drivers. To improve performance, the final label for fatigue detection is calculated by combining these classifiers using the voting method. The results demonstrate that the proposed method accurately recognizes and classifies fatigued drivers with the ensemble classifiers in comparison with other methods.

Wearable brain-computer interface technology and its application

Wearable Brain-Computer Interfaces (BCIs) signify a critical evolution in human-machine communication, driven by the convergence of neuroscience, engineering, and information technology. With applications that span across industrial, medical, and recreational domains, BCIs hold potential to redefine our interaction with the technological landscape. This manuscript elucidates this transformative juncture, bifurcating into passive and active BCIs. In passive BCIs, innovations leveraging Virtual Reality (VR) and Augmented Reality (AR) are delineated, progress has been demonstrated in the classification of passively induced emotional signals, highlighting the emergence of hands-free control systems like quadcopter control and industrial inspections. The discourse on active BCIs unveils advancements such as real-time handwriting and speech decoding, driver drowsiness detection, and emotion recognition, aided by machine learning techniques. Despite groundbreaking progress, challenges in algorithm optimization, adaptability, multimodal signal complexity, and ethics persist. Future directions emphasize the potential of deep learning and multimode input signals collaboration. The manuscript underscores the societal implications, particularly in rehabilitation, communication, and entertainment. The review, therefore, serves as both an appraisal and a roadmap for the burgeoning field of wearable BCIs, underlining its role as a pathway to enhance human capabilities and quality of life.

EEG-based detection of driving fatigue using a novel electrode

In view of the serious harm caused by driving fatigue, this paper investigated driving fatigue detection based on electroencephalogram (EEG) features, designed a novel semi-dry electrode for acquisitioning drivers' EEG signals, and performed refined composite multiscale fluctuation dispersion entropy (RCMFDE) feature extraction of the θ and β bands rhythm signals in the acquisitioned EEG signals. The research results showed that the novel semi-dry electrode designed in this paper had the advantages of convenient replenishment of conductive liquid, early warning of insufficient conductive liquid, comfortable to wear compared with the traditional wet and dry electrodes under the premise of being able to collect EEG signals with qualified quality. And compared with traditional methods such as multiscale sample entropy (MSE), multiscale permutation entropy (MPE), multiscale symbolic dynamic entropy (MSDE), multiscale dispersion entropy (MDE), and refined composite multiscale dispersion entropy (RCMDE), the EEG features extracted by the RCMFDE method in this paper have a more obvious fatigue feature tendency, and thus can identify the driving fatigue state more effectively.

Spatiotemporal Pyramidal CNN with Depth-Wise Separable Convolution for Eye Blinking Detection in the Wild

Eye blinking detection in the wild plays an essential role in deception detection, driving fatigue detection, etc. Despite the fact that numerous attempts have already been made, the majority of them have encountered difficulties, such as the derived eye images having different resolutions as the distance between the face and the camera changes; or the requirement of a lightweight detection model to obtain a short inference time in order to perform in realtime. In this research, two problems are addressed: how the eye blinking detection model can learn efficiently from different resolutions of eye pictures in diverse conditions; and how to reduce the size of the detection model for faster inference time. We propose to utilize upsampling and downsampling the input eye images to the same resolution as one potential solution for the first problem, then find out which interpolation method can result in the highest performance of the detection model. For the second problem, although a recent spatiotemporal convolutional neural network used for eye blinking detection has a strong capacity to extract both spatial and temporal characteristics, it remains having a high number of network parameters, leading to high inference time. Therefore, using Depth-wise Separable Convolution rather than conventional convolution layers inside each branch is considered in this paper as a feasible solution.

Transfer Learning-based Drowsiness Detection System for Driver Assistance and Classification of Traffic Signs Employing a Deep Convolutional Neural Network

In this research paper, a comparative analysis of various image enhancements in the spatial domain techniques was performed based on three-dimensional image quality statistics such as mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity for measuring the image quality (SSIM). The conditions for choosing the best method are low mean square error (MSE) and high peak signal-to-noise ratio as well as the structural similarity for measuring the image quality. The pre-processed image was subjected to a classification task using a stepwise version of pre-trained deep convolutional neural networks. The problem of driver drowsiness caused by fatigue, lack of sleep, medication, etc. will have to be solved by improving the efficiency of driver drowsiness detection. Many traffic accidents are largely caused by drowsy drivers. Driver fatigue and distraction can cause a lack of alertness, which can lead to driver inattention. In this paper, we proposed a new deep-learning technique for driver drowsiness detection. Two methods using convolutional deep neural network and GoogleNet transfer learning are compared to achieve a better accuracy of 99%.

Behavioral drowsiness detection system execution based on digital camera and MTCNN deep learning

Drowsy driving is a major cause of road accidents worldwide, necessitating the development of effective drowsiness detection systems. Each year, there are more accidents and fatalities than ever before for a variety of causes. For instance, there were 22,952 fatalities and 79,545 injuries as a result of nearly 66,500 vehicle accidents in the last 10 years. In this paper, we propose a novel approach for detecting drowsiness based on behavioral cues captured by a digital camera and utilizing the multi-task cascaded convolutional neural network (MTCNN) deep learning algorithm. A high-resolution camera records visual indications like closed or open eye movement to base the technique on the driver's behavior. In order to measure a car user's weariness in the present frame of reference, eyes landmarks are evaluated, which results in the identification of a fresh constraint known as "eyes aspect ratio." A picture with a frame rate of 60 frames per second (f/s) and a resolution of 4,320 eyeballs was used. The accuracy of sleepiness detection was more than 99.9% in excellent lighting and higher than 99.8% in poor lighting, according to testing data. The current study did better in terms of sleepiness detection accuracy than a lot of earlier investigations.