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Survey; Review; Driver monitoring; Driver state; Sensor; Indicator; Drowsiness; Mental workload; Distraction; Emotions; Under the influence
Abstract :
[en] Road vehicle accidents are mostly due to human errors, and many such accidents could be avoided by continuously monitoring the driver. Driver monitoring (DM) is a topic of growing interest in the automotive industry, and it will remain relevant for all vehicles that are not fully autonomous, and thus for decades for the average vehicle owner. The present paper focuses on the first step of DM, which consists of characterizing the state of the driver. Since DM will be increasingly linked to driving automation (DA), this paper presents a clear view of the role of DM at each of the six SAE levels of DA. This paper surveys the state of the art of DM, and then synthesizes it, providing a unique, structured, polychotomous view of the many characterization techniques of DM. Informed by the survey, the paper characterizes the driver state along the five main dimensions—called here “(sub)states”—of drowsiness, mental workload, distraction, emotions, and under the influence. The polychotomous view of DM is presented through a pair of interlocked tables that relate these states to their indicators (e.g., the eye-blink rate) and the sensors that can access each of these indicators (e.g., a camera). The tables factor in not only the effects linked directly to the driver, but also those linked to the (driven) vehicle and the (driving) environment. They show, at a glance, to concerned researchers, equipment providers, and vehicle manufacturers (1) most of the options they have to implement various forms of advanced DM systems, and (2) fruitful areas for further research and innovation.
Research Center/Unit :
Montefiore Institute - Montefiore Institute of Electrical Engineering and Computer Science - ULiège Telim
Disciplines :
Electrical & electronics engineering
Author, co-author :
Halin, Anaïs ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications
Verly, Jacques ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Dép. d'électric., électron. et informat. (Inst.Montefiore)
Van Droogenbroeck, Marc ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications
Language :
English
Title :
Survey and synthesis of state of the art in driver monitoring
Publication date :
18 August 2021
Journal title :
Sensors
ISSN :
1424-8220
eISSN :
1424-3210
Publisher :
Multidisciplinary Digital Publishing Institute (MDPI), Switzerland
Singh, S., (2018) Critical Reasons for Crashes Investigated in the National Motor Vehicle Crash Causation Survey, , Technical Report
National Highway Traffic Safety Administration: Washington, DC, USA
Wouters, P., Bos, J., Traffic accident reduction by monitoring driver behaviour with in-car data recorders (2000) Accid. Anal. Prev, 32, pp. 643-650
Aidman, E., Chadunow, C., Johnson, K., Reece, J., Real-time driver drowsiness feedback improves driver alertness and selfreported driving performance (2015) Accid. Anal. Prev, 81, pp. 8-13
(2021) Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles, , SAE International. Technical Report SAE Standard J3016_202104
Society of Automobile Engineers: Warrendale, PA, USA
El Khatib, A., Ou, C., Karray, F., Driver inattention detection in the context of next-generation autonomous vehicles design: A survey (2020) IEEE Trans. Intell. Transp. Syst, 21, pp. 4483-4496
Johns, M., Sibi, S., Ju, W., Effect of cognitive load in autonomous vehicles on driver performance during transfer of control (2014) Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 1-4. , Association for Computing Machinery: Seattle, WA, USA
Gutiérrez, J., Rodríguez, V., Martin, S., Comprehensive Review of Vision-Based Fall Detection Systems (2021) Sensors, 21, p. 947
Ahir, A., Gohokar, V., Driver inattention monitoring system: A review Proceedings of the 2019 International Conference on Innovative Trends and Advances in Engineering and Technology (ICITAET), pp. 188-194. , Shegoaon, India, 27–28 December 2019
Alluhaibi, S., Al-Din, M., Moyaid, A., Driver behavior detection techniques: A survey (2018) Int. J. Appl. Eng. Res, 13, pp. 8856-8861
Arun, S., Sundaraj, K., Murugappan, M., Driver inattention detection methods: A review Proceedings of the 2012 IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (STUDENT), pp. 1-6. , Kuala Lumpur, Malaysia, 6–9 October 2012
Balandong, R., Ahmad, R., Saad, M., Malik, A., A Review on EEG-Based Automatic Sleepiness Detection Systems for Driver (2018) IEEE Access, 6, pp. 22908-22919
Begum, S., Intelligent driver monitoring systems based on physiological sensor signals: A review Proceedings of the 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), pp. 282-289. , The Hague, The Netherlands, 6–9 October 2013
Chacon-Murguia, M., Prieto-Resendiz, C., Detecting driver drowsiness: A survey of system designs and technology (2015) IEEE Consum. Electron. Mag, 4, pp. 107-119
Chan, T., Chin, C., Chen, H., Zhong, X., A Comprehensive Review of Driver Behavior Analysis Utilizing Smartphones (2020) IEEE Trans. Intell. Transp. Syst, 21, pp. 4444-4475
Chhabra, R., Verma, S., Krishna, C.R., A survey on driver behavior detection techniques for intelligent transportation systems Proceedings of the 2017 7th International Conference on Cloud Computing, Data Science & Engineering—Confluence, pp. 36-41. , Noida, India, 12–13 January 2017
Chowdhury, A., Shankaran, R., Kavakli, M., Haque, M.M., Sensor applications and physiological features in drivers’ drowsiness detection: A review (2018) IEEE Sens. J, 18, pp. 3055-3067
Chung, W.Y., Chong, T.W., Lee, B.G., Methods to detect and reduce driver stress: A review (2019) Int. J. Automot. Technol, 20, pp. 1051-1063
Coetzer, R., Hancke, G., Driver fatigue detection: A survey Proceedings of the 2006 6th World Congress on Intelligent Control and Automation, pp. 1-6. , Dalian, China, 21–23 June 2006
Dababneh, L., El-Gindy, M., Driver vigilance level detection systems: A literature survey (2015) Int. J. Veh. Perform. (IJVP), 2, pp. 1-29
Dahiphale, V., Rao, S., A review paper on portable driver monitoring system for teal time fatigue Proceedings of the 2015 International Conference on Computing Communication Control and Automation, pp. 558-560. , Pune, India, 26–27 Febuary 2015
Dong, Y., Hu, Z., Uchimura, K., Murayama, N., Driver inattention monitoring system for intelligent vehicles: A review (2011) IEEE Trans. Intell. Transp. Syst, 12, pp. 596-614
Ghandour, R., Neji, B., El-Rifaie, A., Al Barakeh, Z., Driver distraction and stress detection systems: A review (2020) Int. J. Eng. Appl. Sci. (IJEAS), p. 7
Hecht, T., Feldhütter, A., Radlmayr, J., Nakano, Y., Miki, Y., Henle, C., Bengler, K., A review of driver state monitoring systems in the context of automated driving (2018) Congress of the International Ergonomics Association (IEA), pp. 398-408. , Springer: Florence, Italy
Kang, H., Various approaches for driver and driving behavior monitoring: A review Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 616-623. , Sydney, NSW, Australia, 2–8 December 2013
Kaplan, S., Guvensan, M., Yavuz, A., Karalurt, Y., Driver behavior analysis for safe driving: A survey (2015) IEEE Trans. Intell. Transp. Syst, 16, pp. 3017-3032
Kaye, S.A., Lewis, I., Freeman, J., Comparison of self-report and objective measures of driving behavior and road safety: A systematic review (2018) J. Saf. Res, 65, pp. 141-151
Khan, M., Lee, S., A Comprehensive Survey of Driving Monitoring and Assistance Systems (2019) Sensors, 19, p. 2574
Kumari, B., Kumar, P., A survey on drowsy driver detection system Proceedings of the International Conference on Big Data Analytics and Computational Intelligence (ICBDAC), pp. 272-279. , Chirala, India, 23–25 March 2017
Lal, S., Craig, A., A critical review of the psychophysiology of driver fatigue (2001) Biol. Psychol, 55, pp. 173-194
Laouz, H., Ayad, S., Terrissa, L., Literature review on driverś drowsiness and fatigue detection Proceedings of the International Conference on Intelligent Systems and Computer Vision (ISCV), pp. 1-7. , Fez, Morocco, 9–11 June 2020
Leonhardt, S., Leicht, L., Teichmann, D., Unobtrusive Vital Sign Monitoring in Automotive Environments—A Review (2018) Sensors, 18, p. 3080
Liu, F., Li, X., Lv, T., Xu, F., A review of driver fatigue detection: Progress and prospect Proceedings of the IEEE International Conference on Consumer Electronics (ICCE), pp. 1-6. , Las Vegas, NV, USA, 11–13 January 2019
Marquart, G., Cabrall, C., de Winter, J., Review of Eye-related Measures of Drivers’ Mental Workload (2015) Procedia Manuf, 3, pp. 2854-2861
Marina Martinez, C., Heucke, M., Wang, F., Gao, B., Cao, D., Driving style recognition for intelligent vehicle control and advanced driver assistance: A survey (2018) IEEE Trans. Intell. Transp. Syst, 19, pp. 666-676
Mashko, A., Review of approaches to the problem of driver fatigue and drowsiness Proceedings of the Smart Cities Symposium Prague (SCSP), pp. 1-5. , Prague, Czech Republic, 24–25 June 2015
Mashru, D., Gandhi, V., Detection of a drowsy state of the driver on road using wearable sensors: A survey Proceedings of the International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 691-695. , Coimbatore, India, 20–21 April 2018
Melnicuk, V., Birrell, S., Crundall, E., Jennings, P., Towards hybrid driver state monitoring: Review, future perspectives and the role of consumer electronics Proceedings of the IEEE Intelligent Vehicles Symposium, IV, pp. 1392-1397. , Gothenburg, Sweden, 19–22 June 2016
Mittal, A., Kumar, K., Dhamija, S., Kaur, M., Head movement-based driver drowsiness detection: A review of state-of-art techniques Proceedings of the IEEE International Conference on Engineering and Technology (ICETECH), pp. 903-908. , Coimbatore, India, 17–18 March 2016
Murugan, S., Selvaraj, J., Sahayadhas, A., Analysis of different measures to detect driver states: A review Proceedings of the IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), pp. 1-6. , Pondicherry, India, 29–30 March 2019
Nair, I., Ebrahimkutty, N., Priyanka, B., Sreeja, M., Gopu, D., A survey on driver fatigue-drowsiness detection system (2016) Int. J. Eng. Comput. Sci, 5, pp. 19237-19240
Němcová, A., Svozilová, V., Bucsuházy, K., Smišek, R., Mézl, M., Hesko, B., Belák, M., Seitl, M., Multimodal features for detection of driver stress and fatigue: Review (2021) IEEE Trans. Intell. Transp. Syst, 22, pp. 3214-3233
Ngxande, M., Tapamo, J., Burke, M., Driver drowsiness detection using behavioral measures and machine learning techniques: A review of state-of-art techniques (2017) Proceedings of the Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA-RobMech), pp. 156-161. , Bloemfontein, South Africa, 30 November–1 December
Oviedo-Trespalacios, O., Haque, M., King, M., Washington, S., Understanding the impacts of mobile phone distraction on driving performance: A systematic review (2016) Transp. Res. Part C Emerg. Technol, 72, pp. 360-380
Papantoniou, P., Papadimitriou, E., Yannis, G., Review of driving performance parameters critical for distracted driving research (2017) Transp. Res. Procedia, 25, pp. 1796-1805
Pratama, B., Ardiyanto, I., Adji, T., A review on driver drowsiness based on image, bio-signal, and driver behavior Proceedings of the International Conference on Science and Technology— Computer (ICST), pp. 70-75. , Yogyakarta, Indonesia, 11–12 July 2017
Ramzan, M., Khan, H., Awan, S., Ismail, A., Ilyas, M., Mahmood, A., A Survey on State-of-the-Art Drowsiness Detection Techniques (2019) IEEE Access, 7, pp. 61904-61919
Sahayadhas, A., Sundaraj, K., Murugappan, M., Detecting driver drowsiness based on sensors: A review (2012) Sensors, 12, pp. 16937-16953
Scott-Parker, B., Emotions, behaviour, and the adolescent driver: A literature review (2017) Transp. Res. Part F Traffic Psychol. Behav, 50, pp. 1-37
Seth, I., A Survey on Driver Behavior Detection Techniques (2020) Int. J. Sci. Res. Sci. Technol, 7, pp. 401-404
Shameen, Z., Yusoff, M., Saad, M., Malik, A., Muzammel, M., Electroencephalography (EEG) based drowsiness detection for drivers: A review (2018) ARPN J. Eng. Appl. Sci, 13, pp. 1458-1464
Sigari, M., Pourshahabi, M., Soryani, M., Fathy, M., A review on driver face monitoring systems for fatigue and distraction detection (2014) Int. J. Adv. Sci. Technol, 64, pp. 73-100
Sikander, G., Anwar, S., Driver Fatigue Detection Systems: A Review (2019) IEEE Trans. Intell. Transp. Syst, 20, pp. 2339-2352
Singh, H., Kathuria, A., Analyzing driver behavior under naturalistic driving conditions: A review (2021) Accid. Anal. Prev, 150, pp. 1-21
Subbaiah, D., Reddy, P., Rao, K., Driver drowsiness detection methods: A comprehensive survey (2019) Int. J. Res. Advent Technol, 7, pp. 992-997
Tu, W., Wei, L., Hu, W., Sheng, Z., Nicanfar, H., Hu, X., Ngai, E., Leung, V., A Survey on Mobile Sensing Based Mood-Fatigue Detection for Drivers (2016) Smart City 3600, 166, pp. 3-15. , Springer: Berlin/Heidelberg, Germany
Ukwuoma, C., Bo, C., Deep learning review on drivers drowsiness detection Proceedings of the Technology Innovation Management and Engineering Science International Conference (TIMES-iCON), pp. 1-5. , Bangkok, Thailand, 11–13 December 2019
Vilaca, A., Cunha, P., Ferreira, A., Systematic literature review on driving behavior (2017) Proceedings of the International Conference on Intelligent Transportation Systems (ITSC), pp. 1-8. , Yokohama, Japan, 16-19 October
Vismaya, U., Saritha, E., A review on driver distraction detection methods Proceedings of the International Conference on Communication and Signal Processing (ICCSP), pp. 483-487. , Chennai, India, 28–30 July 2020
Wang, Q., Yang, J., Ren, M., Zheng, Y., Driver fatigue detection: A survey (2006) Proceedings of the World Congress on Intelligent Control and Automation, 2, pp. 8587-8591. , Dalian, China, 21–23 June
Welch, K., Harnett, C., Lee, Y.C., A Review on Measuring Affect with Practical Sensors to Monitor Driver Behavior (2019) Safety, 5, p. 72
Yusoff, N., Ahmad, R., Guillet, C., Malik, A., Saad, N., Mérienne, F., Selection of measurement method for detection of driver visual cognitive distraction: A review (2017) IEEE Access, 5, pp. 22844-22854
Zhang, J., Qiu, W., Fu, H., Zhang, M., Ma, Q., Review of Techniques for Driver Fatigue Detection (2013) Appl. Mech. Mater, pp. 928-931. , 433, –435
Johns, M., A sleep physiologist’s view of the drowsy driver (2000) Transp. Res. Part F Traffic Psychol. Behav, 3, pp. 241-249
Massoz, Q., (2019) Non-Invasive, Automatic, and Real-Time Characterization of Drowsiness Based on Eye Closure Dynamics, , Ph.D. Thesis, University of Liège, Liège, Belgium
Johns, M., (2001) Assessing the Drowsiness of Drivers, , Unpublished Report
(2020) Overview of Motor Vehicle Crashes in 2019
Traffic Safety Facts Research Note. Report No. DOT HS 813 060, , National Center for Statistics and Analysis. Technical Report
National Highway Traffic Safety Administration: Washington, DC, USA
Critchley, M., On Sleepening (1992) Clin. Neurol. Neurosurg, 94, pp. 121-122
May, J., Baldwin, C., Driver fatigue: The importance of identifying causal factors of fatigue when considering detection and countermeasure technologies (2008) Transp. Res. Part F Traffic Psychol. Behav, 12, pp. 218-224
Ebrahimbabaie, P., (2020) Prediction of Risk of an Event Using Sensor Signals, with Application to the Prevention of Driving Accidents Due to Drowsiness, , Ph.D. Thesis, University of Liège, Liège, Belgium
François, C., (2018) Development and Validation of Algorithms for Automatic and Real-Time Characterization of Drowsiness, , Ph.D. Thesis, University of Liège, Liège, Belgium
Johns, M., Tucker, A., Chapman, R., Monitoring the drowsiness of drivers: A new method based on the velocity of eyelid movements Proceedings of the World Congress on Intelligent Transport Systems, pp. 1-16. , San Francisco, CA, USA, 6–11 November 2005
Aaronson, L., Teel, C., Cassmeyer, V., Neuberger, G., Pallikkathayil, L., Pierce, J., Press, A., Wingate, A., Defining and Measuring Fatigue (2007) J. Nurs. Scholarsh, 31, pp. 45-50
Shen, J., Barbera, J., Shapiro, C., Distinguishing sleepiness and fatigue: Focus on definition and measurement (2006) Sleep Med. Rev, 10, pp. 63-76
Tantisatirapong, S., Senavongse, W., Phothisonothai, M., Fractal dimension based electroencephalogram analysis of drowsiness patterns Proceedings of the International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), pp. 497-500. , Chiang Mai, Thailand, 19–21 May 2010
Vicente, J., Laguna, P., Bartra, A., Bailón, R., Drowsiness detection using heart rate variability (2016) Med. Biol. Eng. Comput, 54, pp. 927-937
Persson, A., Jonasson, H., Fredriksson, I., Wiklund, U., Ahlström, C., Heart rate variability for classification of alert versus sleep deprived drivers in real road driving conditions (2021) IEEE Trans. Intell. Transp. Syst, 22, pp. 3316-3325
Kiashari, S., Nahvi, A., Bakhoda, H., Homayounfard, A., Tashakori, M., Evaluation of driver drowsiness using respiration analysis by thermal imaging on a driving simulator (2020) Multimed. Tools Appl, 79, pp. 17793-17815
Michael, L., Passmann, S., Becker, R., Electrodermal lability as an indicator for subjective sleepiness during total sleep deprivation (2012) J. Sleep Res, 21, pp. 470-478
Lowenstein, O., Feinberg, R., Loewenfeld, I., Pupillary movements during acute and chronic fatigue: A new test for the objective evaluation of tiredness (1963) Investig. Ophthalmol. Vis. Sci, 2, pp. 138-157
Nishiyama, J., Tanida, K., Kusumi, M., Hirata, Y., The pupil as a possible premonitor of drowsiness Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1586-1589. , Lyon, France, 22–26 August 2007
Wilhelm, B., Wilhelm, H., Lüdtke, H., Streicher, P., Adler, M., Pupillographic Assessment of Sleepiness in Sleep-deprived Healthy Subjects (1998) Sleep, 21, pp. 258-265
Brown, M., Marmor, M., Zrenner, E., Brigell, M., Bach, M., ISCEV Standard for Clinical Electro-oculography (EOG) 2006 (2006) Doc. Ophthalmol, 113, pp. 205-212. , Vaegan
Schleicher, R., Galley, N., Briest, S., Galley, L., Blinks and saccades as indicators of fatigue in sleepiness warnings: Looking tired? (2008) Ergonomics, 51, pp. 982-1010
Dinges, D., Mallis, M., Maislin, G., Powell, J., (1998) Evaluation of Techniques for Ocular Measurement as an Index of Fatigue and the Basis for Alertness Management, , Technical Report DOT HS 808 762
National Highway Traffic Safety Administration: Washington, DC, USA
Dinges, D., Mallis, M., Maislin, G., Powell, J., (1998) PERCLOS, a Valid Psychophysiological Measure of Alertness as Assessed by Psychomotor Vigilance, , Technical Report FHWA-MCRT-98-006
FHWA: Washington, DC, USA
Wierwille, W., Ellsworth, L., Wreggit, S., Fairbanks, R., Kirn, C., Research on Vehicle-Based Driver Status/performance Monitoring
Development, Validation, and Refinement of Algorithms for Detection of Driver Drowsiness (2008) Scand. J. Work. Environ. Health, 34, pp. 142-150. , Technical Report DOT HS 808 247
National Highway Traffic Safety Administration: Washington, DC, USA, 1994. 87. Anund, A.
Kecklund, G.
Peters, B.
Forsman, Å.
Arne, L.
Åkerstedt, T. Driver impairment at night and its relation to physiological sleepiness
Lisper, H.O., Laurell, H., van Loon, J., Relation between time to falling asleep behind the wheel on a closed track and changes in subsidiary reaction time during prolonged driving on a motorway (1986) Ergonomics, 29, pp. 445-453
Hultman, M., Johansson, I., Lindqvist, F., Ahlström, C., Driver sleepiness detection with deep neural networks using electrophysiological data (2021) Int. J. Neurosci, 42, p. 29. , Physiol. Meas, 034001, 10.1088/1361-6579/abe91e. 90, Åkerstedt, T.
Gillberg, M. Subjective and objective sleepiness in the active individual. 1990, 52, –37
Hoddes, E., Zarcone, V., Smythe, H., Phillips, R., Dement, W., Quantification of sleepiness: A new approach (1973) Psychophysiology, 10, pp. 431-436
Monk, T., A visual analogue scale technique to measure global vigor and affect (1989) Psychiatry Res, 27, pp. 89-99
Forsman, P., Vila, B., Short, R., Mott, C., Van Dongen, H., Efficient driver drowsiness detection at moderate levels of drowsiness (2013) Accid. Anal. Prev, 50, pp. 341-350
Kircher, A., Uddman, M., Sandin, J., (2002) Vehicle Control and Drowsiness, , Technical Report
VTI: Linköping, Sweden
Wierwille, W., Ellsworth, L., Evaluation of driver drowsiness by trained raters (1994) Accid. Anal. Prev, 26, pp. 571-581
Godthelp, H., Milgram, P., Blaauw, G., The Development of a Time-Related Measure to Describe Driving Strategy (1984) Hum. Factors, 26, pp. 257-268
Liang, Y., Horrey, W., Howard, M., Lee, M., Anderson, C., Shreeve, M., O’Brien, C., Czeisler, C., Prediction of drowsiness events in night shift workers during morning driving (2019) Accid. Anal. Prev, 126, pp. 105-114
Liu, C., Hosking, S., Lenné, M., Predicting driver drowsiness using vehicle measures: Recent insights and future challenges (2009) J. Saf. Res, 40, pp. 239-245
Verwey, W., Zaidel, D., Predicting drowsiness accidents from personal attributes, eye blinks and ongoing driving behaviour (2000) Personal. Individ. Differ, 28, pp. 123-142
Arnedt, J., Wilde, G., Munt, P., MacLean, A., Simulated driving performance following prolonged wakefulness and alcohol consumption: Separate and combined contributions to impairment (2000) J. Sleep Res, 9, pp. 233-241
Thiffault, P., Bergeron, J., Monotony of road environment and driver fatigue: A simulator study (2003) Accid. Anal. Prev, 35, pp. 381-391
Jacobé de Naurois, C., Bourdin, C., Stratulat, A., Diaz, E., Vercher, J.L., Detection and prediction of driver drowsiness using artificial neural network models (2019) Accid. Anal. Prev, 126, pp. 95-104
Ebrahimbabaie, P., Verly, J., Excellent Potential of Geometric Brownian Motion (GBM) as a Random Process Model for Level of Drowsiness Signals (2018) International Joint Conference on Biomedical Engineering Systems and Technologies—BIOSIGNAL, pp. 105-112. , SciTePress: Madeira, Portugal
François, C., Hoyoux, T., Langohr, T., Wertz, J., Verly, J., Tests of a New Drowsiness Characterization and Monitoring System Based on Ocular Parameters (2016) Int. J. Environ. Res. Public Health, 13, p. 174
Silva, H., Lourenço, A., Fred, A., In-vehicle driver recognition based on hand ECG signals Proceedings of the ACM International Conference on Intelligent User Interfaces, pp. 25-28. , 14–17 February 2012
Leicht, L., Skobel, E., Mathissen, M., Leonhardt, S., Weyer, S., Wartzek, T., Reith, S., Teichmann, D., Capacitive ECG recording and beat-to-beat interval estimation after major cardiac event Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 7614-7617. , Milan, Italy, 25–29 August 2015
Wusk, G., Gabler, H., Non-invasive detection of respiration and heart rate with a vehicle seat sensor (2018) Sensors, 18, p. 1463
Zhang, Q., Wu, Q., Zhou, Y., Wu, X., Ou, Y., Zhou, H., Webcam-based, non-contact, real-time measurement for the physiological parameters of drivers (2017) Measurement, 100, pp. 311-321
Izumi, S., Matsunaga, D., Nakamura, R., Kawaguchi, H., Yoshimoto, M., (2017) A Contact-Less Heart Rate Sensor System for Driver Health Monitoring, , https://pdfs.semanticscholar.org/9059/6a41f8642c5854f88e02a3e121a151747434.pdf, (accessed on 17 August 2021)
Schires, E., Georgiou, P., Lande, T., Vital sign monitoring through the back using an UWB impulse radar with body coupled antennas (2018) IEEE Trans. Biomed. Circuits Syst, 12, pp. 292-302
Zhang, K., Zhang, L., Liu, Q., Zhang, D., Yang, M.H., Fast Visual Tracking via Dense Spatio-temporal Context Learning (2014) European Conference on Computer Vision (ECCV), 8693, pp. 127-141. , Springer: Berlin/Heidelberg, Germany
Massoz, Q., Verly, J., Van Droogenbroeck, M., Multi-Timescale Drowsiness Characterization Based on a Video of a Driver’s Face (2018) Sensors, 18, p. 2801
Zin, Z., Rodzi, A., Ibrahim, N., Vision based eye closeness classification for driver’s distraction and drowsiness using PERCLOS and support vector machines (2018) International Conference on Machine Vision (ICMV), 11041. , SPIE: Bellingham, WA, USA
Teyeb, I., Jemai, O., Zaied, M., Amar, C., Vigilance measurement system through analysis of visual and emotional driver’s signs using wavelet networks Proceedings of the International Conference on Intelligent Systems Design and Applications (ISDA), pp. 140-147. , Marrakech, Morocco, 14–16 December 2015
Teyeb, I., Jemai, O., Zaied, M., Amar, C., Towards a smart car seat design for drowsiness detection based on pressure distribution of the driver’s body Proceedings of the International Conference on Software Engineering Advances (ICSEA), pp. 217-222. , Rome, Italy, 21–25 August 2016
Bergasa, L., Nuevo, J., Sotelo, M., Barea, R., Lopez, M., Real-time system for monitoring driver vigilance (2006) IEEE Trans. Intell. Transp. Syst, 7, pp. 63-77
Baccour, M., Driewer, F., Schack, T., Kasneci, E., Camera-based driver drowsiness state classification using logistic regression models Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1-8. , Toronto, ON, Canada, 11–14 October 2020
Dreißig, M., Baccour, M., Schäck, T., Kasneci, E., Driver drowsiness classification based on eye blink and head movement features using the k-NN algorithm Proceedings of the Symposium Series on Computational Intelligence (SSCI), pp. 889-896. , Canberra, ACT, Australia, 1–4 December 2020
Fridman, L., Brown, D., Glazer, M., Angell, W., Dodd, S., Jenik, B., Terwilliger, J., Ding, L., MIT advanced vehicle technology study: Large-scale naturalistic driving study of driver behavior and interaction with automation (2019) IEEE Access, 7, pp. 102021-102038
Li, R., Liu, C., Luo, F., A design for automotive CAN bus monitoring system (2008) Proceedings of the IEEE Vehicle Power and Propulsion Conference, pp. 1-5. , Harbin, China, 3–5 September
Campbell, K., The SHRP 2 Naturalistic Driving Study (2012) TR News, 282, pp. 30-35
Apostoloff, N., Zelinsky, A., Robust vision based lane tracking using multiple cues and particle filtering Proceedings of the IEEE Intelligent Vehicles Symposium, pp. 558-563. , Columbus, OH, USA, 9–11 June 2003
Bakker, B., Zabłocki, B., Baker, A., Riethmeister, V., Marx, B., Iyer, G., Anund, A., Ahlström, C., A Multi-Stage, Multi-Feature Machine Learning Approach to Detect Driver Sleepiness in Naturalistic Road Driving Conditions (2021) IEEE Trans. Intell. Transp. Syst, pp. 1-10
Marquart, G., de Winter, J., Workload assessment for mental arithmetic tasks using the task-evoked pupillary response (2015) PeerJ Comput. Sci, 1, pp. 1-20
Hart, S., Staveland, L., Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research (1988) Adv. Psychol, 52, pp. 139-183
Borghini, G., Astolfi, L., Vecchiato, G., Mattia, D., Babiloni, F., Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness (2014) Neurosci. Biobehav. Rev, 44, pp. 58-75
O’Donnel, R., Eggemeier, F., Workload assessment methodology (1986) Cognitive Processes and Performance, pp. 1-49. , Wiley: Hoboken, NJ, USA, Chapter 42
Sanders, M., McCormick, E., (1998) Human Factors in Engineering and Design, 25. , Mcgraw-Hill Book Company: New York, NY, USA
Wickens, C., Hollands, J., Banbury, S., Parasuraman, R., (2015) Engineering Psychology and Human Performance, , Psychology Press: New York, NY, USA, doi
Schaap, T., Van der Horst, A., van Arem, B., Brookhuis, K., The relationship between driver distraction and mental workload (2013) Driver Distraction and Inattention: Advances in Research and Countermeasures, 1, pp. 63-80. , CRC Press: Boca Raton, FL, USA
Kajiwara, S., Evaluation of driver’s mental workload by facial temperature and electrodermal activity under simulated driving conditions (2014) Int. J. Autom. Technol, 15, pp. 65-70
Gable, T., Kun, A., Walker, B., Winton, R., Comparing heart rate and pupil size as objective measures of workload in the driving context: Initial look Proceedings of the 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 20-25. , Nottingham, UK, 1–3 September 2015
Paxion, J., Galy, E., Berthelon, C., Mental workload and driving (2014) Front. Psychol, 5, p. 1344
Reimer, B., Mehler, B., Coughlin, J., Godfrey, K., Tan, C., An on-road assessment of the impact of cognitive workload on physiological arousal in young adult drivers (2009) Proceedings of the International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 115-118. , Essen, Germany, September 21–22
Fournier, L., Wilson, G., Swain, C., Electrophysiological, behavioral, and subjective indexes of workload when performing multiple tasks: Manipulations of task difficulty and training (1999) Int. J. Psychophysiol, 31, pp. 129-145
Kim, J., Jeong, C., Jung, M., Park, J., Jung, D., Highly reliable driving workload analysis using driver electroencephalogram (EEG) activities during driving (2013) Int. J. Autom. Technol, 14, pp. 965-970
Kosch, T., Hassib, M., Buschek, D., Schmidt, A., Look into my eyes: Using pupil dilation to estimate mental workload for task complexity adaptation (2018) Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, pp. 1-6. , ACM: Montréal, QC, Canada
Pfleging, B., Fekety, D., Schmidt, A., Kun, A., A model relating pupil diameter to mental workload and lighting conditions (2016) Proceedings of the CHI Conference on Human Factors in Computing Systems, pp. 5776-5788. , San Jose, CA, USA, 7–12 May
Yokoyama, H., Eihata, K., Muramatsu, J., Fujiwara, Y., Prediction of driver’s workload from slow fluctuations of pupil diameter Proceedings of the International Conference on Intelligent Transportation Systems (ITSC), pp. 1775-1780. , Maui, HI, USA, 4–7 November 2018
Fridman, L., Reimer, B., Mehler, B., Freeman, W., Cognitive Load Estimation in the Wild Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1-9. , Montreal, Canada, 21–26 April 2018
Liao, Y., Li, S., Wang, W., Wang, Y., Li, G., Cheng, B., Detection of driver cognitive distraction: A comparison study of stop-controlled intersection and speed-limited highway (2016) IEEE Trans. Intell. Transp. Syst, 17, pp. 1628-1637
May, J., Kennedy, R., Williams, M., Dunlap, W., Brannan, J., Eye movement indices of mental workload (1990) Acta Psychol, 75, pp. 75-89
Palasek, P., Lavie, N., Palmer, L., Attentional demand estimation with attentive driving models (2019) Proceedings of the British Machine Vision Conference (BMVC), pp. 1-13. , Cardiff, Wales, UK, 9–12 September
Musabini, A., Chetitah, M., Heatmap-based method for estimating drivers’ cognitive distraction, , arXiv 2020, arXiv:abs/2005.14136
Le, A., Suzuki, T., Aoki, H., Evaluating driver cognitive distraction by eye tracking: From simulator to driving (2020) Transp. Res. Interdiscip. Perspect, 4, pp. 1-7
Hao, X., Wang, Z., Yang, F., Wang, Y., Guo, Y., Zhang, K., The effect of traffic on situation awareness and mental workload: Simulator-based study (2007) International Conference on Engineering Psychology and Cognitive Ergonomics (EPCE), pp. 288-296. , Springer: Berlin/Heidelberg, Germany
Young, K., Regan, M., Lee, J., Measuring the effects of driver distraction: Direct driving performance methods and measures (2009) Driver Distraction: Theory, Effects & Mitigation, pp. 85-105. , CRC Press: Boca Raton, FL, USA, Chapter 7
Ranney, T., Mazzae, E., Garrott, R., Goodman, M., (2000) NHTSA Driver Distraction Research: Past, Present, and Future, , Technical Report
SAE: Warrendale, PA, USA
Regan, M., Lee, J., Young, K., (2008) Driver Distraction: Theory, Effects, and Mitigation, , CRC Press: Boca Raton, FL, USA
Regan, M., Hallett, C., Gordon, C., Driver distraction and driver inattention: Definition, relationship and taxonomy (2011) Accid. Anal. Prev, 43, pp. 1771-1781
Almahasneh, H., Chooi, W.T., Kamel, N., Malik, A., Deep in thought while driving: An EEG study on drivers’ cognitive distraction (2014) Transp. Res. Part F Traffic Psychol. Behav, 26, pp. 218-226
Gonçalves, J., Bengler, K., Driver state monitoring systems–Transferable knowledge manual driving to HAD (2015) Procedia Manuf, 3, pp. 3011-3016
Durso, F., Gronlund, S., Situation awareness (1999) Handbook of Applied Cognition, pp. 283-314. , John Wiley & Sons Ltd.: Hoboken, NJ, USA
Kass, S., Cole, K., Stanny, C., Effects of distraction and experience on situation awareness and simulated driving (2007) Transp. Res. Part F Traffic Psychol. Behav, 10, pp. 321-329
Kircher, K., Ahlström, C., Minimum required attention: A human-centered approach to driver inattention (2016) Hum. Factors, 59, pp. 471-484
Ahlström, C., Georgoulas, G., Kircher, K., Towards a context-dependent multi-buffer driver distraction detection algorithm (2021) IEEE Trans. Intell. Transp. Syst, pp. 1-13
Tijerina, L., Issues in the evaluation of driver distraction associated with in-vehicle information and telecommunications systems (2000) Transp. Res. Inc, 12, pp. 54-67
Li, Z., Bao, S., Kolmanovsky, I., Yin, X., Visual-manual distraction detection using driving performance indicators with naturalistic driving data (2017) IEEE Trans. Intell. Transp. Syst, 19, pp. 2528-2535
Le, T., Zhu, C., Zheng, Y., Luu, K., Savvides, M., Robust hand detection in Vehicles Proceedings of the IEEE International Conference on Pattern Recognition (ICPR), pp. 573-578. , Cancun, Mexico, 4–8 December 2016
Le, T., Quach, K., Zhu, C., Duong, C., Luu, K., Savvides, M., Robust hand detection and classification in vehicles and in the wild Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 39-46. , Honolulu, HI, USA, 21–26 July 2017
Yan, S., Teng, Y., Smith, J., Zhang, B., Driver behavior recognition based on deep convolutional neural networks Proceedings of the International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), pp. 636-641. , Changsha, China, 13–15 August 2016
Baheti, B., Gajre, S., Talbar, S., Detection of distracted driver using convolutional neural network Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1032-1038. , Salt Lake City, UT, USA, 18–22 June 2018
Masood, S., Rai, A., Aggarwal, A., Doja, M., Ahmad, M., Detecting distraction of drivers using Convolutional Neural Network (2020) Pattern Recognit. Lett, 139, pp. 79-85
Young, K., Regan, M., Driver distraction: A review of the literature (2007) Distracted Driving, pp. 379-405. , Australasian College of Road Safety: Pearce, ACT, Australia
Fridman, L., Langhans, P., Lee, J., Reimer, B., Driver Gaze Region Estimation without Use of Eye Movement (2016) IEEE Trans. Intell. Transp. Syst, 31, pp. 49-56
Fridman, L., Lee, J., Reimer, B., Victor, T., ‘Owl’ and ‘Lizard’: Patterns of head pose and eye pose in driver gaze classification (2016) IET Comput. Vis, 10, pp. 308-313
Vicente, F., Huang, Z., Xiong, X., De la Torre, F., Zhang, W., Levi, D., Driver gaze tracking and eyes off the road detection system (2015) IEEE Trans. Intell. Transp. Syst, 16, pp. 2014-2027
Engström, J., Markkula, G., Effects of visual and cognitive distraction on lane change test performance (2007) Proceedings of the International Driving Symposium on Human Factors in Driver Assessment, pp. 199-205. , Stevenson, WA, USA, 10 July
Liang, Y., Lee, J., Combining cognitive and visual distraction: Less than the sum of its parts (2010) Accid. Anal. Prev, 42, pp. 881-890
Naqvi, R., Arsalan, M., Batchuluun, G., Yoon, H., Park, K., Deep learning-based gaze detection system for automobile drivers using a NIR camera sensor (2018) Sensors, 18, p. 456
Mukherjee, S., Robertson, N., Deep head pose: Gaze-direction estimation in multimodal video (2015) IEEE Trans. Multimed, 17, pp. 2094-2107
Sodnik, J., Dicke, C., Tomažič, S., Billinghurst, M., A user study of auditory versus visual interfaces for use while driving (2008) Int. J.-Hum.-Comput. Stud, 66, pp. 318-332
Vincent, E., Gribonval, R., Fevotte, C., Performance measurement in blind audio source separation (2006) IEEE Trans. Audio Speech Lang. Process, 14, pp. 1462-1469
Kahneman, D., Tursky, B., Shapiro, D., Crider, A., Pupillary, heart rate, and skin resistance changes during a mental task (1969) J. Exp. Psychol, 79, pp. 164-167
Hargutt, V., Kruger, H., Eyelid movements and their predictive value for fatigue stages (2000) Proceedings of the International Conference on Traffic and Transport Psychology (ICTTP), , Berne, Switzerland, 4–7 September
Schröger, E., Giard, M.H., Wolff, C., Auditory distraction: Event-related potential and behavioral indices (2000) Clin. Neurophysiol, 111, pp. 1450-1460
Sonnleitner, A., Treder, M., Simon, M., Willmann, S., Ewald, A., Buchner, A., Schrauf, M., EEG alpha spindles and prolonged brake reaction times during auditory distraction in an on-road driving study (2014) Accid. Anal. Prev, 62, pp. 110-118
(2010) Overview of the National Highway Traffic Safety Administration’s Driver Distraction Program, , NHTSA. Technical Report
National Highway Traffic Safety Administration: Washington, DC, USA
Ranney, T., (2008) Driver Distraction: A Review of the Current State-of-Knowledge, , Technical Report
National Highway Traffic Safety Administration: Washington, DC, USA
Strayer, D., Cooper, J., Turrill, J., Coleman, J., Medeiros-Ward, N., Biondi, F., (2013) Measuring Cognitive Distraction in the Automobile, , Technical Report
AAA, Foundation for Traffic Safety: Washington, DC, USA
Harbluk, J., Noy, Y., Trbovich, P., Eizenman, M., An on-road assessment of cognitive distraction: Impacts on drivers’ visual behavior and braking performance (2007) Accid. Anal. Prev, 39, pp. 372-379
Liang, Y., Reyes, M., Lee, J., Real-time detection of driver cognitive distraction using support vector machines (2007) IEEE Trans. Intell. Transp. Syst, 8, pp. 340-350
Son, L., Suzuki, T., Aoki, H., Evaluation of cognitive distraction in a real vehicle based on the reflex eye movement (2018) Int. J. Automot. Eng, 9, pp. 1-8
Strayer, D., Turrill, J., Cooper, J., Coleman, J., Medeiros-Ward, N., Biondi, F., Assessing cognitive distraction in the automobile (2015) Hum. Factors, 57, pp. 1300-1324
Strayer, D., Drews, F., Cell-Phone—Induced Driver Distraction (2007) Curr. Dir. Psychol. Sci, 16, pp. 128-131
Hu, T.Y., Xie, X., Li, J., Negative or positive? The effect of emotion and mood on risky driving (2013) Transp. Res. Part F Traffic Psychol. Behav, 16, pp. 29-40
Pecher, C., Lemercier, C., Cellier, J.M., The Influence of Emotions on Driving Behavior (2010) Traffic Psychology: An International Perspective, pp. 1-27. , Hennessy, D., Ed.
Nova Science Publishers: Hauppauge, NY, USA, Chapter 9
Lu, S., Wei, F., Li, G., The evolution of the concept of stress and the framework of the stress system (2021) Cell Stress, 5, pp. 76-85
Hu, H., Zhu, Z., Gao, Z., Zheng, R., Analysis on biosignal characteristics to evaluate road rage of younger drivers: A driving simulator study (2018) Proceedings of the IEEE Intelligent Vehicles Symposium, IV, pp. 156-161. , Changshu, China, 26–30 June
Gavrilescu, M., Vizireanu, N., Feedforward Neural Network-Based Architecture for Predicting Emotions from Speech (2019) Data, 4, p. 101
Ekman, P., Facial expression and emotion (1993) Am. Psychol, 48, pp. 384-392
Russell, J., Is there universal recognition of emotion from facial expression? A review of the cross-cultural studies (1994) Psychol. Bull, 115, p. 102
Diverrez, J.M., Martin, N., Pallamin, N., Stress interface inducer, a way to generate stress in laboratory conditions Proceedings of the International Conference on Methods and Techniques in Behavioral Research (Measuring Behavior), pp. 25-27. , Dublin, Ireland, 25–27 May 2016
Healey, J., Picard, R., Detecting stress during real-world driving tasks using physiological sensors (2005) IEEE Trans. Intell. Transp. Syst, 6, pp. 156-166
de Santos Sierra, A., Ávila, C., del Pozo, G., Casanova, J., Stress detection by means of stress physiological template Proceedings of the World Congress on Nature and Biologically Inspired Computing, pp. 131-136. , Salamanca, Spain, 19–21 October 2011
Zhao, M., Adib, F., Katabi, D., Emotion recognition using wireless signals Proceedings of the Annual International Conference on Mobile Computing and Networking, pp. 95-108. , New York, NY, USA, 3–7 October 2016
Shi, Y., Ruiz, N., Taib, R., Choi, E., Chen, F., Galvanic skin response (GSR) as an index of cognitive load Proceedings of the CHI Extended Abstracts on Human Factors in Computing Systems, pp. 2651-2656. , San Jose, CA, USA, 28 April–3 May 2007
Partala, T., Surakka, V., Pupil size variation as an indication of affective processing (2003) Int. J.-Hum.-Comput. Stud, 59, pp. 185-198
Wan, P., Wu, C., Lin, Y., Ma, X., On-road experimental study on driving anger identification model based on physiological features by ROC curve analysis (2017) IET Intell. Transp. Syst, 11, pp. 290-298
Bradley, M., Lang, P., Measuring emotion: The self-assessment manikin and the semantic differential (1994) J. Behav. Ther. Exp. Psychiatry, 25, pp. 49-59
Li, H., Sun, J., Xu, Z., Chen, L., Multimodal 2D+3D facial expression recognition with deep fusion convolutional neural network (2017) IEEE Trans. Multimed, 19, pp. 2816-2831
Zhang, T., Zheng, W., Cui, Z., Zong, Y., Yan, J., Yan, K., A deep neural network-driven feature learning method for multi-view facial expression recognition (2016) IEEE Trans. Multimed, 18, pp. 2528-2536
Gao, H., Yüce, A., Thiran, J.P., Detecting emotional stress from facial expressions for driving safety Proceedings of the IEEE International Conference on Image Processing (ICIP), pp. 5961-5965. , Paris, France, 27–30 October 2014
Jeong, M., Ko, B., Driver’s Facial Expression Recognition in Real-Time for Safe Driving (2018) Sensors, 18, p. 4270
Melnicuk, V., Birrell, S., Crundall, E., Jennings, P., Employing consumer electronic devices in physiological and emotional evaluation of common driving activities Proceedings of the IEEE Intelligent Vehicles Symposium, IV, pp. 1529-1534. , Los Angeles, CA, USA, 11–14 June 2017
Murthy, R., Pavlidis, I., Tsiamyrtzis, P., Touchless monitoring of breathing function Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1196-1199. , San Francisco, CA, USA, 1–5 September 2004
Ragot, M., Martin, N., Em, S., Pallamin, N., Diverrez, J.M., Emotion recognition using physiological signals: Laboratory vs. wearable sensors (2017) International Conference on Applied Human Factors and Ergonomics, 608, pp. 15-22. , Springer: Berlin/Heidelberg, Germany
Gouverneur, P., Jaworek-Korjakowska, J., Köping, L., Shirahama, K., Kleczek, P., Grzegorzek, M., Classification of physiological data for emotion recognition (2017) International Conference on Artificial Intelligence and Soft Computing (ICAISC), 10245, pp. 619-627. , Springer: Berlin/Heidelberg, Germany
Ollander, S., Godin, C., Campagne, A., Charbonnier, S., A comparison of wearable and stationary sensors for stress detection Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 4362-4366. , Budapest, Hungary, 9–12 October 2016
Sevil, M., Hajizadeh, I., Samadi, S., Feng, J., Lazaro, C., Frantz, N., Yu, X., Cinar, A., Social and competition stress detection with wristband physiological signals Proceedings of the IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN), pp. 39-42. , Eindhoven, The Netherlands, 9–12 May 2017
Bořil, H., Boyraz, P., Hansen, J., Towards multimodal driver’s stress detection (2012) Digital Signal Processing for In-Vehicle Systems and Safety, pp. 3-19. , Springer: New York, NY, USA
Basu, S., Chakraborty, J., Bagb, A., Aftabuddin, M., A review on emotion recognition using speech Proceedings of the International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 109-114. , Coimbatore, India, 10–11 March 2017
Zhang, S., Zhang, S., Huang, T., Gao, W., Speech emotion recognition using deep convolutional neural network and discriminant temporal pyramid matching (2018) IEEE Trans. Multimed, 20, pp. 1576-1590
Marillier, M., Verstraete, A., Driving under the influence of drugs (2019) WIREs Forensic Sci, 1, pp. 1-24
Zapata, F., Matey, J., Montalvo, G., García-Ruiz, C., Chemical classification of new psychoactive substances (NPS) (2021) Microchem. J, 163, pp. 1-13
Alonso, F., Driving under the influence (2019) The SAGE Encyclopedia of Criminal Psychology, 1, pp. 392-394. , SAGE: Thousand Oaks, CA, USA
Alonso, F., Pastor, J., Montoro, L., Esteban, C., Driving under the influence of alcohol: Frequency, reasons, perceived risk and punishment (2015) Subst. Abus. Treat. Prev. Policy, 10, pp. 1-9
Attia, H., Takruri, M., Ali, H., Electronic monitoring and protection system for drunk driver based on breath sample testing Proceedings of the International Conference on Electronic Devices, Systems and Applications (ICEDSA), pp. 1-4. , Ras Al Khaimah, United Arab Emirates, 6–8 December 2016
Oscar-Berman, M., Shagrin, B., Evert, D., Epstein, C., Impairments of brain and behavior: The neurological effects of alcohol (1997) Alcohol Health Res. World, 21, pp. 65-75
Garrisson, H., Scholey, A., Ogden, E., Benson, S., The effects of alcohol intoxication on cognitive functions critical for driving: A systematic review (2021) Accid. Anal. Prev, 154, pp. 1-11
(2018) Global Status Report on Road Safety 2018: Summary, , WHO. Technical Report
WHO/NMH/NVI/18.20
World Health Organization: Geneva, Switzerland
Christoforou, Z., Karlaftis, M., Yannis, G., Reaction times of young alcohol-impaired drivers (2013) Accid. Anal. Prev, 61, pp. 54-62
Peck, R., Gebers, M., Voas, R., Romano, E., The relationship between blood alcohol concentration (BAC), age, and crash risk (2008) J. Saf. Res, 39, pp. 311-319
Zador, P., Krawchuk, S., Voas, R., Alcohol-related relative risk of driver fatalities and driver involvement in fatal crashes in relation to driver age and gender: An update using 1996 data (2000) J. Stud. Alcohol, 61, pp. 387-395
Gunn, C., Mackus, M., Griffin, C., Munafò, M., Adams, S., A Systematic Review of the Next-Day Effects of Heavy Alcohol Consumption on Cognitive Performance (2018) Addiction, 113, pp. 2182-2193
Verster, J., Bervoets, A., de Klerk, S., Vreman, R., Olivier, B., Roth, T., Brookhuis, K., Effects of alcohol hangover on simulated highway driving performance (2014) Psychopharmacology, 231, pp. 2999-3008
(2018) Drinking and Driving, , PAHO. Technical Report PAHO/NMH/18-011
Pan American Health Organization: Washington, DC, USA
Rosero-Montalvo, P., López-Batista, V., Peluffo-Ordóñez, D., Hybrid embedded-systems-based approach to in-driver drunk status detection using image processing and sensor networks (2021) IEEE Sensors J, 21, pp. 15729-15740
Sanghvi, K., Drunk Driving Detection (2018) Comput. Sci. Inf. Technol, 6, pp. 24-30
(1998) The Visual Detection of DWI Motorists, , NHTSA. Technical Report DOT HS 808 677
National Highway Traffic Safety Administration: Washington, DC, USA
Irwin, C., Iudakhina, E., Desbrow, B., McCartney, D., Effects of acute alcohol consumption on measures of simulated driving: A systematic review and meta-analysis (2017) Accid. Anal. Prev, 102, pp. 248-266
Martin, T., Solbeck, P., Mayers, D., Langille, R., Buczek, Y., Pelletier, M., A review of alcohol-impaired driving: The role of blood alcohol concentration and complexity of the driving task (2013) J. Forensic Sci, 58, pp. 1238-1250
Mets, M., Kuipers, E., de Senerpont Domis, L., Leenders, M., Olivier, B., Verster, J., Effects of alcohol on highway driving in the STISIM driving simulator (2011) Hum. Psychopharmacol. Clin. Exp, 26, pp. 434-439
Joye, T., Rocher, K., Déglon, J., Sidibé, J., Favrat, B., Augsburger, M., Thomas, A., Driving under the influence of drugs: A single parallel monitoring-based quantification approach on whole blood (2020) Front. Chem, 8, pp. 1-10
Charniya, N., Nair, V., Drunk driving and drowsiness detection Proceedings of the International Conference on Intelligent Computing and Control (I2C2), pp. 1-6. , Coimbatore, India, 23–24 June 2017
Ray, A., Das, A., Kundu, A., Ghosh, A., Rana, T., Prevention of driving under influence using microcontroller Proceedings of the International Conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech), pp. 1-2. , Kolkata, India, 28–29 April 2017
Sakairi, M., Water-Cluster-Detecting Breath Sensor and Applications in Cars for Detecting Drunk or Drowsy Driving (2012) IEEE Sensors J, 12, pp. 1078-1083
Kojima, S., Maeda, S., Ogura, Y., Fujita, E., Murata, K., Kamei, T., Tsuji, T., Yoshizumi, M., Noninvasive biological sensor system for detection of drunk driving Proceedings of the International Conference on Information Technology and Applications in Biomedicine (ITAB), pp. 1-4. , Larnaka, Cyprus, 4–7 November 2009
Murata, K., Fujita, E., Kojima, S., Maeda, S., Ogura, Y., Kamei, T., Tsuji, T., Suzuki, N., Noninvasive Biological Sensor System for Detection of Drunk Driving (2011) IEEE Trans. Inf. Technol. Biomed, 15, pp. 19-25
Wu, C., Tsang, K., Chi, H., A wearable drunk detection scheme for healthcare applications Proceedings of the IEEE International Conference on Industrial Informatics (INDIN), pp. 878-881. , Poitiers, France, 19–21 July 2016
Wu, C., Tsang, K., Chi, H., Hung, F., A precise drunk driving detection using weighted kernel based on electrocardiogram (2016) Sensors, 16, p. 659
Hermosilla, G., Verdugo, J., Farias, G., Vera, E., Pizarro, F., Machuca, M., Face Recognition and Drunk Classification Using Infrared Face Images (2018) J. Sens, 2018, p. 5813514
Koukiou, G., Anastassopoulos, V., Local difference patterns for drunk person identification (2018) Multimed. Tools Appl, 77, pp. 9293-9305
Menon, S., Swathi, J., Anit, S., Nair, A., Sarath, S., Driver face recognition and sober drunk classification using thermal images Proceedings of the International Conference on Communication and Signal Processing (ICCSP), pp. 400-404. , Chennai, India, 4–6 April 2019
Berri, R., Osório, F., A nonintrusive system for detecting drunk drivers in modern vehicles Proceedings of the Brazilian Conference on Intelligent Systems (BRACIS), pp. 73-78. , São Paulo, Brazil, 22–25 October 2018
Berri, R., Osório, F., A nonintrusive system for detecting drunk drivers in modern vehicles Proceedings of the Brazilian Conference on Intelligent Systems (BRACIS), pp. 73-78. , São Paulo, Brazil, 22–25 October 2018
El Basiouni El Masri, A., Artail, H., Akkary, H., Toward self-policing: Detecting drunk driving behaviors through sampling CAN bus data Proceedings of the International Conference on Electrical and Computing Technologies and Applications (ICECTA), pp. 1-5. , Ras Al Khaimah, United Arab Emirates, 21–23 November 2017
Harkous, H., Bardawil, C., Artail, H., Daher, N., Application of hidden Markov model on car sensors for detecting drunk drivers (2018) Proceedings of the IEEE International Multidisciplinary Conference on Engineering Technology (IMCET), pp. 1-6. , Beirut, Lebanon, 14–16 Novmber
Harkous, H., Bardawil, C., Artail, H., Daher, N., Application of hidden Markov model on car sensors for detecting drunk drivers (2018) Proceedings of the IEEE International Multidisciplinary Conference on Engineering Technology (IMCET), pp. 1-6. , Beirut, Lebanon, 14–16 Novmber
Harkous, H., Artail, H., A two-stage machine learning method for highly-accurate drunk driving detection Proceedings of the International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pp. 1-6. , Barcelona, Spain, 21–23 October 2019
Li, Z., Jin, X., Zhao, X., Drunk driving detection based on classification of multivariate time series (2015) J. Saf. Res, 54, pp. 61-67
Shirazi, M., Rad, A., Detection of Intoxicated Drivers Using Online System Identification of Steering Behavior (2014) IEEE Trans. Intell. Transp. Syst, 15, pp. 1738-1747
Dai, J., Teng, J., Bai, X., Shen, Z., Xuan, D., Mobile phone based drunk driving detection Proceedings of the International ICST Conference on Pervasive Computing Technologies for Healthcare, pp. 1-8. , Munich, Germany, 22–25 March 2010
Saponara, S., Greco, M., Gini, F., Radar-on-chip/in-package in autonomous driving vehicles and intelligent transport systems: Opportunities and challenges (2019) IEEE Signal Process. Mag, 36, pp. 71-84
Linardatos, P., Papastefanopoulos, V., Kotsiantis, S., Explainable AI: A Review of Machine Learning Interpretability Methods (2021) Entropy, 23, p. 18
Zablocki, É., Ben-Younes, H., Pérez, P., Cord, M., Explainability of vision-based autonomous driving systems: Review and challenges, , arXiv 2021, arXiv:abs/2101.05307