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Relevant bibliographies by topics / Driver's drowsiness

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Academic literature on the topic 'Driver's drowsiness'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Driver's drowsiness"

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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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Dissertations / Theses on the topic "Driver's drowsiness"

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Fernandes, Dias Claudio. "Driver’s Safety Analyzer: Sobriety, Drowsiness, Tiredness, and Focus." Youngstown State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1587477829716502.

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Abas, Ashardi B. "Non-intrusive driver drowsiness detection system." Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5521.

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The development of technologies for preventing drowsiness at the wheel is a major challenge in the field of accident avoidance systems. Preventing drowsiness during driving requires a method for accurately detecting a decline in driver alertness and a method for alerting and refreshing the driver. As a detection method, the authors have developed a system that uses image processing technology to analyse images of the road lane with a video camera integrated with steering wheel angle data collection from a car simulation system. The main contribution of this study is a novel algorithm for drows

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Svensson, Ulrika. "Blink behaviour based drowsiness detection : method development and validation /." [Linköping, Sweden] : Swedish National Road and Transport Research Institute, 2004. http://www.vti.se.

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Yue, Chongshi. "EOG Signals in Drowsiness Research." Thesis, Linköpings universitet, Biomedicinsk instrumentteknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-81761.

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Blink waveform in electrooculogram (EOG) data was used to develop and adjust the method of drowsiness detection in drivers. The origins of some other waveforms in EOG signal were not very clearly understood. The purpose of this thesis work is to study the EOG signal and give explanation of different kind of waveforms in EOG signal, and give suggestions to improve the blink detection algorithm. The road driving test video records and synchronized EOG signal were used to build an EOG library. By comparing the video record of the driver’s face and the EOG data, the origin of the unknown waveforms

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Abtahi, Shabnam. "Driver Drowsiness Monitoring Based on Yawning Detection." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/23295.

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Driving while drowsy is a major cause behind road accidents, and exposes the driver to a much higher crash risk compared to driving while alert. Therefore, the use of assistive systems that monitor a driver’s level of vigilance and alert the fatigue driver can be significant in the prevention of accidents. This thesis introduces three different methods towards the detection of drivers’ drowsiness based on yawning measurement. All three approaches involve several steps, including the real time detection of the driver’s face, mouth and yawning. The last approach, which is the most accurate, is b

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Bandara, Indrachapa Buwaneka. "Driver drowsiness detection based on eye blink." Thesis, Bucks New University, 2009. http://bucks.collections.crest.ac.uk/9782/.

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Accidents caused by drivers’ drowsiness behind the steering wheel have a high fatality rate because of the discernible decline in the driver’s abilities of perception, recognition, and vehicle control abilities while sleepy. Preventing such accidents caused by drowsiness is highly desirable but requires techniques for continuously detecting, estimating, and predicting the level of alertness of drivers and delivering effective feedback to maintain maximum performance. The main objective of this research study is to develop a reliable metric and system for the detection of driver impairment due

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Kannanthanathu, Amal Francis. "Wavelet Transform and Ensemble Logistic Regression for Driver Drowsiness Detection." Thesis, California State University, Long Beach, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10639615.

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<p> Drowsy driving has become a serious concern over the last few decades. The rise in the number of automobiles as well as the stress and fatigue induced due to lifestyle factors have been major contributors to this problem. Accidents due to drowsy driving have caused innumerable deaths and losses to the state. Therefore, detecting drowsiness accurately and within a short period of time before it impairs the driver has become a major challenge. Previous researchers have found that the Electrocardiogram (ECG/EKG) is an important parameter to detect drowsiness. Incorporating machine learning (M

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Bagarotti, Valentina Maria. "DRIVER DROWSINESS ATTENTION WARNING - Integrazione all'interno di un veicolo commerciale." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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The use of the Driver Monitoring Systems (DMC) , combined with Advanced Driver Assistance Systems (ADAS), will lead to a different consideration of safety by car manufacturers and designers. They will have to respond to the institutions’ demand for safer vehicles in order to reduce the number of road accidents and the related social costs. In addition, a more or less impactfull presence of these systems inside the cars could help to raise users’ awareness of the risks that are involved when they are on board a vehicle. The analysis carried out proved to be of great importance for the deve

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Gargano, Ivan Enzo. "Model-Based validation of Driver Drowsiness Detection System for ADAS." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25716/.

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The work described in this Master’s Degree thesis was born after the collaboration with the company Maserati S.p.a, an Italian luxury car maker with its headquarters located in Modena, in the heart of the Italian Motor Valley, where I worked as a stagiaire in the Virtual Engineering team between September 2021 and February 2022. This work proposes the validation using real-world ECUs of a Driver Drowsiness Detection (DDD) system prototype based on different detection methods with the goal to overcome input signal losses and system failures. Detection methods of different categories have been

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Altmüller, Tobias [Verfasser]. "Driver Monitoring and Drowsiness Detection by Steering Signal Analysis / Tobias Altmüller." Aachen : Shaker, 2007. http://d-nb.info/1164338684/34.

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Books on the topic "Driver's drowsiness"

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Čolić, Aleksandar, Oge Marques, and Borko Furht. Driver Drowsiness Detection. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11535-1.

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Čolić, Aleksandar, Borko Furht, and Oge Marques. Driver Drowsiness Detection: Systems and Solutions. Springer London, Limited, 2014.

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Driver Drowsiness Detection: Systems and Solutions. Springer London, Limited, 2014.

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Book chapters on the topic "Driver's drowsiness"

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Khursheed, Mohd Danish, Mohd Maaz Khan, and Sara Parveen. "Real-time driver's drowsiness detection using transfer learning." In Application of Communication Computational Intelligence and Learning. Routledge, 2022. http://dx.doi.org/10.1201/9781003340867-8.

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Srivastava, Swati. "Driver's drowsiness identification using eye aspect ratio with adaptive thresholding." In Recent Trends in Communication and Electronics. CRC Press, 2021. http://dx.doi.org/10.1201/9781003193838-29.

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Jindal, Pardeep Kumar, Gurvinder Singh, Bhupinder Kaur, and Manbir Kaur. "Driver's Drowsiness Monitoring System for Vehicles to Avoid Accidents—A Review." In Latest Trends in Engineering and Technology. CRC Press, 2024. http://dx.doi.org/10.1201/9781032665443-47.

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Revathi, N., R. Reeba Jennifer, S. Praseetha, and K. Sivakami. "A Novel Machine Learning Model on EEG Signals-Based Driver's Drowsiness Detection System." In Machine Learning for Neurodegenerative Disorders. CRC Press, 2025. https://doi.org/10.1201/9781032661025-10.

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Čolić, Aleksandar, Oge Marques, and Borko Furht. "Introduction." In Driver Drowsiness Detection. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11535-1_1.

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Čolić, Aleksandar, Oge Marques, and Borko Furht. "Driver Drowsiness Detection and Measurement Methods." In Driver Drowsiness Detection. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11535-1_2.

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Čolić, Aleksandar, Oge Marques, and Borko Furht. "Commercial Solutions." In Driver Drowsiness Detection. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11535-1_3.

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Čolić, Aleksandar, Oge Marques, and Borko Furht. "Research Aspects." In Driver Drowsiness Detection. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11535-1_4.

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Čolić, Aleksandar, Oge Marques, and Borko Furht. "Examples." In Driver Drowsiness Detection. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11535-1_5.

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Rezaei, Mahdi, and Reinhard Klette. "Driver Drowsiness Detection." In Computer Vision for Driver Assistance. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50551-0_5.

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Conference papers on the topic "Driver's drowsiness"

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Satti, Satish Kumar, Goluguri N. V. Rajareddy, N. V. Vishnumurthy Ravipati, and S. P. N. L. Gayatri Samanvita. "Drowsy Alert: A System to Detect and Alert Driver's Drowsiness for Road Safety." In 2024 IEEE Students Conference on Engineering and Systems (SCES). IEEE, 2024. http://dx.doi.org/10.1109/sces61914.2024.10652546.

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Kumar, Vinay, Aarav Dwivedi, Aryan Vishwakarma, and Nidhi Gupta. "Driver Drowsiness Alert System." In 2024 International Conference on Computing, Sciences and Communications (ICCSC). IEEE, 2024. https://doi.org/10.1109/iccsc62048.2024.10830397.

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Khan, Rimsha Mehnaaz, B. P. Pradeep Kumar, Sidra Kabir, and Yash Raj. "Driver Drowsiness Detection System." In 2025 3rd International Conference on Smart Systems for applications in Electrical Sciences (ICSSES). IEEE, 2025. https://doi.org/10.1109/icsses64899.2025.11010064.

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Murali, E., Ch G. Vignesh, and G. Prasanth Varma. "Driver Drowsiness Detection Using YOLO." In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2024. http://dx.doi.org/10.1109/icaccs60874.2024.10717282.

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Suresh, Yeresime, Akshay Kumar, K. Bhaskar Reddy, H. Bhuvan Sai, and K. Chetan Sai. "Driver Drowsiness Detection using Machine Learning." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724107.

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Naik, Aditya, Priyadarshan Wagh, Riya Kosta, Vijay Oli, and Shailendra Aswale. "Driver Drowsiness Detection using Smart Glasses." In 2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS). IEEE, 2024. https://doi.org/10.1109/ictacs62700.2024.10841148.

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Yashwanth, Challa, and Jyoti Singh Kirar. "Driver's Drowsiness Detection." In TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON). IEEE, 2019. http://dx.doi.org/10.1109/tencon.2019.8929429.

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Balomenos, Athanasios, Dimitrios Xynogalas, Vasileios-Marios Panagakis, Andreas Georgis, Georgios Siokas, and Yannis Kopsinis. "Real Time Drowsiness Detection, Alerting and Reporting." In Emerging Tech Conference Edge Intelligence 2022. Hellenic Emerging Technology Industry Association, 2025. https://doi.org/10.63438/wyjb9531.

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Driver drowsiness is one of the leading causes of motor vehicle crashes, an issue which the industry and academia wants to tackle. Currently, different methodologies focus on combining drowsiness detection technologies and machine learning. The proposed solution is a computer vision based solution for detecting drowsiness based on a video feed of the driver's face. This helps monitor a driver's fatigue condition in real-time. The system is based on a hybrid approach, combining the decision of far-edge and near-edge submodules to detect drowsiness signs of the truck driver. According to the dro

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Singh, Amandeep, Siby Samuel, Jagmeet Singh, and Yash Kumar Dhabi. "Internet of Things (IoT) based Drowsiness Detection and Intervention System." In 9th International Conference on Human Interaction and Emerging Technologies - Artificial Intelligence and Future Applications. AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1002955.

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This study aimed to develop a non-intrusive smart monitoring system that could identify and prevent drowsy driving, reducing the risk of accidents. The study developed a system that uses video processing to measure the Euclidean distance of the eye and an eye aspect ratio (EAR) in order to detect drowsiness. The system employed face recognition to accurately identify the driver's eye aspect ratio. An Internet of Things (IoT) module used for remote assessment of the driver's drowsiness response in real-time. If the driver is in a drowsy state, the system sends an alert/warning to the driver and

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Clerence, Angelo, Rizma Nalmi, and Prabhath Buddhika. "Real-Time Embedded System for Inattentive Driver Monitoring." In The SLIIT International Conference on Engineering and Technology 2022. Faculty of Engineering, SLIIT, 2022. http://dx.doi.org/10.54389/euah8717.

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One of the causes of motor vehicle accidents in Sri Lanka is driver inattention or drowsiness. In the field of intelligent transportation systems, continuous research and development are conducted to address this contemporary issue. Many approaches, such as driver assistance and drowsiness detection systems, have been proposed to overcome this fatality. The purpose of this research was to implement a product that can maximise road safety while improving the transport sector's efficiency and reliability of the logistics chain to reinforce the country's economic growth. In this paper, the correl

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Reports on the topic "Driver's drowsiness"

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Kulhandjian, Hovannes. Detecting Driver Drowsiness with Multi-Sensor Data Fusion Combined with Machine Learning. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.2015.

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In this research work, we develop a drowsy driver detection system through the application of visual and radar sensors combined with machine learning. The system concept was derived from the desire to achieve a high level of driver safety through the prevention of potentially fatal accidents involving drowsy drivers. According to the National Highway Traffic Safety Administration, drowsy driving resulted in 50,000 injuries across 91,000 police-reported accidents, and a death toll of nearly 800 in 2017. The objective of this research work is to provide a working prototype of Advanced Driver Ass

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Pyta, V., Bharti Gupta, Shaun Helman, Neale Kinnear, and Nathan Stuttard. Update of INDG382 to include vehicle safety technologies. TRL, 2020. http://dx.doi.org/10.58446/thco7462.

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Driving is one of the riskiest work tasks, accounting for around one third of fatal crashes in the UK. Organisations are expected to manage work-related road safety (WRRS) in the same way that they manage other health and safety risks. The Health and Safety Executive (HSE) and Department for Transport (DFT) issue joint guidance on this in INDG382 ‘Driving at work: managing work-related road safety’. HSE and DFT were seeking to update INDG382 to include reference to vehicle safety technologies that could enable employers to monitor safety related events or driver behaviours, to support learning

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