Driver Drowsiness Detection Research Articles

Effect of distance information and number of warning signs on driving safety of young adults near road work zones in China

The continuous growth of automobile ownership and road mileage in China has brought a lot of road maintenance work. The work zone shall be set for road maintenance work. Due to the road characteristics near the work zone, more accidents and a higher accident risk exist. Traffic signs are one of the essential means to ensure traffic safety. The location and number of road work signs (sign level) play a crucial role in driver safety near the work zone. The repeated road work signs can form a warning system to regulate driver behavior. This paper evaluates the effect of the number of warning signs with and without distance information on driver behavior (psychological load and physiological characteristics) near the work zone. The matter-element extension model is used for comprehensive evaluation. The study resulted in several findings. First, adding distance information improves the difficulty and time of visual recognition and enhances driver alertness. On the other hand, too many road signs are not conducive to maintaining driver alertness. Second, distance information on the work signs and the repeated setting of road warning signs add extra burden on the driver and increase driver fatigue. Third, driver's response to the repeated road work signs will gradually become stable; The speed transition under the warning signs Levels 2 and 3 are more stable than that under the warning sign Levels 1 and 4. Finally, the warning sign level shall not be more than 3. If the advanced warning area is short, it is recommended to select a warning sign Level 1. If the advanced warning area is relatively long, it is recommended to choose a warning sign Level 2. Finally, adding distance information will improve the overall effect of the warning scheme. These findings can provide a valuable reference for setting the road work signs near the work zone in practice in China.

Real Time Alert System for Slumberous and Fatigue Driver Using Eye Blink Sensor

Various technologies are being created to reduce accidents, which are rising rapidly in the modern day. Driver weariness or drowsiness is a significant factor in the occurrence of accidents. Drowsiness is detected using a variety of deep learning and image processing techniques. However, these techniques are pricey, and they might not work for those who wear glasses. This suggested solution employs an eye blink sensor to stop drowsiness-related accidents. This serves as an accident avoidance mechanism. The eye blink sensor-equipped goggles that the driver is required to wear include these features.To detect driver drowsiness, the goggles must be worn by the driver the entire time they are operating a vehicle and must blink for a few seconds. When the alarm system detects that the driver is dozing off, it will sound an alert until they open their eyes. Along with detecting drowsiness, the LDR sensor is used to control the high intensity of the headlights when two vehicles are approaching from opposite directions. Additionally, it helps to avoid accidents caused by blurry vision. When a driver snoozes more than five times, the GSM modem alerts the vehicle's owner. In this situation, the both owner and the driver could be alerted when the driver is drowsy.

Driver Drowsiness Detection Using Deep Learning

Abstract: The main reason for accidents worldwide is the sleepiness experienced by individuals operating vehicles. Fatigue and drowsiness are prevalent among numerous drivers due to insufficient rest and tiredness. The most effective approach to preventing road accidents resulting from drowsiness is to provide advance warnings to the driver. There are plentiful techniquesto detect drowsiness. The project, driver drowsiness detection using deep learning is implemented using a convolution neural network(CNN), an approach of deep learning. In this project first, we capture a person’s face with the help of a web camera after which with the help of the deep learning algorithm we predict if the person is drowsy or not. If the person is detected as drowsyit immediately rings an alarm to alert the person along with a warning voice message. The prime goal of this project is to alert drowsy people before the cause of accidents which helps us in preventing the cause of road due to drowsiness of the person

Driver Drowsiness Detection using Artificial Intelligence

The goal of the research is to show how artificial intelligence may be used to identify driver tiredness using visual processing. Experts estimate that over a quarter of all serious car accidents are brought on by drivers who are too sleepy to pay attention to the road. As a result, we know that tiredness is a more common contributor to car accidents than intoxication. Vision-based ideas were used to design the Drowsiness Detection System. The gadget relies on a small camera to detect drowsiness in drivers by examining their eyes and scanning their face. The Viola-Jones and Hough transform are the techniques utilised by the system to first scan the driver's face, then the eyes, and then check whether the eyes are open or closed using artificial intelligence software. The system works with binary pictures to scan the sides of the face, reducing the space where the eyes will be located. Let's say that the eyes are shown to be closed for five or more consecutive frames. When this occurs, the system tracks the driver's level of activity and determines that the driver is dozing off, so it sounds an alert or produces an alarm signal to wake him up.

Vigilance Decrement During On-Road Partially Automated Driving Across Four Systems.

This study uses a detection task to measure changes in driver vigilance when operating four different partially automated systems. Research show temporal declines in detection task performance during manual and fully automated driving, but the accuracy of using this approach for measuring changes in driver vigilance during on-road partially automated driving is yet unproven. Participants drove four different vehicles (Tesla Model 3, Cadillac CT6, Volvo XC90, and Nissan Rogue) equipped with level-2 systems in manual and partially automated modes. Response times to a detection task were recorded over eight consecutive time periods. Bayesian analysis revealed a main effect of time period and an interaction between mode and time period. A main effect of vehicle and a time period x vehicle interaction were also found. Results indicated that the reduction in detection task performance over time was worse during partially automated driving. Vehicle-specific analysis also revealed that detection task performance changed across vehicles, with slowest response time found for the Volvo. The greater decline in detection performance found in automated mode suggests that operating level-2 systems incurred in a greater vigilance decrement, a phenomenon that is of interest for Human Factors practitioners and regulators. We also argue that the observed vehicle-related differences are attributable to the unique design of their in-vehicle interfaces.

An EEG Channel Selection Framework for Driver Drowsiness Detection via Interpretability Guidance.

Drowsy driving has a crucial influence on driving safety, creating an urgent demand for driver drowsiness detection. Electroencephalogram (EEG) signal can accurately reflect the mental fatigue state and thus has been widely studied in drowsiness monitoring. However, the raw EEG data is inherently noisy and redundant, which is neglected by existing works that just use single-channel EEG data or full-head channel EEG data for model training, resulting in limited performance of driver drowsiness detection. In this paper, we are the first to propose an Interpretability-guided Channel Selection (ICS) framework for the driver drowsiness detection task. Specifically, we design a two-stage training strategy to progressively select the key contributing channels with the guidance of interpretability. We first train a teacher network in the first stage using full-head channel EEG data. Then we apply the class activation mapping (CAM) to the trained teacher model to highlight the high-contributing EEG channels and further propose a channel voting scheme to select the top N contributing EEG channels. Finally, we train a student network with the selected channels of EEG data in the second stage for driver drowsiness detection. Experiments are designed on a public dataset, and the results demonstrate that our method is highly applicable and can significantly improve the performance of cross-subject driver drowsiness detection.

Real-Time Deep Learning-Based Drowsiness Detection: Leveraging Computer-Vision and Eye-Blink Analyses for Enhanced Road Safety.

Drowsy driving can significantly affect driving performance and overall road safety. Statistically, the main causes are decreased alertness and attention of the drivers. The combination of deep learning and computer-vision algorithm applications has been proven to be one of the most effective approaches for the detection of drowsiness. Robust and accurate drowsiness detection systems can be developed by leveraging deep learning to learn complex coordinate patterns using visual data. Deep learning algorithms have emerged as powerful techniques for drowsiness detection because of their ability to learn automatically from given inputs and feature extractions from raw data. Eye-blinking-based drowsiness detection was applied in this study, which utilized the analysis of eye-blink patterns. In this study, we used custom data for model training and experimental results were obtained for different candidates. The blinking of the eye and mouth region coordinates were obtained by applying landmarks. The rate of eye-blinking and changes in the shape of the mouth were analyzed using computer-vision techniques by measuring eye landmarks with real-time fluctuation representations. An experimental analysis was performed in real time and the results proved the existence of a correlation between yawning and closed eyes, classified as drowsy. The overall performance of the drowsiness detection model was 95.8% accuracy for drowsy-eye detection, 97% for open-eye detection, 0.84% for yawning detection, 0.98% for right-sided falling, and 100% for left-sided falling. Furthermore, the proposed method allowed a real-time eye rate analysis, where the threshold served as a separator of the eye into two classes, the "Open" and "Closed" states.

A High Performance and Robust FPGA Implementation of a Driver State Monitoring Application.

A high-performance Driver State Monitoring (DSM) application for the detection of driver drowsiness is presented in this paper. The popular Ensemble of Regression Trees (ERTs) machine learning method has been employed for the alignment of 68 facial landmarks. Open-source implementation of ERTs for facial shape alignment has been ported to different platforms and adapted for the acceleration of the frame processing speed using reconfigurable hardware. Reducing the frame processing latency saves time that can be used to apply frame-to-frame facial shape coherency rules. False face detection and false shape estimations can be ignored for higher robustness and accuracy in the operation of the DSM application without sacrificing the frame processing rate that can reach 65 frames per second. The sensitivity and precision in yawning recognition can reach 93% and 97%, respectively. The implementation of the employed DSM algorithm in reconfigurable hardware is challenging since the kernel arguments require large data transfers and the degree of data reuse in the computational kernel is low. Hence, unconventional hardware acceleration techniques have been employed that can also be useful for the acceleration of several other machine learning applications that require large data transfers to their kernels with low reusability.

Driver Drowsiness Detection and Alert System Implemented on Web

Driver Drowsiness Detection and Alert System Implemented on Web

Driver fatigue detection and human-machine cooperative decision-making for road scenarios

Driver fatigue detection and human-machine cooperative decision-making for road scenarios

A CNN-Based Approach for Driver Drowsiness Detection by Real-Time Eye State Identification

Drowsiness detection is an important task in road safety and other areas that require sustained attention. In this article, an approach to detect drowsiness in drivers is presented, focusing on the eye region, since eye fatigue is one of the first symptoms of drowsiness. The method used for the extraction of the eye region is Mediapipe, chosen for its high accuracy and robustness. Three neural networks were analyzed based on InceptionV3, VGG16 and ResNet50V2, which implement deep learning. The database used is NITYMED, which contains videos of drivers with different levels of drowsiness. The three networks were evaluated in terms of accuracy, precision and recall in detecting drowsiness in the eye region. The results of the study show that all three convolutional neural networks have high accuracy in detecting drowsiness in the eye region. In particular, the Resnet50V2 network achieved the highest accuracy, with a rate of 99.71% on average. For better visualization of the data, the Grad-CAM technique is used, with which we obtain a better understanding of the performance of the algorithms in the classification process.

A Product Fuzzy Convolutional Network for Detecting Driving Fatigue.

Existing driving fatigue detection methods rarely consider how to effectively fuse the advantages of the electroencephalogram (EEG) and electrocardiogram (ECG) signals to enhance detection performance under noise conditions. To address the issues, this article proposes a new type of the deep learning (DL) framework based on EEG and ECG called the product fuzzy convolutional network (PFCN). It should be noted that this article first investigates how to fuse EEG and ECG signals to deal with driving fatigue detection under noise conditions in both simulated and real-field driving environments. Specifically, the PFCN includes three subnetworks. The first uses a fuzzy neural network (FNN) with feedback and a product layer, effectively capturing the particularity and temporal variation of high-dimensional EEG signals and reducing the time-space complexity. The second subnetwork uses a 1-D convolution to convert the ECG data into feature sequences, providing high accuracy and low computational complexity in ECG data classification. The third subnetwork proposes a fusion-separation mechanism to effectively fuse the extracted ECG and EEG features, suppressing the noise interference and ensuring higher detection accuracy. To evaluate the performance of PFCN, a series of experiments has been set up in both simulated and real-field driving environments. The results indicate that the proposed PFCN model has better robustness and detection accuracy compared with several mainstream fatigue detection models.

The Effectiveness of a Drowsiness Detection System in Reducing Drowsy Driving Accidents

Abstract: The identification of drowsiness is critical for human-computer interaction and transportation safety. We present a unique approach that combines physiological and behavioural markers to identify drowsiness in real time. A camera, an electroencephalogram (EEG), and a body-worn accelerometer are all part of our system. To identify tiredness based on changes in the eyes and head, the camera collects face pictures and extracts information such as eye aspect ratio and head position. The EEG examines brain activity, detecting tiredness based on changes in brain activity by extracting components such as alpha and theta waves. The body-worn accelerometer detects tiredness by measuring body motions and extracting information such as body acceleration and angular velocity. We propose an ensemble technique for improving overall accuracy by combining the outputs of many classifiers. When we tested our system on a dataset of participants, it detected drowsiness with 96.5% accuracy, beating state-of-the-art approaches.

A multi-feature fusion algorithm for driver fatigue detection based on a lightweight convolutional neural network

A multi-feature fusion algorithm for driver fatigue detection based on a lightweight convolutional neural network

Drowsiness Detection and Alertness Using Eye Motion Monitoring

The driver felt sleepy when they don’t take proper rest while driving on long routes. The restless driving careless mistake could be fatal to driver as well as others’ lives. This issue has been increased to such a level that a system required to avoid accidents and save life. The driver alertness detection can play a significant role to avoid such hazards. The system can identify the drowsiness on the face of driver and can generate an alarm for them to stop or take necessary actions. Eye state analysis is a key step for alertness detection that helps to identify the state of the eye whether it is open or close. In this paper, the method has been proposed for eye state analysis following face and eye detection to detect driver’s alertness. This system has been integrated into a four-steps includes detection of face, detection of eye, analysis of eye state, and decision regarding driver's drowsiness. A warning signal has been buzzed on drowsiness detection to alarm the driver. Simulation results validate that our proposed idea attains high accuracy and low error rate as compared to state-of-art.

3D-STCNN: Spatiotemporal Convolutional Neural Network Based on EEG 3D Features for Detecting Driving Fatigue

Fatigue driving has become one of the main causes of traffic accidents, and driving fatigue detection based on electroencephalogram (EEG) can effectively evaluate the driver's mental state and avoid the occurrence of traffic accidents. This article evaluates a feature extraction method for extracting multiple features of EEG signals and establishes a spatiotemporal convolutional neural network (STCNN) to detect driver fatigue. Firstly, we constructed a three-dimensional feature of the EEG signal, which includes the frequency domain, time domain, and spatial features of the EEG signal. Then, we use STCNN for fatigue state classification. STCNN is composed of an attention time network based on attention mechanism and an attention convolutional neural network based on attention mechanism. In addition, we conducted fatigue driving experiments and collected EEG signals from 14 subjects in both awake and fatigued states, ultimately collecting EEG data under three different driving task loads. We conducted extensive experiments on this basis and compared the effectiveness of STCNN and six competitive methods. The results show that the classification accuracy of STCNN is 87.55%, which can effectively detect the fatigue status of drivers.

DDD TinyML: A TinyML-Based Driver Drowsiness Detection Model Using Deep Learning.

Driver drowsiness is one of the main causes of traffic accidents today. In recent years, driver drowsiness detection has suffered from issues integrating deep learning (DL) with Internet-of-things (IoT) devices due to the limited resources of IoT devices, which pose a challenge to fulfilling DL models that demand large storage and computation. Thus, there are challenges to meeting the requirements of real-time driver drowsiness detection applications that need short latency and lightweight computation. To this end, we applied Tiny Machine Learning (TinyML) to a driver drowsiness detection case study. In this paper, we first present an overview of TinyML. After conducting some preliminary experiments, we proposed five lightweight DL models that can be deployed on a microcontroller. We applied three DL models: SqueezeNet, AlexNet, and CNN. In addition, we adopted two pretrained models (MobileNet-V2 and MobileNet-V3) to find the best model in terms of size and accuracy results. After that, we applied the optimization methods to DL models using quantization. Three quantization methods were applied: quantization-aware training (QAT), full-integer quantization (FIQ), and dynamic range quantization (DRQ). The obtained results in terms of the model size show that the CNN model achieved the smallest size of 0.05 MB using the DRQ method, followed by SqueezeNet, AlexNet MobileNet-V3, and MobileNet-V2, with 0.141 MB, 0.58 MB, 1.16 MB, and 1.55 MB, respectively. The result after applying the optimization method was 0.9964 accuracy using DRQ in the MobileNet-V2 model, which outperformed the other models, followed by the SqueezeNet and AlexNet models, with 0.9951 and 0.9924 accuracies, respectively, using DRQ.

Driver vigilance detection based on deep learning with fused thermal image information for public transportation

Autonomous-rail Rapid Transit (ART) is a new type of road public transportation with an automatic driving function, and it is one of the main places for epidemic spread. Scientific wearing of masks has been proven to be an extremely effective way to prevent an epidemic. However, drivers wearing masks make it difficult for traditional driver alert detection methods based on facial information to operate effectively. This paper proposes an ART driver alert detection system based on multimodal information fusion and deep learning, which can be applied to drivers wearing masks. The detection system is mainly divided into three modules: data acquisition module, preprocessing module, and vigilance detection module. The data acquisition module collects real-time face thermal imaging data, and environment temperature information of 13 people in the vigilance and fatigue state of the ART simulated driving environment. The preprocessing module proposes a face thermal imaging key information extraction method based on the constraints between Yolov5 and thermal image interframe constraints and extracts the mean temperature information and the hot voxel information (HV) of the eyes, forehead, and mask areas. Then the extracted information and the environment temperature information are transmitted together to the vigilance detection module for learning and classification. The vigilance detection module proposes a lightweight hybrid-attention optimization network, and this paper extracts training sets and testing sets from the data of 13 subjects. And the training set data is enhanced. Each sample is 90 frames. The maximum accuracy rate of the testing set reaches 99.57%. The feasibility of the detection system proposed in this paper is proved, which makes the thermal imaging and attention model show great potential in the application of traffic safety operation and maintenance.

Eye-Blink Event Detection Using a Neural-Network-Trained Frame Segment for Woman Drivers in Saudi Arabia

Women have been allowed to drive in Saudi Arabia since 2018, revoking a 30-year ban that also adhered to the traffic rules provided in the country. Conventional drivers are often monitored for safe driving by monitoring their facial reactions, eye blinks, and expressions. As driving experience and vehicle handling features have been less exposed to novice women drivers in Saudi Arabia, technical assistance and physical observations are mandatory. Such observations are sensed as images/video frames for computer-based analyses. Precise computer vision processes are employed for detecting and classifying events using image processing. The identified events are unique to novice women drivers in Saudi Arabia, assisting with their vehicle usage. This article introduces the Event Detection using Segmented Frame (ED-SF) method to improve the abnormal Eye-Blink Detection (EBD) of women drivers. The eye region is segmented using variation pixel extraction in this process. The pixel extraction process requires textural variation identified from different frames. The condition is that the frames are to be continuous in the event detection. This method employs a convolution neural network with two hidden layer processes. In the first layer, continuous and discrete frame differentiations are identified. The second layer is responsible for segmenting the eye region, devouring the textural variation. The variations and discrete frames are used for training the neural network to prevent segment errors in the extraction process. Therefore, the frame segment changes are used for Identifying the expressions through different inputs across different texture luminosities. This method applies to less-experienced and road-safety-knowledge-lacking woman drivers who have initiated their driving journey in Saudi-Arabia-like countries. Thus the proposed method improves the EBD accuracy by 9.5% compared to Hybrid Convolutional Neural Networks (HCNN), Long Short-Term Neural Networks (HCNN + LSTM), Two-Stream Spatial-Temporal Graph Convolutional Networks (2S-STGCN), and the Customized Driving Fatigue Detection Method CDFDM.

Driver Vigilance Detection Based on Limited EEG Signals

Driver vigilance detection is an important topic for traffic safety improvement and brain-computer interface design. Electroencephalography (EEG) can directly provide real-time information and is sensitive to the change of vigilance. Thus, EEG signals are widely used for driver vigilance detection. Previous studies commonly acquired EEG signals from a large number of electrode channels and utilized all the collected data for recognition tasks, which may introduce a large amount of noise and redundant data and limit their application potentials in practical systems. This paper examines the potential of using EEG signals from only one frequency band or from only a small subset of related electrode channels in recognizing driver vigilance state. The widely used SEED VIG dataset is adopt for experiments. Four candidate sets of EEG frequency bands and four candidate sets of EEG electrode channels are determined according to the statistical significance results of EEG power spectral density (PSD) features and differential entropy (DE) features. The experimental results show that the recognition accuracy is higher when using EEG signals from the selected frequency band (i.e., alpha band) or the selected electrodes (i.e., T7, TP7, CP1) than when using all the data. Specifically, the best recognition accuracy is 91.31% for the alpha band and 76.81% for the selected electrodes. These results indicate that higher driver vigilance recognition accuracy can be achieved with much less amount of data, which would facilitate the development of wearable equipment based on EEG signals.