{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:00:41Z","timestamp":1777705241941,"version":"3.51.4"},"reference-count":143,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T00:00:00Z","timestamp":1700265600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems: Applications in Engineering and Technology"],"published-print":{"date-parts":[[2024,1,10]]},"abstract":"<jats:p>Fatigue driving is one of the primary causative factors of road accidents. It is of great significance to discern, identify and warn drivers in time for traffic safety and reduce traffic accidents. In this paper, a systematic review for the fatigue driving behavior recognition method is developed to analyze its research status and development trends. Firstly, the data information and its application scenarios related to fatigue driving is detailed. Three driving behavior recognition methods based on different types of signal data are summarized and analyzed, and this signal data can be divided into physiological signal characteristics, visual signal characteristics, vehicle sensor data characteristics and multi-data information fusion. By summarizing and comparing the recognition effect of existing fatigue driving recognition methods, combined with deep learning technology, the paper concludes the fatigue driving behavior recognition method based on single data source has some shortcomings such as low accuracy and easy to be affected by external factors, but the recognition method based on multi-feature information fusion can achieve a exhilarated recognition result. Finally, some prospects are given to analyze the development trend of fatigue driving behavior recognition in the future.<\/jats:p>","DOI":"10.3233\/jifs-235075","type":"journal-article","created":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T11:11:51Z","timestamp":1700565111000},"page":"1407-1427","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":7,"title":["A systematic review for the fatigue driving behavior recognition method"],"prefix":"10.1177","volume":"46","author":[{"given":"Junjian","family":"Hou","sequence":"first","affiliation":[{"name":"Zhengzhou University of Light Industry","place":["China"]}]},{"given":"Yaxiong","family":"Xu","sequence":"additional","affiliation":[{"name":"Zhengzhou University of Light Industry","place":["China"]}]},{"given":"Wenbin","family":"He","sequence":"additional","affiliation":[{"name":"Zhengzhou University of Light Industry","place":["China"]}]},{"given":"Yudong","family":"Zhong","sequence":"additional","affiliation":[{"name":"Zhengzhou University of Light Industry","place":["China"]}]},{"given":"Dengfeng","family":"Zhao","sequence":"additional","affiliation":[{"name":"Zhengzhou University of Light Industry","place":["China"]}]},{"given":"Fang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Zhengzhou University of Light Industry","place":["China"]}]},{"given":"Mingyuan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Zhengzhou Senpeng Electronic Technology Co., LTD","place":["China"]}]},{"given":"Shesen","family":"Dong","sequence":"additional","affiliation":[{"name":"Zhengzhou Senpeng Electronic Technology Co., LTD","place":["China"]}]}],"member":"179","published-online":{"date-parts":[[2023,11,18]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"W. H. Organization \u201cGlobal status report on road safety 2018.\u201d In.: World Health Organization. 2018."},{"key":"e_1_3_2_3_2","unstructured":"ZhengW.L. GaoK.P. LiG. et al\u2018Vigilance Estimation Using a Wearable EOG Device in Real Driving Environment\u2019 IEEE Transactions on Intelligent Transportation Systems. 2019."},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2995063"},{"key":"e_1_3_2_5_2","unstructured":"N. B. of S. of China China Statistical Yearbook(http:\/\/www.stats.gov.cn\/english\/)."},{"key":"e_1_3_2_6_2","first-page":"903","article-title":"Head movement-based driver drowsiness detection: A review ofstate-of-art techniques","volume":"2016","author":"Mittal A.","year":"2016","unstructured":"MittalA., KumarK., DhamijaS. and KaurM., Head movement-based driver drowsiness detection: A review ofstate-of-art techniques. In 2nd IEEE International Conference on Engineering and Technology, ICETECH 2016 (2016), 903\u2013908 Institute of Electrical and Electronics Engineers Inc.","journal-title":"In 2nd IEEE International Conference on Engineering and Technology, ICETECH"},{"key":"e_1_3_2_7_2","doi-asserted-by":"crossref","unstructured":"KamranM.A. Malik Naeem MannanM. Yung JeongM.\u2018Drowsiness Fatigue and Poor Sleep\u2019s Causes and Detection: A Comprehensive Study\u2019 IEEE Access 2019.","DOI":"10.1109\/ACCESS.2019.2951028"},{"key":"e_1_3_2_8_2","first-page":"1777","article-title":"A driver fatigue recognition model using fusion of multiple features[C]\/\/2005 IEEE international conference on systems, man and cybernetics","volume":"2","author":"Yang G.","year":"2005","unstructured":"YangG., LinY. and BhattacharyaP., A driver fatigue recognition model using fusion of multiple features[C]\/\/2005 IEEE international conference on systems, man and cybernetics. IEEE 2 (2005), 1777\u20131784.","journal-title":"IEEE"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2010.08.009"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2016.00304"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2016.2544061"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1039\/C8RA04846K"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aap.2011.11.019"},{"key":"e_1_3_2_14_2","first-page":"61904","article-title":"A Survey on State-of-the-Art Drowsiness Detection Techniques","volume":"7","author":"Ramzan M.","year":"2019","unstructured":"RamzanM., Ullah KhanH., Mahmood AwanS., IsmailA., IlyasM. and MahmoodA., A Survey on State-of-the-Art Drowsiness Detection Techniques. Digital Object Identifier 7 (2019), 61904\u201361919.","journal-title":"Digital Object Identifier"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-76167-7_10"},{"key":"e_1_3_2_16_2","doi-asserted-by":"crossref","unstructured":"QingW. BingXiS. BinX. et al. A perclos-based driver fatigue recognition application for smart vehicle 1335 space[C]\/\/2010 Third International Symposium on Information Processing. IEEE (2010) 437\u2013441.","DOI":"10.1109\/ISIP.2010.116"},{"key":"e_1_3_2_17_2","doi-asserted-by":"crossref","unstructured":"Bagus PratamaG. ArdiyantoI. AdjiT.B. A Review on Driver Drowsiness Based on Image Bio- Signal and Driver Behavior. In 2017 3rd International Conference on Science and Technology - Computer (ICST) 2017.","DOI":"10.1109\/ICSTC.2017.8011855"},{"key":"e_1_3_2_18_2","unstructured":"TakeiY. FurukawaY.Estimate of driver\u2019s fatigue through steering motion[C]\/\/ Systems Man and Cybernetics 2005 IEEE International Conference on. IEEE 2005."},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3185251"},{"key":"e_1_3_2_20_2","first-page":"01019","article-title":"A review on fatigue driving detection[C]\/\/ITM Web of Conferences","volume":"12","author":"Shi S.Y.","year":"2017","unstructured":"ShiS.Y., TangW.Z. and WangY.Y., A review on fatigue driving detection[C]\/\/ITM Web of Conferences. EDP Sciences 12 (2017), 01019.","journal-title":"EDP Sciences"},{"issue":"5","key":"e_1_3_2_21_2","first-page":"2012","article-title":"Driver drowsiness detection based on multisource information[J]","volume":"22","author":"Cheng B.","unstructured":"ChengB., ZhangW., LinY., et al. Driver drowsiness detection based on multisource information[J], Human Factors and Ergonomics in Manufacturing & Service Industries 22(5), 2012.","journal-title":"Human Factors and Ergonomics in Manufacturing & Service Industries"},{"issue":"9","key":"e_1_3_2_22_2","first-page":"2014","article-title":"Data Fusion to Develop a Driver Drowsiness Detection System with Robustness to Signal Loss[J]","volume":"14","author":"Samiee S.","unstructured":"SamieeS., AzadiS., KazemiR., et al. Data Fusion to Develop a Driver Drowsiness Detection System with Robustness to Signal Loss[J], Sensors (Basel, Switzerland) 14(9), 2014.","journal-title":"Sensors (Basel, Switzerland)"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2018.2868499"},{"key":"e_1_3_2_24_2","unstructured":"OpGuard by GuardVant. Accessed: Aug. 11 2017. [Online].Available: https:\/\/www.guardvant.com\/opguard\/."},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2018.2868499"},{"key":"e_1_3_2_26_2","first-page":"01019","article-title":"A review on fatigue driving detection[C]\/\/ITM Web of Conferences","volume":"12","author":"Shi S.Y.","year":"2017","unstructured":"ShiS.Y., TangW.Z. and WangY.Y., A review on fatigue driving detection[C]\/\/ITM Web of Conferences. EDP Sciences 12 (2017), 01019.","journal-title":"EDP Sciences"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/0926-6410(95)00042-9"},{"key":"e_1_3_2_28_2","doi-asserted-by":"crossref","unstructured":"TsuchidaA. BhuiyanM.S. OguriK.Estimation of drowsiness level based on eyelid closure and heart rate variability[C]\/\/ International Conference of the IEEE Engineering in Medicine & Biology Society. IEEE 2009.","DOI":"10.1109\/IEMBS.2009.5334766"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/0013-4694(93)90111-8"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2006.03.011"},{"key":"e_1_3_2_31_2","unstructured":"GromerM. SalbD. WalzerT. et al. ECG Sensor for Detection of a Driver\u2019s Drowsiness[C]\/\/ 23rd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems 2019."},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-its.2012.0032"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.3390\/s131216494"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSI.2012.2185290"},{"key":"e_1_3_2_35_2","doi-asserted-by":"crossref","unstructured":"MoralesJ.M. Ruiz-RabeloJ.F. Diaz-PiedraC. et al. Detecting Mental Workload in Surgical Teams Using a Wearable Single-Channel Electroencephalographic Device[J] Journal of Surgical Education 2019.","DOI":"10.1016\/j.jsurg.2019.01.005"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2019.02.005"},{"key":"e_1_3_2_37_2","doi-asserted-by":"crossref","unstructured":"ZhangY.F. GaoX.Y. ZhuJ.Y. et al. A novel approach to driving fatigue detection using forehead EOG[C]\/\/2015 7th International IEEE\/EMBS Conference on Neural Engineering (NER). IEEE (2015) 707\u2013710.","DOI":"10.1109\/NER.2015.7146721"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2008.01.085"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2010.2077291"},{"key":"e_1_3_2_40_2","doi-asserted-by":"crossref","unstructured":"ArtantoD. SulistyantoM.P. PranowoI.D. et al. Drowsiness detection system based on eye-closure using a low-cost EMG and ESP8266[C]\/\/ 2017 2nd International conferences on Information Technology Information Systems and Electrical Engineering (ICITISEE). 2017.","DOI":"10.1109\/ICITISEE.2017.8285502"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jelekin.2004.08.002"},{"issue":"3","key":"e_1_3_2_42_2","first-page":"494","article-title":"Muscular fatigue detection using sEMG in dynamic contractions[J]","volume":"2015","author":"Bueno D.R.","year":"2015","unstructured":"BuenoD.R., LizanoJ.M. and MontanoL., Muscular fatigue detection using sEMG in dynamic contractions[J]. Conf Proc IEEE Eng Med Biol Soc 2015(3) (2015), 494\u2013497.","journal-title":"Conf Proc IEEE Eng Med Biol Soc"},{"key":"e_1_3_2_43_2","first-page":"01019","article-title":"A review on fatigue driving detection[C]\/\/ITM Web of Conferences","volume":"12","author":"Shi S.Y.","year":"2017","unstructured":"ShiS.Y., TangW.Z. and WangY.Y., A review on fatigue driving detection[C]\/\/ITM Web of Conferences. EDP Sciences 12 (2017), 01019.","journal-title":"EDP Sciences"},{"key":"e_1_3_2_44_2","unstructured":"HongW.Detection and Alleviation of Driving Fatigue Based on EMG and EMS\/EEG Using Wearable Sensor[C]\/\/ 5th EAI International Conference on Wireless Mobile Communication and Healthcare - \u201cTransforming healthcare through innovations in mobile and wireless technologies\". ICST (Institute for Computer Sciences Social-Informatics and Telecommunications Engineering) 2015."},{"issue":"99","key":"e_1_3_2_45_2","first-page":"1","article-title":"Detecting Human Driver Inattentive and Aggressive Driving Behavior using Deep Learning: Recent Advances Requirements and Open Challenges[J]","author":"Alkinani M.H.","year":"2020","unstructured":"AlkinaniM.H., KhanW.Z. and ArshadQ., Detecting Human Driver Inattentive and Aggressive Driving Behavior using Deep Learning: Recent Advances Requirements and Open Challenges[J]. IEEE Access PP(99) (2020), 1\u20131.","journal-title":"IEEE Access"},{"key":"e_1_3_2_46_2","doi-asserted-by":"crossref","unstructured":"GuJ. WangZ. et al. Recent advances in convolutional neural networks[J] Pattern Recognition the Journal of the Pattern Recognition Society 2018.","DOI":"10.1016\/j.patcog.2017.10.013"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1561\/2000000039"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1109\/12.106218"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1016\/0165-0114(92)90086-J"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2014.2375591"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.5958\/2249-7315.2016.00153.2"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2020.2992856"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSI.2006.884408"},{"key":"e_1_3_2_55_2","doi-asserted-by":"crossref","unstructured":"ViswanathanM. ZhangZ.X. TianX.W. et al. A fatigue detection algorithm by heart rate variability based on a neuro-fuzzy network[C]\/\/ International Conference on Ubiquitous Information Management & Communication. DBLP 2011.","DOI":"10.1145\/1968613.1968712"},{"key":"e_1_3_2_56_2","doi-asserted-by":"crossref","unstructured":"NgxandeM. TapamoJ.R. BurkeM.Driver drowsiness detection using behavioral measures and machine learning techniques: A review of state-of-art techniques[C]\/\/ 2017 Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASARobMech). IEEE 2018.","DOI":"10.1109\/RoboMech.2017.8261140"},{"issue":"3","key":"e_1_3_2_57_2","first-page":"1","article-title":"Towards Detection of Bus Driver Fatigue Based on Robust Visual Analysis of Eye State[J]","author":"Mandal B.","year":"2017","unstructured":"MandalB., LiL., GangS.W., et al. Towards Detection of Bus Driver Fatigue Based on Robust Visual Analysis of Eye State[J]. IEEE Transactions on Intelligent Transportation Systems PP(3) (2017), 1\u201313.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"e_1_3_2_58_2","doi-asserted-by":"crossref","unstructured":"MittalA. KumarK. DhamijaS. et al. Head movementbased driver drowsiness detection: A review of state-of-art techniques[C]\/\/ IEEE International Conference on Engineering & Technology. IEEE 2016.","DOI":"10.1109\/ICETECH.2016.7569378"},{"key":"e_1_3_2_59_2","first-page":"01019","article-title":"A review on fatigue driving detection[C]\/\/ITM Web of Conferences","volume":"12","author":"Shi S.Y.","year":"2017","unstructured":"ShiS.Y., TangW.Z. and WangY.Y., A review on fatigue driving detection[C]\/\/ITM Web of Conferences. EDP Sciences 12 (2017), 01019.","journal-title":"EDP Sciences"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-com.2010.0925"},{"key":"e_1_3_2_61_2","doi-asserted-by":"crossref","unstructured":"OmidyeganehM. JavadtalabA. ShirmohammadiS.Intelligent driver drowsiness detection through fusion of yawning and eye closure[C]\/\/ IEEE International Conference on Virtual Environments Human-computer Interfaces & Measurement Systems. IEEE 2011.","DOI":"10.1109\/VECIMS.2011.6053857"},{"key":"e_1_3_2_62_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3025818"},{"key":"e_1_3_2_63_2","doi-asserted-by":"crossref","unstructured":"TaoH. ZhangG. YongZ. et al. Real-time driver fatigue detection based on face alignment[C]\/\/ International Conference on Digital Image Processing. 2017.","DOI":"10.1117\/12.2282043"},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci11020240"},{"key":"e_1_3_2_65_2","doi-asserted-by":"crossref","unstructured":"AkroutB. MahdiW.Yawning detection by the analysis of variational descriptor for monitoring driver drowsiness[C]\/\/ Image Processing Applications & Systems. IEEE 2016.","DOI":"10.1109\/IPAS.2016.7880127"},{"key":"e_1_3_2_66_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.02.014"},{"key":"e_1_3_2_67_2","doi-asserted-by":"publisher","DOI":"10.20473\/jisebi.7.1.22-30"},{"key":"e_1_3_2_68_2","doi-asserted-by":"crossref","unstructured":"RuizN. ChongE. RehgJ.M.Fine-Grained Head Pose EstimationWithout Keypoints[C]\/\/ 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE 2018.","DOI":"10.1109\/CVPRW.2018.00281"},{"issue":"99","key":"e_1_3_2_69_2","first-page":"1","article-title":"Unsupervised Drowsy Driving Detection with RFID[J]","author":"Yang C.","year":"2020","unstructured":"YangC., WangX. and MaoS., Unsupervised Drowsy Driving Detection with RFID[J]. IEEE Transactions on Vehicular Technology PP(99) (2020), 1\u20131.","journal-title":"IEEE Transactions on Vehicular Technology"},{"key":"e_1_3_2_70_2","doi-asserted-by":"crossref","unstructured":"ZhangN. ZhangH. HuangJ.Driver Fatigue State Detection Based on Facial Key Points[C]\/\/ 2019 6th International Conference on Systems and Informatics (ICSAI). 2019.","DOI":"10.1109\/ICSAI48974.2019.9010478"},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-its.2017.0183"},{"key":"e_1_3_2_72_2","doi-asserted-by":"publisher","DOI":"10.3390\/fi11050115"},{"issue":"99","key":"e_1_3_2_73_2","first-page":"1","article-title":"Fatigue state detection based on multi-index fusion and state recognition network[J]","author":"Ji Y.","year":"2019","unstructured":"JiY., WangS., ZhaoY., et al. Fatigue state detection based on multi-index fusion and state recognition network[J]. IEEE Access PP(99) (2019), 1\u20131.","journal-title":"IEEE Access"},{"key":"e_1_3_2_74_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/8851485"},{"key":"e_1_3_2_75_2","doi-asserted-by":"crossref","unstructured":"XingJ. FangG. ZhongJ. et al. Application of Face Recognition Based on CNN in Fatigue Driving Detection[J] Journal of Guangdong Polytechnic Normal University 2019.","DOI":"10.1145\/3358331.3358387"},{"key":"e_1_3_2_76_2","doi-asserted-by":"crossref","unstructured":"JabbarR. ShinoyM. KharbecheM. et al. Driver drowsiness detection model using convolutional neural networks techniques for android application[C]\/\/2020 IEEE International Conference on Informatics IoT and Enabling Technologies (ICIoT). IEEE (2020) 237\u2013242.","DOI":"10.1109\/ICIoT48696.2020.9089484"},{"key":"e_1_3_2_77_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2958667"},{"key":"e_1_3_2_78_2","doi-asserted-by":"crossref","unstructured":"AnsariS. DuH. NaghdyF.Driver\u2019s Foot Trajectory Tracking for Safe Maneuverability Using New Modified reLU-BiLSTM Deep Neural Network[C]\/\/ 2020 IEEE International Conference on Systems Man and Cybernetics (SMC). IEEE 2020.","DOI":"10.1109\/SMC42975.2020.9283169"},{"key":"e_1_3_2_79_2","doi-asserted-by":"publisher","DOI":"10.1049\/ipr2.12373"},{"key":"e_1_3_2_80_2","doi-asserted-by":"crossref","unstructured":"AliN. HasanI. \u00d6zyerT. et al. Driver drowsiness detection by employing CNN and DLIB[C]\/\/2021 22nd International Arab Conference on Information Technology (ACIT). IEEE (2021) 1\u20135.","DOI":"10.1109\/ACIT53391.2021.9677197"},{"key":"e_1_3_2_81_2","doi-asserted-by":"crossref","unstructured":"XingJ. FangG. ZhongJ. et al. Application of face recognition based on CNN in fatigue driving 1587 detection[C]\/\/Proceedings of the 2019 International Conference on Artificial Intelligence and Advanced Manufacturing (2019) 1\u20135.","DOI":"10.1145\/3358331.3358387"},{"key":"e_1_3_2_82_2","doi-asserted-by":"crossref","unstructured":"KushwahJ.S. JainD. SinghP. et al. A Comprehensive System for Detecting Profound Tiredness for Automobile Drivers Using a CNN[M]\/\/Advanced Computing and Intelligent Technologies: Proceedings of ICACIT 2022. Singapore: Springer Nature Singapore (2022) 407\u2013415.","DOI":"10.1007\/978-981-19-2980-9_33"},{"key":"e_1_3_2_83_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3134222"},{"key":"e_1_3_2_84_2","doi-asserted-by":"crossref","unstructured":"MaX. ChauL.P. YapK.H.Depth video-based twostream convolutional neural networks for driver fatigue detection[C]\/\/2017 International Conference on Orange Technologies (ICOT). IEEE (2017) 155\u2013158.","DOI":"10.1109\/ICOT.2017.8336111"},{"key":"e_1_3_2_85_2","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging6030008"},{"key":"e_1_3_2_86_2","doi-asserted-by":"publisher","DOI":"10.3390\/app12031145"},{"issue":"1","key":"e_1_3_2_87_2","first-page":"2201","article-title":"Real-time detection of drowsiness related lane departures using steering wheel angle[C]\/\/Proceedings of the Human Factors and Ergonomics Society Annual Meeting","volume":"56","author":"McDonald A.D.","year":"2012","unstructured":"McDonaldA.D., SchwarzC., LeeJ.D., et al. Real-time detection of drowsiness related lane departures using steering wheel angle[C]\/\/Proceedings of the Human Factors and Ergonomics Society Annual Meeting. Sage CA: Los Angeles, CA: Sage Publications 56(1) (2012), 2201\u20132205.","journal-title":"Sage CA: Los Angeles, CA: Sage Publications"},{"key":"e_1_3_2_88_2","first-page":"549","article-title":"Fatigue driving detection system design based on driving behavior[C]\/\/2010 International Conference on Optoelectronics and Image Processing.","volume":"1","author":"Hailin W.","year":"2010","unstructured":"HailinW., HanhuiL. and ZhumeiS., Fatigue driving detection system design based on driving behavior[C]\/\/2010 International Conference on Optoelectronics and Image Processing.. IEEE 1 (2010), 549\u2013552.","journal-title":"IEEE"},{"key":"e_1_3_2_89_2","first-page":"1765","article-title":"Estimate of driver\u2019s fatigue through steering motion[C]\/\/2005 IEEE international conference on systems, man and cybernetics.","volume":"2","author":"Takei Y.","year":"2005","unstructured":"TakeiY. and FurukawaY., Estimate of driver\u2019s fatigue through steering motion[C]\/\/2005 IEEE international conference on systems, man and cybernetics.. IEEE 2 (2005), 1765\u20131770.","journal-title":"IEEE"},{"key":"e_1_3_2_90_2","unstructured":"DengQ. SoeffkerD. A Review of HMM-based Approaches of Driving Behaviors Recognition and Prediction[J] IEEE Transactions on Intelligent Vehicles 2021."},{"key":"e_1_3_2_91_2","doi-asserted-by":"crossref","unstructured":"SiegmundG.P. KingD.J. MumfordD.K.Correlation of Heavy-Truck Driver Fatigue with Vehicle-Based Control Measures[C]\/\/ International Truck & Bus Meeting & Exposition. 1995.","DOI":"10.4271\/952594"},{"key":"e_1_3_2_92_2","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-6670(17)55320-3"},{"key":"e_1_3_2_93_2","unstructured":"TakeiY. FurukawaY.Estimate of driver\u2019s fatigue through steering motion[C]\/\/ Systems Man and Cybernetics 2005 IEEE International Conference on. IEEE 2005."},{"key":"e_1_3_2_94_2","doi-asserted-by":"publisher","DOI":"10.3390\/s17030495"},{"key":"e_1_3_2_95_2","doi-asserted-by":"crossref","unstructured":"Zhen HaiG. DinhdatL. HongyuH. et al.Driver Drowsiness Detection Based on Time Series Analysis of Steering Wheel Angular Velocity[C]\/\/ International Conference on Measuring Technology & Mechatronics Automation. IEEE 2017.","DOI":"10.1109\/ICMTMA.2017.0031"},{"key":"e_1_3_2_96_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ergon.2021.103083"},{"key":"e_1_3_2_97_2","doi-asserted-by":"publisher","DOI":"10.1006\/cviu.2002.0958"},{"key":"e_1_3_2_98_2","unstructured":"BerglundJ.In-Vehicle Prediction of Truck Driver Sleepiness: Steering Related Variables[J] Institutionen Fr Systemteknik 2007."},{"key":"e_1_3_2_99_2","doi-asserted-by":"crossref","unstructured":"PomerleauD. JochemT.Rapidly Adapting Machine Vision for Automated Vehicle Steering[J] IEEE Expert Intelligent Systems & Thr App 1996.","DOI":"10.1109\/64.491277"},{"key":"e_1_3_2_100_2","doi-asserted-by":"crossref","unstructured":"BroggiA.A massively parallel approach to real time vision based road marking detection[J] Proc. IEEE Intelligent Vehicles\u201995 (1995) 84\u201389.","DOI":"10.1109\/IVS.1995.528262"},{"key":"e_1_3_2_101_2","doi-asserted-by":"crossref","unstructured":"BertozziM. BroggiA.Real-time Lane and obstacle detection on the gold system[C]\/\/Proceedings of Conference on Intelligent Vehicles IEEE (1996) 213\u2013218.","DOI":"10.1109\/IVS.1996.566380"},{"key":"e_1_3_2_102_2","doi-asserted-by":"crossref","unstructured":"HofmannU. RiederA. DickmannsE.D.Dickmanns EMS-vision: application to hybrid adaptive cruise control[C]\/\/ IEEE Intelligent Vehicles Symposium. IEEE 2000.","DOI":"10.1109\/IVS.2000.898387"},{"key":"e_1_3_2_103_2","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-6670(17)55320-3"},{"key":"e_1_3_2_104_2","doi-asserted-by":"crossref","unstructured":"PomerleauD.A.Neural Network Perception for Mobile Robot Guidance[M] Kluwer Academic Pub 1993.","DOI":"10.1007\/978-1-4615-3192-0"},{"key":"e_1_3_2_105_2","doi-asserted-by":"publisher","DOI":"10.1016\/0165-1684(93)90040-H"},{"key":"e_1_3_2_106_2","doi-asserted-by":"publisher","DOI":"10.1109\/70.210794"},{"key":"e_1_3_2_107_2","unstructured":"SeoJ.H. AnH.C. JeongS.S. KongY.G.Development of lane deviation warning and preventing system through vision system and steering control in 1998 Seoul ITS Congress CD-Rom 1998."},{"key":"e_1_3_2_108_2","unstructured":"SatoK. GotoT. KubotaY.Astudy on a lane departure warning system using a steering torque as awarning signal in Proc. AVEC\u2019 98 (1998) pp. 479\u2013484."},{"issue":"2","key":"e_1_3_2_109_2","first-page":"910","article-title":"Lane Departure Identification for Advanced Driver Assistance[J]","volume":"16","author":"Gaikwad V.","year":"2015","unstructured":"GaikwadV. and LokhandeS., Lane Departure Identification for Advanced Driver Assistance[J]. IEEE Transactions on Intelligent Transportation Systems 16(2) (2015), 910\u2013918.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"e_1_3_2_110_2","unstructured":"LeeJ.W. KeeC.D. YiU.K.A new approach for lane departure identification[C]\/\/ 4th Intelligent Vehicles Symposium. 2003."},{"key":"e_1_3_2_111_2","doi-asserted-by":"crossref","unstructured":"JungH. MinJ. KimJ.An efficient lane detection algorithm for lane departure detection[C]\/\/ Intelligent Vehicles Symposium. IEEE 2013.","DOI":"10.1109\/IVS.2013.6629593"},{"key":"e_1_3_2_112_2","unstructured":"BingY. ZhangW. CaiY.A Lane Departure Warning System Based on Machine Vision[C]\/\/Workshop on Computational Intelligence&Industrial Application. IEEE Computer Society 2008."},{"key":"e_1_3_2_113_2","doi-asserted-by":"crossref","unstructured":"HeJ. RongH. GongJ. et al.A Lane Detection Method for Lane Departure Warning System[C]\/\/ International Conference on Optoelectronics&Image Processing. IEEE 2010.","DOI":"10.1109\/ICOIP.2010.307"},{"key":"e_1_3_2_114_2","unstructured":"MiyajimaN.O. et al.Cepstral Analysis of Driving Behavioral Signals for Driver Identification[C]\/\/ IEEE International Conference on Acoustics. IEEE 2006."},{"key":"e_1_3_2_115_2","doi-asserted-by":"publisher","DOI":"10.1631\/jzus.C11a0195"},{"key":"e_1_3_2_116_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.egypro.2018.09.221"},{"key":"e_1_3_2_117_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.amar.2020.100114"},{"key":"e_1_3_2_118_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.sbspro.2014.01.130"},{"key":"e_1_3_2_119_2","doi-asserted-by":"publisher","DOI":"10.3390\/s17030495"},{"key":"e_1_3_2_120_2","unstructured":"FriedrichsF. YangB.Drowsiness monitoring by steering and lane data based features under real driving conditions[C]\/\/ European Signal Processing Conference. IEEE 2010."},{"key":"e_1_3_2_121_2","doi-asserted-by":"crossref","unstructured":"McDonaldA.D. Lee et al.Steering in a Random Forest: Ensemble Learning for Detecting Drowsiness-Related Lane Departures[J] HUMAN FACTORS -NEW YORK THEN SANTA MONICA- 2014.","DOI":"10.1177\/0018720813515272"},{"key":"e_1_3_2_122_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trd.2018.07.007"},{"key":"e_1_3_2_123_2","doi-asserted-by":"crossref","unstructured":"WakitaT.Driver Identification Using Driving Behavior Signals[J] IEICE Transactions on Information and Systems E89-D(3) (2006) 1188\u20131194.","DOI":"10.1093\/ietisy\/e89-d.3.1188"},{"key":"e_1_3_2_124_2","doi-asserted-by":"crossref","unstructured":"HailinW. HanhuiL. ZhumeiS.Fatigue Driving Detection System Design Based on Driving Behavior[C]\/\/ International Conference on Optoelectronics&Image Processing. IEEE 2011.","DOI":"10.1109\/ICOIP.2010.101"},{"key":"e_1_3_2_125_2","doi-asserted-by":"crossref","unstructured":"ZhangJ. ZhangY. ChenL. et al.A fuzzy control strategy and optimization for four wheel steering system[C]\/\/ Vehicular Electronics and Safety 2007. ICVES. IEEE International Conference on. IEEE 2008.","DOI":"10.1109\/ICVES.2007.4456359"},{"key":"e_1_3_2_126_2","doi-asserted-by":"publisher","DOI":"10.1109\/MCE.2015.2463373"},{"key":"e_1_3_2_127_2","doi-asserted-by":"crossref","unstructured":"SandbergD. WahdeM.Particle swarm optimization of feedforward neural networks for the detection of drowsy driving[C]\/\/2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence). IEEE (2008) 788\u2013793.","DOI":"10.1109\/IJCNN.2008.4633886"},{"key":"e_1_3_2_128_2","doi-asserted-by":"publisher","DOI":"10.1243\/0954407011528536"},{"key":"e_1_3_2_129_2","doi-asserted-by":"crossref","unstructured":"HailinW. HanhuiL. ZhumeiS.Fatigue Driving Detection System Design Based on Driving Behavior[C]\/\/ International Conference on Optoelectronics&Image Processing. IEEE 2011.","DOI":"10.1109\/ICOIP.2010.101"},{"key":"e_1_3_2_130_2","doi-asserted-by":"publisher","DOI":"10.1177\/1550147719872452"},{"key":"e_1_3_2_131_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trd.2018.07.007"},{"key":"e_1_3_2_132_2","doi-asserted-by":"publisher","DOI":"10.3390\/s21072372"},{"key":"e_1_3_2_133_2","doi-asserted-by":"publisher","DOI":"10.1177\/1550147719872452"},{"key":"e_1_3_2_134_2","doi-asserted-by":"publisher","DOI":"10.3390\/s150924191"},{"key":"e_1_3_2_135_2","doi-asserted-by":"crossref","unstructured":"SinghP. SharmaR. TomarY. et al.Exploring CNN for Driver Drowsiness Detection Towards Smart Vehicle Development[M]\/\/Revolutionizing Industrial Automation Through the Convergence of Artificial Intelligence and the Internet of Things. IGI Global (2023) 213\u2013228.","DOI":"10.4018\/978-1-6684-4991-2.ch011"},{"issue":"1","key":"e_1_3_2_136_2","article-title":"HybridFatigue: A real-time driver drowsiness detection using hybrid features and transfer learning[J],","author":"Abbas Q.","year":"2020","unstructured":"AbbasQ., HybridFatigue: A real-time driver drowsiness detection using hybrid features and transfer learning[J],. International Journal of Advanced Computer Science and Applications 11 (1) (2020).","journal-title":"International Journal of Advanced Computer Science and Applications 11"},{"key":"e_1_3_2_137_2","doi-asserted-by":"crossref","unstructured":"SaA. SsA. AeA. et al.Applying deep neural networks for multi-level classification of driver drowsiness using Vehicle-based measures[J] Expert Systems with Applications (2020) 162.","DOI":"10.1016\/j.eswa.2020.113778"},{"key":"e_1_3_2_138_2","unstructured":"GriesbachK. BeggiatoM. Hoffmann LaneK.H. change prediction with an echo state network and recurrentneural network in the urban area[J] IEEE Transactions on Intelligent Transportation Systems 2021."},{"key":"e_1_3_2_139_2","doi-asserted-by":"crossref","unstructured":"YarlagaddaV. KoolagudiS.G. ManojK.M.V. et al. Driver Drowsiness Detection Using Facial Parameters and RNNs with LSTM[C]\/\/ 2020 IEEE 17th India Council International Conference (INDICON). IEEE 2020.","DOI":"10.1109\/INDICON49873.2020.9342348"},{"key":"e_1_3_2_140_2","doi-asserted-by":"publisher","DOI":"10.1049\/ipr2.12373"},{"key":"e_1_3_2_141_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2993285"},{"key":"e_1_3_2_142_2","doi-asserted-by":"crossref","unstructured":"ZhuW.B. YangH. JinY. et al.A method for recognizing fatigue driving based on dempster-shafer theory and fuzzy neural network[J] Mathematical Problems in Engineering 2017.","DOI":"10.1155\/2017\/6191035"},{"issue":"99","key":"e_1_3_2_143_2","article-title":"A Survey on state-of-the-art Drowsiness Detection Techniques[J]","author":"Ramzan M.","year":"2019","unstructured":"RamzanM., KhanH.U., AwanS.M., et al. A Survey on state-of-the-art Drowsiness Detection Techniques[J]. IEEE Access PP(99) (2019).","journal-title":"IEEE Access"},{"key":"e_1_3_2_144_2","first-page":"01019","article-title":"A review on fatigue driving detection[C]\/\/ITM Web of Conferences","volume":"12","author":"Shi S.Y.","year":"2017","unstructured":"ShiS.Y., TangW.Z. and WangY.Y., A review on fatigue driving detection[C]\/\/ITM Web of Conferences. EDP Sciences 12 (2017), 01019.","journal-title":"EDP Sciences"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems: Applications in Engineering and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-235075","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-235075","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-235075","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:43:17Z","timestamp":1777455797000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-235075"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,18]]},"references-count":143,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1,10]]}},"alternative-id":["10.3233\/JIFS-235075"],"URL":"https:\/\/doi.org\/10.3233\/jifs-235075","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,18]]}}}