Driver Fatigue Research Articles

A Deep Learning Approach to Automated Drowsiness Detection in Drivers Using CNN Models for Enhanced Road Safety

Traffic accidents are responsible for the majority of fatalities and injuries worldwide. According to the World Health Organization, around one million people die each year due to traffic-related injuries. Drivers who are sleep-deprived, fatigued, or not well-rested may fall asleep behind the wheel, putting both themselves and other road users at risk. Research indicates that drowsiness is a leading cause of major road accidents. In recent times, tired driving has emerged as the primary factor contributing to driver drowsiness, which in turn has led to an increase in road accidents. This has become a critical issue that needs urgent attention. The goal of many technologies is to improve real-time drowsiness detection. Numerous devices have been developed using various artificial intelligence algorithms to address this issue. My research focuses on detecting driver drowsiness by analyzing the driver’s face, tracking eye movement, and alerting the driver with an alarm when drowsiness is detected. In this paper, I utilized various Convolutional Neural Network (CNN) models, including Small CNN, VGG16, VGG19, and Inception, to classify distracted drivers based on the State Farm Distracted Driver Detection challenge on Kaggle. The deep learning framework employed for this task is Keras, which runs on top of TensorFlow. The system compares the extracted eye images with a dataset to detect drowsiness. If the system identifies closed eyes within a certain range, it triggers an alarm to alert the driver. If the driver opens their eyes after the alert, the system resumes tracking. The system adjusts the score based on eye status: the score decreases when eyes are open and increases when eyes are closed. This paper aims to address drowsiness detection with an accuracy of 94%, contributing to the reduction of road accidents.

Real-Time Driver Drowsiness Detection Using Facial Analysis and Machine Learning Techniques

Drowsy driving poses a significant challenge to road safety worldwide, contributing to thousands of accidents and fatalities annually. Despite advancements in driver drowsiness detection (DDD) systems, many existing methods face limitations such as intrusiveness and delayed reaction times. This research addresses these gaps by leveraging facial analysis and state-of-the-art machine learning techniques to develop a real-time, non-intrusive DDD system. A distinctive aspect of this research is its systematic assessment of various machine and deep learning algorithms across three pivotal public datasets, the NTHUDDD, YawDD, and UTA-RLDD, known for their widespread use in drowsiness detection studies. Our evaluation covered techniques including the K-Nearest Neighbors (KNNs), support vector machines (SVMs), convolutional neural networks (CNNs), and advanced computer vision (CV) models such as YOLOv5, YOLOv8, and Faster R-CNN. Notably, the KNNs classifier reported the highest accuracy of 98.89%, a precision of 99.27%, and an F1 score of 98.86% on the UTA-RLDD. Among the CV methods, YOLOv5 and YOLOv8 demonstrated exceptional performance, achieving 100% precision and recall with mAP@0.5 values of 99.5% on the UTA-RLDD. In contrast, Faster R-CNN showed an accuracy of 81.0% and a precision of 63.4% on the same dataset. These results demonstrate the potential of our system to significantly enhance road safety by providing proactive alerts in real time.

A Review on Smart Specs

The recent study highlights the critical issue of driver drowsiness, a leading cause of road accidents resulting from fatigue. A system capable of detecting drowsiness and warning drivers at an early stage could significantly reduce the number of drowsiness-related road accidents. Drowsiness refers to a state of being sleepy or having a compelling desire to fall asleep. It is often characterized by reduced alertness, slowness, and difficulty staying awake or maintaining focus. This paper presents a literature review of driver drowsiness detection systems based on the analysis of physiological signals, facial features, driving patterns, and steering wheel monitoring. "Smart glasses," which utilize an eye blink sensor and Arduino, monitor the driver's eye movements and trigger a warning using vibrations and a loud buzzer if the driver begins to doze off. Keywords:- : Facial features ,Eye Blink sensor, Arduino ,Vibration sensor,

Driver muscle fatigue analysis based on monorail crane operating environment biomechanical coupling model and surface electromyography signal

To quantitatively analyze and evaluate the driving fatigue of monorail crane drivers, this research established a biomechanical coupling model of the cab-driver operation environment within the Anybody. Muscle activation and muscle force were utilized as evaluation indicators to analyze the driving fatigue characteristics of monorail crane drivers. The research revealed that during the driving process, the biceps brachii displays the most pronounced variation for muscle activation and muscle force. In contrast, the trapezius and latissimus dorsi show less force and activation, with smoother curve changes. The extensor digitorum significantly exerts force during driving times of 1∼2, 12∼15.8, and 27∼30 s, with minimal activity otherwise. The rectus femoris, gastrocnemius, and soleus exhibit a cyclical pattern of increasing and then decreasing muscle force and activation. Peak durations for these muscles are 3∼21 and 27∼39 s, while the biceps femoris has the lowest muscle force and activation with minimal variation. A comprehensive analysis indicated that the overall fatigue of the lower limbs showed a continuous increase, with the fatigue being greater for lower limbs than upper limbs. Subsequently, surface electromyography experiment was conducted to validate the model by testing muscle electrical signal during maximum voluntary contraction (MVC). The results demonstrated that the relative error between the model and the experimental data was less than 15%, and the correlation coefficients were all greater than 0.966 ( p < 0.05), indicating a strong consistency between the model and the experimental data. Finally, EMG was employed to calculate and analyze the changes in muscle activity during the driving process. The findings from this research can serve as a theoretical foundation and provide data references for the optimization design of monorail crane.

Classification Method for Fatigue Driving Signals Based on Multiple Classifier Analysis

Classification Method for Fatigue Driving Signals Based on Multiple Classifier Analysis

AI- and Deep Learning-Powered Driver Drowsiness Detection Method Using Facial Analysis

The significant number of road traffic accidents caused by fatigued drivers presents substantial risks to the public’s overall safety. In recent years, there has been a notable convergence of intelligent cameras and artificial intelligence (AI), leading to significant advancements in identifying driver drowsiness. Advances in computer vision technology allow for the identification of driver drowsiness by monitoring facial expressions such as yawning, eye movements, and head movements. These physical indications, together with assessments of the driver’s physiological condition and behavior, aid in assessing fatigue and lowering the likelihood of drowsy driving-related incidents. This study presents an extensive variety of meticulously designed algorithms that were thoroughly analyzed to assess their effectiveness in detecting drowsiness. At the core of this attempt lay the essential concept of feature extraction, an efficient technique for isolating facial and ocular regions from a particular set of input images. Following this, various deep learning models, such as a traditional CNN, VGG16, and MobileNet, facilitated detecting drowsiness. Among these approaches, the MobileNet model was a valuable choice for drowsiness detection in drivers due to its real-time processing capability and suitability for deployment in resource-constrained environments, with the highest achieved accuracy of 92.75%.

Research progress on thermal comfort evaluation in vehicle cab

In order to improve thermal comfort of vehicle cab, reduce driver fatigue and further improve work efficiency, researches on thermal comfort of vehicle cab are summarized. Research background of thermal comfort for vehicle cab is analyzed. And then related research progress on thermal environment in vehicle cab is studied from aspect of time and space, and thermal environment inside and outside vehicle are compared. Affecting factors of thermal comfort in vehicle cab are discussed in depth, which conclude thermophysical parameters, human physiological factors, clothing thermal resistance and other secondary factors. And thermal comfort evaluation indexes are analyzed in depth. Evaluation methods of thermal comfort in uniform environment are analyzed, related experimental research and theoretical analysis are summarized, and it also points out some problems in thermal comfort of vehicle at this stage, and also gives corresponding solutions. The future trend of thermal comfort of vehicle cab is predicted. Analysis results can provide theoretical guidance for optimization design of air conditioning supply parameters and structural parameters, and has significant meaning of improving thermal comfort of vehicle cab.

Mediating role of fatigue driving in the influence of job demand and insecurity on safety incidents among bus drivers

BackgroundIn developing countries, driving long-distance buses presents substantial road-traffic challenges. These are intensified by demanding job conditions and job insecurity, potentially leading to increased safety incidents linked to driver fatigue. This study sought to investigate how driving fatigue mediates the relationship between job demands, job security, and safety incidents among long-distance bus drivers in Ghana.MethodsThis survey sampled 7315 long-distance commercial bus drivers using a convenient sampling technique. Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS version 4.FindingsBoth job demands and job security showed significant positive associations with safety incidents among the drivers. Driving fatigue produced a partial mediation effect of job demands and job security on safety incidents of the drivers. This highlights crucial link between job stressors and driving performance.ConclusionThe study indicates critical impact of job demands and job security on driving fatigue and subsequent safety incidents among bus these drivers in Ghana. Thus, addressing these stressors through targeted interventions, such as improved working conditions and fatigue management programmes, become essential for enhancing driver well-being and road safety in Ghana. Importantly, addressing these challenges requires collaborative efforts among transport authorities, policymakers, and other industry stakeholders to enforce regulations and promote supportive work environments for the drivers.

Design of a System for Driver Drowsiness Detection and Seat Belt Monitoring Using Raspberry Pi 4 and Arduino Nano

This research explores the design of a system for monitoring driver drowsiness and supervising seat belt usage in interprovincial buses. In Peru, road accidents involving long-distance bus transportation amounted to 5449 in 2022, and the human factor plays a significant role. It is essential to understand how the use of non-invasive sensors for monitoring and supervising passengers and drivers can enhance safety in interprovincial transportation. The objective of this research is to develop a system using a Raspberry Pi 4 and Arduino Nano that allows for the storage of monitoring data. To achieve this, a conventional camera and MediaPipe were used for driver drowsiness detection, while passenger supervision was carried out using a combination of commercially available sensors as well as custom-built sensors. RS485 communication was utilized to store data related to both the driver and passengers. The simulations conducted demonstrate a high level of reliability in detecting driver drowsiness under specific conditions and the correct operation of the sensors for passenger supervision. Therefore, the proposed system is feasible and can be implemented for real-world testing. The implications of this research suggest that the system’s cost is not a barrier to its implementation, thus contributing to improved safety in interprovincial transportation.

Open source head and face estimation as a complement to commercial eye-tracking

Eye-tracking solutions are a mainstay in driver modeling research; however, their limitations motivate exploration of complementary methods. One such is available in a class of machine vision models that estimate head pose, eye gaze, facial landmarks, and facial action units. In this article, we share lessons learned from using an open source tool to measure driver behavior. We selected a tool called OpenFace and applied it to an existing drowsy driving dataset that was collected with a commercial camera-based driver monitoring system. Forty participants each drove for around three hours in the early morning. Variables from the driver monitoring system combined with other simulator data and a set of videos from different cameras were recorded as part of the simulator video capture software. OpenFace was used to process the driver-facing video and integrate its output into the dataset. We considered a set of measures appropriate for modeling driver drowsiness, including eye closure, gaze dispersion, head movements, head stillness, and facial contortions (e.g., yawns, brow furrowing, etc.). We present comparisons of similar data between the commercial system and open-source model. Several lessons learned on topics including camera placement, ambient lighting, and data synchronization are shared, along with recommendations for future work.

Real-Time Driver Drowsiness Detection System Using Machine Learning

Driver fatigue is a leading cause of road accidents globally, with significant fatalities and injuries reported annually. This paper presents a real-time Driver Drowsiness Detection System leveraging computer vision and machine learning techniques. Using Convolutional Neural Networks (CNNs), the system monitors key behavioural indicators, such as eye closure and yawning frequency, from live video feeds. Preprocessing techniques like grayscale conversion and normalization ensure robustness under varied conditions. Alerts are triggered immediately upon detecting drowsiness, enhancing road safety. The system’s non-intrusive design ensures scalability and affordability, making it suitable for integration into Advanced Driver Assistance Systems (ADAS). This paper also discusses challenges and potential enhancements, such as IoT integration and edge computing. Key Words: Driver Drowsiness Detection, Machine Learning, CNNs, Real-Time Monitoring, Road Safety.

Fuzzy-based driver monitoring system

This paper presents an all-around approach to enhancing road safety through a fuzzy driver tracking system integrated with VANET technology, IoT devices, and hardware wristbands. The increasing prevalence of road accidents is caused by factors such as driver fatigue, health disorders, or inattention to this system, which effectively detects and responds to such situations and provides real-time solutions. It comes with advanced sensors The hardware wristband continuously monitors the driver's pulse, like heart rate. Body temperature and movement Patterns for identifying signs of sleepiness abnormal health parameters or erratic behavior are processed using fuzzy logic algorithms. The in-car software interface gets real-time information from the wristband, processes it, and unlocks several safety protocols, which include visual notifications, buzzing alerts, and communication with other vehicles through VANET technology. The system also logs highly detailed records of incidents it detects, which will be analyzed later for long-term behavioral profiling. It will leverage the power of IoT-enabled cloud storage to optimize data management and analysis, thus fostering continuous improvement in traffic safety protocols. The integration of fuzzy logic ensures accurate and context-sensitive decision-making, addressing uncertainties inherent in dynamic driving environments. Meanwhile, the utilization of VANET enables seamless communication between vehicles, enhancing collective road safety. This paper provides an in-depth exploration of the system's architecture, design principles, algorithms, implementation strategies, and expected outcomes. It has several aspects, thereby changing the face of drivers' safety through reduced accidents, faster response mechanisms to accidents at a given time, and thus a safer ecosystem of transport for all other road users. This system represents a crucial step forward in intelligent transportation technologies. Combining the features of real-time monitoring, predictive analytics, and robust communication networks, the solution not only mitigates short-term risks but also works toward long-term safety improvement. The modular design adapts to different vehicle types and driving environments, making this a scalable solution for widespread adoption. This paper outlines the capacity of the system to transform traditional road safety mechanisms into proactive and intelligent ones. Index Terms—Internet of Things (IoT), secrecy enhancing, smart cities, space shift keying (SSK).

Analysis of Driver Drowsiness Detection System Based on Landmarks and MediaPipe

Analysis of Driver Drowsiness Detection System Based on Landmarks and MediaPipe

Highly sensitive strain sensors with ultra-low detection limit based on pre-defined serpentine cracks.

Flexible and stretchable strain sensors have garnered significant interest due to their potential applications in various fields including human health monitoring and human-machine interfaces. Previous studies have shown that strain sensors based on microcracks can exhibit both high sensitivity and a wide sensing range by manipulating the opening and closing of randomly generated cracks within conductive thin films. However, the uncontrolled nature of microcrack formation can cause a drift in the sensor's performance over time, affecting its accuracy and reliability. In this study, by pre-defining the cracks, we introduce a novel resistive strain sensor with high sensitivity, excellent linearity, an ultra-low detection limit, and robustness against off-axis deformation. The sensor operates on a simple mechanism involving the modulation of ohmic contact within intricately designed conductive serpentine curves, which are encapsulated by pre-stretched thin films. This design facilitates a high gauge factor of 495, exceptional linearity (R2 > 0.98), and an ultra-low detection threshold of 0.01% strain. Moreover, it maintains performance integrity during off-axis deformations such as bending and twisting, features that are indispensable for accurately monitoring human motion. To explore practical applications, a driving scenario was simulated where a sensor array was positioned on the driver's neck. The sensor output was analyzed using machine learning algorithms to successfully determine the presence of driver fatigue. This demonstration underlines the potential of our sensor technology in applications ranging from healthcare monitoring to wearable biomechanical systems and human-machine interfaces.

A Multi-Modality Attention Network for Driver Fatigue Detection Based on Frontal EEG, EDA and PPG Signals

A Multi-Modality Attention Network for Driver Fatigue Detection Based on Frontal EEG, EDA and PPG Signals

Lightweight YOLOM-Net for Automatic Identification and Real-Time Detection of Fatigue Driving

Lightweight YOLOM-Net for Automatic Identification and Real-Time Detection of Fatigue Driving

Analysis of Factors Affecting Fatigue Level in Online Motorcycle Taxi Drivers in Denpasar City

Ojek online is an online-based transportation that is currently popular. Along with the rise of this drivers, it causes an increase in the number of accidents that occur. Factors that cause fatigue consist of age, gender, workload, length of service, and duration of work. Fatigue is the most workload experienced by online motorcycle taxi drivers in Denpasar. The purpose of this research is to determine the factors that affect job fatigue in online motorcycle taxi drivers, such as age, gender, smoking habits, length of work, duration of work, and body mass index. This study is an observational study with a cross sectional design and used the Industrial Fatigue Research Committee questionnaire. The subjects of this study were online motorcycle taxi drivers in Bali, especially in Denpasar City, totaling 106 people selected based on the inclusion and exclusion criteria using purposive sampling technique. The majority of this subjects are male (51,2%), duration of work of >8 hours (69,1%), work for >2 years (61,5%), aged 37 years (66,1%), and obese (100%) experiencing job fatigue. The results of the analysis obtained smoking factors, work duration, length of work, age, and body mass index affect work fatigue in online motorcycle taxi drivers.

Detection of Drowsiness in Drivers Using Image Processing and Support Vector Machine (SVM) Classification

Accidents can be caused by external factors on the road, vehicle conditions, or internal factors such as drowsiness. Drowsiness while driving poses risks to the driver and others. An early detection system is crucial to alert drivers to stop or rest if they show signs of drowsiness. Physical signs of drowsiness include a lethargic facial expression, frequent eye blinking, continuous yawning, or nodding off. A detection system utilizing image processing and machine learning can observe these signs by detecting facial landmarks and analyzing activities such as eye blinking, yawning, and head tilt. This study aims to classify the drowsiness condition based on these three factors. The classification process is conducted using machine learning with the Support Vector Machine (SVM) method to determine whether a person is drowsy or not. The dataset consists of the number of eye blinks, head tilts, and yawns. Conditions are classified into two classes, drowsy and not drowsy. In this study, the SVM classification method can predict drowsiness with an accuracy of up to 77% in the conducted tests.

COMPARISON OF ROBUSTNESS TEST RESULTS OF THE EYE ASPECT RATIO METHOD AND IRIS-SCLERA PATTERN ANALYSIS TO DETECT DROWSINESS WHILE DRIVING

Traffic accidents caused by driver drowsiness are a leading factor in fatal road incidents. This study introduces a computer vision-based drowsiness detection system utilizing two methods: Eye Aspect Ratio (EAR) and Iris-Sclera Pattern Analysis (ISPA). The EAR method measures the eye aspect ratio to determine whether the eyes are open or closed. This involves calculating the vertical distance between specific landmark points on the eyelids and comparing it to the horizontal distance between points on the eye. A decrease in this ratio serves as an early indicator of drowsiness. The ISPA method employs symmetry analysis between the iris and sclera. This approach relies on the visual pattern formed when the eyes are open, where the sclera appears symmetrically distributed around the iris. During this process, eye images are processed to extract iris and sclera features, which are then analyzed for symmetry to detect signs of drowsiness. The study evaluates the reliability of both methods under varying conditions, such as changes in lighting, viewing distances, head movements, and the use of eyeglasses. The results show that the EAR method achieved an accuracy of 83.33% in distance testing, indicating its effectiveness in stable lighting environments. In contrast, the ISPA method achieved an accuracy of 59.25% under low and variable lighting conditions and proved more reliable for detecting the eyes of users wearing glasses.

Lightweight Research on Fatigue Driving Face Detection Based on YOLOv8

Introduction: With the rapid development of society, motor vehicles have become one of the main means of transportation. However, as the number of motor vehicles continues to increase, traffic safety accidents also continue to appear, bringing serious threats to people's lives, and property safety. Fatigue driving is one of the important causes of traffic safety accidents. Method: To address this problem, a target detection algorithm called VA-YOLO is designed to improve the speed and accuracy of facial recognition for fatigue checking. The algorithm employs a lightweight backbone network, VanillaNet, instead of the traditional backbone network, which reduces the computational and parametric quantities of the model. The SE attention mechanism is also introduced to enhance the model's attention to the target features, which further improves the accuracy of target detection. Finally, in terms of the bounding box regression loss function, the SIoU loss function is used to reduce the error. Result: The experimental results show that, compared toYolov8n, the VA-YOLO algorithm improves the accuracy by 1.3% while the number of parameters decreases by 30%. Conclusion: This shows that the VA-YOLO algorithm has a significant advantage in realizing the balance between the number of parameters and accuracy, which is important for improving the speed and accuracy of fatigue driving detection.