Driver Drowsiness Detection Research Articles

Fatigue Driving Detection and Early Warning System based on EEG Signals

The fatigue driving detection and warning system based on EEG signals aims to use EEG technology to monitor the mental state of drivers in real time, so as to identify and warn fatigue driving behavior in time, so as to improve road safety. The design of the system is based on a deep understanding of the connection between EEG signals and the fatigue state, by analyzing the changes in frequency bands such as α wave, β wave, θ wave and δ wave, which are closely related to the alert state and fatigue in humans. The development of the system first considers the main factors induced by fatigue, such as sleep deprivation, long work hours and environmental monotony, which can significantly affect the activity of brain waves. From these physiological and environmental factors, the system requirements analysis emphasizes the need for real-time data processing and high accuracy, as well as a user interface design that provides clear and intuitive feedback to users (drivers and regulators). In terms of implementation, the system integrates EEG monitoring equipment for data acquisition, uses advanced signal processing algorithm to extract fatigue-related features, and uses machine learning model for real-time analysis and fatigue state determination. In addition, the system design includes an emergency response mechanism, once signs of fatigue are detected, it can automatically trigger an alarm, issue a warning to the driver, and if necessary through the vehicle control system to ensure driving safety. This comprehensive system not only improves driving safety, but also provides strong data support for traffic management departments.

Real-time Driver Drowsiness Detection Techniques using Machine Learning Algorithms and Models

This paper is a report on the research conducted in the field of electronics and communication engineering to develop a system for driver drowsiness detection to prevent accidents happening because of driver fatigue and sleepiness. The document discusses the solution by Tensor flow machine learning tool and open CV image pre-processing available for detecting fatigue and their efficacy in preventing accidents in the current state of traffic. Furthermore, the paper states the overview of the observations made by the authors in order to help further optimization in the mentioned field to achieve the utility at a better efficiency for a safer road. Key Words: Deep Learning, Convolutional Neural Networks (CNNs), Real-time Monitoring, Facial Feature Analysis, Eye Closure Detection, EEG Signal Analysis

Driver drowsiness is associated with altered facial thermal patterns: Machine learning insights from a thermal imaging approach

Driver drowsiness is a significant factor in road accidents. Thermal imaging has emerged as an effective tool for detecting drowsiness by enabling the analysis of facial thermal patterns. However, it is not clear which facial areas are most affected and correlate most strongly with drowsiness. This study examines the variations and importance of various facial areas and proposes an approach for detecting driver drowsiness. Twenty participants underwent tests in a driving simulator, and temperature changes in various facial regions were measured. The random forest method was employed to evaluate the importance of each facial region. The results revealed that temperature changes in the nasal area exhibited the highest value, while the eyes had the most correlated changes with drowsiness. Furthermore, drowsiness was classified with an accuracy of 88 % utilizing thermal variations in the facial region identified as the most important regions by the random forest feature importance model. These findings provide a comprehensive overview of facial thermal imaging for detecting driver drowsiness and introduce eye temperature as a novel and effective measure for investigating cognitive activities.

Designing a practical fatigue detection system: A review on recent developments and challenges

IntroductionFatigue is considered to have a life-threatening effect on human health and it has been an active field of research in different sectors. Deploying wearable physiological sensors helps to detect the level of fatigue objectively without any concern of bias in subjective assessment and interfering with work. MethodsThis paper provides an in-depth review of fatigue detection approaches using physiological signals to pinpoint their main achievements, identify research gaps, and recommend avenues for future research. The review results are presented under three headings, including: signal modality, experimental environments, and fatigue detection models. Fatigue detection studies are first divided based on signal modality into uni-modal and multi-modal approaches. Then, the experimental environments utilized for fatigue data collection are critically analyzed. At the end, the machine learning models used for the classification of fatigue state are reviewed. Practical ApplicationsThe directions for future research are provided based on critical analysis of past studies. Finally, the challenges of objective fatigue detection in the real-world scenario are discussed.

Ultra-Wide Band Radar Empowered Driver Drowsiness Detection with Convolutional Spatial Feature Engineering and Artificial Intelligence

Driving while drowsy poses significant risks, including reduced cognitive function and the potential for accidents, which can lead to severe consequences such as trauma, economic losses, injuries, or death. The use of artificial intelligence can enable effective detection of driver drowsiness, helping to prevent accidents and enhance driver performance. This research aims to address the crucial need for real-time and accurate drowsiness detection to mitigate the impact of fatigue-related accidents. Leveraging ultra-wideband radar data collected over five minutes, the dataset was segmented into one-minute chunks and transformed into grayscale images. Spatial features are retrieved from the images using a two-dimensional Convolutional Neural Network. Following that, these features were used to train and test multiple machine learning classifiers. The ensemble classifier RF-XGB-SVM, which combines Random Forest, XGBoost, and Support Vector Machine using a hard voting criterion, performed admirably with an accuracy of 96.6%. Additionally, the proposed approach was validated with a robust k-fold score of 97% and a standard deviation of 0.018, demonstrating significant results. The dataset is augmented using Generative Adversarial Networks, resulting in improved accuracies for all models. Among them, the RF-XGB-SVM model outperformed the rest with an accuracy score of 99.58%.

Enhancing Driver Safety: Deep Learning Approach for Drowsiness Detection and Accident Prevention

Road safety concerns have spurred the development of innovative technologies aimed at reducing accidents, particularly those caused by driver fatigue. The main scope of the driver drowsiness detection system is to minimize road accidents caused by fatigue or sleepiness of drivers. This system leverages deep learning and computer vision, employing a Raspberry Pi camera to monitor facial expressions, including yawning, to assess the driver's alertness. Upon detecting signs of drowsiness, such as prolonged eye closure or altered facial expressions, the system triggers a buzzer alert by analyzing the Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) ratios. Integrated within an embedded system, it utilizes frontal face detection algorithms and Haar cascade classifiers to localize key facial features in real-time, facilitating efficient monitoring for signs of fatigue or distraction. Additionally, it issues vibration alerts if the seat belt is not fastened. The seat belt remainder will be helpful to ensure the safety of the driver, reduces accidents and monitors the driver's heart rate, triggering a PANIC message if irregularities are detected. At traffic signals, the system automatically reduces vehicle speed using a vibration motor. In the unfortunate event of an accident, the system initiates speed reduction and promptly notifies registered contacts through the Blynk application, thereby significantly reducing accidents caused by drowsy driving.

Enhancing Road Safety: A Driver Fatigue Detection and Behaviour Monitoring System using Advanced Computer Vision Techniques

Driver drowsiness is a major hazard to road safety, necessitating the development of reliable detection technologies. This study describes a revolutionary driver fatigue detection system that uses cutting-edge computer vision technologies. This system uses the MediaPipe framework for accurate face and hand detection and the Eye Aspect Ratio (EAR) for drowsiness detection. Furthermore, it uses the OpenCV solvePnP function for estimating rotation vector, and converting it to a rotation angle of the head, to check driver attention. By continuously monitoring these indicators, when the system successfully detects any instances of driver tiredness or inattention, it records his or her behavior and delivers notifications to help prevent accidents. This study helps to improve road safety by utilizing cutting-edge computer vision techniques to prevent driver fatigue and boost attentive driving practices.

Drowsy Driver Detection System

Abstract: Drowsy driver detection system is a safety technology designed to identify the potential signs of fatigue or drowsiness of the driver to prevent accidents caused as a result of not being alert. It is a significant risk factor for traffic accidents which has led to thousands of accidents, injuries, and fatalities each year. In this system we have used some techniques to monitor the driver’s behavior or state. This involves tracking facial features, eye movements or blink patterns and yawning. The system identifies signs of fatigue, such as slow eyelid closure, increased blink duration and yawn which often indicate drowsiness. As technology advances, these systems are becoming more accurate, efficient and integrated with other vehicle safety features. The continued development of drowsy driver detection technology holds significant promise for reducing traffic accidents and improving overall road safety.

Driver Drowsiness Detection using CNN

Abstract: Driver drowsiness is a significant factor contributing to road accidents worldwide, posing a major threat to public safety. This project presents a robust and efficient approach to detecting driver drowsiness using Convolutional Neural Networks (CNN). The proposed system aims to enhance road safety by monitoring drivers in real-time and providing timely warnings to prevent accidents caused by fatigue. The methodology involves capturing video frames of the driver's face using a camera installed in the vehicle. The CNN model is trained on a comprehensive dataset containing images of alert and drowsy states. Key facial landmarks and features, such as eye closure duration, yawning frequency, and head position, are extracted and analyzed to determine the driver's level of alertness. By leveraging the powerful feature extraction capabilities of CNNs, the system can accurately distinguish between alert and drowsy states with high precision. Experimental results demonstrate the effectiveness of the proposed system in real-world scenarios, achieving a high accuracy rate in detecting drowsiness. The system's performance is evaluated against various benchmarks, showcasing its potential for integration into modern vehicles as a preventive safety measure. The deployment of this CNN-based drowsiness detection system can significantly reduce the risk of accidents, contributing to safer driving conditions and saving lives.

Driver drowsiness detection system

Driver drowsiness detection system

Correction: Driver fatigue detection based on improved YOLOv7

Correction: Driver fatigue detection based on improved YOLOv7

Drowsiness detection in real-time via convolutional neural networks and transfer learning

Drowsiness detection is a critical aspect of ensuring safety in various domains, including transportation, online learning, and multimedia consumption. This research paper presents a comprehensive investigation into drowsiness detection methods, with a specific focus on utilizing convolutional neural networks (CNN) and transfer learning. Notably, the proposed study extends beyond theoretical exploration to practical application, as we have developed a user-friendly mobile application incorporating these advanced techniques. Diverse datasets are integrated to systematically evaluate the implemented model, and the results showcase its remarkable effectiveness. For both multi-class and binary classification scenarios, our drowsiness detection system achieves impressive accuracy rates ranging from 90 to 99.86%. This research not only contributes to the academic understanding of drowsiness detection but also highlights the successful implementation of such methodologies in real-world scenarios through the development of our application.

Advancements in driver fatigue detection: A comprehensive analysis of eye movement and facial feature approaches

This article goes into sophisticated tiredness detection methods that make use of computer vision algorithms, including the Eye Movement and Facial Feature approaches. Eye movement analysis, which emphasizes blink frequency, Scanning speed, and gaze length, excels in tasks requiring sustained visual attention, which is critical in high-attention vocations. Facial Feature detection, on the other hand, monitors changes in expressions, muscle activity, and emotions, providing flexibility across a wide range of settings. Eye movement enables accurate indications, real-time responsiveness, and task-specific precision, which is especially important in high-attention occupations. Facial Feature is ideal for a variety of scenarios since it gives complete assessments, flexibility, contextual analysis, and non-intrusiveness. Case examples show how eye-tracking and convolutional neural networks may be used to improve accuracy. This study helps to weariness management by detailing the advantages and disadvantages of Eye Movement and Facial Feature methods. Given the pervasiveness of fatigue, knowing these sensing systems is critical for maintaining safety, productivity, and general well-being in a variety of professional and everyday life scenarios.

Advancements in CMOS image sensors for enhanced vehicle assistance technologies

The burgeoning adoption of new energy vehicles has precipitated a surge in advanced vehicular assistant technologies, encompassing systems such as assisted driving and parking. The efficacy of these technologies hinges critically on the selection of suitable image sensors, which are integral to fulfilling the stringent image quality requisites necessary for the implementation of these technologies. Predominantly, these assistant systems are reliant on sophisticated image processing techniques, necessitating high-quality images as a fundamental input for efficacious analysis and recognition. Currently, the Complementary Metal-Oxide-Semiconductor (CMOS) image sensor is predominantly employed in vehicular systems, a choice underpinned by its advantageous attributes such as resistance to interference, cost-effectiveness, and high degree of integration. This paper aims to elucidate the developmental trajectory and fundamental principles of CMOS technology and its application in image sensors. It delves into an analysis of the salient features of CMOS image sensors, especially in their application to parking assistance, autonomous driving support, and fatigue detection systems. Conclusively, the paper offers an exhaustive overview of the current state of CMOS image sensor development, alongside a projection of future trends and an exploration of the challenges that must be surmounted to advance this field further.

Drowsiness Detection using Python

The Driver Drowsiness Detection System is a sophisticated safety solution designed to mitigate the risks associated with driver fatigue and drowsiness while operating a vehicle. This project aims to develop an intelligent system capable of monitoring driver behavior and physiological signals in real-time, detecting signs of drowsiness, and issuing timely alerts to prevent accidents. The system utilizes a combination of hardware sensors, including cameras, infrared sensors, EEG sensors, and heart rate monitors, to capture and analyze various indicators of driver drowsiness, such as facial expressions, eye movements, head gestures, body temperature, brainwave patterns, and heart rate variability.

Detection of Drowsy Drivers in Video Sequences via LSTM with CNN Features

Fatigued driving is one of the main causes of traffic accidents. This paper developed a method for sleep drive detection that makes use of the convolutional neural network (CNN), recurrent neural network (RNN), short-term neural network (LSTM), cascade, and cam switching algorithms. Using CNN, the algorithm initially extracts the driver's face traits. The RNN that predicts the driver's degree of tiredness is then fed these features. The technology tracks the driver's eyes and eyelids using algorithms called Cascades and Cam shifts. The accuracy of the sleepiness prediction is increased further with the usage of this data. Using datasets of video recordings of attentive and somnolent drivers, the system was assessed. The outcomes demonstrated how well the algorithm could identify sleepy drivers. This system may be able to stop sleepy driving-related accidents. Ten thousand videos of awake and sleepy drivers were used to train the algorithm. Drivers can be observed in real time by the system. If the system detects that the driver is sleepy, it has the ability to sound an alarm. The technology can be used to stop driving when sleepy from causing accidents. Keywords: Convolution neural network, Recurrent neural networks, long short-term memory, eye tracking, face detection, drowsy driver detection, deep learning, computer vision, machine learning, and deep learning.

DROWSINESS DETECTION USING

This review paper delves into the critical realm of drowsiness detection systems designed to mitigate the perilous consequences of driver fatigue. As instances of accidents caused by drowsy driving continue to pose significant risks on roadways, the proposed work adopts a comprehensive approach. Leveraging advanced technologies such as machine learning, computer vision, and sensor integration, the system focuses on early detection of drowsiness indicators. The algorithm, refined through iterative development, analyses facial expressions, eye movements, and physiological data for enhanced accuracy. Customizable alert mechanisms and autonomous responses, including auto-driver modes and forced parking, aim to provide timely interventions. Prospects involve multi-sensor fusion, adaptive machine learning, and integration with autonomous vehicles. As technology evolves, the goal is to create a user friendly, adaptive system that not only detects drowsiness effectively but also actively prevents potential accidents, contributing to a safer driving environment. Keywords: Drowsiness Detection, Adaptive Machine Learning, Forced Parking, Human- Machine Collaboration, Digital Object Identifier (DOI), Real- time Processing, - Friendly Design.

LBP-CNN Fusion for Driver Fatigue Detection

Nowadays, individuals tend to rely on their own means of transportation. As the number of vehicles on the road continues to rise, the occurrence of accidents has also increased. Specifically, road accidents are becoming more prevalent, with driver drowsiness being a significant contributing factor. Approximately 2.3 lakh out of the 23 lakh road mishaps that transpire in the country each year are attributed to drowsiness. Recent data indicates that an estimated 2.3 lakh to 3.5 lakh road accidents are a direct consequence of sleepiness[9]. To mitigate the occurrence of such accidents, we propose the implementation of a drowsy detection system for drivers. This system ensures that both the passengers and the driver can embark on their journey with the assurance of safety. The system utilizes a camera to monitor the driver's facial expressions and issues alerts accordingly. It analyzes various facial features such as eye closure, yawning, and signs of fatigue, subsequently notifying the driver to remain attentive. This approach involves the utilization of the local binary pattern texture analysis method in conjunction with convolutional neural networks (CNNs). By doing so, it effectively highlights the textural variations in the driver's face, thereby clearly identifying the relevant facial features. The camera detects the driver's face, captures the image, and subsequently converts it into LBP images. This process enables the system to detect subtle indicators of drowsiness, ultimately aiding in the prevention of accidents. Consequently, this system significantly reduces the occurrence of drowsiness-related accidents, thereby saving numerous lives

Reducing Road Mishaps: A CNN Model for Driver Fatigue Detection

Over the last few decades, there have been substantial developments in a variety of domains, including computer science, artificial intelligence, and machine learning, which has accelerated the evolution and application of intelligent systems in specific areas such as transportation. One way these systems are used in transportation is through fatigue detection, and to enable this technology, Convolutional Neural Networks serve as the foundation on which this system is built. CNNs are frequently employed in computer vision applications because they can automatically extract pertinent features from input data without the need for manual feature engineering. This CNN-based fatigue detection system was built in-order to address the repercussions of fatigued driving by monitoring the driver’s facial features in real-time so as to predict the fatigue levels and send out an alert to restore the driver’s alertness level so as to reduce road mishap. The system relies on visual input from a camera, which sends this input to a fatigue detection program installed on a Raspberry Pi microprocessor and sends out via alerts via a display and audio alerts via a piezoelectric buzzer. The model shows excellent performance, consistently achieving high accuracy, peaking at 97.34% on the 64th epoch, and consistently maintaining high validation accuracy, reaching 96.02% in the 67th epoch, indicating its ability to function effectively.

Attentiveness in urban spaces: The rhythm of the street

Driver vigilance research focuses on the safety impacts of maintaining sustained attention in underload conditions when automatic responses are adequate to the task and responses are sparse. However, urban environments have frequent risks that appear chaotically and require high levels of engagement—a very different vigilance task. Although drivers perceive a wide range of task demands as manageable, their movement patterns hint at more active engagement as they approach overload. This condition we are calling “Attentiveness,” to distinguish it from the classic exploration of vigilance in underload conditions. To understand how the built environment impacts driving automaticity and vigilance, data from the SHRP2 Naturalistic Driving Study (NDS) was used to evaluate driving patterns within urban multimodal street sections. Data for acceleration, jerk, lane position, and speed were tabulated for ten, 5-second epochs at 200 multimodal locations and compared to the behavior for the entire length of the drive. The built environment variables that demonstrated a strong correlation and impact size for a change in driver behavior included doorway density, block length, Walkscore, corridor aspect ratio (height over width), and terminated vistas. The commonality for these variables is that each one impacts the rate that novel stimuli arrive as drivers progress sequentially along their path. Acceleration, jerk, and lane position appear to be related to the rhythm of interruptions that occur along the length of the roadway.