Journal articles: '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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Lee, Michael L., Mark E. Howard, William J. Horrey, Yulan Liang, Clare Anderson, Michael S. Shreeve, Conor S. O’Brien, and Charles A. Czeisler. "High risk of near-crash driving events following night-shift work." Proceedings of the National Academy of Sciences 113, no. 1 (December 22, 2015): 176–81. http://dx.doi.org/10.1073/pnas.1510383112.

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Night-shift workers are at high risk of drowsiness-related motor vehicle crashes as a result of circadian disruption and sleep restriction. However, the impact of actual night-shift work on measures of drowsiness and driving performance while operating a real motor vehicle remains unknown. Sixteen night-shift workers completed two 2-h daytime driving sessions on a closed driving track at the Liberty Mutual Research Institute for Safety: (i) a postsleep baseline driving session after an average of 7.6 ± 2.4 h sleep the previous night with no night-shift work, and (ii) a postnight-shift driving session following night-shift work. Physiological measures of drowsiness were collected, including infrared reflectance oculography, electroencephalography, and electrooculography. Driving performance measures included lane excursions, near-crash events, and drives terminated because of failure to maintain control of the vehicle. Eleven near-crashes occurred in 6 of 16 postnight-shift drives (37.5%), and 7 of 16 postnight-shift drives (43.8%) were terminated early for safety reasons, compared with zero near-crashes or early drive terminations during 16 postsleep drives (Fishers exact:P= 0.0088 andP= 0.0034, respectively). Participants had a significantly higher rate of lane excursions, average Johns Drowsiness Scale, blink duration, and number of slow eye movements during postnight-shift drives compared with postsleep drives (3.09/min vs. 1.49/min; 1.71 vs. 0.97; 125 ms vs. 100 ms; 35.8 vs. 19.1; respectively,P< 0.05 for all). Night-shift work increases driver drowsiness, degrading driving performance and increasing the risk of near-crash drive events. With more than 9.5 million Americans working overnight or rotating shifts and one-third of United States commutes exceeding 30 min, these results have implications for traffic and occupational safety.

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Ma, Yuliang, Songjie Zhang, Donglian Qi, Zhizeng Luo, Rihui Li, Thomas Potter, and Yingchun Zhang. "Driving Drowsiness Detection with EEG Using a Modified Hierarchical Extreme Learning Machine Algorithm with Particle Swarm Optimization: A Pilot Study." Electronics 9, no. 5 (May 8, 2020): 775. http://dx.doi.org/10.3390/electronics9050775.

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Driving fatigue accounts for a large number of traffic accidents in modern life nowadays. It is therefore of great importance to reduce this risky factor by detecting the driver’s drowsiness condition. This study aimed to detect drivers’ drowsiness using an advanced electroencephalography (EEG)-based classification technique. We first collected EEG data from six healthy adults under two different awareness conditions (wakefulness and drowsiness) in a virtual driving experiment. Five different machine learning techniques, including the K-nearest neighbor (KNN), support vector machine (SVM), extreme learning machine (ELM), hierarchical extreme learning machine (H-ELM), and the proposed modified hierarchical extreme learning machine algorithm with particle swarm optimization (PSO-H-ELM), were applied to classify the subject’s drowsiness based on the power spectral density (PSD) feature extracted from the EEG data. The mean accuracies of the five classifiers were 79.31%, 79.31%, 74.08%, 81.67%, and 83.12%, respectively, demonstrating the superior performance of our new PSO-H-ELM algorithm in detecting drivers’ drowsiness, compared to the other techniques.

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Azizan, Amzar, and Husna Padil. "Lane keeping performances subjected to whole-body vibrations." International Journal of Engineering & Technology 7, no. 4.13 (October 9, 2018): 1. http://dx.doi.org/10.14419/ijet.v7i4.13.21318.

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Despite the fact that many research have been carried out on the characterization of the effects of whole-body vibration on seated occupants’ comfort, there is still very little scientific knowledge regarding drowsiness caused by the vibrations. Furthermore, there are less verified measurement methods available to quantify the whole body vibration-induced drowsiness of the vehicle occupants. This study is therefore set out to evaluate the effect of vibrations on drowsiness. 20 male volunteers have been recruited for this experiment. The data for this study is gathered from 10-minute simulated driving sessions under both no-vibration conditions and with a vibration that is randomly organized. Gaussian random vibration, with 1-15 Hz frequency bandwidth at 0.2 ms-2 r.m.s. for 30 minutes, is applied. During the driving session, the volunteers are required to obey the speed limit of a 100 kph and keep a consistent position in the left-hand lane. The deviation in the lateral position are recorded and analyzed. Additionally, the volunteers are also asked to rate their subjective drowsiness level by means of Karolinska Sleepiness Scale (KSS) scores for every five minutes. Based on the results, the role of vibration in promoting drowsiness can be observed from the driving impairment following 30-mins exposure to vibration.

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YUDA, Emi, Yutaka YOSHIDA, and Junichiro HAYANO. "Changes in Respiration Pattern Preceding Drowsiness During Driving." International Symposium on Affective Science and Engineering ISASE2020 (2020): 1–2. http://dx.doi.org/10.5057/isase.2020-c000018.

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Cho, SeongSe, and WonHyuk Choi. "Implementation of Drowsiness Detection and Safe Driving System." International Journal of IT-based Public Health Management 7, no. 1 (March 30, 2020): 1–8. http://dx.doi.org/10.21742/ijiphm.2020.7.1.01.

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Lin, S. T., Y. Y. Tan, P. Y. Chua, L. K. Tey, and C. H. Ang. "PERCLOS Threshold for Drowsiness Detection during Real Driving." Journal of Vision 12, no. 9 (August 10, 2012): 546. http://dx.doi.org/10.1167/12.9.546.

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Prasath, T. Manoj, S. Geetha, R. Kishore Kanna, and R. Vasuki. "IR Sensor Based Drowsiness Detecting During Driving System." Indian Journal of Public Health Research & Development 10, no. 11 (2019): 2587. http://dx.doi.org/10.5958/0976-5506.2019.04001.4.

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Mikaeili, Mohammad, and SeyedMohammad Reza Noori. "Driving drowsiness detection using fusion of electroencephalography, electrooculography, and driving quality signals." Journal of Medical Signals & Sensors 6, no. 1 (2016): 39. http://dx.doi.org/10.4103/2228-7477.175868.

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Sun, Yifan, Chaozhong Wu, Hui Zhang, Wenhui Chu, Yiying Xiao, and Yijun Zhang. "Effects of Individual Differences on Measurements’ Drowsiness-Detection Performance." Promet - Traffic&Transportation 33, no. 4 (August 5, 2021): 565–78. http://dx.doi.org/10.7307/ptt.v33i4.3668.

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Individual differences (IDs) may reduce the detection-accuracy of drowsiness-driving by influencing measurements’ drowsiness-detection performance (MDDP). The purpose of this paper is to propose a model that can quantify the effects of IDs on MDDP and find measurements with less impact by IDs to build drowsiness-detection models. Through field experiments, drivers’ naturalistic driving data and subjective-drowsiness levels were collected, and drowsiness-related measurements were calculated using the double-layer sliding time window. In the model, MDDP was represented by |Z-statistics| of the Wilcoxon-test. First, the individual driver’s measurements were analysed by Wilcoxon-test. Next, drivers were combined in pairs, measurements of paired-driver combinations were analysed by Wilcoxon-test, and measurement’s IDs of paired-driver combinations were calculated. Finally, linear regression was used to fit the measurements’ IDs and changes of MDDP that equalled the individual driver’s |Z-statistics| minus the paired-driver combination’s |Z-statistics|, and the slope’s absolute value (|k|) indicated the effects of ID on the MDDP. As a result, |k| of the mean of the percentage of eyelid closure (MPECL) is the lowest (4.95), which illustrates MPECL is the least affected by IDs. The results contribute to the measurement selection of drowsiness-detection models considering IDs.

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Br. Pasaribu, Novie Theresia, Timotius Halim, Ratnadewi Ratnadewi, and Agus Prijono. "EEG signal classification for drowsiness detection using wavelet transform and support vector machine." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (June 1, 2021): 501. http://dx.doi.org/10.11591/ijai.v10.i2.pp501-509.

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There are several categories to detect and measure driver drowsiness such as physiological methods, subjective methods and behavioral methods. The most objective method for drowsiness detection is the physiological method. One of the physiological methods used is an electroencephalogram (EEG). In this research wavelet transform is used as a feature extraction and using support vector machine (SVM) as a classifier. We proposed an experiment of retrieval data which is designed by using modified-EAR and EEG signal. From the SVM training process, with the 5-fold cross validation, Quadratic kernel has the highest accuracy 84.5% then others. In testing Driving-2 process 7 respondents were detected as drowsiness class, and 3 respondents were detected as awake class. In the testing of Driving-3 process, 6 respondents were detected as drowsiness class, and 4 respondents were detected as awake class.

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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 (June 25, 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 inertial measurement unit and gas pedal sensors. The principle component analysis was utilized to choose the components that have high change. The difference esteems were utilized to separate weakness drivers, which are accepted to have higher driving exercises, from typical drivers.

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Jose, Jitha. "Real-Time Driver Drowsiness Detection and Alert System." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 31, 2021): 3118–26. http://dx.doi.org/10.22214/ijraset.2021.37001.

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Nowadays, road accidents have become really high and is causing severe physical injuries, deaths etc, mainly in India. India has become the highest in the world in case of road accidents, recording 53 road crashes per hour. Drowsy driving is one of the reasons for road accidents. Increasing road accidents due to drowsy driving indicate the need of system that detect the drowsiness of the driver and alert them at the correct time. “Researchers have attempted to determine driver drowsiness using the following measures: (1) vehicle-based measures; (2) behavioral measures and (3) physiological measures”[13]. Rate of using physiological measures to detect drowsiness is high; vehicle-based measures are affected by construction of the street, driving ability of the driver, type of vehicle etc; some methodologies use mental measures for which some anodes are put on the head and body. So here we have used much more feasible method. In this paper we discuss a method which is non-intrusive. In our proposed system, the main data we use to detect driver drowsiness is the eye conclusion proportion, its duration etc. Eyes opening and closing ratio reflects a person's mind status and attention and therefore, it can be potentially used to indicate driver fatigue levels[14]. We keep a value as the threshold value, if the eye conclusion proportion goes below the threshold value, then we alert the driver using a buzzer and provide tips to get rid of the drowsiness through an audio message. For continuously detecting the driver’s eye we use a camera. We also use an LED light to alert the co-passengers about the drowsiness of the driver.

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Gwak, Jongseong, Akinari Hirao, and Motoki Shino. "An Investigation of Early Detection of Driver Drowsiness Using Ensemble Machine Learning Based on Hybrid Sensing." Applied Sciences 10, no. 8 (April 22, 2020): 2890. http://dx.doi.org/10.3390/app10082890.

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Drowsy driving is one of the main causes of traffic accidents. To reduce such accidents, early detection of drowsy driving is needed. In previous studies, it was shown that driver drowsiness affected driving performance, behavioral indices, and physiological indices. The purpose of this study is to investigate the feasibility of classification of the alert states of drivers, particularly the slightly drowsy state, based on hybrid sensing of vehicle-based, behavioral, and physiological indicators with consideration for the implementation of these identifications into a detection system. First, we measured the drowsiness level, driving performance, physiological signals (from electroencephalogram and electrocardiogram results), and behavioral indices of a driver using a driving simulator and driver monitoring system. Next, driver alert and drowsy states were identified by machine learning algorithms, and a dataset was constructed from the extracted indices over a period of 10 s. Finally, ensemble algorithms were used for classification. The results showed that the ensemble algorithm can obtain 82.4% classification accuracy using hybrid methods to identify the alert and slightly drowsy states, and 95.4% accuracy classifying the alert and moderately drowsy states. Additionally, the results show that the random forest algorithm can obtain 78.7% accuracy when classifying the alert vs. slightly drowsy states if physiological indicators are excluded and can obtain 89.8% accuracy when classifying the alert vs. moderately drowsy states. These results represent the feasibility of highly accurate early detection of driver drowsiness and the feasibility of implementing a driver drowsiness detection system based on hybrid sensing using non-contact sensors.

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Zandi, Ali Shahidi, Azhar Quddus, Laura Prest, and Felix J. E. Comeau. "Non-Intrusive Detection of Drowsy Driving Based on Eye Tracking Data." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 6 (May 16, 2019): 247–57. http://dx.doi.org/10.1177/0361198119847985.

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Drowsy driving is one of the leading causes of motor vehicle accidents in North America. This paper presents the use of eye tracking data as a non-intrusive measure of driver behavior for detection of drowsiness. Eye tracking data were acquired from 53 subjects in a simulated driving experiment, whereas the simultaneously recorded multichannel electroencephalogram (EEG) signals were used as the baseline. A random forest (RF) and a non-linear support vector machine (SVM) were employed for binary classification of the state of vigilance. Different lengths of eye tracking epoch were selected for feature extraction, and the performance of each classifier was investigated for every epoch length. Results revealed a high accuracy for the RF classifier in the range of 88.37% to 91.18% across all epoch lengths, outperforming the SVM with 77.12% to 82.62% accuracy. A feature analysis approach was presented and top eye tracking features for drowsiness detection were identified. Altogether, this study showed a high correspondence between the extracted eye tracking features and EEG as a physiological measure of vigilance and verified the potential of these features along with a proper classification technique, such as the RF, for non-intrusive long-term assessment of drowsiness in drivers. This research would ultimately lead to development of technologies for real-time assessment of the state of vigilance, providing early warning of fatigue and drowsiness in drivers.

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Ahn*, Byeongtae. "A Study for Drowsy Detection & Prevention System." International Journal of Recent Technology and Engineering 10, no. 1 (May 30, 2021): 67–72. http://dx.doi.org/10.35940/ijrte.a5652.0510121.

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Recently, the casualties of automobile traffic accidents are rapidly increasing, and serious accidents involving serious injury and death are increasing more than those of ordinary people. More than 70% of major accidents occur in drowsy driving. Therefore, in this paper, we studied the drowsiness prevention system to prevent large - scale disasters of traffic accidents. In this paper, we propose a real - time flicker recognition method for drowsy driving detection system and drowsy recognition according to the increase of carbon dioxide. The efficiency of the drowsiness prevention system using these two techniques is improved.

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Aidman, E., K. Johnson, G. M. Paech, C. Della Vedova, M. Pajcin, C. Grant, G. Kamimori, et al. "Caffeine reduces the impact of drowsiness on driving errors." Transportation Research Part F: Traffic Psychology and Behaviour 54 (April 2018): 236–47. http://dx.doi.org/10.1016/j.trf.2018.01.008.

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B., Hemantkumar, and Shashikant D. "A Novel Method for Identifying the Drowsiness while Driving." International Journal of Computer Applications 132, no. 13 (December 17, 2015): 33–36. http://dx.doi.org/10.5120/ijca2015907611.

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Soares, Sónia, Sara Ferreira, and António Couto. "Driving simulator experiments to study drowsiness: A systematic review." Traffic Injury Prevention 21, no. 1 (January 2, 2020): 29–37. http://dx.doi.org/10.1080/15389588.2019.1706088.

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Li, Xuanpeng, and Emmanuel Seignez. "Driver inattention monitoring system based on multimodal fusion with visual cues to improve driving safety." Transactions of the Institute of Measurement and Control 40, no. 3 (October 7, 2016): 885–95. http://dx.doi.org/10.1177/0142331216670451.

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Driver inattention, either driver drowsiness or distraction, is a major contributor to serious traffic crashes. In general, most research on this topic studies driver drowsiness and distraction separately, and is often conducted in a well-controlled, simulated environment. By considering the reliability and flexibility of real-time driver monitoring systems, it is possible to evaluate driver inattention by the fusion of multiple selected cues in real life scenarios. This paper presents a real-time, visual-cue-based driver monitoring system, which can track both multi-level driver drowsiness and distraction simultaneously. A set of visual cues are adopted via analysis of drivers’ physical behaviour and driving performance. Driver drowsiness is evaluated using a multi-level scale, by applying evidence theory. Additionally, a general framework of extensive hierarchical combinations is used to generate a probabilistic evaluation of driving risk in real time. This driver inattention monitoring system with multimodal fusion has been proven to improve the accuracy of risk evaluation and reduce the rate of false alarms, and acceptance of the system is recommended.

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Hong, Seunghyeok, and Hyun Jae Baek. "Drowsiness Detection Based on Intelligent Systems with Nonlinear Features for Optimal Placement of Encephalogram Electrodes on the Cerebral Area." Sensors 21, no. 4 (February 10, 2021): 1255. http://dx.doi.org/10.3390/s21041255.

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Drowsiness while driving can lead to accidents that are related to the loss of perception during emergencies that harm the health. Among physiological signals, brain waves have been used as informative signals for the analyses of behavioral observations, steering information, and other biosignals during drowsiness. We inspected the machine learning methods for drowsiness detection based on brain signals with varying quantities of information. The results demonstrated that machine learning could be utilized to compensate for a lack of information and to account for individual differences. Cerebral area selection approaches to decide optimal measurement locations could be utilized to minimize the discomfort of participants. Although other statistics could provide additional information in further study, the optimized machine learning method could prevent the dangers of drowsiness while driving by considering a transitional state with nonlinear features. Because brain signals can be altered not only by mental fatigue but also by health status, the optimization analysis of the system hardware and software will be able to increase the power-efficiency and accessibility in acquiring brain waves for health enhancements in daily life.

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Dabbu, Suman, M. Malini, B. Ram Reddy, and Yashwanth Sai Reddy Vyza. "ANN based Joint Time and frequency analysis of EEG for detection of driver drowsiness." Defence Life Science Journal 2, no. 4 (November 10, 2017): 406. http://dx.doi.org/10.14429/dlsj.2.10370.

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Drowsiness detection plays a vital role in accidents avoidance systems, thereby saving many precious lives. According to the World Health Organization, Drowsiness has been the radical contributor of road fatalities. Electroencephalogram (EEG) is a physiological signal which relays the functioning of Brain and widely used in the diagnosis of Neurological Disorders. This study extrapolates the EEG signal analysis to examine several cognitive tasks. In this report, the EEG signal is processed to detect the behavioural patterns of the brain and drowsiness state of the drivers while performing monotonous driving for long distances. An eight-channel EEG data acquisition system is used to acquire the EEG data from 20 male volunteers. The EEG signal is pre-processed and decomposed into various rhythms by applying Digital filter in MATLAB 2007b (Mathworks, Inc., USA). Time-Frequency Domain analysis has been done to extract certain features PSG and PRMSD which are statistically significant (ρ < 0.05) in the detection of drowsiness. The driving profile is classified into Active and Drowsy by a threshold, and linear regression analysis has been performed on the features extracted. A Drowsiness index is proposed stating a positive correlation (0.8-0.9) between the Total mean and the drowsy mean of the subject.

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Tran, Chinh, and Nader Namazi. "Real-time Detection of Early Drowsiness Using Convolution Neural Networks." Electronic Imaging 2021, no. 8 (January 18, 2021): 233–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.8.imawm-233.

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Drowsiness driving is one of the major reasons causing deadly traffic accidents in the United States of America. This paper intends to propose a system to detect different levels of drowsiness, which can help drivers to have enough time to handle sleepiness. Furthermore, we use distinct sound alarms to warn the user to prevent early accidents. The basis of the proposed approach is to consider symptoms of drowsiness, including the amount of eye closure, yawning, eye blinking, and head position to classify the level of drowsiness. We design a method to extract eye and mouth features from 68 key points of facial landmark. These features will help the system to detect the level of drowsiness in realtime video stream based on different symptoms. The experiential results show that the average accuracy of the system that has the capability to detect drowsiness intensity scale in different light conditions is approximately 96.6%.

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Cheng, Eric Juwei, Ku-Young Young, and Chin-Teng Lin. "Temporal EEG Imaging for Drowsy Driving Prediction." Applied Sciences 9, no. 23 (November 25, 2019): 5078. http://dx.doi.org/10.3390/app9235078.

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As a major cause of vehicle accidents, the prevention of drowsy driving has received increasing public attention. Precisely identifying the drowsy state of drivers is difficult since it is an ambiguous event that does not occur at a single point in time. In this paper, we use an electroencephalography (EEG) image-based method to estimate the drowsiness state of drivers. The driver’s EEG measurement is transformed into an RGB image that contains the spatial knowledge of the EEG. Moreover, for considering the temporal behavior of the data, we generate these images using the EEG data over a sequence of time points. The generated EEG images are passed into a convolutional neural network (CNN) to perform the prediction task. In the experiment, the proposed method is compared with an EEG image generated from a single data time point, and the results indicate that the approach of combining EEG images in multiple time points is able to improve the performance for drowsiness prediction.

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Phulari, Shivanand. "Driver Drowsiness Detection using Machine Learning with Visual Behaviour." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 20, 2021): 1800–1805. http://dx.doi.org/10.22214/ijraset.2021.35348.

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A person while driving a vehicle - if does not have proper sleep or rest, is more inclined to fall asleep which may cause a traffic accident. This is why a system is required which will detect the drowsiness of the driver. Recently, in research and development, machine learning methods have been used to predict a driver's conditions. Those conditions can be used as information that will improve road safety. A driver's condition can be estimated by basic characteristics age, gender and driving experience. Also, driver's driving behaviours, facial expressions, bio-signals can prove helpful in the estimation. Machine Learning has brought progression in video processing which enables images to be analysed with accuracy. In this paper, we proposed a method for detecting drowsiness by using convolution neural network model over position of eyes and extracting detailed features of the mouth using OpenCV and Dlib to count the yawning.

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Yan, Xiao, and Ashardi Abas. "Preliminary on Human Driver Behavior: A Review." International Journal of Artificial Intelligence 7, no. 2 (December 7, 2020): 29–34. http://dx.doi.org/10.36079/lamintang.ijai-0702.146.

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Drowsiness is one of the main factors causing traffic accidents. Research on drowsiness can effectively reduce the traffic accident rate. According to the existing literature, this paper divides the current measurement techniques into subjective and objective ones. Among them, invasive detection and non-invasive detection based on vehicles or drivers are the main objective detection methods.Then, this paper studies the characteristics of drowsiness, and analyzes the advantages and disadvantages of each detection method in practical application. Finally, the development of detection technology is prospected, and provides ideas for the follow-up development of fatigue driving detection technology.

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Lobo, António, Sara Ferreira, and António Couto. "Exploring Monitoring Systems Data for Driver Distraction and Drowsiness Research." Sensors 20, no. 14 (July 9, 2020): 3836. http://dx.doi.org/10.3390/s20143836.

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Driver inattention is a major contributor to road crashes. The emerging of new driver monitoring systems represents an opportunity for researchers to explore new data sources to understand driver inattention, even if the technology was not developed with this purpose in mind. This study is based on retrospective data obtained from two driver monitoring systems to study distraction and drowsiness risk factors. The data includes information about the trips performed by 330 drivers and corresponding distraction and drowsiness alerts emitted by the systems. The drivers’ historical travel data allowed defining two groups with different mobility patterns (short-distance and long-distance drivers) through a cluster analysis. Then, the impacts of the driver’s profile and trip characteristics (e.g., driving time, average speed, and breaking time and frequency) on inattention were analyzed using ordered probit models. The results show that long-distance drivers, typically associated with professionals, are less prone to distraction and drowsiness than short-distance drivers. The driving time increases the probability of inattention, while the breaking frequency is more important to mitigate inattention than the breaking time. Higher average speeds increase the inattention risk, being associated with road facilities featuring a monotonous driving environment.

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Smith, Alec, Farzan Sasangohar, Anthony D. McDonald, Nena Bonuel, Holly Shui, Christine Ouko, and Lorelie Lazaro. "Drowsy Driving Among Shift Work Nurses: A Qualitative Data Analysis." Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 8, no. 1 (September 2019): 167–71. http://dx.doi.org/10.1177/2327857919081041.

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Drowsy driving claims many lives every year. While all drivers are susceptible to the problem of drowsy driving, the nurse population is of particular concern. Studies have shown the severity of drowsiness for night shift nurses both at work and on the drive home. Many work and non-work factors contribute to the drowsiness that nurses experience. This study used a semi-structured interview approach to gain the perception and experiences of nurses concerning drowsy driving and possible interventions. Interviews were conducted at a large hospital in south central Texas with 30 night shift nurses. Visualizations depicting nurses’ responses are presented to aid in the understanding of the themes derived from the interviews. The nurses experience drowsy driving on a regular basis, use ineffective mitigation techniques and have differing preferences for an educational and technological intervention for drowsy driving. An emergent theme was how work and non-work factors work in conjunction to impact the nurses’ experiences of drowsy driving. Potential, implementable solutions regarding some of these factors are presented.

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Kundinger, Thomas, Phani Krishna Yalavarthi, Andreas Riener, Philipp Wintersberger, and Clemens Schartmüller. "Feasibility of smart wearables for driver drowsiness detection and its potential among different age groups." International Journal of Pervasive Computing and Communications 16, no. 1 (January 2, 2020): 1–23. http://dx.doi.org/10.1108/ijpcc-03-2019-0017.

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Purpose Drowsiness is a common cause of severe road accidents. Therefore, numerous drowsiness detection methods were developed and explored in recent years, especially concepts using physiological measurements achieved promising results. Nevertheless, existing systems have some limitations that hinder their use in vehicles. To overcome these limitations, this paper aims to investigate the development of a low-cost, non-invasive drowsiness detection system, using physiological signals obtained from conventional wearable devices. Design/methodology/approach Two simulator studies, the first study in a low-level driving simulator (N = 10) to check feasibility and efficiency, and the second study in a high-fidelity driving simulator (N = 30) including two age groups, were conducted. An algorithm was developed to extract features from the heart rate signals and a data set was created by labelling these features according to the identified driver state in the simulator study. Using this data set, binary classifiers were trained and tested using various machine learning algorithms. Findings The trained classifiers reached a classification accuracy of 99.9%, which is similar to the results obtained by the studies which used intrusive electrodes to detect ECG. The results revealed that heart rate patterns are sensitive to the drivers’ age, i.e. models trained with data from one age group are not efficient in detecting drowsiness for another age group, suggesting to develop universal driver models with data from different age groups combined with individual driver models. Originality/value This work investigated the feasibility of driver drowsiness detection by solely using physiological data from wrist-worn wearable devices, such as smartwatches or fitness trackers that are readily available in the consumer market. It was found that such devices are reliable in drowsiness detection.

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Jiang, De Rong. "Study on Driving System Based on EEG." Applied Mechanics and Materials 63-64 (June 2011): 579–82. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.579.

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Driving System, which is programmed by C#, is a car driving manipulation to simulate the real car driving movement, obtain sense of simulation system. Obviously, Driving System has a lot of advantages as a driving system for fatigue detecting monitor. In driving system, there are three parts, which are the driving Environment, Data acquisition and Data analysis. Driving System can fulfill the interactivity and real time effectively, and has been used successfully in the drowsiness of driving, which can be familiarly analyzed with arithmetic of EEG.

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Gielen, Jasper, and Jean-Marie Aerts. "Feature Extraction and Evaluation for Driver Drowsiness Detection Based on Thermoregulation." Applied Sciences 9, no. 17 (August 30, 2019): 3555. http://dx.doi.org/10.3390/app9173555.

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Numerous reports state that drowsiness is one of the major factors affecting driving performance and resulting in traffic accidents. In the past, methods to detect driver drowsiness have been developed based on physiological, behavioral, and vehicular features. In this pilot study, we test the use of a new set of features for detecting driver drowsiness based on physiological changes related to thermoregulation. Nineteen participants successfully performed a driving simulation, while the temperature of the nose (Tnose) and wrist (Twrist) as well as the heart rate (HR) were monitored. On average, an initial increase in temperature followed by a gradual decrease was observed in drivers who experienced drowsiness. For non-drowsy drivers, no such trends were observed. In addition, HR decreased on average in both groups, yet the decrease in the drowsy group was more distinct. Next, a classification based on each of these variables resulted in an accuracy of 68.4%, 88.9%, and 70.6% for Tnose, Twrist, and HR, respectively. Combining the information of all variables resulted in an accuracy of 89.5%, meaning that ultimately the state of 17 out of 19 drivers was detected correctly. Hence, we conclude that the use of physiological features related to thermoregulation shows potential for future research in this field.

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Ann, Ik-Soo. "A study on warning sound for drowsiness driving prevention system." Journal of the Acoustical Society of America 143, no. 3 (March 2018): 1961. http://dx.doi.org/10.1121/1.5036446.

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Hardee, H. Lenora, Thomas A. Dingus, and Walter W. Wierwille. "Driver Drowsiness Detection Using Subsidiary Task and Driving Performance Measures." Proceedings of the Human Factors Society Annual Meeting 30, no. 4 (September 1986): 398–402. http://dx.doi.org/10.1177/154193128603000421.

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Six male and six female subjects operated a moving-base automobile simulator for 2 1/2 hours after having been kept continuously awake for approximately 19 hours past their normal wake-up times. During this session, subjects drove both while performing three subsidiary tasks (auditory, visual, and tactual) which required a tactual output but which otherwise differed only in terms of stimulus input modality, and while performing no task. The driving performance, behavioral, physiological, and subsidiary task response measures collected during the experiment were subjected to a series of linear discriminant analyses, and the results indicated that the detection of driver impairment due to drowsiness may indeed be possible by monitoring combinations of subsidiary task and physiological measures.

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De Rosario, H., J. S. Solaz, N. Rodrı́guez, and L. M. Bergasa. "Controlled inducement and measurement of drowsiness in a driving simulator." IET Intelligent Transport Systems 4, no. 4 (2010): 280. http://dx.doi.org/10.1049/iet-its.2009.0110.

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Joly, Antonin, Rencheng Zheng, Tsutomu Kaizuka, and Kimihiko Nakano. "Effect of drowsiness on mechanical arm admittance and driving performances." IET Intelligent Transport Systems 12, no. 3 (April 1, 2018): 220–26. http://dx.doi.org/10.1049/iet-its.2016.0249.

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Eoh, Hong J., Min K. Chung, and Seong-Han Kim. "Electroencephalographic study of drowsiness in simulated driving with sleep deprivation." International Journal of Industrial Ergonomics 35, no. 4 (April 2005): 307–20. http://dx.doi.org/10.1016/j.ergon.2004.09.006.

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Manu, M. Agna, Dayana Jaijan, S. N. Nissa, S. Jesna, Abin Shukoor, and A. R. Shamna. "A Novel Approach to Detect Driver Drowsiness and Alcohol Intoxication using Haar Algorithm with Raspberry Pi." International Journal of Research in Engineering, Science and Management 3, no. 9 (September 15, 2020): 48–51. http://dx.doi.org/10.47607/ijresm.2020.284.

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Drowsiness in driver and alcohol consumption are the critical cause of road accident and death. Lives of pedestrian and passengers are put to risk as drivers tend to fall asleep and also when the driver is in his drunken state. Detection of driver drowsiness and its indication is an active research area now. There are 3 methods for detection of driver fatigue which includes vehicle-based method, behavioural method, and physiological based method. We adopt behavioural method. This project is aimed towards developing a prototype of drowsiness and alcohol detection system using Haar algorithm with raspberry pi. This project proposes a real time detection of driver’s drowsiness as well as alcohol intoxication and subsequently alerting them. The primary purpose of this drowsiness and alcohol detection system is to develop a system that can reduce the number of accidents from drowsiness and drunk driving of vehicle. It consists of camera which is placed in front of the driver to detect the face. An alcohol sensor which is a gas sensor used to sense the drinking state of driver. Haar algorithm is used for face detection. The results demonstrate the accuracy and robustness of the hybridized of image processing technique. Thus, it can be concluded the proposed approach is an effective solution for a real-time of driver drowsiness and alcohol detection.

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Mohd Adib, M. A. H., and N. H. M. Hasni. "Enhanced Brady-Tachy Heart Automotive (BT-Heartomotive) Device for Heart Rate Monitoring during Driving." International Journal of Automotive and Mechanical Engineering 17, no. 1 (April 8, 2020): 7599–606. http://dx.doi.org/10.15282/ijame.17.1.2020.09.0564.

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Driving with brady-tachy syndrome is one of the main causes of car accidents. In order to prevent drivers from brady-tachy driving, there is a strong demand for driver monitoring systems. Other than problems in driving attitudes and skills, road accidents are also caused by uncontrollable factors such as medical conditions and drowsiness. These factors can be avoided by having early detection. Therefore, the brady-tachy heart automotive so-called BT-Heartomotive device is developed. This BT-Heartomotive device can detect early signs of drowsiness and health problems by measuring the heart rate of the drivers during driving. The device also could use the data to send an alert to the passengers that they’re in precaution. The device shows a good accuracy in the detection of the heart rate level. The device comprised three main components; wristband, monitor and integrated mobile applications. Heart rate measurement can reveal a lot about the physical conditions of an individual. The BT-Heartomotive device is simple, easy to use and automated.

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Subramonian*, Kavyashree, and Sumathi, G. "Drowsiness Detection System with Speed Limit Recommendation using Sentiment Analysis." International Journal of Recent Technology and Engineering 10, no. 1 (May 30, 2021): 184–90. http://dx.doi.org/10.35940/ijrte.a5848.0510121.

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Driving while drowsy is a ubiquitous and extremely grave public health hazard that requires immediate consideration. Through studies in recent years, it has been proved that about 20 percent of all car accidents have occurred as a result of dozy driving. The main objective of new drowsiness detection systems is accurate doziness recognition. In this regard, the face is the most important part of the body as it sends a lot of essential information. The facial expressions of a drowsy driver include frequency of blinking and yawning. This paper proposes a model which detects the drivers' awareness using video stills of the driver’s face and improves the tracking accuracy. Further, we introduce the auxiliary functionality of speed limit recommendations based on the driver’s present state of mind. The various facial features are evaluated to determine the drivers' current state. By combining the features of the eyes and mouth, the driver is alerted with a fatigue warning and also suggested a safe speed limit. This system is very essential so as to prevent and hence reduce the number of fatal accidents that occur as a result of dozy driving saving a lot of lives and damage to property.

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Kundinger, Thomas, Nikoletta Sofra, and Andreas Riener. "Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection." Sensors 20, no. 4 (February 14, 2020): 1029. http://dx.doi.org/10.3390/s20041029.

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Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such methods. Especially, techniques that include physiological measurements seem to be a promising alternative. However, in a dynamic environment such as driving, only non- or minimal intrusive methods are accepted, and vibrations from the roadbed could lead to degraded sensor technology. This work contributes to driver drowsiness detection with a machine learning approach applied solely to physiological data collected from a non-intrusive retrofittable system in the form of a wrist-worn wearable sensor. To check accuracy and feasibility, results are compared with reference data from a medical-grade ECG device. A user study with 30 participants in a high-fidelity driving simulator was conducted. Several machine learning algorithms for binary classification were applied in user-dependent and independent tests. Results provide evidence that the non-intrusive setting achieves a similar accuracy as compared to the medical-grade device, and high accuracies (>92%) could be achieved, especially in a user-dependent scenario. The proposed approach offers new possibilities for human–machine interaction in a car and especially for driver state monitoring in the field of automated driving.

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Schömig, Nadja, Volker Hargutt, Alexandra Neukum, Ina Petermann-Stock, and Ina Othersen. "The Interaction Between Highly Automated Driving and the Development of Drowsiness." Procedia Manufacturing 3 (2015): 6652–59. http://dx.doi.org/10.1016/j.promfg.2015.11.005.

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Journal articles on the topic 'Driving drowsiness'

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