Drowsiness Detection Research Articles

Drowsiness detection using Raspberry Pi for EVs and smart cars

Drowsiness detection using Raspberry Pi for EVs and smart cars

SLEEPINESS OR DROWZINESS AND FATIGUE DETECTION SYSTEM OF DRIVER IN THE ROAD NETWORK

On the basis of data, it is analyzed that drivers feeling drowsy are responsible for accidents on the roads.[3], posing a serious threat to public safety. To tackle this issue and take advantage of the mobile devices, a unique solution for Driver Drowsiness or sleepiness and fatigue Detection[2] system has been proposed. This solution utilizes Convolutional Neural Networks along with Flask for image/live input. By following this two-step technique, the mobile devices in the car record and evaluate the driver's current state while also ensuring their privacy. Then, boundaries verify sleepiness by comparing the data from the mobile client with the real-time input that was observed. The suggested framework uses a data fusion technique and is based on the distributed boundaries architecture, which ensures effective administration of the area of interest. This method uses flask manual input or real-time input of the car environment and the CNN model to identify driver fatigue locally based on facial expressions. With an impressive average accuracy, the framework's sleepiness detection performance is remarkable.

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

Driver Drowsiness Detection System

Abstract: Drowsy driving poses a significant threat to road safety, necessitating the development of effective drowsiness detection systems. This research paper provides a thorough review of current technologies employed in drowsiness detection, encompassing image-based, physiological, and behavioural approaches. Evaluating the strengths and limitations of existing systems, the study identifies key challenges, including false positives and negatives, adaptability to diverse conditions, and integration complexities. Recent advancements, such as deep learning, sensor fusion, and real-time processing, are explored, offering insights into their impact on system accuracy and usability. The paper proposes hybrid approaches, personalized algorithms, and integration with smart infrastructure as potential enhancements. Through case studies, the effectiveness of drowsiness detection in real-world scenarios is highlighted, emphasizing the positive influence on road safety. The research concludes with a discussion on future directions, outlining emerging technologies and identifying research gaps. This paper aims to contribute to the ongoing evolution of drowsiness detection systems, fostering innovation for improved safety on the roads

Drowsiness Detection using CNN

Abstract: This research introduces a Driver Drowsiness Detection system employing Convolutional Neural Networks (CNN). The system analyses real-time facial features from in-vehicle cameras to determine a driver's alertness level. Trained on diverse datasets, the CNN model demonstrates high accuracy in identifying drowsiness signs, making it suitable for real-world deployment. This system contributes to road safety by providing timely alerts to prevent accidents caused by driver fatigue. As road safety remains a critical concern, the development of intelligent systems to mitigate driver-related risks has become imperative.

Driver Drowsiness Detection and Alert System

Abstract: Driver drowsiness is a significant factor in road accidents, necessitating the development of effective detection and alert systems to mitigate this risk. This paper provides a comprehensive review of driver drowsiness detection and alert systems, examining the technologies, methodologies, and challenges associated with these systems. Our goal is to provide an interface where the program can automatically detect the driver's drowsiness and detect it in the event of an accident by using the image of a person captured by the webcam and examining how this information can be used to improve driving safety can be used. . a vehicle safety project that helps prevent accidents caused by the driver's sleep. Basically, you're collecting a human image from the webcam and exploring how that information could be used to improve driving safety. Collect images from the live webcam stream and apply machine learning algorithm to the image and recognize the drowsy driver or not. Despite advancements, challenges remain in achieving high accuracy and minimizing false alarms. Future directions include improving sensor technology, enhancing algorithm robustness, and addressing user acceptance issues. Overall, driver drowsiness detection and alert systems play a crucial role in enhancing road safety and warrant continued research and development efforts.

Fatigue Driver Alert System

The rise in accidents caused by drivers who are too sleepy to drive has made advanced driver alert systems necessary. This research proposes a novel method for identifying drowsy drivers through the integration of GPS technology and physiological data, offering an inventive solution to this pressing problem. We explore the complexities of drowsiness detection, highlighting the difficulties and introducing an improved approach to improve performance. Using real-time GPS data, our system not only warns drivers when something is wrong, but it also suggests appropriate rest spots depending on the driver's present location. Our method, which emphasizes proactive measures to limit the risks associated with drowsy driving, pioneers safer driving habits by seamlessly combining physiological measurements, GPS technology, and algorithmic enhancements. Performance assessments show encouraging outcomes, highlighting the potential advantages and importance of these devices in lowering driver fatigue-related incidents. This research advances vehicle safety and emphasizes the need of taking preventative action to reduce the risks associated with sleepy driving.

Driver Drowsiness Detection System Based on Eye State Analysis

Driver Drowsiness Detection System Based on Eye State Analysis

DRIVER DROWSINESS DETECTION AND CREATING SAFETY MEASURES USING OPENCV,PYTHON

Accidents due to driver drowsy driving is significantly increasing in high ratio now days. So we proposed an AI based solution to reduce the driver drossy driving accident by alerting the driver when they fall asleep. This is an brief abstract that explains system working and principles. This paper present a novel, Real-Time Driver Drowsiness Detection System(DDDS) that utilizes a multi-model approach for fatigue assessment. The system incorporates computer vision and machine learning techniques to analyze facial features, eye movements, and physical signal(if applicable) captured through a webcam processing techniques enhance data processing techniques enhanced data quality for better analysis.Facial landmarks are identified to locate the eyes, and features like Eye Aspect Ratio(EAR) are used to detect signs of fatigue. The system employs a multimodal approach, utilizing computer vision techniques to analyze facial features and eye movements captured through a webcam. Specifically, the DDDS leverages the Haar cascade algorithm for efficient facial landmark detection and the Histogram of Oriented Gradients (HOG) algorithm for feature extraction from facial regions of interest. This combination allows for accurate localization and analysis of key features like eyes. Additionally, the system explores the potential of physiological data (e.g., heart rate variability) for a more comprehensive analysis (if applicable). Machine learning techniques (if applicable) can be employed on the combined features, including those extracted using Haar and HOG, for enhanced fatigue classification. Upon detecting drowsiness, the DDDS triggers customizable alerts (visual, audio, or haptic) to warn the driver. The system additionally explores the potential of physiological data (e.g., heart rate variability) for fatigue detection, offering a more comprehensive approach (if applicable).A machine learning model can be employed on the combined features for robust fatigue classification. Upon detection of drowsiness,the DDDS triggers customizable alerts ( visual, audio, or haptic) to warn the driver. This system addresses limitations of existing solution by: 1.Real-Time processing:Ensuring immediate detection and response to fatigue signs. 2.Multi feature analysis: Combining facial features, eye movements,and physiological data for robust detection. 3.Customizable alerts: Providing drivers control over alert type for a personalized experience. The evaluation methodology employs a dataset of [data type e.g.., image, videos] to access the system’s performance.Metrics like accuracy,precision,recall,and F1-score will be used to evaluate the effectiveness of the DDDS in detecting drowsy driving. This research aims to contribute to safer roads by developing a comprehensive and adaptable Driver Drowsiness Detection System. II.KEYWORDS: Drowsy Driving, Driver Monitoring,Computer Vision, Machine Learning,Eye Tracking, Fatigue Detection,Physiological Signs(if applicable)

FATIGUE DETECTION OF DRIVERS USING MACHINE LEARNING

In this project, the aim is to develop a prototype drowsiness detection system to address driver fatigue, a major cause of accidents worldwide. The system detects driver drowsiness by monitoring eye blinks and yawning. If the driver's eyes remain closed for an extended period or if yawning is detected, an alarm is sounded. Programming for this system is conducted in OpenCV, utilizing the Haar cascade library for face detection and other machine learning libraries to detect facial features. The system operates in real-time and is nonintrusive, prioritizing driver safety without being obtrusive. By effectively monitoring driver alertness and issuing timely warnings, this system contributes significantly to enhancing road safety. Keywords - Driver fatigue, drowsiness detection, eye blink, yawn detection, OpenCV, Haar cascade, machine learning, facial features.

DROWSINESS AND ALCOHOL DETECTION USING ARDUINO

Accidents on road ways are increasing in our day to day lives. These accidents pose significant threats to human safety and well-being. There are several ways for reduction of accidents. Some of them are Drowsiness detection and Alcoholic detection. By using sensors, communication devices and algorithms, the system aims to identify potential hazards. This abstract presents a comprehensive approach to accident reduction using Internet of Things (IoT) technology. Drowsiness Detection System aims to mitigate the risks of drowsy driving and enhance road safety. By utilizing an Arduino microcontroller and various sensors, this system continuously monitors the driver’s vital signs and facial expressions. It analyzes parameters such as eye blink patterns, head movements, and heart rate to accurately identify signs of drowsiness. Upon detecting drowsiness, the system promptly alerts the driver through alarms or visual cues, prompting them to take necessary actions to prevent accidents. This project serves as a vital tool in reducing road accidents caused by driver drowsiness, ensuring safer journeys for all. This project presents the design and implementation of an Alcohol Detection with Engine Locking for cars using Arduino UNO as the MCU (Master Control Unit). The system will continuously monitor level of alcohol concentration in alcohol detection sensor and thus turn off the engine of vehicle if the alcohol concentration is above threshold level. The model will also send the message of whereabouts of the vehicle through SIM900A. The project provides an efficient solution to control accidents due to drunk driving Key Words: Arduino UNO, Sensor Fusion, Real time monitoring, MQ-3 sensor, GPS module, GSM module, Telematics.

Real Time Driver Drowsiness Detection Using Visual Behavior

One of the main factors contributing to traffic accidents is falling asleep behind the wheel. Although it cannot be totally avoided, this model can help prevent it. A system called drowsiness detection keeps drivers from nodding off while operating a vehicle. Both passengers and other drivers will be safer thanks to this technology. The algorithm can monitor the space between the driver's eyes by employing an infrared camera in front of the vehicle. The technology sounds an alert to alert the driver to take control of the vehicle when the region comes under curtain measurement for a number of consecutive frames. The alarm goes off when the driver opens their eyes again and the area passes above the safe zone. As a result, this may wake up the motorist and save lives. The algorithm's accuracy is 96%.

Association of Visual-Based Signals with Electroencephalography Patterns in Enhancing the Drowsiness Detection in Drivers with Obstructive Sleep Apnea.

Individuals with obstructive sleep apnea (OSA) face increased accident risks due to excessive daytime sleepiness. PERCLOS, a recognized drowsiness detection method, encounters challenges from image quality, eyewear interference, and lighting variations, impacting its performance, and requiring validation through physiological signals. We propose visual-based scoring using adaptive thresholding for eye aspect ratio with OpenCV for face detection and Dlib for eye detection from video recordings. This technique identified 453 drowsiness (PERCLOS ≥ 0.3 || CLOSDUR ≥ 2 s) and 474 wakefulness episodes (PERCLOS < 0.3 and CLOSDUR < 2 s) among fifty OSA drivers in a 50 min driving simulation while wearing six-channel EEG electrodes. Applying discrete wavelet transform, we derived ten EEG features, correlated them with visual-based episodes using various criteria, and assessed the sensitivity of brain regions and individual EEG channels. Among these features, theta-alpha-ratio exhibited robust mapping (94.7%) with visual-based scoring, followed by delta-alpha-ratio (87.2%) and delta-theta-ratio (86.7%). Frontal area (86.4%) and channel F4 (75.4%) aligned most episodes with theta-alpha-ratio, while frontal, and occipital regions, particularly channels F4 and O2, displayed superior alignment across multiple features. Adding frontal or occipital channels could correlate all episodes with EEG patterns, reducing hardware needs. Our work could potentially enhance real-time drowsiness detection reliability and assess fitness to drive in OSA drivers.

DRIVER DROWSINESS DETECTION SYSTEMS

Car accidents are frequently caused due to many reasons like drowsiness, drunkenness or tiredness, which has serious consequences for traffic safety. Numerous factors, such as fatigue, intoxication, or drowsiness, commonly contribute to car accidents, which have detrimental effects on traffic safety. Many fatal collisions might be avoided if sleepy drivers were warned beforehand. Many sleepiness detection technologies are available to identify and warn drivers of any indications of inattention while driving. Sensors in self-driving cars must be able to detect whether a driver is sleepy, agitated, or experiencing sharp emotional fluctuations. Thus far There have been numerous recommendations for enhanced warning systems to prevent sleepy driving. When these systems detect a significant lane departure, a change in the headway distance, or other indications of subpar driving, they sound an alarm. These systems rely heavily on machine learning algorithms, which allow them to learn and adjust to unique driving patterns and characteristics. In supervised learning, models are trained on labelled datasets to discriminate between awake and sleepy states. These approaches are widely used for classification tasks. Unsupervised learning techniques are also applied for anomaly detection, which finds departures from typical driving behaviour. The incorporation of cutting-edge sensors like facial recognition cameras and brainwave sensors, which offer more thorough insights into the driver's condition, has recently improved driver sleepiness detection systems. Moreover, these systems are more accurate and responsive when real-time data processing and cloud-based analytics are used. KEYWORDS Drowsiness-detection, Driver-Fatigue, Computer Vision, Real Time, survey, Eye Tracking Technology, Automotive Safety Systems, Driver Assistance Systems, Real-time Drowsiness Detection, Alertness Monitoring.

Drowsiness detection system based on machine learning using eye state

Drowsiness detection system based on machine learning using eye state

Quantum machine learning for drowsiness detection with EEG signals

Human reliability is an increasingly important area in various fields for accident prevention. Monitoring human biological parameters, such as metabolic agents, through techniques like an electroencephalogram (EEG), data analysis can help detect patterns indicating drowsiness, a major cause of fatigue that may impact tasks in various industries, including oil and gas, aviation, naval, railway, and others that involve shift work. While traditional machine learning methods, such as Multilayer Perceptron (MLP), have been explored in the literature for EEG-based drowsiness detection, advancements in computing technology have brought quantum mechanics concepts into play, offering potential advantages in computational efficiency for problem-solving. This work explores drowsiness detection via Quantum Machine Learning (QML). EEG signals are preprocessed to extract features specific to this type of data, such as Higuchi Fractal Dimension, Complexity, and Mobility, as well as statistical features such as mean, variance, root mean square, peak-to-peak, and maximum amplitude. We employ different quantum circuit architectures involving operations such as rotation gates (Ry, Rz, Ry), and entangling gates (CNOT, CZ, and iSWAP). We also combine those configurations considering different numbers of layers (1, 5, and 10). The models are trained and compared with classical MLP, considering five subjects. The main findings indicate that one subject (10) showed better results with the classic MLP model. However, for two subjects (1 and 8), iSWAP gates with 1 and 10 layers were notable, whereas for the last two subjects (5 and 6), configurations of CZ gates with 1 and 10 layers displayed the best results. This proof-of-principle study shows that QML models are suitable for analyzing EEG data related to drowsiness and can be further improved as quantum computing continues to evolve.

Algorithm for drowsiness detection based on hybrid brain network parameter optimization

Drowsiness detection is a test designed to detect a person’s reaction ability and speed while in a state of fatigue. This type of drowsiness detection has many practical applications in areas like driver monitoring, transportation industry, and workplace safety. In this paper, a new machine learning drowsiness detection algorithm based on hybrid brain network feature extraction is proposed. First, the raw electroencephalogram signals are segmented into 30 s epochs, which are decomposed with wavelet packet transform into 7 layers to extract 30 s sleep characteristic subbands and the 7-th layer subbands. After that, 30 s subbands are divided into 5 s subbands with 50% overlap. At the same time, 30 s subbands are reconstructed by phase space reconstruction (PSR), whose parameters are optimized through energy valley optimizer. Then, based on mutual information and horizontal visual graph, a hybrid brain network is constructed with the 7-th layer subbands, 5 s subbands and PSR subbands, respectively. Finally, network features and time–frequency features are extracted and input into random forest classifier. The performance of the algorithm is evaluated through two tests: subject-independent test and subject-non-independent test. The results show that in Sleep-EDF dataset, accuracy, κ coefficient, recall, precision and F1-score obtained by the proposed algorithm can reach 94.19%, 88.39%, 95.37%, 92.53% and 93.93%, respectively. In Sleep-EDF Expanded dataset, accuracy, κ coefficient, recall, precision and F1-score can reach 93.65%, 87.65%, 94.53%, 92.11%, and 92.89%, respectively.

A hybrid approach for driver drowsiness detection utilizing practical data to improve performance system and applicability.

Numerous systems for detecting driver drowsiness have been developed; however, these systems have not yet been widely used in real-time. The purpose of this study was to investigate at the feasibility of detecting alert and drowsy states in drivers using an integration of features from respiratory signals, vehicle lateral position, and reaction time and out-of-vehicle ways of data collection in order to improve the system's performance and applicability in the real world. Data was collected from 25 healthy volunteers in a driving simulator-based study. Their respiratory activity was recorded using a wearable belt and their reaction time and vehicle lateral position were measured using tests developed on the driving simulator. To induce drowsiness, a monotonous driving environment was used. Different time domain features have been extracted from respiratory signals and combined with the reaction time and lateral position of the vehicle for modeling. The observer of rating drowsiness (ORD) scale was used to label the driver's actual states. The t-tests and Man-Whitney test was used to select only statistically significant features (p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features then combined to investigate the improvement in performance using the Multilayer Perceptron (MLP), the Support Vector Machines (SVMs), the Decision Trees (DTs), and the Long Short Term Memory (LSTM) classifiers. The models were implemented in Python library 3.6. The experimental results illustrate that the support vector machine classifier achieved accuracy of 88%, precision of 85%, recall of 83%, and F1 score of 84% using selected features. These results indicate the possibility of very accurate detection of driver drowsiness and a viable solution for a practical driver drowsiness system based on combined measurement using less-intrusive and out-of-vehicle recording methods.

Driver Drowsiness Detection Using Raspberry Pi

Driver Drowsiness Is A Significant Factor Contributing To Road Accidents Worldwide. This Paper Proposes A Novel Approach To Mitigate This Problem By Developing A Driver Drowsiness Detection System (Ddds) Using Raspberry Pi. The System Utilizes Image Processing Techniques To Monitor The Driver's Facial Features And Detect Signs Of Drowsiness In Real-Time. A Combination Of Computer Vision Algorithms And Models Is Employed To Accurately Identify Fatigue-Related Symptoms Such As Eye Closure And Head Nodding. The Proposed System Offers A Cost-Effective And Efficient Solution For Enhancing Road Safety By Alerting Drivers When They Exhibit Signs Of Drowsiness, Thereby Reducing The Risk Of Accidents. Keywords: Driver Drowsiness Detection, Raspberry Pi, Image Processing, Computer Vision, Road Safety.