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Journal articles on the topic 'Driver's drowsiness'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Deshmukh, Sarang Sunilrao, Nikhil Sahebrao Ghagre, Pratiksha Ganesh Dange, and Prof G. N. Gaikwad. "Driver’s Anti Sleep Devices using IOT." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 3663–68. http://dx.doi.org/10.22214/ijraset.2023.51034.

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Abstract: Drowsy driving is a serious issue that causes a lot of accidents on the road all around the world. Due to the driver's inability to stay awake while driving, many accidents happen. The existing techniques utilised to do so are ineffective, and it is difficult to spot drowsy drivers. So, in order to prevent accidents, there is a need for a gadget that can recognise driver drowsiness in real-time and inform the driver. The design and development of a driver's anti-sleep device employing a Node MCU, an IR sensor, a gyroscope, and a buzzer are presented in this work. The proposed device

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Permatasari, Dinda Ayu, Gillang Al Azhar, Muhammad Rifqi Zharfan, Anindya Dwi Risdhayanti, Arief Rahman Hidayat, and Denda Dewatama. "Design of Emergency Alarm System for Drowsiness Detection Using YOLO Method Based on Raspberry Pi." Journal of Electrical, Electronic, Information, and Communication Technology 6, no. 2 (2024): 78. https://doi.org/10.20961/jeeict.6.2.93201.

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<p style="text-align: justify;">Drowsiness is one of the main factors causing traffic accidents that often lead to fatalities, as drowsy drivers lose concentration. Therefore, drowsiness detection in car drivers is very important to prevent accidents. In this research, an emergency alarm system for drowsiness detection using YOLO method based on Mini PC is designed. This drowsiness detection system uses a camera to take pictures of the driver's face and the YOLO algorithm to detect the eyes. If the driver's eyes are detected to be closed, the system will give a warning in the form of a b

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Mohan Kumar, Ugra, Devendra Singh, Sudhir Jugran, Pankaj Punia, and Vinay Negi. "A System on Intelligent Driver Drowsiness Detection Method." International Journal of Engineering & Technology 7, no. 3.4 (2018): 160. http://dx.doi.org/10.14419/ijet.v7i3.4.16765.

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We actualized a fatigue driver recognition framework utilizing a mix of driver's state and driving conduct pointers. For driver's express, the framework observed the eyes' blinking rate and the flickering span. Fatigue drivers have these qualities higher than ordinary levels. We utilized a camera with machine vision procedures to find out and watch driver's blinking behavior. Harr's feature classifier was utilized to first find the eye's range, and once found, a layout coordinating was utilized to track the eye for fast preparing. For driving conduct, we gained the vehicle's state from inertia

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S, Manjunath, Banashree P, Shreya M, Sneha Manjunath Hegde, and Nischal H P. "Driver Drowsiness Detection System." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 129–35. http://dx.doi.org/10.22214/ijraset.2022.42109.

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Abstract: Recently, in addition to autonomous vehicle technology research and development, machine learning methods have been used to predict a driver's condition and emotions in order to provide information that will improve road safety. A driver's condition can be estimated not only by basic characteristics such as gender, age, and driving experience, but also by a driver's facial expressions, bio-signals, and driving behaviours. Recent developments in video processing using machine learning have enabled images obtained from cameras to be analysed with high accuracy. Therefore, based on the

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Pendyala, Pranavi, Aviva Munshi, and Anoushka Mehra. "Vehicular Security Drowsy Driver Detection System." International Journal of Engineering and Advanced Technology 10, no. 5 (2021): 206–9. http://dx.doi.org/10.35940/ijeat.e2751.0610521.

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Detecting the driver's drowsiness in a consistent and confident manner is a difficult job because it necessitates careful observation of facial behaviour such as eye-closure, blinking, and yawning. It's much more difficult to deal with when they're wearing sunglasses or a scarf, as seen in the data collection for this competition. A drowsy person makes a variety of facial gestures, such as quick and repetitive blinking, shaking their heads, and yawning often. Drivers' drowsiness levels are commonly determined by assessing their abnormal behaviours using computerised, nonintrusive behavioural a

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Pushkar, Piyush, Rohan Khandare, Yasharth Prasad, Vishal Kumar, and Dr Megha Kadam. "Real Time Drowsiness Detection System Using CNN." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 3884–87. http://dx.doi.org/10.22214/ijraset.2023.49112.

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Abstract: Driver fatigue and rash driving are the leading causes of road accidents, which result in the loss of valued life and decrease road traffic safety. Driver drowsiness solutions that are reliable and precise are essential to prevent accidents and increase road traffic safety. Various driver drowsiness detection systems have been developed using various technologies that are geared at the specific parameter of detecting the driver's tiredness. This research offers a unique multi-level distribution model for detecting driver drowsiness utilising Convolution Neural Networks (CNN) and. To

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Pushkar, Piyush, Rohan Khandare, Yasharth Prasad, and Vishal Kumar. "Real Time Drowsiness Detection System Using CNN." International Journal for Research in Applied Science and Engineering Technology 11, no. 1 (2023): 1487–90. http://dx.doi.org/10.22214/ijraset.2023.48847.

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Abstract: Driver fatigue and rash driving are the leading causes of road accidents, which result in the loss of valued life and decrease road traffic safety. Driver drowsiness solutions that are reliable and precise are essential to prevent accidents and increase road traffic safety. Various driver drowsiness detection systems have been developed using various technologies that are geared at the specific parameter of detecting the driver's tiredness. This research offers a unique multi-level distribution model for detecting driver drowsiness utilising Convolution Neural Networks (CNN) and. To

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Khanorkar, Vedang. "Driver Drowsiness Detection Using Raspberry Pi." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30372.

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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 Safe

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Rokade, Prof J. R. "An Automatic Driver’s Drowsiness Alert System." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 2519–23. http://dx.doi.org/10.22214/ijraset.2023.52096.

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Abstract: The Automatic Driver's Drowsiness Alert System (ADDAS) using a PIC microcontroller is a technological solution that aims to prevent accidents caused by drowsy driving. This system uses a combination of sensors and algorithms to detect the driver's level of alertness and sends a warning when the driver is experiencing drowsiness or fatigue. The system is based on a PIC microcontroller that processes the data obtained from the sensors, which include an eyeblink sensor, a temperature sensor, a Heartbeat sensor, GSM (Global System for Mobile Communication) module and GPS (Global Position

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Aanchal Takkar, Sumer Yadav, Radhika Gupta, Swati Sah,. "Project Awakesure: Intelligent Drowsiness Detection Using Eye Tracking." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 11 (2024): 906–14. http://dx.doi.org/10.17762/ijritcc.v11i11.10362.

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Being sleepy or drowsy is referred to as being drowsy. A person who is sleepy may feel exhausted or lethargic and struggle to stay awake. People who are sleepy tend to be less attentive and may even nod off, though they can still be awakened. An increasing number of vocations nowadays call for sustained focus. In order for drivers to respond quickly to unexpected incidents, they must maintain a watchful eye on the road. Many road incidents are directly caused by tired drivers. In order to drastically lower the frequency of fatigue-related auto accidents, it is crucial to develop technologies t

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Faisal, Tarig, Isaias Negassi, Ghebrehiwet Goitom, Mohammed Yassin, Anees Bashir, and Moath Awawdeh. "Systematic development of real-time driver drowsiness detection system using deep learning." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 1 (2022): 148. http://dx.doi.org/10.11591/ijai.v11.i1.pp148-160.

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Advancements in globalization have significantly seen a rise in road travel. This has also led to increased car accidents and fatalities, which become a global cause of concern. Driver's behavior, including drowsiness, contributes to many of the road deaths. The main objective of this study is to develop a system to diminish mishaps caused by the driver's drowsiness. Recently deep convolutional neural networks have been used in multiple applications, including identifying and anticipate driver drowsiness. However, limited studies investigated the systematic optimization of convolutional neural

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Raghav, Ishol, Ginni Kumar Singh, and Aarti Verma. "Driver Drowsiness Detection using Artificial Intelligence." International Journal of Recent Technology and Engineering (IJRTE) 12, no. 2 (2023): 63–65. http://dx.doi.org/10.35940/ijrte.b7784.0712223.

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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

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Bankar, Jaitee. "Driver Drowsiness Detection using CNN." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 5000–5004. http://dx.doi.org/10.22214/ijraset.2024.62704.

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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 al

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J, Jayaresmi. "Advanced Driver Drowsiness Detection System Using Machine Learning and Arduino." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41742.

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Driver drowsiness is a critical issue in vehicle safety, with statistics showing it to be a significant factor in 10-40 percent of highway accidents. The consequences of falling asleep while driving is severe, leading to increased injury severity and a higher occurrence among sleep-deprived individuals. Drowsiness negatively impacts mental alertness, impairs judgment, and slows reaction time, posing a risk of human error that can result in death or injury. Previous techniques for detecting drowsiness, such as using sensitive electrodes directly attached to the driver's body, proved impractical

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mane, Damini. "A Review on Smart Specs." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem41163.

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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

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Aher, Prof Shital. "A Implementation on Deep Learning Techniques for Detecting Driver Fatigue." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47332.

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Abstract - Driver fatigue is a significant contributor to road accidents worldwide. This paper introduces a smart and reliable method for detecting driver drowsiness through a combination of eye blink rate and yawning analysis. The system captures the driver's facial expressions using a camera positioned within the vehicle. Initially, the driver’s face is detected and monitored using advanced image processing algorithms. Subsequently, the regions around the eyes and mouth are isolated for further analysis to identify signs of sleepiness. These indicators are then integrated in a decision-makin

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Bankar, Jaitee. "Driver Drowsiness Detection using CNN." International Journal for Research in Applied Science and Engineering Technology 12, no. 1 (2024): 687–68. http://dx.doi.org/10.22214/ijraset.2024.57981.

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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

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Vaidya, Prof R. S. "Driving Alertness System Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 256–60. http://dx.doi.org/10.22214/ijraset.2024.58722.

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Abstract: Driver fatigue, a major contributor to road accidents, poses a significant challenge to road safety. Numerous fatal collisions could be prevented by promptly alerting drivers experiencing drowsiness. Several drowsiness detection methods have been developed to monitor driver alertness while driving and issue warnings when attention lapses. These systems employ various techniques to assess drowsiness levels, including extracting key features from facial expressions such as yawning, eye closure, and head movements. Evaluating both the driver's physiological state and vehicle behavior is

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Walke, Ms K. G. "Survey on Driver Drowsiness Detection System." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (2021): 1850–54. http://dx.doi.org/10.22214/ijraset.2021.39236.

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Abstract: We proposed to use this system to minimise the frequency of accidents caused by driver exhaustion, hence improving road safety. This device uses optical information and artificial intelligence to identify driver sleepiness automatically. We use Softmax to find, monitor, and analyse the driver's face and eyes in order to calculate PERCLOS (% of eye closure). It will also employ alcohol pulse detection to determine whether or not the person is normal. Due to extended driving durations and boredom in crowded settings, driver weariness is one of the leading causes of traffic accidents, p

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Deshmukh, Pranjali. "Driver Drowsiness Detection System." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem03981.

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Abstract: This system is designed to enhance road safety by detecting and preventing accidents caused by driver drowsiness, a leading cause of traffic incidents. Using computer vision, the system monitors the driver's facial features, such as the eyes and mouth, to identify signs of fatigue like blinking patterns and yawning. The detection uses Haar cascade classifiers to track these features, while a Convolutional Neural Network (CNN) identifies complex patterns, distinguishing between normal behavior and drowsiness. This deep learning technique helps adapt to various driving conditions, ensu

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C, Pavilaa. "Driver Drowsiness Detection System Based on Eye State Analysis." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 4791–97. http://dx.doi.org/10.22214/ijraset.2024.61050.

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Abstract: The Driver Drowsiness Detection System, utilizing eye state analysis, introduces an innovative approach with OpenCV for real-time monitoring of eye movements. This combination enables precise eye tracking and analysis, essential for assessing driver alertness. Upon detecting drowsiness, the system employs a modified Convolutional Neural Network (CNN) architecture to evaluate its severity. This neural network processes extracted features from the driver's eyes, providing a nuanced assessment of drowsiness levels. By leveraging these technologies, the system enhances safety by promptly

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Srivastava, Sanjeevani. "Driver Drowsiness Monitoring System using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 1344–50. http://dx.doi.org/10.22214/ijraset.2023.51769.

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Abstract: In today's world, sleepiness is one of the main causes of road accidents, many of which have tragic outcomes. Statistics show that the majority of traffic collisions, which frequently result in fatalities and serious injuries, are caused by sleepy driving. As a result, various studies have been done to develop software that can recognize driver tiredness and alert them before making a major error. Using methods from the automobile industry, several of the more popular ways to design their own systems. However, other factors, such as vehicle type, road design, and the capacity to oper

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Walke, Ms K. G., Harshvardhan Shete, Amjad Mulani, Darshan Asknani, and Rushikesh Gadekar. "Driver Drowsiness Detection System Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 4306–10. http://dx.doi.org/10.22214/ijraset.2022.43105.

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Abstract: We proposed to use this system to minimise the frequency of accidents caused by driver exhaustion, hence improving road safety. This device uses optical information and artificial intelligence to identify driver sleepiness automatically. We use Softmax to find, monitor, and analyse the driver's face and eyes in order to calculate PERCLOS (% of eye closure). It will also employ alcohol pulse detection to determine whether or not the person is normal. Due to extended driving durations and boredom in crowded settings, driver weariness is one of the leading causes of traffic accidents, p

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Yunidar, Yunidar, Melinda Melinda, Khairani Khairani, Muhammad Irhamsyah, and Nurlida Basir. "IoT-based Heart Signal Processing System for Driver Drowsiness Detection." Green Intelligent Systems and Applications 3, no. 2 (2023): 98–110. http://dx.doi.org/10.53623/gisa.v3i2.323.

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Traffic accidents often result in loss of life and significant economic losses. Indonesia's high number of traffic accidents indicates the need for effective solutions to overcome this problem. Developing a drowsiness detection device is one effort that can be made to reduce accidents caused by drowsy drivers. The data obtained in this study used driver heart rate data. The drowsiness detection tool was developed using the Wemos D1 Pro Esp8266 microcontroller and MAX30102 sensor. Testing was carried out on 25 subjects under two conditions: 'Drowsy' and 'Normal.' The driver's level of drowsines

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Rajkumar, V. P., P. Papitha, M. Rashiyadevi, G. Shalini, and G. Thrisha. "Heart rate variability-based detection of driver drowsiness and its validation using EEG." i-manager’s Journal on Embedded Systems 12, no. 2 (2024): 20. http://dx.doi.org/10.26634/jes.12.2.20922.

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Being drowsy while driving is considered highly dangerous. Addressing this issue is crucial because drivers' lives are at risk. Preventing accidents becomes challenging if drivers experience drowsiness. This study aims to develop a device to assist drivers, especially at night, in preventing accidents caused by drowsiness or sleepiness. The goal is to design an electronic device capable of detecting driver drowsiness by monitoring random changes in steering movement and wheel speed reduction. The vibration sensor's threshold can be adjusted accordingly to take appropriate action. If the driver

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Krity, Kaushiki, Kunal Goyal, Mohit Khatri, Naman Pandey, and Suguna M. K. "Using Facial Features to Detect Driver’s Drowsiness State." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 1797–805. http://dx.doi.org/10.22214/ijraset.2023.51947.

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Abstract: Drowsiness and fatigue are one of the main causes leading to road accidents. They can be prevented by taking effort to get enough sleep before driving, drink coffee or energy drink, or have a rest when the signs of drowsiness occur. The popular drowsiness detection method uses complex methods, such as EEG and ECG [19]. This method has high accuracy for its measurement but it need to use contact measurement and it has many limitations on driver fatigue and drowsiness monitor. Thus, it is not comfortable to be used in real time driving. This paper proposes a way to detect the drowsines

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Bhosale, Ashwini. "Drowsiness Detection using CNN." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 1377–79. http://dx.doi.org/10.22214/ijraset.2024.59959.

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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

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J, Sathiaparkavi, and Vennila C. "Drowsiness Alert Control System using 68 Landmark Predictor." International Journal of Multidisciplinary Research Transactions 6, no. 5 (2024): 136–44. https://doi.org/10.5281/zenodo.11350228.

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This paper proposes a novel approach to detect drowsiness of driver by utilizing 68 landmark predictor which captures facial feature to avoid loss of human life due to accidents. Driver, emphasizing the need for advanced driver monitoring systems to create alertness. The 68 landmark predictor is based on the dlib library which precisely locates 68 key points on the face and captures subtle changes indicative of drowsiness. The paper employs computer vision techniques to continuously monitor the driver's facial landmarks in real-time. By analyzing variations in facial expressions, eye movements

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Kumar, K. Tulasi Krishna, B. Keerthi, C. SP Vishwak Sen, and T. Karthikeya. "Driver Drowsiness Detection System Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 1–6. http://dx.doi.org/10.22214/ijraset.2023.50031.

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Abstract: Drowsy driving is one of the major causes of road accidents and death. Hence, detection of driver's fatigue and its indication is an active research area. Most of the conventional methods are either vehicle based, or behavioral based or physiological based. Few methods are intrusive and distract the driver, some require expensive sensors and data handling. Therefore, in my literature survey, a low cost, real time driver's drowsiness detection system is developed with acceptable accuracy. The proposed work mainly focus on a webcam records the video and driver's face is detected in eac

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Louis, Twali Mugisha, and G. Glorindal. "A review on the effectiveness of driver drowsiness detection system using computerised devices." i-manager’s Journal on Image Processing 10, no. 3 (2023): 17. http://dx.doi.org/10.26634/jip.10.3.20055.

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Driver drowsiness is a critical factor contributing to road accidents worldwide, with potentially devastating consequences. To mitigate this problem, numerous driver drowsiness detection systems have been developed, employing various computerized devices and technologies. The development of technologies for detecting drowsiness is a major challenge in the field of accident-avoidance systems. Since the dangers of drowsiness exist on the road, techniques need to be advanced to prevent its consequences. The most crucial aspect of this work is to develop a drowsiness detection system by tracking t

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Thomas,, Mrs Athira M. "IoT- Based Drowsiness Monitoring and Crash Detection System to Enhance Road Safety." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31368.

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This project is a solution designed to address the critical issue of driver drowsiness and enhance road safety. The system leverages the power of the Internet of Things (IoT) to continuously monitor driver alertness and promptly detect potential accidents. It comprises a network of sensors strategically placed within the vehicle, including facial recognition cameras and accelerometers. These sensors work together to capture and analyze a driver's physiological and behavioural data in real-time. Facial recognition technology detects signs of drowsiness such as drooping eyelids and yawning. Thro

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Wefa, Indah Dara, and Riki Mukhaiyar. "Facial Expression Detector While Driving." JTEIN: Jurnal Teknik Elektro Indonesia 5, no. 1 (2024): 138–46. http://dx.doi.org/10.24036/jtein.v5i1.606.

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Facial expression when a person is sleepy can be seen from eye behavior and facial behavior. To detect the driver's drowsiness, facial expression is a form of non-verbal communication that results from one or more facial muscle movements that can indicate a person's emotional state. This detector works automatically when the system runs without external control. The system is controlled only via the desktop using the Convolutional Neural Network (CNN) method. The working principle of this tool is to start by connecting the power when the system and the Desktop are turned on, then activate the

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Jimenez-Pinto, J., and M. Torres-Torriti. "Face salient points and eyes tracking for robust drowsiness detection." Robotica 30, no. 5 (2011): 731–41. http://dx.doi.org/10.1017/s0263574711000749.

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SUMMARYMeasuring a driver's level of attention and drowsiness is fundamental to reducing the number of traffic accidents that often involve bus and truck drivers, who must work for long periods of time under monotonous road conditions. Determining a driver's state of alert in a noninvasive way can be achieved using computer vision techniques. However, two main difficulties must be solved in order to measure drowsiness in a robust way: first, detecting the driver's face location despite variations in pose or illumination; secondly, recognizing the driver's facial cues, such as blinks, yawns, an

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Anis Hazirah Rodzi, Zalhan Bin Mohd Zin, and Norazlin Ibrahim. "Classification for Driver’s Distraction and Drowsiness Through Eye Closeness Using Receiver Operating Curve (ROC)." Data Science: Journal of Computing and Applied Informatics 4, no. 1 (2020): 15–26. http://dx.doi.org/10.32734/jocai.v4.i1-3516.

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In Malaysia, driver inattention and drowsiness becomes one of the causes of road accidents which sometime could lead to fatal ones. From the data provided by Malaysian Police Force, Polis Di Raja Malaysia or PDRM in 2016, deaths from road accidents increased from 6,706 in 2015 to 7,512 in 2016. Some accidents were caused by human factor such as driver's inattention and drowsiness. This problem motivates many parties to look for better solution in dealing with this human factor. Some of the car manufacturers have introduced to their certain models of car with an assistant system to oversee driv

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Patil, Girish Ananda. "DRIVER DROWSINESS DETECTION SYSTEMS." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30811.

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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, agitat

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Teja ,, Bandaru Varun. "Real Time Driver Drowsiness Detection Using Visual Behavior." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31195.

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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

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Turki, Amina, Omar Kahouli, Saleh Albadran, Mohamed Ksantini, Ali Aloui, and Mouldi Ben Amara. "A sophisticated Drowsiness Detection System via Deep Transfer Learning for real time scenarios." AIMS Mathematics 9, no. 2 (2024): 3211–34. http://dx.doi.org/10.3934/math.2024156.

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<abstract> <p>Driver drowsiness is one of the leading causes of road accidents resulting in serious physical injuries, fatalities, and substantial economic losses. A sophisticated Driver Drowsiness Detection (DDD) system can alert the driver in case of abnormal behavior and avoid catastrophes. Several studies have already addressed driver drowsiness through behavioral measures and facial features. In this paper, we propose a hybrid real-time DDD system based on the Eyes Closure Ratio and Mouth Opening Ratio using simple camera and deep learning techniques. This system seeks to mode

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Patle, Abhishek, Jayesh Gotephode, Mohit Lanjewar, Samiksha Gedam, and Prof Anuja Ghasad. "Driver Drowsiness Detection and Alert System." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 4440–43. http://dx.doi.org/10.22214/ijraset.2024.61007.

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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 impr

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Maktouf, Naba Ali, and Sajid Saleh Khalaf. "HEART RATE VARIABILITY BASED DRIVER DROWSINESS DETECTION." European Journal of Medical Genetics and Clinical Biology 1, no. 4 (2024): 78–86. http://dx.doi.org/10.61796/jmgcb.v1i4.408.

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Driver drowsiness may cause traffic injuries and death. Various methods have been given to detect drowsiness, for instance, image-based biometric-signals-based. In this project, a new approach using heart rate and head tilt is discussed. The aim of the project is to provide a device with simple components that enables the driver to save his life by avoiding accidents caused by frequent sleepiness by giving a sound alert (alarm), which forces the driver to wake up and regain control of the car This device is not considered a treatment for the problem of sleep, but rather a preventive tool from

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AlKishri, Wasin, Abdallah Abualkishik, and Mahmood Al-Bahri. "Enhanced Image Processing and Fuzzy Logic Approach for Optimizing Driver Drowsiness Detection." Applied Computational Intelligence and Soft Computing 2022 (March 19, 2022): 1–14. http://dx.doi.org/10.1155/2022/9551203.

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Driver drowsiness is a severe problem that usually causes traffic accidents, classified as more dangerous. The record of the National Safety Council reported that drowsy driving is caused by 9.5% of all crashes (100,000 cases). Therefore, preventing and minimizing driver fatigue is a significant research area. This study aims to design a nonintrusive real-time drowsiness system based on image processing and fuzzy logic techniques. It is an enhanced approach for Viola–Jones to examine different visual signs to detect the driver's drowsiness level. It extracted eye blink duration and mouth featu

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Sachin Kumar, Priya Devi, Meghna Singh, and Meetu Rani. "Driver drowsiness detection system." International Journal of Science and Research Archive 12, no. 1 (2024): 1017–22. http://dx.doi.org/10.30574/ijsra.2024.12.1.0935.

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In contemporary times, the escalating incidence of accidents attributable to drowsy driving presents a formidable challenge. Acknowledging the pivotal role of driver fatigue and intermittent inattention in these occurrences, this research endeavors to optimize efforts towards the real-time identification of drowsiness in drivers under authentic driving conditions, with the overarching objective of mitigating the incidence of traffic accidents. Drawing upon a corpus of secondary data gleaned from prior studies on drowsiness detection systems, a diverse array of methodological approaches has bee

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Sheetal Chauhan and Sanu Singh. "Driver Drowsiness Detection and Alarm System Using Deep Learning." International Journal of Scientific Research in Science and Technology 12, no. 3 (2025): 141–49. https://doi.org/10.32628/ijsrst2512312.

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Accidents caused by drowsy drivers have become a big problem worldwide. These accidents lead to a lot of deaths and injuries every year. Despite various efforts, drowsiness remains a primary factor contributing to these accidents. This study proposes a system that analyzes facial expressions in real time to check the drowsiness of drivers and will sound an alarm when the system notices the driver is getting sleepy. The proposed system uses facial landmark detection to find important spots on the driver's face, especially around the eyes. These spots help calculate eye aspect ratio(EAR), which

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Yagna V. Bhatt. "State-of-the-Art in Driver’s Drowsiness Detection: A Comprehensive Survey." Journal of Information Systems Engineering and Management 10, no. 36s (2025): 1024–40. https://doi.org/10.52783/jisem.v10i36s.6630.

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Recent developments in computing technology and advancements in artificial intelligence have led to major improvements in driver monitoring systems. These enhancements are crucial given the substantial risk posed by fatigue or drowsiness on roads, leading to numerous accidents and impacting overall road safety. Timely detection and alert mechanisms for fatigued drivers can prevent many such accidents. Various methods have been formulated to monitor driver drowsiness during operation and alert drivers when their attention wanes. These methods utilize facial expressions like yawning, eye closure

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OKUMOTO, Yasuhisa, and Hiroaki HIRAMATU. "1508 Fundamental Study on Driver's Drowsiness." Proceedings of Conference of Chugoku-Shikoku Branch 2009.47 (2009): 527–28. http://dx.doi.org/10.1299/jsmecs.2009.47.527.

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Adarsh, Gauri, and Vineet Singh. "Drowsiness Detection System in Real Time Based on Behavioral Characteristics of Driver using Machine Learning Approach." Journal of Informatics Electrical and Electronics Engineering (JIEEE) 4, no. 1 (2023): 1–10. http://dx.doi.org/10.54060/jieee.v4i1.84.

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The process of determining if a person, generally a driver, is becoming sleepy or drowsy while performing a task such as driving is known as drowsiness detection. It is a necessary system for detecting and alerting drivers to their tiredness, which might impair their driving ability and lead to accidents. The project aims to create a reliable and efficient system capable of real-time detection of drowsiness using OpenCV, Dlib, and facial landmark detection technologies. The project's results show that the sleepiness detection method can accurately and precisely identify tiredness in real time.

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Widyastuti, Nur Rachmi, and Dani Fitria Brilianti. "The Impact of Drowsiness on Road Traffic Accidents in Yogyakarta." Journal of Scientific Research, Education, and Technology (JSRET) 3, no. 4 (2024): 1651–61. https://doi.org/10.58526/jsret.v3i4.555.

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Traffic accidents are a serious issue in Yogyakarta, with one of the leading causes being drowsy driving. Drowsiness while driving can reduce alertness, response time, and the driver's ability to react to emergency situations, thereby increasing the risk of accidents. This phenomenon not only causes material losses but also endangers the lives of drivers, passengers, and other road users.This study aims to analyze the impact of drowsiness on traffic accidents in Yogyakarta, identify factors influencing drowsiness while driving, and provide recommendations to reduce the risk of accidents caused

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R, Aditya. "Design of Real-Time Driver Drowsiness Detection Based Electromechanical Braking System for Heavy-Duty Vehicles." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29932.

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The increasing number of road accidents involving heavy vehicles due to driver drowsiness necessitates the development of advanced safety systems. This abstract presents a real-time driver-drowsiness-based electromechanical braking system designed specifically for heavy vehicles. The system uses a combination of sensors, including facial recognition cameras and fatigue monitoring sensors, to continuously monitor the driver's physiological and behavioral parameters. Machine learning algorithms analyze these data streams in real-time to identify signs of drowsiness, such as drooping eyelids, err

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Mr. T. Nandhakumar, Ms. S. Swetha, Ms. T. Thrisha, and Ms. M. Varunapriya. "Driver Distraction and Drowsiness Detection Based on Object Detection Using Deep Learning Algorithm." International Journal of Innovative Technology and Exploring Engineering 13, no. 6 (2024): 18–22. http://dx.doi.org/10.35940/ijitee.f9888.13060524.

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Distracted driving is a major global contributing factor to traffic accidents. Distracted drivers are three times more likely to be involved in an accident than non-distracted drivers. This is why detecting driver distraction is essential to improving road safety. Several prior studies have proposed a range of methods for identifying driver distraction, including as image, sensor, and machine learning-based approaches. However, these methods have limitations in terms of accuracy, complexity, and real-time performance. By combining a convolutional neural network (CNN) with the You Only Look Onc

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Ms., S. Swetha. "Driver Distraction and Drowsiness Detection Based on Object Detection Using Deep Learning Algorithm." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 13, no. 6 (2024): 18–22. https://doi.org/10.35940/ijitee.F9888.13060524.

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<strong>Abstract: </strong>Distracted driving is a major global contributing factor to traffic accidents. Distracted drivers are three times more likely to be involved in an accident than non-distracted drivers. This is why detecting driver distraction is essential to improving road safety. Several prior studies have proposed a range of methods for identifying driver distraction, including as image, sensor, and machine learning-based approaches. However, these methods have limitations in terms of accuracy, complexity, and real-time performance. By combining a convolutional neural network (CNN)

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Phulari, Shivanand. "Driver Drowsiness Detection using Machine Learning with Visual Behaviour." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 1800–1805. http://dx.doi.org/10.22214/ijraset.2021.35348.

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A person while driving a vehicle - if does not have proper sleep or rest, is more inclined to fall asleep which may cause a traffic accident. This is why a system is required which will detect the drowsiness of the driver. Recently, in research and development, machine learning methods have been used to predict a driver's conditions. Those conditions can be used as information that will improve road safety. A driver's condition can be estimated by basic characteristics age, gender and driving experience. Also, driver's driving behaviours, facial expressions, bio-signals can prove helpful in th

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