Abstract
Conditionally automated driving (CAD) will lead to a paradigm shift in the field of driver state monitoring systems. High underload and the possibility of engaging in non-driving related activities will greatly influence the driver state. Level 3 also requires drivers to act as a fallback level in a take-over situation. Drivers have to get back in the loop and regain control with possible challenges due to their state. Therefore, driver state assessment will gain importance in order to ensure a safe and comfortable hand-over. The purpose of this paper is to provide an overview of driver state models and monitoring systems in the context of automated driving. Based on three driver state models, we focus on the commonly used driver state constructs fatigue, attention and workload. As part of this review, different definitions are summarized and possible metrics to operationalize these constructs were identified and critically reviewed. When reviewing the literature, it became apparent that driver state and the different constructs lack a common definition. Overall, eye-tracking is the technology with the most potential, but it needs further development to increase reliability. EEG lacks practicability and subjective measures are prone to misjudgement and may counteract extreme levels of fatigue.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
SAE International (2016) Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems. SAE Standard J 3016_201609
Gold CG (2016) Modeling of Take-Over Performance in Highly Automated Vehicle Guidance. Dissertation, Technische Universität München. http://mediatum.ub.tum.de?id=1296132
Bhatt PP, Trivedi JA (2017) Various methods for driver drowsiness detection: an overview. Int J Comput Sci Eng (IJCSE) 9(03):70–74
Heikoop DD, de Winter JCF, van Arem B, Stanton NA (2015) Psychological constructs in driving automation. A consensus model and critical comment on construct proliferation. Theor Issues Ergon Sci. https://doi.org/10.1080/1463922X.2015.1101507
Stanton N, Young M (2000) A proposed psychological model of driving automation. Theor Issues Ergon Sci. https://doi.org/10.1080/14639220052399131
Rauch N, Kaussner A, Boverie S, Giralt A (2009) HAVEit Deliverable D32.1 Report on driver assessment methodology. HAVEit - Highly automated vehicles for intelligent transport, Regensburg
Marberger C, Mielenz H, Naujoks F, Radlmayr J, Bengler K, Wandtner B (2018) Understanding and applying the concept of “Driver Availability” in automated driving. In: Stanton NA (ed) Advances in human aspects of transportation: proceedings of the AHFE 2017 international conference on human factors in transportation. Springer International Publishing, Cham, pp 595–605
Croo HD, Bandmann M, Mackay GM, Rumar K, Vollenhoven P (2001) The role of driver fatigue in commercial road transport crashes. European Transport Safety Council, Brussels, Belgium
Karrer-Gauß K (2012) Prospektive Bewertung von Systemen zur Müdigkeitserkennung - Ableitung von Gestaltungsempfehlungen zur Vermeidung von Risikokompensation aus empirischen Untersuchungen. Technische Universität Berlin (2012)
May JF, Baldwin CL (2009) Driver fatigue. The importance of identifying causal factors of fatigue when considering detection and countermeasure technologies. Transp Res Part F Traffic Psychol Behav. https://doi.org/10.1016/j.trf.2008.11.005
Åkerstedt T, Gillberg M (2009) Subjective and objective sleepiness in the active individual. Int J Neurosci. https://doi.org/10.3109/00207459008994241
Hoddes E, Zarcone V, Smythe H, Phillips R, Dement WC (1973) Quantification of sleepiness. A new approach. Psychophysiology. https://doi.org/10.1111/j.1469-8986.1973.tb00801.x
Goncalves J, Happee R, Bengler K (2016) Drowsiness in conditional automation. Proneness, diagnosis and driving performance effects. In: Proceedings of the 2016 IEEE 19th international conference on intelligent transportation systems (ITSC), Rio de Janeiro, Brazil, pp 873–878
Jarosch O, Kuhnt M, Paradies S, Bengler K (2017) It’s out of our hands now! effects of non-driving related tasks during highly automated driving on drivers’ fatigue. In: Proceedings of the 9th international driving symposium on human factors in driver assessment, training, and vehicle design: driving assessment 2017. Driving assessment conference, Manchester Village, Vermont, USA. University of Iowa, Iowa City, Iowa, 26–29 June 2017, pp 319–325. https://doi.org/10.17077/drivingassessment.1653
Knipling RR, Wierwille WW (1994) Vehicle-based drowsy driver detection. Current status and future prospects. In: IVHS America fourth annual meeting, Atlanta
Feldhütter A, Feierle A, Kalb L, Bengler K (2018) A new approach for a real-time non-invasive fatigue assessment system for automated driving. In: Proceedings of the human factors and ergonomics society (HFES) (in Press)
Dong Y, Hu Z, Uchimura K, Murayama N (2011) Driver inattention monitoring system for intelligent vehicles. A review. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/tits.2010.2092770
Coetzer RC, Hancke GP (2009) Driver fatigue detection. A survey. In: Proceedings of the AFRICON, AFRICON 2009, Nairobi, Kenya. IEEE, pp 1–6. https://doi.org/10.1109/afrcon.2009.5308101
Vicente J, Laguna P, Bartra A, Bailón R (2016) Drowsiness detection using heart rate variability. Med Biol Eng Comput. https://doi.org/10.1007/s11517-015-1448-7
Hargutt V (2000) Eyelid movements and their predictive value of fatigue stages. In: 3rd international conference of psychophysiology in ergonomics, San Diego, California, 30.07.2000 (2000)
Wierwille WW, Wreggit SS, Kirn CL, La Ellsworth, Fairbanks, R.J (1994) Research on vehicle-based driver status/performance monitoring; development, validation, and refinement of algorithms for detection of driver drowsiness. Final report. U.S. Department of Transportation. Springfield, Virginia
Murata A, Koriyama T, Hayami T (2012) A basic study on the prevention of drowsy driving using the change of neck bending and the sitting pressure distribution. In: Proceedings of SICE (Society of Instrument and Control Engineers) Annual Conference 2012, Akita, pp 274–279
Schmidt J. Braunagel C, Stolzmann W, Karrer-Gauss K (2016) Driver drowsiness and behavior detection in prolonged conditionally automated drives. In: 2016 IEEE intelligent vehicles symposium (IV), Gotenburg, Sweden. IEEE, Piscataway, NJ, 19–22 June 2016, pp 400–405. https://doi.org/10.1109/ivs.2016.7535417
Regan MA, Hallett C, Gordon CP (2011) Driver distraction and driver inattention. Definition, relationship and taxonomy. Accident; analysis and prevention. https://doi.org/10.1016/j.aap.2011.04.008
Schooler JW, Smallwood J, Christoff K, Handy TC, Reichle ED, Sayette MA (2011) Meta-awareness, perceptual decoupling and the wandering mind. Trends Cogn Sci. https://doi.org/10.1016/j.tics.2011.05.006
Oken BS, Salinsky MC, Elsas SM (2006) Vigilance, alertness, or sustained attention. physiological basis and measurement. Clin Neurophysiol. https://doi.org/10.1016/j.clinph.2006.01.017
Schmidt EA, Schrauf M, Simon M, Fritzsche M, Buchner A, Kincses WE (2009) Drivers’ mis-judgement of vigilance state during prolonged monotonous daytime driving. Accid Anal Prev. https://doi.org/10.1016/j.aap.2009.06.007
Sathyanarayana A, Nageswaren S, Ghasemzadeh H, Jafari R, Hansen JHL (2008) Body sensor networks for driver distraction identification. In: Proceedings of the 2008 IEEE international conference on vehicular electronics and safety (ICVES 2008), Columbus, OH, 22.09.2008–24.09.2008, Columbus, USA, pp 120–125. https://doi.org/10.1109/icves.2008.4640876
Azman A, Meng Q, Edirisinghe E (2010) Non intrusive physiological measurement for driver cognitive distraction detection. Eye and Mouth Movements. In: 2010 3rd international conference on advanced computer theory and engineering (ICACTE). IEEE, pp 595–599 (2010)
Körber M, Cingel A, Zimmermann M, Bengler K (2015) Vigilance decrement and passive fatigue caused by monotony in automated driving. Procedia Manuf. https://doi.org/10.1016/j.promfg.2015.07.499
Young K, Regan M (2007) Driver distraction. A review of the literature. In: Faulks IJ, Regan M, Stevenson M, Brown J, Porter A, Irwin JD (ed) Distracted driving, NSW, Sydney, pp 379–405 (2007)
Selye H (1980) Selye’s guide to stress research. Van Nostrand Reinhold, New York
Conti-Kufner A-S (2017) Measuring cognitive task load: an evaluation of the Detection Response Task and its implications for driver distraction assessment. Dissertation, Technische Universität München. http://mediatum.ub.tum.de?id=1340561
Wickens CD (2008) multiple resources and mental workload. Hum Factors. https://doi.org/10.1518/001872008X288394
Hart SG, Staveland LE (1988) Development of NASA-TLX (Task Load Index). Results of empirical and theoretical research. In: Meshkati N, Hancock PA (eds) Human Mental Workload. Advances in Psychology, vol 52, 1st edn. Elsevier textbooks, s.l., pp 139–183 (1988)
Pauzié A (2008) A method to assess the driver mental workload. The driving activity load index (DALI). IET Intell Transp Syst. https://doi.org/10.1049/iet-its:20080023
Kahneman D, Tursky B, Shapiro D, Crider A (1969) Pupillary, heart rate, and skin resistance changes during a mental task. J Exp Psychol. https://doi.org/10.1037/h0026952
Itoh M (2009) Individual differences in effects of secondary cognitive activity during driving on temperature at the nose tip. In: Proceedings of the 2009 international conference on mechatronics and automation (ICMA), Changchun, China, 09.08.2009–12.08.2009, IEEE, Changchun, China, pp 7–11. https://doi.org/10.1109/icma.2009.5246188
Marquart G, Cabrall C, de Winter J (2015) Review of eye-related measures of drivers’ mental workload. Procedia Manuf. https://doi.org/10.1016/j.promfg.2015.07.783
Wang Y, Reimer B, Dobres J, Mehler B (2014) The sensitivity of different methodologies for characterizing drivers’ gaze concentration under increased cognitive demand. Transp Res Part F Traffic Psychol Behav. https://doi.org/10.1016/j.trf.2014.08.003
Healey JA, Picard RW (2005) Detecting stress during real-world driving tasks using physiological sensors. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2005.848368
Yamakoshi T, Yamakoshi K, Tanaka S, Nogawa M, Shibata M, Sawada Y, Rolfe P, Hirose Y (2007) A preliminary study on driver’s stress index using a new method based on differential skin temperature measurement. In: Proceedings of the 29th annual international conference of the IEEE EMBS, vol. 2007, Lyon, France, pp 722–725
Lee HB, Choi JM, Kim JS, Kim YS, Baek HJ, Ryu MS, Sohn RH, Park KS (2007) Nonintrusive biosignal measurement system in a vehicle. In: Proceedings of the 29th annual international conference of the IEEE EMBS, Lyon, France, pp 2303–2306
Jeong IC, Jun Sh, Lee DH, Yoon HR (2007) Development of bio signal measurement system for vehicles. In: Proceedings of the 2007 international conference on convergence, pp 1091–1096
Schmidt EA, Schrauf M, Simon M, Buchner A, Kincses WE (2011) The short-term effect of verbally assessing drivers’ state on vigilance indices during monotonous daytime driving. Transp Res Part F Traffic Psychol Behav. https://doi.org/10.1016/j.trf.2011.01.005
Lenné MG, Jacobs EE (2016) Predicting drowsiness-related driving events. a review of recent research methods and future opportunities. Theor Issues Ergon Sci. https://doi.org/10.1080/1463922X.2016.1155239
Heuer S, Chamadiya B, Gharbi A, Kunze C, Wagner M (2010) Unobtrusive in-vehicle biosignal instrumentation for advanced driver assistance and active safety. In: Proceedings of the IEEE EMBS conference on biomedical engineering and sciences (IECBES), Kuala Lumpur, Malaysia. IEEE, Piscataway, pp 252–256. https://doi.org/10.1109/iecbes.2010.5742238
Bergasa LM, Nuevo J, Sotelo MA, Barea R, Lopez ME (2006) Real-Time system for monitoring driver vigilance. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2006.869598
Danisman T, Bilasco IM, Djeraba C, Ihaddadene N (2010) Drowsy driver detection system using eye blink patterns. In: 2010 international conference on machine and web intelligence (ICMWI), Algiers, 03.10.2010–05.10.2010. IEEE, pp 230–233. https://doi.org/10.1109/icmwi.2010.5648121
Friedrichs, F., Yang, B.: Camera-based drowsiness reference for driver state classification under real driving conditions. In: Proceedings of the 2010 IEEE intelligent vehicles symposium (IV), La Jolla, CA, USA, 21.06.2010–24.06.2010. IEEE, La Jolla, USA, pp 101–106. https://doi.org/10.1109/ivs.2010.5548039
Fors C, Ahlström C, Sörner P, Kovaceva J, Hasselberg E, Krantz M, Grönvall J-F, Kircher K, Anund A (2011) Camera-based sleepiness detection. Final report of the project SleepEYE. ViP publication: ViP - Virtual Prototyping and Assessment by Simulation. Statens vägoch transport-forskningsinstitut, Linköping
ISO/TS 15007-2:2014 (2014) Road vehicles - Measurement of driver visual behaviour with respect to transport information and control systems: Part 2: Equipment and procedures. International Organization for Standardization, Switzerland
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Hecht, T. et al. (2019). A Review of Driver State Monitoring Systems in the Context of Automated Driving. In: Bagnara, S., Tartaglia, R., Albolino, S., Alexander, T., Fujita, Y. (eds) Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018). IEA 2018. Advances in Intelligent Systems and Computing, vol 823. Springer, Cham. https://doi.org/10.1007/978-3-319-96074-6_43
Download citation
DOI: https://doi.org/10.1007/978-3-319-96074-6_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-96073-9
Online ISBN: 978-3-319-96074-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)