SURVEY PAPER ON DRIVER SLEEPINESS DETECTION SYSTEM

Driver drowsiness is one of the leading causes of traffic accidents and has a substantial impact on road safety. Many traffic accidents can be avoided if sleepy drivers were given early warnings. Drowsiness detection systems monitor the driver condition and generate an alarm if drowsiness signs are detected. In this paper, a real-time visual-based driver drowsiness detection system is presented aiming to detect drowsiness by extracting an eye feature called the eye aspect ratio. In the proposed system, which is applied on videos obtained from a public drowsiness detection dataset, the face region is first localized in each frame. Then, the eye region is detected and extracted as the region of interest using facial landmarks detector. Following that, the eye aspect ratio value of each frame is calculated, analyzed, and recorded. Finally, three different classifiers, namely, linear support vector machine, random forest, and sequential neural network, are employed to improve the detection accuracy. Subsequently, the extracted data are classified to determine if the driver's eyes are closed or open. An alarm will then be triggered to alert the drowsy driver if an eye closure is recognized for a specified duration of time.

Vehicle crashes lead to huge economic and social consequences, and one non-negligible cause of accident is driver sleepiness. Driver sleepiness analysis based on the monitoring of vehicle acceleration, steering and deviation from the road or physiological and behavioral monitoring of the driver, e.g., monitoring of yawning, head pose, eye blinks and eye closures, electroencephalogram, electrooculogram, electromyogram and electrocardiogram (ECG), have been used as a part of sleepiness alert systems. Heart rate variability (HRV) is a potential method for monitoring of driver sleepiness. Despite previous positive reports from the use of HRV for sleepiness detection, results are often inconsistent between studies. In this work, we have re-evaluated the feasibility of using HRV for detecting drivers’ sleepiness during real road driving. A database consists of ECG measurements from 10 drivers, driving during morning, afternoon and night sessions on real road were used. Drivers have reported their average sleepiness level by using the Karolinska sleepiness scale once every five minutes. Statistical analysis was performed to evaluate the potential of HRV indexes to distinguish between alert, first signs of sleepiness and severe sleepiness states. The results suggest that individual subjects show different reactions to sleepiness, which produces an individual change in HRV indicators. The results motivate future work for more personalized approaches in sleepiness detection.

Fatigue driving is the main cause of traffic accidents. Analysis of electroencephalogram (EEG) signals has attracted wide attention for identifying fatigue driving. With the development of the Internet of Vehicles (IoV), we hope to establish an EEG-based IoV traffic management system to improve traffic safety. In the proposed system, real-time diagnosis is a significant factor, and improvement of the detection speed is our main concern. EEG signals generate a large amount of spatially oriented data over a relatively short duration; hence, their dimension needs to be reduced effectively before being analysed. We proposes a feature reduction method, based on a novel weighted principal component analysis (WPCA) algorithm for EEG signals. First, the EEG features are extracted by an autoregressive (AR) model. Second, we calculate the influence of different features on the classified performance of fatigue state. The accuracy reduction values of different features are normalised as the weights of the features. Finally, these weights are assigned to the WPCA to reduce the EEG features. To verify the effectiveness of the algorithm, we carried out a simulated driving experiment involving eight participants. For comparison, power spectral density and differential entropy models were also introduced to extract EEG features. Support Vector Machine was adopted as a classifier to establish a fatigue driving classification experiment. The experimental results show that the WPCA method can effectively reduce the feature dimension of different EEG feature extraction methods, speed up calculations, and achieve a much higher classification accuracy of fatigue driving.

Nowadays, drowsiness is a serious cause of traffic accidents, a problem of major concern to society. Driver fatigue or sleepiness decreases the driver’s reaction time, reduces attention, and affects the quality of decision making which impairs the driving experience. Therefore, in this paper, a drowsiness detection system is designed based on computer vision, using a cascade of classifiers based on Haar-like features. The system is able to detect the face and eyes of the driver and determine the eyes closure or opening, which concludes the drowsiness of the driver. The paper presents the five primary steps involves which are: video acquirement, frame separation, face detection, eyes detection and drowsiness detection.

Driver fatigue is one of the main causes of traffic accidents. Many researchers believe that electroencephalography is very accurate and promising to detect driving fatigue, but so far, the research results have been variable. Through simulation experiments, this paper found when driving at night, delta band varies significantly with the degree of fatigue. The results have implications for real-time automatic detecting driver fatigue at night and are helpful for future development of driver fatigue countermeasures.

Driver fatigue is a leading cause of road accidents, particularly during long-distance driving, and necessitates reliable, real-time detection methods to prevent safety compromises. This study presents a drowsiness detection system designed to provide early warnings by analyzing eye-blinking patterns through real-time facial recognition and eye tracking using an in-vehicle camera. The system leverages MediaPipe to extract facial landmarks and compute the Eye Aspect Ratio (EAR), a geometric measure that quantifies eye openness. By employing a threshold-based decision mechanism, the system triggers audible alerts when EAR values indicate signs of drowsiness, such as prolonged or frequent eye closures. Evaluated on a dataset of 667 driver images, the system achieved an accuracy of 85% in drowsiness detection, with an optimal EAR threshold of 0.289 identified for maximizing performance. The approach demonstrates robustness against common real-world challenges, including variations in lighting conditions and camera positioning, offering a non-intrusive, adaptive, and highly reliable solution. By focusing on eye-based biometric features derived from facial landmarks, this system contributes an original and practical advancement in driver monitoring technology, with significant potential to reduce fatigue-related accidents and enhance overall road safety.

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.

With the rapid growth of the number of motor vehicles, road traffic accidents are becoming more and more serious. Fatigue driving has become one of the main causes of traffic accidents. It is particularly critical to find effective solutions to fatigue driving. At present, the academic community believes that the phenomenon of fatigue driving can be controlled by means of prevention and intervention. In terms of intervention countermeasures, using sound stimulation as a wake-up method has the advantages of easy implementation and significant effect, which has become the research focus in the driving fatigue mitigation scheme. Through the establishment of a simulation driving platform, driving fatigue detection system and driving fatigue wake-up system in the laboratory, the experimental research is carried out to study the fatigue generation of drivers in different emotional types of music environment and the effectiveness of wake-up countermeasures. Three sound environments were constructed in the experiment, which were driving noise environment, positive emotional music mixed with driving noise environment and negative emotional music mixed with driving noise environment. The subjects were tested in three sound environments in turn. When entering fatigue, they were given no stimulation, positive emotional music stimulation or negative emotional music stimulation according to the experimental design. The experimental results were obtained after considering the subjective evaluation questionnaire and the experimental data analysis based on pupil diameter variation coefficient, average blink rate and other indicators. The experimental results show that when driving for a long time, the driver will enter the fatigue state regardless of whether the driving background has different emotional music. The driver's fatigue depth in the positive emotional music environment is shallower than that in the negative emotional music environment and the music-free environment; there is no significant difference in the degree of fatigue between the driver 's fatigue in the negative emotional music environment and the fatigue in the absence of music environment. In the comparison of music arousal stimuli with different emotions, the introduction of positive emotional music as arousal stimulus has the best effect.

Fatigue driving is an important cause of traffic accidents, the key to avoid fatigue driving is to detect the driver's fatigue condition in real time. Driving behaviors such as accelerate, brake, shift and steer, continuous change when drivers become more and more fatigue. By detecting and analyzing the changing signals of them, it can show whether the driver is fatigue or not. This fatigue driving detection system is composed of driving simulator, data acquisition system, PC serial communication System and fatigue identification model.

Driver's drowsiness is one of the main causes of traffic accidents. It accounts for up to 20% of serious or fatal accidents on the roads. In this paper, we propose a novel sensor-based driver condition recognition to prevent drowsiness-related accidents by using Support Vector Machine (SVM). It helps to determine the driver's condition by using sensors of the wearable device. The process for the proposed warning system is as follows: First, we acquire bio-data from a PhotoPlethysmoGraphy(PPG) sensor in the device to understand the characteristics of the driver's condition. Then, the acquired data is processed through segmentation and averaging to increase classification accuracy. The processed data is used as feature vectors in the SVM for the driver's condition classification. To evaluate performance of proposed method, the following metrics were used: accuracy, error rate, precision and recall. From the calculated results, the proposed method had high accuracy of 96.3 %. Finally, we could create the driver's condition recognition model that can be applied to a system which can alert the driver.

Driver fatigue is one of the main causes for traffic accidents. It is very important to identify fatigued driving to prevent traffic incidents objectively and accurately. Detecting drivers' physiological signals is an important method of identifying fatigued driving. Moreover, special attention has been paid to physiological signals as methods for detecting fatigues driving, such as with ECG (electroencephalography) and EEG (electrocardiogram) tests. In this paper, ECG and EEG data were collected through experiments conducted through a driving simulator. The ECG trend was obtained based on statistical analysis. Moreover, the authors compared EEG signals under the awake and fatigued states by combining the video and the ECG trend. The EEG characteristics were then extracted, and the EEG threshold value is obtained. The effectiveness of these indices is evaluated in further experiments. The results could be used as the foundation for future studies on driver fatigue.

Driver sleepiness has become one of the main global contributing elements to auto crashes in the past years. Quantifying sleepiness, or the state of the driver, is a direct approach to evaluate driver fatigue. Hence, to safeguard people and property, it is quite essential to know whether the motorist is drowsy. The ultimate goal of our study is to develop a technology that can ensure an adequate warning. Long distance travelers are known to fall into drowsiness, which is not noticed on time by such drivers. In fact, drowsy drivers who need to take a break cause about 25% of all catastrophic motorway accidents; this means sleepiness is the cause of traffic accidents more often than drunk driving. Our aim is to build a prototype of drowsiness detecting technology. This system continuously takes pictures, uses a methodology to identify the condition of the eye, and issues a warning when necessary. There are many techniques to gauge fatigue, but one approach is the most accurate since it is totally non-intrusive and doesn’t have any negative effects on the driver. Thus, when the heart rate of a driver is below a certain limit, it means they are exhausted or experiencing a heart attack or failure.

Driver fatigue is one of the leading causes of traffic accidents. At present, fatigue driving detection has disadvant ages such as low practical application effect and high equipment requirements. This paper proposes a multi-feature point non-invasive fatigue monitoring system based on a support vector machine with a hybrid kernel function. The system detects feature points through a gradient descent tree algorithm based on a cascaded regression and calculates the eye aspect ratio (EAR) and mouth aspect ratio (MAR). The heart rate is obtained through RGB image analysis combined with Euler’s video magnification algorithm. Classify facial features to get fatigued. This paper is based on the Logistic and Radial Basis Polynomial Kernel (RBPK) function to improve the support vector machine, which has better learning and generalization. Finally, this paper uses the Driver Drowsiness Detection Dataset and the author’s dataset to test. The classification accuracy rate for a single picture is 96.92%. In summary, the system proposed in this paper has a better recognition rate for fatigue driving detection.

Fatigue driving of drivers will lead to traffic accidents. Effectively monitoring and detecting driver fatigue in real-time is critical for traffic safety enhancement. However, the current approaches usually use a single measure to determine driver fatigue status, which is not robust or comprehensive and may make mistakes in some situations. To this end, a new detection method based on Yolo V5 is proposed to comprehensively judge driver fatigue based on the state of driver eyes and mouth. This method comprehensively considers the state of driver eyes and mouth, which overcomes the limitation of using a single measure. Bio-face and GI4E datasets are used to test the effectiveness of our method. The obtained results show that our method can detect driver fatigue more accurately with a low computation burden. The real-time processing speed of our method is 42 FPS on GTX 1650, which should work in practical applications.

Fatigue driving is one of the primary causes of traffic accidents, posing a serious threat not only to the driver’s life and property but also to the safety of surrounding vehicles and pedestrians. Therefore, accurate and efficient detection of driver fatigue is essential for preventing traffic incidents. This study proposes a novel driver fatigue detection framework that integrates AlphaPose* with a Long Short-Term Memory (LSTM) network. To enhance the performance of AlphaPose, the original human detector is replaced with an optimized YOLOv11n model, which incorporates a Hybrid Pooling Fusion Block (HPFB) to improve feature representation and meet the requirements of keypoint estimation. Furthermore, a multi-view data processing strategy based on the Driving Monitoring Dataset (DMD) is introduced to capture driver behavior from frontal, lateral, and hand views. To effectively model the temporal dynamics of driver behavior, a fatigue behavior monitoring network is designed using LSTM. Comparative experiments demonstrate that the proposed AlphaPose*-LSTM-based system achieves superior performance in fatigue detection tasks compared to existing state-of-the-art approaches.