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

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Published: 4 June 2025
Last updated: 1 August 2025

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1

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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2

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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4

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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5

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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8

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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10

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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11

Skipper, Julie Hamilton. "An investigation of low-level stimulus-induced measures of driver drowsiness." Diss., Virginia Polytechnic Institute and State University, 1985. http://hdl.handle.net/10919/49799.

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Few attempts have been made to use physical and physiological driver characteristics to predict driver drowsiness. As a result, a reliable drowsy driver detection system has yet to be devised. Thus, the primary objectives of this research were to determine whether driving characteristics and response variables could be used to detect eyelid closure associated with edrowsiness, and. to provide ‘potential measures of driver· drowsiness. In. the study, eyelid closure was defined as the measurement standard of drowsiness. Eyelid closure, in studies conducted at Duke University, was a rel
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12

Hardee, Helen Lenora. "A comparison of three subsidiary tasks used as driver drowsiness countermeasures." Diss., Virginia Polytechnic Institute and State University, 1985. http://hdl.handle.net/10919/54294.

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Two previous studies performed at Virginia Tech have shown that it is feasible to detect drowsy drivers using driving performance and physiological measures. Therefore, assuming that drowsiness can be detected, it becomes important to develop methods (countermeasures) by which drivers can regain and maintain alertness. The current study was thus undertaken in an attempt to evaluate three subsidiary tasks which differed only in regard to input modality (auditory, tactual, or visual) in terms of: 1) the degree to which they aided the driver by maintaining or restoring alertness; and 2) the degre
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13

Hanowski, Richard J. "The Impact of Local/Short Haul Operations on Driver Fatigue." Diss., Virginia Tech, 2000. http://hdl.handle.net/10919/28416.

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Massie, Blower, and Campbell (1997) indicate that trucks that operate less than 50 miles from the vehicle's home base comprise 58% of the trucking industry. However, despite being the largest segment, research involving local/short haul (L/SH) operations has been scant. In fact, little is known about the general safety issues in L/SH operations. As a precursor to the present research, Hanowski, Wierwille, Gellatly, Early, and Dingus (1998) conducted a series of focus groups in which L/SH drivers provided their perspective on safety issues, including fatigue, in their industry. As a follow-up
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Wreggit, Steven S. "The development and validation of algorithms for the detection of driver drowsiness." Diss., Virginia Tech, 1994. http://hdl.handle.net/10919/39041.

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15

Toole, Laura Marie. "Crash Risk and Mobile Device Use Based on Fatigue and Drowsiness Factors in Truck Drivers." Thesis, Virginia Tech, 2001. http://hdl.handle.net/10919/47599.

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Driver distraction has become a major concern for the U.S. Department of Transportation (US DOT).  Performance decrements are typically the result of driver distraction because attentional resources are limited, which are limited; fatigue and drowsiness limit attentional resources further.  The purpose of the current research is to gain an understanding of the relationship between mobile device use (MDU), fatigue, through driving time and time on duty, and drowsiness, through time of day and amount of sleep, for commercial motor vehicle drivers.  A re-analysis of naturalistic driving data was
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Toole, Laura. "Crash Risk and Mobile Device Use Based on Fatigue and Drowsiness Factors in Truck Drivers." Thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/47599.

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Driver distraction has become a major concern for the U.S. Department of Transportation (US DOT).  Performance decrements are typically the result of driver distraction because attentional resources are limited, which are limited; fatigue and drowsiness limit attentional resources further.  The purpose of the current research is to gain an understanding of the relationship between mobile device use (MDU), fatigue, through driving time and time on duty, and drowsiness, through time of day and amount of sleep, for commercial motor vehicle drivers.  A re-analysis of naturalistic driving data was
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17

Lewin, Mark Gustav. "Simulator test and evaluation of a drowsy driver detection system and revisions to drowsiness detection algorithms." Thesis, This resource online, 1996. http://scholar.lib.vt.edu/theses/available/etd-08222008-063044/.

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18

Ebrahim, Parisa [Verfasser], and Bin [Akademischer Betreuer] Yang. "Driver drowsiness monitoring using eye movement features derived from electrooculography / Parisa Ebrahim ; Betreuer: Bin Yang." Stuttgart : Universitätsbibliothek der Universität Stuttgart, 2016. http://d-nb.info/1118370554/34.

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19

Hasan, Md Mahmudul. "Biomedical signal based drowsiness detection using machine learning: Singular and hybrid signal approaches." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/211388/1/Md%20Mahmudul_Hasan_Thesis.pdf.

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Drowsiness is one of the main contributors to road crashes. This research program examines the utility of drowsiness detection based on singular and hybrid approaches using physiological signals of EEG, EOG, and ECG. Four supervised machine learning models were developed to detect drowsiness levels, using physiological features known to be associated with drowsiness and performance impairment. The ground truth was subjective sleepiness responses while performing a repetitive reaction time task. The outcome of the study indicates that the selected features provided higher performance in the hyb
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20

Persson, Anna. "Heart rate variability for driver sleepiness assessment." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157187.

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Studies have reported that around 20 % of all traffic accidents are caused by a sleepy driver. Sleepy driving has been compared to drunk driving. A sleepy driver is also an issue in the case of automated vehicles in the future. Handing back the control of the vehicle to a sleepy driver is a serious risk. This has increased the need for a sleepiness estimation system that can be used in the car to warn the driver when driving is not recommended. One commonly used method to estimate sleepiness is to study the heart rate variability, HRV, which is said to reflect the activity of the autonomous ne
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Svensson, Ulrika. "Blinkbeteendebaserad trötthetsdetektering : metodutveckling och validering." Thesis, Linköping University, Department of Biomedical Engineering, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2578.

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<p>Electrooculogram (EOG) data was used to develop, adjust and validate a method for drowsiness detection in drivers. The drowsiness detection was based on changes in blink behaviour and classification was made on a four graded scale. The purpose was to detect early signs of drowsiness in order to warn a driver. MATLAB was used for implementation. For adjustment and validatation, two different reference measures were used; driver reported ratings of drowsiness and an electroencephalogram (EEG) based scoring scale. A correspondence of 70 % was obtained between the program and the self ratings a
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22

Belz, Steven Mark. "An On-Road Investigation of Commercial Motor Vehicle Operators and Self-Rating of Alertness and Temporal Separation as Indicators of Driver Fatigue." Diss., Virginia Tech, 2000. http://hdl.handle.net/10919/29589.

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This on-road field investigation employed, for the first time, a completely automated, trigger-based data collection system capable of evaluating driver performance in an extended duration real-world commercial motor vehicle environment. The complexities associated with the development of the system, both technological and logistical and the necessary modifications to the plan of research are presented herein This study, performed in conjunction with an on-going three year contract with the Federal Highway Administration, examined the use of self-rating of alertness and temporal separation (m
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23

Hošek, Roman. "Systém pro sledování únavy řidiče." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219790.

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This diploma thesis deals with the options of image processing on mobile platforms, especially on Android operating system, and their use in a driver drowsiness detection system. The introductory part analyses the influence of drowsiness on drivers, focusing chiefly on the microsleep, and describes the already existing driver drowsiness detection systems. The thesis proceeds by the description of possibilities of image processing on mobile platforms with the emphasis on Android operating system together with the OpenCV library, known from the desktop interface. This is followed by comparison o
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Lawoyin, Samuel. "Novel technologies for the detection and mitigation of drowsy driving." VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/3639.

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In the human control of motor vehicles, there are situations regularly encountered wherein the vehicle operator becomes drowsy and fatigued due to the influence of long work days, long driving hours, or low amounts of sleep. Although various methods are currently proposed to detect drowsiness in the operator, they are either obtrusive, expensive, or otherwise impractical. The method of drowsy driving detection through the collection of Steering Wheel Movement (SWM) signals has become an important measure as it lends itself to accurate, effective, and cost-effective drowsiness detection. In thi
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Yao, K. P., and 姚國鵬. "An In-Vehicle Vision-Based Driver's Drowsiness Detection System." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/6nstuq.

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碩士<br>國立臺灣師範大學<br>資訊工程研究所<br>96<br>Many traffic accidents have been reported due to driver’s drowsiness/fatigue. Drowsiness degrades driving performance due to the declinations of visibility, situational awareness and decision-making capability. In this study, a vision-based drowsiness detection and warning system is presented, which attempts to bring to the attention of a driver to his/her own potential drowsiness. The information provided by the system can also be utilized by adaptive systems to manage noncritical operations, such as starting a ventilator, spreading fragrance, turning on a r
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Chen, Po Chuan, and 陳柏銓. "Using Forehead-Channel Activities to Detect Driver's Drowsiness in a VR Based Driving Environment." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/78095657086703671457.

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碩士<br>國立交通大學<br>多媒體工程研究所<br>96<br>Previous studies showed that the alpha power increases in the occipital lobe highly related to human drowsiness. However, the acquisition of occipital EEG signals with the traditional electrode cap is inconvenient. Thus, the main purpose of this study was to confirm whether the forehead EEG signals could reflect the driver’s drowsiness and be able to use to estimate driver’s driving trajectory for constructing a feasible detecting system that can be applied in real life. Brain signals acquired from the occipital and the frontal lobe were analyzed and compa
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Gau, Shir-Cheng, and 高士政. "Development of Dual-Core-Processor based Real-Time Wireless Embedded Brain Signal Acquisition / Processing System and its Application on Driver's Drowsiness Estimation." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/77390002178270000538.

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碩士<br>國立交通大學<br>電機與控制工程系所<br>93<br>In this thesis, a portable Real-Time Wireless Embedded Brain Signal Acquisition / Processing System is developed. It combines electroencephalogram signal amplifier technique, wireless transimission technique, and embedded real-time system. This system is convenient for people used in daily life. The developed strategy contain three parts: First, the bluetooth protocol is used as a transmission interface and integrated with the bio-signal amplifier to transmit the measured physiological signals wirelessly. Then, the OMAP is used as a development platform and a
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Sahoo, Chandraprakash. "Driver Drowsiness Detection System." Thesis, 2016. http://ethesis.nitrkl.ac.in/8057/1/2016_BT_CSahoo_112EI0563_Driver.pdf.

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In recent years’ driver fatigue is one of the major causes of vehicle accidents in the world. A direct way of measuring driver fatigue is measuring the state of the driver i.e. drowsiness. So it is very important to detect the drowsiness of the driver to save life and property. This project is aimed towards developing a prototype of drowsiness detection system. This system is a real time system which captures image continuously and measures the state of the eye according to the specified algorithm and gives warning if required. Though there are several methods for measuring the drowsiness bu
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Huang, Chen-Yi, and 黃振彝. "Driver''s Drowsiness Detecting and Warning System." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/52186516881796403677.

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碩士<br>國立臺灣大學<br>機械工程學研究所<br>93<br>Road accidents often result in injuries and deaths, many of which are caused by dozing drivers. How to reduce victims by the accidents of this kind has become a long term issue all over the world. This work firstly gives a survey on drowsiness detecting systems, inclusive of those by the brainwave, pulsation, heartbeat, and eyelid blinking frequency, followed by focusing on the detail of eyelid blinking detection by image processing and its current development trend. This thesis then introduces an image processing system, with the application of techniques of
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Oliveira, Licínio Manuel França de. "Driver drowsiness detection using non-intrusive signal acquisition." Master's thesis, 2018. https://repositorio-aberto.up.pt/handle/10216/113802.

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31

LIN, FANG-YU, and 林芳瑜. "An EEG-based Driver Drowsiness Detection System Design." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/jv33ev.

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碩士<br>國立中正大學<br>資訊工程研究所<br>106<br>Every year tens of thousands of traffic accidents occur. Most of them are related to fatigue. Fatigue driving has always been a very serious traffic problem. In the worst case, such traffic accidents can be fatal. In order to ensure road safety and increase driving safety, we must use an effective method to detect the driver’s drowsiness level to prevent serious traffic accidents. Thus, we expect to be able to build an innovative system to solve this serious problem. In order to detect driving fatigue, we propose a drowsiness detection system which is based on
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Oliveira, Licínio Manuel França de. "Driver drowsiness detection using non-intrusive signal acquisition." Dissertação, 2018. https://repositorio-aberto.up.pt/handle/10216/113802.

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33

Parikh, Prachi. "Drowsiness detection while driving using fractal analysis and wavelet transform." 2007. http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.16757.

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34

Culp, Jonathan. "Non-intrusive driver drowsiness monitoring via artificial neural networks." 2008. http://etda.libraries.psu.edu/theses/approved/WorldWideIndex/ETD-2679/index.html.

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35

Lin, Shi-Ming, and 林士銘. "A Real-Time Driver Drowsiness Detection andAlertness Monitor System." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/13679828824797476760.

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碩士<br>國立中央大學<br>資訊工程研究所<br>95<br>Recently, the issue of driver assistance for safety becomes more attractive. In this thesis, we propose a computer vision system for monitoring the driver’s vigilance. The proposed system consists of seven parts: (1) developing an active image acquisition equipment, (2) eye detection, (3) eye tracking, (4) face detection , (5) face orientation estimation, (6) gaze estimation, (7) vigilance decision. In order to deal with various ambient light conditions, we utilize an IR camera equipped with an active IR illuminator to extract several visual cues such as close/
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Hsu, Wen-Ling, and 徐溫嶺. "Real-Time Driver Drowsiness Detection System for Intelligent Vehicles." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/10582733189205739099.

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碩士<br>國立勤益科技大學<br>電機工程系<br>98<br>Driver Mental Detection which based on image processing technique is very important in Advanced Vehicle Control and Safety Services, AVCSS. When drivers are drowsy or doze off, the system will issue warnings to remind drivers in order to avoid accidents due to drowsiness. We consider the research of this type in demand of operational efficiency and real-time system, so we use personal computer to accomplish this system in this thesis. This system is to detect driver’s facial features and then determine his physiological conditions based on eye feature in va
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Chen, Chia-Lin, and 陳佳鈴. "The research of the brain networks in different drowsiness stages of drivers." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/78159320653796569016.

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碩士<br>國立交通大學<br>多媒體工程研究所<br>99<br>Driver drowsiness was generally regarded as a main reason of causing car accidents. Our team had investigated EEG signals in drowsy state of driver, designed the algorithm that detecting drowsiness by EEG signals, and developed the wireless and portable application of detecting drowsiness for drivers. Although we already had the good indicators of detecting drowsiness by EEG signals, detecting the levels of drowsiness remained unsolved nowadays. The aim of this study is to explore the changes of brain signal transferring network from alertness to drowsiness an
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Chang, Kuang-Siyong, and 張光雄. "A Driver Drowsiness Detection Based on An Active IR illumination." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/65289931355882648513.

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碩士<br>國立中央大學<br>資訊工程研究所<br>93<br>An active computer vision system is proposed to extract various visual cues for drowsiness detection of drivers. The visual cues include eye close/ open, eye blinking, eyelid movement, and face direction. The proposed system consists of four parts: an active image acquisition equipment, eye detector, eye tracker, and visual cue extractor. For working in various ambient light conditions, we used an IR camera equipped with a blinking IR illuminator to acquire deriver’s pupils and face for detecting and tracking eyes. The bright and dark pupil images acquired by t
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Ribeiro, António Miguel Fernandes. "Brain machine interface in automotive: drowsiness detection." Master's thesis, 2016. http://hdl.handle.net/1822/46591.

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Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e Computadores<br>Com o crescimento progressivo da indústria automóvel, assim como o grande número de mortes que ocorrem nas estradas em todo o mundo, existe uma necessidade de monitorizar a atenção do condutor de forma a melhorar estas estatísticas. Mais de 1,25 milhões de pessoas morrem nas estradas, por ano, em todo o mundo como consequência de acidentes de tráfego, sendo que 20% dessas fatalidades são causadas por fadiga do condutor. Sendo esta uma das mais proeminentes causas destes números, uma BrainMachine Inte
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Fernandes, Margarida João Castro Neves. "Driver Drowsiness Detection Using Non-Intrusive Eletrocardiogram and Steering Wheel Angle Signals." Master's thesis, 2019. https://hdl.handle.net/10216/122936.

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Fernandes, Margarida João Castro Neves. "Driver Drowsiness Detection Using Non-Intrusive Eletrocardiogram and Steering Wheel Angle Signals." Dissertação, 2019. https://hdl.handle.net/10216/122936.

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Lin, Cyun-Yi, and 林群益. "Machine Learning and Gradient Statistics Based Real-Time Driver Drowsiness and Behavior Detection." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/45018109574447682059.

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碩士<br>國立中興大學<br>電機工程學系所<br>105<br>For the past few years, many accidents caused by the fatigue driving have occurred frequently. Therefore, many researchers and experts all over the world have paid great efforts in this issue. For the fatigue detection issue, the driver’s spirit status can be evaluated through the eye blinking condition. In this thesis, by recognizing the accurate eye position, the eye detection methodology is proposed to enhance the accuracy of fatigue detections. The proposed system includes four parts, which are the face detection, the eye-glasses bridge detection, the eye
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Lal, Saroj Kusum Lata. "The psychophysiology of driver fatigue/drowsiness : electroencephalography, electro-oculogram, electrocardiogram and psychological effects." 2001. http://hdl.handle.net/2100/1031.

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University of Technology, Sydney. Faculty of Science.<br>Driver fatigue is a major cause of road accidents and has implications for road safety. Investigating the psychophysiological links to fatigue can enhance our understanding and management of fatigue in the transport industry. A variety of psychophysiological parameters have been identified as indicators of fatigue, with electroencephalography (EEG) perhaps being the most promising. Therefore, monitoring EEG during driver fatigue may be a promising variable for use in fatigue countermeasure devices. However, most previous fatigue-based st
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Ying-FanLin and 林映帆. "Real-time Driver Drowsiness Detection System Based on PERCLOS and Grayscale Image Processing." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/76964000392371475381.

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碩士<br>國立成功大學<br>工程科學系碩博士班<br>101<br>Fatigue reduces a driver’s attention, especially when driving long distances or at night, when reaction ability declines. The fatigue effect is a common, yet dangerous, driving experience that may even include a few seconds of shallow sleep. In response to this problem, several automobile plants have begun installing onboard computers in their cars featuring a driver drowsiness detection system. However, many of these products require physical contact, which is inconvenient for drivers and easily forgotten. Further some products currently on the market actua
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Markoni, Herleeyandi, and 呂小龍. "Driver Drowsiness Detection Using Hybrid Convolutional Neural Network and Long Short-Term Memory." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/qgmqn4.

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碩士<br>國立臺灣科技大學<br>電機工程系<br>106<br>Drowsiness and fatigue of the drivers are amongst the significant causes of the accident. Every year they increase the number of deaths and fatalities to the human population. To prevent the impact that caused by this problem, the driver drowsiness system is proposed and examined in this study. The challenge of this problem is the variation of the human face, the accuracy of the system which respected to the time that needed by the system to analyze with the real-time requirement. The first challenge pertaining the facial variation has been handled well using
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Hsieh, Hung-Yi, and 謝弘義. "Development of Wireless Brain Computer Interface with Embedded Multi-task Scheduling and its Application on Real-time Driver’s Drowsiness Detection and Warning." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/75843462213122318903.

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碩士<br>國立交通大學<br>電機與控制工程系所<br>94<br>Recently, many traffic accidents on the highway are caused by the drivers’ drowsiness. If the driver is drowsy, there are some features in EEG signals. We can use these features to estimate the driver’s drowsiness. But the past bio-signal monitor system usually can only record the signals and is unable of real-time signal processing. We propose the Brain Computer Interface (BCI) system in this thesis, which can process bio-signal real-time, and we apply it to detect the driver’s drowsiness and warn the driver when the driver’s drowsiness occurs. The goal of t
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Soares, Rui Emanuel Paixão. "Driver monitoring systems of fatigue based on eye tracking." Master's thesis, 2017. http://hdl.handle.net/1822/54750.

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Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e Computadores<br>Nowadays, road deaths as well as the injuries and monetary losses has become a global crisis. One of the main causes of road accidents is related to driver fatigue caused by sleep deprivation or disorders, being present in about 20% of accidents. Therefore, there is a growing interest in developing equipments capable to detect driver’s drowsiness to avoid potential accidents. In order to detect driver’s drowsiness, several private and public entities from around the world have been working on different
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