Relevant bibliographies by topics / Driving drowsiness

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Driving drowsiness'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Driving drowsiness"

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Sun, Yifan, Chaozhong Wu, Hui Zhang, Yijun Zhang, Shaopeng Li, and Hongxia Feng. "Extraction of Optimal Measurements for Drowsy Driving Detection considering Driver Fingerprinting Differences." Journal of Advanced Transportation 2021 (August 31, 2021): 1–17. http://dx.doi.org/10.1155/2021/5546127.

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Contributions of measurements for detecting drowsy driving are determined by calculation parameters, which are directly related to the accuracy of drowsiness detection. The previous studies utilized the same Unified Calculation Parameters (UCPs) to compute each driver’s measurements. However, since each driver has unique driving behavior characteristics, namely, driver fingerprinting, Individual Drivers’ Best Calculation Parameters (IDBCPs) making measurements more discriminative for drowsiness are various. Regardless of the difference in driver fingerprinting among the drivers being tested, using UCPs instead of IDBCPs to compute measurements will limit the drowsiness-detection performance of the measurements and reduce drowsiness-detection accuracies at the individual driver level. Thus, this paper proposed a model to optimize calculation parameters of individual driver’s measurements and to extract individual driver’s measurements that effectively distinguish drowsy driving. Through real vehicle experiments, we collected naturalistic driving data and subjective drowsy levels evaluated by the Karolinska Sleepiness Scale. Eight nonintrusive drowsiness-related measurements were calculated by double-layer sliding time windows. In the proposed model, we firstly applied the Wilcoxon test to analyze differences between measurements of the awake state and drowsy state, and constructed the fitness function reflecting the relationship between the calculation parameters and measurement’s drowsiness-detection performance. Secondly, the genetic algorithms were used to optimize fitness functions to obtain measured IDBCPs. Finally, we selected measurements calculated by IDBCPs that can distinguish drowsy driving to constitute individual drivers’ optimal drowsiness-detection measurement set. To verify the advantages of IDBCPs, the measurements calculated by UCPs and IDBCPs were, respectively, used to build driver-specific drowsiness-detection models: DF_U and DF_I based on the Fisher discriminant algorithm. The mean drowsiness-detection accuracies of DF_U and DF_I were, respectively, 85.25% and 91.06%. It indicated that IDBCPs could enhance measurements’ drowsiness-detection performance and improve the drowsiness-detection accuracies. This paper contributed to the establishment of personalized drowsiness-detection models considering driver fingerprinting differences.
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Rumagit, Arthur Mourits, Izzat Aulia Akbar, Mitaku Utsunomiya, Takamasa Morie, and Tomohiko Igasaki. "Gazing as actual parameter for drowsiness assessment in driving simulators." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 1 (January 1, 2019): 170. http://dx.doi.org/10.11591/ijeecs.v13.i1.pp170-178.

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Many traffic accidents are due to drowsy driving. However, to date, only a few studies have been conducted on the gazing properties related to drowsiness. This study was conducted with the objective of estimating the relationship between gazing properties and drowsiness in three facial expression evaluation (FEE) categories: alert (FEE = 0), lightly drowsy (FEE = 1−2), heavily drowsy (FEE = 3−4). Drowsiness was investigated based on these eye-gazing properties by analyzing the gazing signal utilizing an eye gaze tracker and FEE in a driving simulator environment. The results obtained indicate that gazing properties have significant differences among the three drowsiness conditions, with p < 0.001 in a Kruskal–Wallis test. Furthermore, the overall classification accuracy of the three drowsiness conditions based on gazing properties using a support vector machine was 76.3%. This indicates that our proposed gazing properties can be used to quantitatively assess drowsiness.
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Barr, Lawrence C., C. Y. David Yang, Richard J. Hanowski, and Rebecca Olson. "Assessment of Driver Fatigue, Distraction, and Performance in a Naturalistic Setting." Transportation Research Record: Journal of the Transportation Research Board 1937, no. 1 (January 2005): 51–60. http://dx.doi.org/10.1177/0361198105193700108.

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The results of a study to characterize episodes of driver fatigue and drowsiness and to assess the impact of driver fatigue on driving performance are documented. This data-mining effort performed additional analyses on data collected in an earlier study by the Federal Motor Carrier Safety Administration of the effects of fatigue on drivers in local and short-haul operations. The primary objectives of the study were to investigate fatigue as a naturally occurring phenomenon by identifying and characterizing episodes of drowsiness during all periods of driving and to determine the operational or driving environment factors associated with drowsy driving. A total of 2,745 drowsy events were identified in approximately 900 total hours of naturalistic driving video data. Higher levels of fatigue were associated with younger and less experienced drivers. In addition, a strong and consistent relationship was found between drowsiness and time of day. Drowsiness was twice as likely to occur between 6:00 a.m. and 9:00 a.m., and approximately 30% of all observed incidences of drowsiness occurred within the first hour of the work shift. Insights about the relationship between driver fatigue and driver distraction and inattention are provided. This study presents an analytical framework for quantitatively assessing driver fatigue and drowsiness as a function of driver characteristics and the driving environment. It is hoped that the results will help to identify effective countermeasures for drowsy driving that will reduce the number of commercial-vehicle-related fatalities and injuries.
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Soares, Sónia, Tiago Monteiro, António Lobo, António Couto, Liliana Cunha, and Sara Ferreira. "Analyzing Driver Drowsiness: From Causes to Effects." Sustainability 12, no. 5 (March 5, 2020): 1971. http://dx.doi.org/10.3390/su12051971.

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Drowsiness and fatigue are major safety issues that cannot be measured directly. Their measurements are sustained on indirect parameters such as the effects on driving performance, changes in physiological states, and subjective measures. We divided this study into two distinct lines. First, we wanted to find if any driver’s physiological characteristic, habit, or recent event could interfere with the results. Second, we aimed to analyze the effects of subjective sleepiness on driving behavior. On driving simulator experiments, the driver information and driving performance were collected, and responses to the Karolinska Sleepiness Scale (KSS) were compared with these parameters. The results showed that drowsiness increases when the driver has suffered a recent stress situation, has taken medication, or has slept fewer hours. An increasing driving time is also a strong factor in drowsiness development. On the other hand, robustness, smoking habits, being older, and being a man were revealed to be factors that make the participant less prone to getting drowsy. From another point of view, the speed and lane departures increased with the sleepiness feeling. Subjective drowsiness has a great correlation to drivers’ personal aspects and the driving behavior. In addition, the KSS shows a great potential to be used as a predictor of drowsiness.
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Jang, Seok-Woo, and Byeongtae Ahn. "Implementation of Detection System for Drowsy Driving Prevention Using Image Recognition and IoT." Sustainability 12, no. 7 (April 10, 2020): 3037. http://dx.doi.org/10.3390/su12073037.

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In recent years, the casualties of traffic accidents caused by driving cars have been gradually increasing. In particular, there are more serious injuries and deaths than minor injuries, and the damage due to major accidents is increasing. In particular, heavy cargo trucks and high-speed bus accidents that occur during driving in the middle of the night have emerged as serious social problems. Therefore, in this study, a drowsiness prevention system was developed to prevent large-scale disasters caused by traffic accidents. In this study, machine learning was applied to predict drowsiness and improve drowsiness prediction using facial recognition technology and eye-blink recognition technology. Additionally, a CO2 sensor chip was used to detect additional drowsiness. Speech recognition technology can also be used to apply Speech to Text (STT), allowing a driver to request their desired music or make a call to avoid drowsiness while driving.
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Satti, Afraiz Tariq, Jiyoun Kim, Eunsurk Yi, Hwi-young Cho, and Sungbo Cho. "Microneedle Array Electrode-Based Wearable EMG System for Detection of Driver Drowsiness through Steering Wheel Grip." Sensors 21, no. 15 (July 27, 2021): 5091. http://dx.doi.org/10.3390/s21155091.

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Driver drowsiness is a major cause of fatal accidents throughout the world. Recently, some studies have investigated steering wheel grip force-based alternative methods for detecting driver drowsiness. In this study, a driver drowsiness detection system was developed by investigating the electromyography (EMG) signal of the muscles involved in steering wheel grip during driving. The EMG signal was measured from the forearm position of the driver during a one-hour interactive driving task. Additionally, the participant’s drowsiness level was also measured to investigate the relationship between muscle activity and driver’s drowsiness level. Frequency domain analysis was performed using the short-time Fourier transform (STFT) and spectrogram to assess the frequency response of the resultant signal. An EMG signal magnitude-based driver drowsiness detection and alertness algorithm is also proposed. The algorithm detects weak muscle activity by detecting the fall in EMG signal magnitude due to an increase in driver drowsiness. The previously presented microneedle electrode (MNE) was used to acquire the EMG signal and compared with the signal obtained using silver-silver chloride (Ag/AgCl) wet electrodes. The results indicated that during the driving task, participants’ drowsiness level increased while the activity of the muscles involved in steering wheel grip decreased concurrently over time. Frequency domain analysis showed that the frequency components shifted from the high to low-frequency spectrum during the one-hour driving task. The proposed algorithm showed good performance for the detection of low muscle activity in real time. MNE showed highly comparable results with dry Ag/AgCl electrodes, which confirm its use for EMG signal monitoring. The overall results indicate that the presented method has good potential to be used as a driver’s drowsiness detection and alertness system.
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Kozak, Ksenia, Reates Curry, Jeff Greenberg, Bruce Artz, Mike Blommer, and Larry Cathey. "Leading Indicators of Drowsiness in Simulated Driving." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 49, no. 22 (September 2005): 1917–21. http://dx.doi.org/10.1177/154193120504902207.

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Drowsiness while driving was measured using three measures: a physiological measure of eye closure, a sustained reaction time task and a subjective assessment. The study was conducted in Ford's VIRTTEX driving simulator. Thirty-two adults who were sleep deprived for 24 hours and six adults who had a full night of sleep participated in the study. The performance of the sleep-deprived group was compared with that of the control group. Sleep-deprived drivers had significantly longer PVT reaction times, a greater number of lapses, higher PERCLOS values and perceived themselves as sleepier than did the control group. This study demonstrated the ability to successfully implement drowsiness measures in a driving simulator. The advantage of a three-hour simulator drive in providing increasing levels of drowsiness in each subject was established. These findings provide metrics that can be used to evaluate the efficacy and acceptability of safety systems for drowsy drivers.
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Caponecchia, Carlo, and Ann Williamson. "Drowsiness and driving performance on commuter trips." Journal of Safety Research 66 (September 2018): 179–86. http://dx.doi.org/10.1016/j.jsr.2018.07.003.

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Shekari Soleimanloo, S., T. L. Sletten, A. Clark, J. M. Cori, A. P. Wolkow, C. Beatty, B. Shiferaw, et al. "0286 Schedule Characteristics of Heavy Vehicle Drivers are Associated with Eye-Blink Indicators of Real-Time Drowsiness on the Road." Sleep 43, Supplement_1 (April 2020): A108—A109. http://dx.doi.org/10.1093/sleep/zsaa056.284.

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Abstract Introduction While up to 52% of heavy vehicle crashes are drowsiness-related, the contributions of schedule factors to real-time objective drowsiness in heavy vehicle drivers (HVDs) have not been studied. Eye-blink parameters are a reliable indicator of driver drowsiness. This study aimed to examine the relationship between work-related factors and objective drowsiness in HVDs. Methods HVDs (all males, aged 49.5 ± 8 years) undertook 5- weeks of sleep-wake monitoring (Philips Actiwatch, N=15), and 4-weeks of infrared oculography (Optalert, Melbourne, Australia) to monitor their eye-blink parameters (averaged each minute) while driving their own vehicle (N=12). Participants slept for 5.75± 1.4 hours before the drives. Drowsiness events were defined as any Johns Drowsiness Scores (JDS) scores larger than 2.6 based on prior research. The relationships of schedule factors and drowsiness events per hour were assessed via mixed linear regression models. Results Drowsiness event rates were 3–5 times greater between 22:00 and 03:00 hours compared to between 16:00 and 17:00 hours (17- 25 events/h vs 5 events/h, P= 0.0001 to 0.007). The frequency of drowsiness events at night varied with shift start time and time into shift (P= 0.0001 to 0.001). Compared to the first hour of driving, drowsiness event rates rose significantly during the 13th to the 21st hours into the shift (13- 59 events/h vs 5.5 events/h, P= 0.0001 to 0.007). During sequential night shifts drowsiness events were 1.8 times more common compared to 1–3 sequential day shifts (9 events/h vs 5 events/h, P= 0.012 to 0.019). Conclusion Driving at night, for more than 12 hours and sequential night shifts increase real-time drowsiness in HVDs, with these factors interacting resulting in even higher rates of drowsiness events. Longitudinal studies in larger populations will further define how these factors interact to inform the work scheduling of HVDs to reduce the risk of drowsiness. Support This research was supported by the CRC for Alertness, Safety and Productivity.
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Mahmoodi, Mohammad, and Ali Nahvi. "Driver drowsiness detection based on classification of surface electromyography features in a driving simulator." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 233, no. 4 (March 1, 2019): 395–406. http://dx.doi.org/10.1177/0954411919831313.

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Driver drowsiness is a significant cause of fatal crashes every year in the world. In this research, driver’s drowsiness is detected by classifying surface electromyography signal features. The tests are conducted on 13 healthy subjects in a driving simulator with a monotonous route. The surface electromyography signal from the upper arm and shoulder muscles are measured including mid deltoid, clavicular portion of the pectoralis major, and triceps and biceps long heads. Signals are separated into 30-s epochs. Five features including range, variance, relative spectral power, kurtosis, and shape factor are extracted. The Observer Rating of Drowsiness evaluates the level of drowsiness. A binormal function is fitted for each feature. For classification, six classifiers are applied. The results show that the k-nearest neighbor classifier predicts drowsiness by 90% accuracy, 82% precision, 77% sensitivity, and 92% specificity.
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Dissertations / Theses on the topic "Driving drowsiness"

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Wang, Ivy, and Sebastian Lindberg. "Detecting Drowsiness in Driving Using EEG Sensors." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-200520.

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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 reliable measure of drowsiness. A computer simulated nighttime driving task introduced 90 minutes of typical highway driving to twenty driver/subjects seated ixx a moving-base driving simulator. Each driver/subject drove under two conditions--rested and after 19 hours of being awake. During the 90 minutes of driving, two types of low—level stimuli, steering wheel torque and front wheel displacement, were applied to the simulation. Responses to these stimuli as well as driving I measures from the intervals between stimuli were analyzed for variations associated with eyelid closure. Seventeen dependent variables were investigated.
Ph. D.
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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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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 drowsiness detection and tracking, which is based on the incorporation of information from a road vision system and vehicle performance parameters. Refinement of the algorithm is more precisely detected the level of drowsiness by the implementation of a support vector machine classification for robust and accurate drowsiness warning system. The Support Vector Machine (SVM) classification technique diminished drowsiness level by using non intrusive systems, using standard equipment sensors, aim to reduce these road accidents caused by drowsiness drivers. This detection system provides a non-contact technique for judging various levels of driver alertness and facilitates early detection of a decline in alertness during driving. The presented results are based on a selection of drowsiness database, which covers almost 60 hours of driving data collection measurements. All the parameters extracted from vehicle parameter data are collected in a driving simulator. With all the features from a real vehicle, a SVM drowsiness detection model is constructed. After several improvements, the classification results showed a very good indication of drowsiness by using those systems.
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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 degree to which the responses to these tasks could be used to detect drowsiness. Subjective measures of drowsiness were also obtained to provide an additional source of verification of level of drowsiness. To accomplish these objectives, a total of 12 male and female driver-subjects drove a moving-base simulator continuously from 12:30 a.m. to 3:00 a.m. During this time, the subjects performed each of the subsidiary tasks for a 30-minute period; they also drove for a 30-minute period during which no subsidiary task was performed. During the simulated, nighttime, highway driving scenario, 20 driving performance, behavioral, and physiological measures were collected for each 3-minute driving interval, along with 5 subsidiary task measures and subjective alertness ratings. The experimental results indicated that none of the three subsidiary tasks provided an effective means of maintaining driver alertness. However, the results of a second series of discriminant analyses did indicate that driver impairment due to drowsiness could be reliably detected with linear combinations of subsidiary task and driving measures. In fact, promising discriminant models for the auditory and visual tasks were identified which employed a subsidiary task response measure of the number of correct responses to the subsidiary task during each 6-minute driving interval as well as a physiological measure of the subject's heart rate variance; these models showed overall classification error percentages as low as 3% and 8%. Finally, the analyses of the subjective alertness ratings indicated that subjects' ratings were not significantly affected by either the type of subsidiary task performed or time-on-task.
Ph. D.
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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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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 used to obtain information about the factors, MDU, safety-critical events (SCE), and normal driving epochs.  Odds ratios were used to calculate SCE risk for 6 mobile device use subtasks and each of the factors, which were divided into smaller bins of hours for more specific information.  A generalized linear mixed model and chi-square test were used to assess MDU for each factor and the associated bins.  Results indicated visually demanding subtasks were associated with an increase in SCE risk, but conversation on a hands-free cell phone decreased SCE risk.  There was an increase in SCE risk for visual manual subtasks for all bins in which analyses were possible.  Drivers had a higher proportion of MDU in the early morning (circadian low period) than all other times of day that were analyzed.  These results will be used to create recommended training and evaluate policy and technology and will help explain the relationship between MDU, fatigue, and drowsiness.
Master of Science
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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 used to obtain information about the factors, MDU, safety-critical events (SCE), and normal driving epochs.  Odds ratios were used to calculate SCE risk for 6 mobile device use subtasks and each of the factors, which were divided into smaller bins of hours for more specific information.  A generalized linear mixed model and chi-square test were used to assess MDU for each factor and the associated bins.  Results indicated visually demanding subtasks were associated with an increase in SCE risk, but conversation on a hands-free cell phone decreased SCE risk.  There was an increase in SCE risk for visual manual subtasks for all bins in which analyses were possible.  Drivers had a higher proportion of MDU in the early morning (circadian low period) than all other times of day that were analyzed.  These results will be used to create recommended training and evaluate policy and technology and will help explain the relationship between MDU, fatigue, and drowsiness.
Master of Science
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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 this dissertation, novel technologies for drowsiness detection using Inertial Measurement Units (IMUs) are investigated and described. IMUs are an umbrella group of kinetic sensors (including accelerometers and gyroscopes) which transduce physical motions into data. Driving performances were recorded using IMUs as the primary sensors, and the resulting data were used by artificial intelligence algorithms, specifically Support Vector Machines (SVMs) to determine whether or not the individual was still fit to operate a motor vehicle. Results demonstrated high accuracy of the method in classifying drowsiness. It was also shown that the use of a smartphone-based approach to IMU monitoring of drowsiness will result in the initiation of feedback mechanisms upon a positive detection of drowsiness. These feedback mechanisms are intended to notify the driver of their drowsy state, and to dissuade further driving which could lead to crashes and/or fatalities. The novel methods not only demonstrated the ability to qualitatively determine a drivers drowsy state, but they were also low-cost, easy to implement, and unobtrusive to drivers. The efficacy, ease of use, and ease of access to these methods could potentially eliminate many barriers to the implementation of the technologies. Ultimately, it is hoped that these findings will help enhance traveler safety and prevent deaths and injuries to users.
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Ndaki, Ntombikayise. "Investigation of the effect of short duration breaks in delaying the onset of performance related fatigue during long distance monotonous driving at different times of the day." Thesis, Rhodes University, 2012. http://hdl.handle.net/10962/d1016353.

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Road traffic accidents are a serious burden to the health systems of many countries especially in South Africa. Research aimed at reducing traffic related accidents is of importance as traffic crashes are rated as the second leading cause of fatalities in South Africa and ninth in the world. Despite the extensive efforts into research and development of new technology, driver fatigue still remains a cause of vehicle accidents worldwide. Fatigue plays a role in up to 20% of vehicle accidents with many being serious or fatal. Numerous coping behaviours are employed by drivers to counteract the negative effects of fatigue. The most common coping behaviours include taking short naps, talking to passengers, listening to the radio, opening windows and drinking stimulants. Driving breaks have long been identified as an effective countermeasure against fatigue. Most research done in driving breaks has investigated the duration of the breaks, activity undertaken during the break and the frequency of the breaks taken outside the vehicle. However limited literature is available on the effectiveness of breaks in counteracting the effects of fatigue. The objective of the current study was aimed at assessing whether short duration breaks are an effective countermeasure against fatigue. Physiological, neurophysiological, subjective and performance measures were used as indicators for fatigue. Additional focus of the research was determining whether breaks were more or less effective at counteracting the effects of fatigue at different times of day. Twelve participants were recruited for the study, six males and six females. The participants were required to perform a driving task on a simulator for 90 minutes. The study consisted of four independent conditions, namely driving during the day with breaks, driving during the day without breaks, driving during the night with breaks and driving during the night without breaks. The without breaks conditions were similar except that they occurred at different times of the day, one session at night and the other session during day time, as was the case for the conditions with breaks. The driving task used in the current study was a low fidelity simulator tracking task. The participants were required to follow a centre line displayed on a tracking path as accurately as possible. The measurements that were recorded in this study included physiological, performance, subjective and neurophysiological. Physiological measures included heart rate and heart rate variability (frequency domain) and core body temperature. The ascending threshold of the critical flicker fusion frequency was the only neurophysiological measurement included in the current investigation. Performance was quantified by mean deviation from a centre line participants were meant to track. Two rating scales were used: Karolinska sleepiness scale and the Wits sleepiness scale were used for the measurement of subjective sleepiness. Heart rate, heart rate variability and mean deviation were measured continuously throughout the 90 minute driving task. Critical flicker fusion frequency, temperature and the subjective scales were measured before and after the 90 minute driving task. The results indicated that the short duration breaks during day time had a positive effect on driving performance; however the breaks at night had a negative effect on driving performance. Heart rate was higher during the day compared to night time and the heart rate variability high frequency spectrum values were lower during the day condition, to show the activation of the sympathetic nervous system which is characteristic of day time. The night conditions had lower heart rate values and higher heart rate variability high frequency values, which show the activation of the parasympathetic nervous system which is dominant during periods of fatigue and night time. Subjective sleepiness levels were also higher at night compared to day time.
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Book chapters on the topic "Driving drowsiness"

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Nair, Vivek, and Nadir Charniya. "Drunk Driving and Drowsiness Detection Alert System." In Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB), 1191–207. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-00665-5_113.

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Fu, Jia-Wei, Mu Li, and Bao-Liang Lu*. "Detecting Drowsiness in Driving Simulation Based on EEG." In Autonomous Systems – Self-Organization, Management, and Control, 21–28. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-8889-6_3.

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Poorna, S. S., V. V. Arsha, P. T. A. Aparna, Parvathy Gopal, and G. J. Nair. "Drowsiness Detection for Safe Driving Using PCA EEG Signals." In Advances in Intelligent Systems and Computing, 419–28. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7871-2_40.

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Weinbeer, Veronika, Tobias Muhr, and Klaus Bengler. "Automated Driving: The Potential of Non-driving-Related Tasks to Manage Driver Drowsiness." In Advances in Intelligent Systems and Computing, 179–88. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96074-6_19.

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Yadav, Deepanshu, Divya Mohan, and Amrita Jyoti. "Comparison of Computer Vision Techniques for Drowsiness Detection While Driving." In Data Intelligence and Cognitive Informatics, 651–62. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8530-2_51.

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Yadav, Deepanshu, Divya Mohan, and Amrita Jyoti. "Comparison of Computer Vision Techniques for Drowsiness Detection While Driving." In Data Intelligence and Cognitive Informatics, 651–62. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8530-2_51.

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Wu, Yanbin, Ken Kihara, Yuji Takeda, Toshihisa Sato, Motoyuki Akamatsu, and Satoshi Kitazaki. "The Relationship Between Drowsiness Level and Takeover Performance in Automated Driving." In HCI in Mobility, Transport, and Automotive Systems. Driving Behavior, Urban and Smart Mobility, 125–42. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50537-0_11.

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Sato, Toshihisa, Yuji Takeda, Motoyuki Akamatsu, and Satoshi Kitazaki. "Evaluation of Driver Drowsiness While Using Automated Driving Systems on Driving Simulator, Test Course and Public Roads." In HCI in Mobility, Transport, and Automotive Systems. Driving Behavior, Urban and Smart Mobility, 72–85. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50537-0_7.

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Basori, Ahmad Hoirul, and Sharaf J. Malebary. "iDriveAR: In-Vehicle Driver Awareness and Drowsiness Framework Based on Facial Tracking and Augmented Reality." In Internet of Vehicles and its Applications in Autonomous Driving, 93–103. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46335-9_7.

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Khan, Rayyan A., Noman Naseer, and Muhammad J. Khan. "Drowsiness Detection During a Driving Task Using fNIRS." In Neuroergonomics, 79–85. Elsevier, 2019. http://dx.doi.org/10.1016/b978-0-12-811926-6.00013-0.

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Conference papers on the topic "Driving drowsiness"

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Rimini-Doering, Maria, Dietrich Manstetten, Tobias Altmueller, Ulrich Ladstaetter, and Michael Mahler. "Monitoring Driver Drowsiness and Stress in a Driving Simulator." In Driving Assessment Conference. Iowa City, Iowa: University of Iowa, 2001. http://dx.doi.org/10.17077/drivingassessment.1009.

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Charniya, Nadir N., and Vivek R. Nair. "Drunk driving and drowsiness detection." In 2017 International Conference on Intelligent Computing and Control (I2C2). IEEE, 2017. http://dx.doi.org/10.1109/i2c2.2017.8321811.

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Golz, M., D. Sommer, U. Trutschel, J. Krajewski, and B. Sirois. "Driver Drowsiness Immediately before Crashes – A Comparative Investigation of EEG Pattern Recognition." In Driving Assessment Conference. Iowa City, Iowa: University of Iowa, 2013. http://dx.doi.org/10.17077/drivingassessment.1535.

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Noori, Seyed Mohammad Reza, and Mohammad Mikaeili. "Detecting driving drowsiness using EEG, EOG and driving quality signals." In 2015 22nd Iranian Conference on Biomedical Engineering (ICBME). IEEE, 2015. http://dx.doi.org/10.1109/icbme.2015.7404144.

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Kundinger, Thomas, Andreas Riener, Nikoletta Sofra, and Klemens Weigl. "Driver drowsiness in automated and manual driving." In IUI '20: 25th International Conference on Intelligent User Interfaces. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3377325.3377506.

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Horrey, William J., Yulan Liang, Michael L. Lee, Mark E. Howard, Clare Anderson, Michael S. Shreeve, Conor O'Brien, and Charles A. Czeisler. "The Long Road Home: Driving Performance and Ocular Measurements of Drowsiness Following Night Shift-Work." In Driving Assessment Conference. Iowa City, Iowa: University of Iowa, 2013. http://dx.doi.org/10.17077/drivingassessment.1497.

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Tasaki, Masaru, Motoaki Sakai, Mai Watanabe, Hui Wang, and Daming Wei. "Evaluation of Drowsiness During Driving using Electrocardiogram - A Driving Simulation Study." In 2010 IEEE 10th International Conference on Computer and Information Technology (CIT). IEEE, 2010. http://dx.doi.org/10.1109/cit.2010.264.

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Jiang, Derong, and Jianfeng Hu. "Research of Drowsiness in Driving Based on EEG." In 2010 Third International Symposiums on Electronic Commerce and Security (ISECS). IEEE, 2010. http://dx.doi.org/10.1109/isecs.2010.79.

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Garcia, I., S. Bronte, L. M. Bergasa, J. Almazan, and J. Yebes. "Vision-based drowsiness detector for real driving conditions." In 2012 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2012. http://dx.doi.org/10.1109/ivs.2012.6232222.

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Akbar, Izzat Aulia, Tomohiko Igasaki, Nobuki Murayama, and Zhencheng Hu. "Drowsiness assessment using electroencephalogram in driving simulator environment." In 2015 8th International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2015. http://dx.doi.org/10.1109/bmei.2015.7401497.

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