[1]

Knipling RR. 1993. IVHS technologies applied to collision avoidance: Perspectives on six target crash types and countermeasures. Proceedings of the 1993 Annual Meeting of IVHS America: surface transportation: mobility, technology, and society, Washington, D.C., 1993. Washington, D.C.: IVHS America. pp. 249−59
[2]

Treat JR, Tumbas N, McDonald ST, Shinar D, Hume RD, et al. 1979. Tri-level study of the causes of traffic accidents: final report. Executive summary, Indiana University, Bloomington, Institute for Research in Public Safety.
[3]

Williams GW. 1963. Highway hypnosis: An hypothesis. International Journal of Clinical and Experimental Hypnosis 11:143−51

doi: 10.1080/00207146308409239
[4]

Williams GW, Shor RE. 1970. An historical note on highway hypnosis. Accident Analysis & Prevention 2:223−25

doi: 10.1016/0001-4575(70)90044-8
[5]

Shor RE, Thackray RI. 1970. A program of research in "highway hypnosis" a preliminary report. Accident Analysis & Prevention 2:103−9

doi: 10.1016/0001-4575(70)90070-9
[6]

Brown ID. 1991. Highway hypnosis: Implications for road traffic researches and practitioners. Vision In Vehicles. https://api.semanticscholar.org/CorpusID:112304819
[7]

Cerezuela GP, Tejero P, Chóliz M, Chisvert M, Monteagudo MJ. 2004. Wertheim's hypothesis on 'highway hypnosis': empirical evidence from a study on motorway and conventional road driving. Accident Analysis & Prevention 36:1045−54

doi: 10.1016/j.aap.2004.02.002
[8]

Shi H, Chen L, Wang X, Wang B, Wang G, et al. 2023. Research on recognition of road hypnosis in the typical monotonous scene. Sensors 23:1701

doi: 10.3390/s23031701
[9]

Wang B, Shi H, Chen L, Wang X, Wang G, et al. 2023. A recognition method for road hypnosis based on physiological characteristics. Sensors 23:3404

doi: 10.3390/s23073404
[10]

Konstantopoulos P, Chapman P, Crundall D. 2010. Driver's visual attention as a function of driving experience and visibility, using a driving simulator to explore drivers' eye movements in day, night and rain driving. Accident Analysis & Prevention 42:827−34

doi: 10.1016/j.aap.2009.09.022
[11]

Wang YK, Jung TP, Lin CT. 2015. EEG-based attention tracking during distracted driving. IEEE Transactions on Neural Systems & Rehabilitation Engineering 23(6):1085−94

doi: 10.1109/TNSRE.2015.2415520
[12]

Deng T, Yang K, Li Y, Yan H. 2016. Where does the driver look? Top-down-based saliency detection in a traffic driving environment. IEEE Transactions on Intelligent Transportation Systems 17:2051−62

doi: 10.1109/TITS.2016.2535402
[13]

Hills PJ, Thompson C, Pake JM. 2018. Detrimental effects of carryover of eye movement behaviour on hazard perception accuracy: Effects of driver experience, difficulty of task, and hazardousness of road. Transportation Research Part F: Traffic Psychology & Behaviour 58:906−16

doi: 10.1016/j.trf.2018.07.014
[14]

Palazzi A, Abati D, Solera F, Cucchiara R. 2019. Predicting the driver's focus of attention: the DR(eye)VE project. IEEE Transactions on Pattern Analysis & Machine Intelligence 41:1720−33

doi: 10.1109/TPAMI.2018.2845370
[15]

Barlow Z, Jashami H, Sova A, Hurwitz DS, Olsen MJ. 2019. Policy processes and recommendations for Unmanned Aerial System operations near roadways based on visual attention of drivers. Transportation research part C: emerging technologies 108:207−22

doi: 10.1016/j.trc.2019.09.012
[16]

Young KL, Osborne R, Grzebieta R, Williamson A, Haworth N, et al. 2020. Using naturalistic driving data to examine how drivers share attention when engaging in secondary tasks. Safety Science 129:104841

doi: 10.1016/j.ssci.2020.104841
[17]

Kimura M, Kimura K, Takeda Y. 2022. Assessment of driver’s attentional resource allocation to visual, cognitive, and action processing by brain and eye signals. Transportation Research Part F: Traffic Psychology & Behaviour 86:161−77

doi: 10.1016/j.trf.2022.02.009
[18]

Long Y, Huang J, Zhao X, Li Z. 2022. Does LSTM outperform 4DDTW-KNN in lane change identification based on eye gaze data? Transportation Research Part C: Emerging Technologies 137:103583

doi: 10.1016/j.trc.2022.103583
[19]

Habibifar N, Salmanzadeh H. 2022. Improving driving safety by detecting negative emotions with biological signals: Which is the best? Transportation Research Record 2676:334−49

doi: 10.1177/03611981211041594
[20]

Wiberg H, Nilsson E, Lindén P, Svanberg B, Poom L. 2015. Physiological responses related to moderate mental load during car driving in field conditions. Biological Psychology 108:115−25

doi: 10.1016/j.biopsycho.2015.03.017
[21]

Hu J. 2017. Automated detection of driver fatigue based on AdaBoost classifier with EEG signals. Frontiers in Computational Neuroscience 11:72

doi: 10.3389/fncom.2017.00072
[22]

Du G, Wang Z, Li C, Liu PX. 2021. A TSK-type convolutional recurrent fuzzy network for predicting driving fatigue. IEEE Transactions on Fuzzy Systems 29:2100−11

doi: 10.1109/TFUZZ.2020.2992856
[23]

Zhang C, Sun L, Cong F, Kujala T, Ristaniemi T, et al. 2020. Optimal imaging of multi-channel EEG features based on a novel clustering technique for driver fatigue detection. Biomedical Signal Processing and Control 62:102103

doi: 10.1016/j.bspc.2020.102103
[24]

Awais M, Badruddin N, Drieberg M. 2017. A hybrid approach to detect driver drowsiness utilizing physiological signals to improve system performance and wearability. Sensors 17:1991

doi: 10.3390/s17091991
[25]

Murugan S, Selvaraj J, Sahayadhas A. 2020. Detection and analysis: Driver state with electrocardiogram (ECG). Physical & Engineering Sciences in Medicine 43:525−37

doi: 10.1007/s13246-020-00853-8
[26]

Ramos G, Vaz J, Mendonça G, Pezarat-Correia P, Rodrigues J, et al. 2020. Fatigue evaluation through machine learning and a global fatigue descriptor. Journal of Healthcare Engineering 2020:6484129

doi: 10.1155/2020/6484129
[27]

Barua S, Ahmed MU, Ahlström C, Begum S. 2019. Automatic driver sleepiness detection using EEG, EOG and contextual information. Expert Systems with Applications 115:121−35

doi: 10.1016/j.eswa.2018.07.054
[28]

Mårtensson H, Keelan O, Ahlström C. 2019. Driver sleepiness classification based on physiological data and driving performance from real road driving. IEEE Transactions on Intelligent Transportation Systems 20:421−30

doi: 10.1109/TITS.2018.2814207
[29]

Wang L, Wang H, Jiang X. 2017. A new method to detect driver fatigue based on EMG and ECG collected by portable non-contact sensors. Promet - Traffic & Transportation 29:479−88

doi: 10.7307/ptt.v29i5.2244