16.7.2.8.4 Driver Fatigue

Chapter Contents (Back)
Driver Fatigue. Fatigue. Driver Monitoring.

Yang, J.H., Mao, Z.H., Tijerina, L., Pilutti, T., Coughlin, J.F., Feron, E.,
Detection of Driver Fatigue Caused by Sleep Deprivation,
SMC-A(39), No. 4, July 2009, pp. 694-705.
IEEE DOI 0906
BibRef

Fan, X.[Xiao], Sun, Y.F.[Yan-Feng], Yin, B.C.[Bao-Cai], Guo, X.M.[Xiu-Ming],
Gabor-based dynamic representation for human fatigue monitoring in facial image sequences,
PRL(31), No. 3, 1 February 2010, pp. 234-243.
Elsevier DOI 1001
BibRef
Earlier: A1, A2, A3, Only:
Multi-Scale Dynamic Human Fatigue Detection with Feature Level Fusion,
FG08(1-6).
IEEE DOI 0809
BibRef
And: A1, A3, A2, Only:
Dynamic Human Fatigue Detection Using Feature-Level Fusion,
ICISP08(94-102).
Springer DOI 0807
Human fatigue; Multi-scale; Dynamic feature; Feature fusion; AdaBoost algorithm BibRef

Senaratne, R.[Rajinda], Jap, B.[Budi], Lal, S.[Sara], Hsu, A.[Arthur], Halgamuge, S.[Saman], Fischer, P.[Peter],
Comparing two video-based techniques for driver fatigue detection: classification versus optical flow approach,
MVA(22), No. 4, July 2011, pp. 597-618.
WWW Link. 1107
BibRef

Zhao, C., Zhang, X., Zhang, B., Dang, Q., Lian, J.,
Driver's fatigue expressions recognition by combined features from pyramid histogram of oriented gradient and contourlet transform with random subspace ensembles,
IET-ITS(7), No. 1, 2013, pp. 36-45.
DOI Link 1307
BibRef

Hu, S., Zheng, G., Peters, B.,
Driver fatigue detection from electroencephalogram spectrum after electrooculography artefact removal,
IET-ITS(7), No. 1, 2013, pp. 105-113.
DOI Link 1307
BibRef

Zhang, C.[Chi], Wang, H.[Hong], Fu, R.R.[Rong-Rong],
Automated Detection of Driver Fatigue Based on Entropy and Complexity Measures,
ITS(15), No. 1, February 2014, pp. 168-177.
IEEE DOI 1403
accident prevention BibRef

He, Q.C.[Qi-Chang], Li, W.[Wei], Fan, X.[Xiumin], Fei, Z.M.[Zhi-Min],
Driver fatigue evaluation model with integration of multi-indicators based on dynamic Bayesian network,
IET-ITS(9), No. 5, 2015, pp. 547-554.
DOI Link 1507
Bayes methods BibRef

He, Q.C.[Qi-Chang], Li, W.[Wei], Fan, X.[Xiumin], Fei, Z.M.[Zhi-Min],
Evaluation of driver fatigue with multi-indicators based on artificial neural network,
IET-ITS(10), No. 8, 2016, pp. 555-561.
DOI Link 1610
driver information systems BibRef

Alioua, N.[Nawal], Amine, A.[Aouatif], Rogozan, A.[Alexandrina], Bensrhair, A.[Abdelaziz], Rziza, M.[Mohammed],
Driver head pose estimation using efficient descriptor fusion,
JIVP(2016), No. 1, 2016, pp. 2.
DOI Link 1601
BibRef

Alioua, N.[Nawal], Amine, A.[Aouatif], Rziza, M.[Mohammed], Aboutajdine, D.[Driss],
Driver's Fatigue and Drowsiness Detection to Reduce Traffic Accidents on Road,
CAIP11(II: 397-404).
Springer DOI 1109
BibRef

Kaplan, S., Guvensan, M.A., Yavuz, A.G., Karalurt, Y.,
Driver Behavior Analysis for Safe Driving: A Survey,
ITS(16), No. 6, December 2015, pp. 3017-3032.
IEEE DOI 1512
Fatigue BibRef

Li, Z., Sun, G., Zhang, F., Jia, L., Zheng, K., Zhao, D.,
Smartphone-based fatigue detection system using progressive locating method,
IET-ITS(10), No. 3, 2016, pp. 148-156.
DOI Link 1604
driver information systems BibRef

Mandal, B., Li, L., Wang, G.S., Lin, J.,
Towards Detection of Bus Driver Fatigue Based on Robust Visual Analysis of Eye State,
ITS(18), No. 3, March 2017, pp. 545-557.
IEEE DOI 1703
Cameras BibRef

Lee, B.G., Chong, T.W., Lee, B.L., Park, H.J., Kim, Y.N., Kim, B.,
Wearable Mobile-Based Emotional Response-Monitoring System for Drivers,
HMS(47), No. 5, October 2017, pp. 636-649.
IEEE DOI 1709
Accidents, Electromyography, Fatigue, Monitoring, Sensors, Stress, Vehicles, Healthcare, mobile application, negative emotion, roadway accident, stress, wearable, system BibRef

Wang, Z., Zheng, R., Kaizuka, T., Shimono, K., Nakano, K.,
The Effect of a Haptic Guidance Steering System on Fatigue-Related Driver Behavior,
HMS(47), No. 5, October 2017, pp. 741-748.
IEEE DOI 1709
Fatigue, Haptic interfaces, Steering systems, Torque, Trajectory, Vehicles, Wheels, Driver behavior, driving simulator, haptic guidance, passive, fatigue BibRef

Sun, W., Zhang, X., Peeta, S., He, X., Li, Y.,
A Real-Time Fatigue Driving Recognition Method Incorporating Contextual Features and Two Fusion Levels,
ITS(18), No. 12, December 2017, pp. 3408-3420.
IEEE DOI 1712
Brain modeling, Computational modeling, Fatigue, Real-time systems, Reliability, Sleep, Vehicles, Dempster-Shafer evidence theory, multi-class support vector machine classifier BibRef

Yan, R.H.[Rong-Hui], Wu, C.[Cheng], Wang, Y.M.[Yi-Ming],
Exploration and evaluation of individual difference to driving fatigue for high-speed railway: a parametric SVM model based on multidimensional visual cue,
IET-ITS(12), No. 6, August 2018, pp. 504-512.
DOI Link 1807
BibRef

Wang, P.[Ping], Min, J.L.[Jian-Liang], Hu, J.F.[Jian-Feng],
Ensemble classifier for driver's fatigue detection based on a single EEG channel,
IET-ITS(12), No. 10, December 2018, pp. 1322-1328.
DOI Link 1812
BibRef

Gu, W.H.[Wang Huan], Zhu, Y.[Yu], Chen, X.D.[Xu Dong], He, L.F.[Lin Fei], Zheng, B.B.[Bing Bing],
Hierarchical CNN-based real-time fatigue detection system by visual-based technologies using MSP model,
IET-IPR(12), No. 12, December 2018, pp. 2319-2329.
DOI Link 1812
BibRef

Vo, S.A.[Son Anh], Mirowski, L.[Luke], Scanlan, J.[Joel], Turner, P.[Paul],
Verifying driver performance for heavy haulage fatigue management,
IET-ITS(13), No. 6, June 2019, pp. 1033-1040.
DOI Link 1906
BibRef

Sikander, G., Anwar, S.,
Driver Fatigue Detection Systems: A Review,
ITS(20), No. 6, June 2019, pp. 2339-2352.
IEEE DOI 1906
Survey, Driver Fatigue. Fatigue, Vehicles, Sleep, Monitoring, Mathematical model, Feature extraction, Task analysis, Intelligent transportation, driver monitoring BibRef

Amodio, A., Ermidoro, M., Maggi, D., Formentin, S., Savaresi, S.M.,
Automatic Detection of Driver Impairment Based on Pupillary Light Reflex,
ITS(20), No. 8, August 2019, pp. 3038-3048.
IEEE DOI 1908
Vehicles, Fatigue, Video sequences, Sleep, Roads, Monitoring, Cameras, ADAS, system identification, video processing, pupil dynamics, support vector machine BibRef

Li, X.[Xu], Hong, L.[Lin], Wang, J.C.[Jian-Chun], Liu, X.[Xiang],
Fatigue driving detection model based on multi-feature fusion and semi-supervised active learning,
IET-ITS(13), No. 9, September 2019, pp. 1401-1409.
DOI Link 1908
BibRef

Xiao, Z.T.[Zhi-Tao], Hu, Z.Q.A.[Zhi-Qi-Ang], Geng, L.[Lei], Zhang, F.[Fang], Wu, J.[Jun], Li, Y.L.[Yue-Long],
Fatigue driving recognition network: fatigue driving recognition via convolutional neural network and long short-term memory units,
IET-ITS(13), No. 9, September 2019, pp. 1410-1416.
DOI Link 1908
BibRef

Dasgupta, A., Rahman, D., Routray, A.,
A Smartphone-Based Drowsiness Detection and Warning System for Automotive Drivers,
ITS(20), No. 11, November 2019, pp. 4045-4054.
IEEE DOI 1911
Vehicles, Face, Cameras, Lighting, Automotive engineering, Fatigue, Sensors, PERCLOS, voiced-unvoiced ratio, Android, drowsiness BibRef

Chaudhuri, A., Routray, A.,
Driver Fatigue Detection Through Chaotic Entropy Analysis of Cortical Sources Obtained From Scalp EEG Signals,
ITS(21), No. 1, January 2020, pp. 185-198.
IEEE DOI 2001
Electroencephalography, Sleep, Fatigue, Entropy, Scalp, Brain modeling, Vehicles, Chaotic entropy, dipoles, scalp EEG, sleep deprivation, support vector machines BibRef

Kurosawa, Y.[Yuki], Mochiduki, S.[Shinya], Hoshino, Y.[Yuko], Yamada, M.[Mitsuho],
Measurement of Fatigue Based on Changes in Eye Movement during Gaze,
IEICE(E103-D), No. 5, May 2020, pp. 1203-1207.
WWW Link. 2005
BibRef

Liu, Z.M.[Zhong-Min], Peng, Y.X.[Yu-Xi], Hu, W.J.[Wen-Jin],
Driver fatigue detection based on deeply-learned facial expression representation,
JVCIR(71), 2020, pp. 102723.
Elsevier DOI 2009
Fatigue detection, MB-LBP, PERCLOS, Fuzzy reasoning BibRef

Li, F., Chen, C.H., Xu, G., Khoo, L.P.,
Hierarchical Eye-Tracking Data Analytics for Human Fatigue Detection at a Traffic Control Center,
HMS(50), No. 5, October 2020, pp. 465-474.
IEEE DOI 2009
Fatigue, Interpolation, Data integrity, Noise measurement, Data mining, Tracking, Data analysis, Eye movement, traffic management BibRef

Nemcová, A.[Andrea], Svozilová, V.[Veronika], Bucsuházy, K.[Katerina], Smíšek, R.[Radovan], Mézl, M.[Martin], Hesko, B.[Branislav], Belák, M.[Michal], Bilík, M.[Martin], Maxera, P.[Pavel], Seitl, M.[Martin], Dominik, T.[Tomáš], Semela, M.[Marek], Šucha, M.[Matúš], Kolár, R.[Radim],
Multimodal Features for Detection of Driver Stress and Fatigue: Review,
ITS(22), No. 6, June 2021, pp. 3214-3233.
IEEE DOI 2106
Survey, Driver Monitoring. Stress, Fatigue, Databases, Accidents, Automobiles, Sleep, Driver fatigue, driver stress, traffic accident, multimodal features BibRef

Zhao, Q.J.[Qi-Jie], Jiang, J.[Junye], Lei, Z.G.[Zhi-Gao], Yi, J.G.[Jin-Gang],
Detection method of eyes opening and closing ratio for driver's fatigue monitoring,
IET-ITS(15), No. 1, 2021, pp. 31-42.
DOI Link 2106
BibRef

Sun, Y.F.[Yi-Fan], Wu, C.Z.[Chao-Zhong], Zhang, H.[Hui], Zhou, W.[Wei], Li, X.[Xin], Zhang, Q.[Qi],
Extraction of optimal fatigue-driving steering indicators considering individual differences,
IET-ITS(15), No. 5, 2021, pp. 606-618.
DOI Link 2106
BibRef

Liu, M.Z.[Ming-Zhou], Xu, X.[Xin], Hu, J.[Jing], Jiang, Q.N.[Qian-Nan],
Real time detection of driver fatigue based on CNN-LSTM,
IET-IPR(16), No. 2, 2022, pp. 576-595.
DOI Link 2201
BibRef

Zhao, G.Z.[Guang-Zhe], He, Y.Q.[Yan-Qing], Yang, H.[Hanting], Tao, Y.[Yong],
Research on fatigue detection based on visual features,
IET-IPR(16), No. 4, 2022, pp. 1044-1053.
DOI Link 2203
BibRef

Yang, Y.X.[Yu-Xuan], Gao, Z.K.[Zhong-Ke], Li, Y.L.[Yan-Li], Cai, Q.[Qing], Marwan, N.[Norbert], Kurths, J.[Jürgen],
A Complex Network-Based Broad Learning System for Detecting Driver Fatigue From EEG Signals,
SMCS(51), No. 9, September 2021, pp. 5800-5808.
IEEE DOI 2108
Fatigue, Electroencephalography, Vehicles, Learning systems, Indexes, Biomedical monitoring, Task analysis, electroencephalogram (EEG) signals BibRef

Huang, R.[Rui], Wang, Y.[Yan], Li, Z.J.[Zi-Jian], Lei, Z.[Zeyu], Xu, Y.F.[Yu-Fan],
RF-DCM: Multi-Granularity Deep Convolutional Model Based on Feature Recalibration and Fusion for Driver Fatigue Detection,
ITS(23), No. 1, January 2022, pp. 630-640.
IEEE DOI 2201
Fatigue, Feature extraction, Face, Vehicles, Time-domain analysis, Fuses, Videos, Deep learning, fatigue detection, multi-granularity network BibRef

Wu, E.Q.[Edmond Q.], Zhou, M.C.[Meng-Chu], Hu, D.[Dewen], Zhu, L.J.[Long-Jun], Tang, Z.R.[Zhi-Ri], Qiu, X.Y.[Xu-Yi], Deng, P.Y.[Ping-Yu], Zhu, L.M.[Li-Min], Ren, H.[He],
Self-Paced Dynamic Infinite Mixture Model for Fatigue Evaluation of Pilots' Brains,
Cyber(52), No. 7, July 2022, pp. 5623-5638.
IEEE DOI 2207
Electroencephalography, Fatigue, Brain modeling, Mixture models, Task analysis, Regulation, Load modeling, Brain fatigue, self-paced learning BibRef

Wu, E.Q.[Edmond Q.], Zhou, M.C.[Meng-Chu], Xiong, P.W.[Peng-Wen], Tang, Z.R.[Zhi-Ri], Hu, R.H.[Rui-Han], Jie, Y.W.[Yu-Wen],
Inferring Flight Performance Under Different Maneuvers With Pilot's Multi-Physiological Parameters,
ITS(23), No. 8, August 2022, pp. 11338-11348.
IEEE DOI 2208
Gaussian processes, Atmospheric modeling, Task analysis, Brain modeling, Pupils, Heart rate variability, Physiology, workload BibRef

Wu, E.Q.[Edmond Q.], Cao, Z.T.[Zheng-Tao], Sun, P.Z.H.[Poly Z. H.], Li, D.F.[Dong-Fang], Law, R.[Rob], Xu, X.[Xin], Zhu, L.M.[Li-Min], Yu, M.[Mengsun],
Inferring Cognitive State of Pilot's Brain Under Different Maneuvers During Flight,
ITS(23), No. 11, November 2022, pp. 21729-21739.
IEEE DOI 2212
Fatigue, Brain modeling, Electroencephalography, Feature extraction, Generative adversarial networks, Electrodes, brain power map BibRef

Ahlström, C.[Christer], van Leeuwen, W.[Wessel], Krupenia, S.[Stas], Jansson, H.[Herman], Finér, S.[Svitlana], Anund, A.[Anna], Kecklund, G.[Göran],
Real-Time Adaptation of Driving Time and Rest Periods in Automated Long-Haul Trucking: Development of a System Based on Biomathematical Modelling, Fatigue and Relaxation Monitoring,
ITS(23), No. 5, May 2022, pp. 4758-4766.
IEEE DOI 2205
Sleep, Vehicles, Fatigue, Heart rate variability, Regulation, Automobiles, Real-time systems, Hours of service regulations, truck BibRef

Wu, E.Q.[Edmond Q.], Zhu, L.M.[Li-Min], Li, G.J.[Gui-Jiang], Li, H.J.[Hong-Jun], Tang, Z.[Zhiri], Hu, R.[Ruihan], Zhou, G.R.[Gui-Rong],
Nonparametric Hierarchical Hidden Semi-Markov Model for Brain Fatigue Behavior Detection of Pilots During Flight,
ITS(23), No. 6, June 2022, pp. 5245-5256.
IEEE DOI 2206
Hidden Markov models, Brain modeling, Fatigue, Mixture models, Electroencephalography, Vehicles, Feature extraction, t-mixture model BibRef

Horberry, T.[Tim], Mulvihill, C.[Christine], Fitzharris, M.[Michael], Lawrence, B.[Brendan], Lenné, M.[Mike], Kuo, J.[Jonny], Wood, D.[Darren],
Human-Centered Design for an In-Vehicle Truck Driver Fatigue and Distraction Warning System,
ITS(23), No. 6, June 2022, pp. 5350-5359.
IEEE DOI 2206
Vehicles, Fatigue, Alarm systems, Visualization, Safety, Task analysis, Australia, Human centered design, driver fatigue, driver warning systems BibRef

Fan, C.J.[Chao-Jie], Peng, Y.[Yong], Peng, S.L.[Shuang-Ling], Zhang, H.H.[Hong-Hao], Wu, Y.K.[Yuan-Kai], Kwong, S.[Sam],
Detection of Train Driver Fatigue and Distraction Based on Forehead EEG: A Time-Series Ensemble Learning Method,
ITS(23), No. 8, August 2022, pp. 13559-13569.
IEEE DOI 2208
Fatigue, Electroencephalography, Vehicles, Task analysis, Rail transportation, Forehead, Feature extraction, Train driver, time-series ensemble learning method BibRef

Ansari, S.[Shahzeb], Naghdy, F.[Fazel], Du, H.P.[Hai-Ping], Pahnwar, Y.N.[Yasmeen Naz],
Driver Mental Fatigue Detection Based on Head Posture Using New Modified reLU-BiLSTM Deep Neural Network,
ITS(23), No. 8, August 2022, pp. 10957-10969.
IEEE DOI 2208
Vehicles, Fatigue, Magnetic heads, Monitoring, Biomedical monitoring, Tools, Vehicle dynamics, Driver behaviour, reLU-BiLSTM BibRef

Lv, C.[Chao], Nian, J.T.[Jin-Tao], Xu, Y.[Yaru], Song, B.[Bo],
Compact Vehicle Driver Fatigue Recognition Technology Based on EEG Signal,
ITS(23), No. 10, October 2022, pp. 19753-19759.
IEEE DOI 2210
Fatigue, Electroencephalography, Vehicles, Wavelet packets, Brain modeling, Clustering algorithms, Software, convolutional neural network BibRef

Wu, E.Q.[Edmond Q.], Lin, C.T.[Chin-Teng], Zhu, L.M.[Li-Min], Tang, Z.R., Jie, Y.W.[Yu-Wen], Zhou, G.R.[Gui-Rong],
Fatigue Detection of Pilots' Brain Through Brains Cognitive Map and Multilayer Latent Incremental Learning Model,
Cyber(52), No. 11, November 2022, pp. 12302-12314.
IEEE DOI 2211
Fatigue, Brain modeling, Rhythm, Electroencephalography, Market research, Electrodes, Adaptation models, Brain power map, nonparametric prior BibRef

Du, G.L.[Guang-Long], Zhang, L.L.[Lin-Lin], Su, K.[Kang], Wang, X.Q.[Xue-Qian], Teng, S.H.[Shao-Hua], Liu, P.X.[Peter X.],
A Multimodal Fusion Fatigue Driving Detection Method Based on Heart Rate and PERCLOS,
ITS(23), No. 11, November 2022, pp. 21810-21820.
IEEE DOI 2212
Fatigue, Feature extraction, Iris, Heart rate, Brain modeling, Eyelids, Vehicles, Multimodal feature fusion, fatigue driving detection, heart rate BibRef

Bakker, B.[Bram], Zablocki, B.[Bartosz], Baker, A.[Angela], Riethmeister, V.[Vanessa], Marx, B.[Bernd], Iyer, G.[Girish], Anund, A.[Anna], Ahlström, C.[Christer],
A Multi-Stage, Multi-Feature Machine Learning Approach to Detect Driver Sleepiness in Naturalistic Road Driving Conditions,
ITS(23), No. 5, May 2022, pp. 4791-4800.
IEEE DOI 2205
Sleep, Vehicles, Fatigue, Feature extraction, Roads, Hidden Markov models, Faces, Fatigue detection, video-based, field trial BibRef

Zuo, X.[Xin], Zhang, C.[Chi], Cong, F.Y.[Feng-Yu], Zhao, J.[Jian], Hämäläinen, T.[Timo],
Driver Distraction Detection Using Bidirectional Long Short-Term Network Based on Multiscale Entropy of EEG,
ITS(23), No. 10, October 2022, pp. 19309-19322.
IEEE DOI 2210
Vehicles, Electroencephalography, Feature extraction, Task analysis, Entropy, Fatigue, Complexity theory, BiLSTM BibRef

Zhang, Z.M.[Zhi-Min], Ning, H.S.[Huan-Sheng], Zhou, F.[Fang],
A Systematic Survey of Driving Fatigue Monitoring,
ITS(23), No. 11, November 2022, pp. 19999-20020.
IEEE DOI 2212
Fatigue, Monitoring, Vehicles, Sleep, Transportation, Task analysis, Biomedical monitoring, Driving fatigue monitoring, fatigue assessment BibRef

Yang, C.[Cong], Yang, Z.Y.[Zhen-Yu], Li, W.Y.[Wei-Yu], See, J.[John],
FatigueView: A Multi-Camera Video Dataset for Vision-Based Drowsiness Detection,
ITS(24), No. 1, January 2023, pp. 233-246.
IEEE DOI 2301
Visualization, Cameras, Mouth, Vehicles, Annotations, Training data, Intelligent transportation systems, Drowsiness detection, autonomous vehicles BibRef

Zhou, Y.[Yi], Zeng, C.[ChangQing], Mu, Z.[ZhenDong],
Optimal feature-algorithm combination research for EEG fatigue driving detection based on functional brain network,
IET-Bio(12), No. 2, 2023, pp. 65-76.
DOI Link 2305
brain-computer interfaces, feature extraction BibRef

Fa, S.X.[Shu-Xiang], Yang, X.H.[Xiao-Hui], Han, S.Y.[Shi-Yuan], Feng, Z.Q.[Zhi-Quan], Chen, Y.H.[Yue-Hui],
Multi-scale spatial-temporal attention graph convolutional networks for driver fatigue detection,
JVCIR(93), 2023, pp. 103826.
Elsevier DOI 2305
Driver fatigue detection, Driver behavior, Deep learning, Graph convolution networks BibRef

Du, G.L.[Guang-Long], Long, S.Y.[Shuai-Ying], Li, C.Q.[Chun-Quan], Wang, Z.Y.[Zhi-Yao], Liu, P.X.[Peter X.],
A Product Fuzzy Convolutional Network for Detecting Driving Fatigue,
Cyber(53), No. 7, July 2023, pp. 4175-4188.
IEEE DOI 2307
Electroencephalography, Fatigue, Electrocardiography, Feature extraction, Brain modeling, Fuzzy neural networks, product fuzzy convolutional network (PFCN) BibRef

Liu, S.G.[Shu-Gang], Wang, Y.J.[Yu-Jie], Yu, Q.G.[Qiang-Guo], Zhan, J.[Jie], Liu, H.L.[Hong-Li], Liu, J.T.[Jiang-Tao],
A Driver Fatigue Detection Algorithm Based on Dynamic Tracking of Small Facial Targets Using YOLOv7,
IEICE(E106-D), No. 11, November 2023, pp. 1881-1890.
WWW Link. 2311
BibRef

Gu, Y.[Yi], Xia, K.J.[Kai-Jian], Lai, K.W.[Khin-Wee], Jiang, Y.Z.[Yi-Zhang], Qian, P.J.[Peng-Jiang], Gu, X.Q.[Xiao-Qing],
Transferable Takagi-Sugeno-Kang Fuzzy Classifier With Multi-Views for EEG-Based Driving Fatigue Recognition in Intelligent Transportation,
ITS(24), No. 12, December 2023, pp. 15807-15817.
IEEE DOI 2312
BibRef

Xu, T.[Tao], Wang, H.T.[Hong-Tao], Lu, G.[Guanyong], Wan, F.[Feng], Deng, M.Q.[Meng-Qi], Qi, P.[Peng], Bezerianos, A.[Anastasios], Guan, C.T.[Cun-Tai], Sun, Y.[Yu],
E-Key: An EEG-Based Biometric Authentication and Driving Fatigue Detection System,
AffCom(14), No. 2, April 2023, pp. 864-877.
IEEE DOI 2306
Fatigue, Electroencephalography, Brain modeling, Convolutional neural networks, Feature extraction, Data models, biometric BibRef

Gu, Y.[Yi], Jiang, Y.Z.[Yi-Zhang], Wang, T.T.[Ting-Ting], Qian, P.J.[Peng-Jiang], Gu, X.Q.[Xiao-Qing],
EEG-Based Driver Mental Fatigue Recognition in COVID-19 Scenario Using a Semi-Supervised Multi-View Embedding Learning Model,
ITS(25), No. 1, January 2024, pp. 859-868.
IEEE DOI 2402
Electroencephalography, Fatigue, Brain modeling, Vehicles, Kernel, Sparse matrices, Feature extraction, kernel trick BibRef


Lin, J.Q.[Jia-Qin], Du, S.[Shaoyi], Liu, Y.Y.[Yu-Ying], Tian, Z.Q.[Zhi-Qiang], Qu, T.[Ting], Zheng, N.N.[Nan-Ning],
Interpretable Driver Fatigue Estimation Based on Hierarchical Symptom Representations,
MMMod23(II: 647-658).
Springer DOI 2304
BibRef

Laouz, H., Ayad, S., Terrissa, L.S.,
Literature Review on Driver's Drowsiness and Fatigue Detection,
ISCV20(1-7)
IEEE DOI 2011
electrocardiography, electroencephalography, electromyography, electro-oculography, human factors, medical signal processing, Physiological and Behavioral Measures BibRef

Zhang, F., Su, J., Geng, L., Xiao, Z.,
Driver Fatigue Detection Based on Eye State Recognition,
CMVIT17(105-110)
IEEE DOI 1704
feedforward neural nets BibRef

Yin, H., Su, Y., Liu, Y., Zhao, D.,
A driver fatigue detection method based on multi-sensor signals,
WACV16(1-7)
IEEE DOI 1606
Cameras BibRef

Li, Z.[Zibo], Zhang, F.[Fan], Sun, G.M.[Guang-Min], Zhao, D.Q.[De-Qun], Zheng, K.[Kun],
Driver Fatigue Detection System Based on DSP Platform,
MMMod16(II: 47-53).
Springer DOI 1601
BibRef

Rodzik, K.[Konrad], Sawicki, D.[Dariusz],
Recognition of the Human Fatigue Based on the ICAAM Algorithm,
CIAP15(II:373-382).
Springer DOI 1511
BibRef

García, H.F.[Hernán F.], Salazar, A.[Augusto], Alvarez, D.[Damián], Orozco, Á.Á.[Álvaro Á.],
Driving Fatigue Detection Using Active Shape Models,
ISVC10(III: 171-180).
Springer DOI 1011
BibRef

Khan, M.I.[M. Imran], Mansoor, A.B.[A. Bin],
Real Time Eyes Tracking and Classification for Driver Fatigue Detection,
ICIAR08(xx-yy).
Springer DOI 0806
BibRef

Zhang, Z.T.[Zu-Tao], Zhang, J.S.[Jia-Shu],
Driver Fatigue Detection Based Intelligent Vehicle Control,
ICPR06(II: 1262-1265).
IEEE DOI 0609
BibRef

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
License Plate Recognition, Extraction, Analysis .


Last update:Apr 10, 2024 at 09:54:40