Advertisement

Cognition, Technology & Work

, Volume 21, Issue 1, pp 33–40 | Cite as

Effect of prolonged periods of conditionally automated driving on the development of fatigue: with and without non-driving-related activities

  • Anna FeldhütterEmail author
  • Tobias Hecht
  • Luis Kalb
  • Klaus Bengler
Original Article
  • 133 Downloads

Abstract

Due to the ongoing development in automated vehicle technology, conditionally automated driving (CAD) will become a realistic scenario within the next years. However, an increasing automation in driving tasks and taking the driver out of the loop increases the risk of monotony and fatigue brought on by boredom. Whether the driver is still able to take over the vehicle guidance at system limits is questionable. Therefore, the aim of the current driving simulator study is to investigate how prolonged monotonous periods of conditionally automated driving affect passenger fatigue level and the take-over performance and how both is affected by voluntary non-driving-related activities (NDRA). For this purpose, two conditions (encouraging fatigue and encouraging alertness by engaging in voluntary NDRA) were tested in a 60 min conditionally automated drive followed by a take-over situation. Twenty-five percent of the participants in the fatigue encouraging condition temporarily showed strong evidence of fatigue or they fell asleep. However, the time of occurrence of fatigue phases varied among individuals (occurrence mainly after 20–40 min of automated driving). The take-over performance in the take-over situation after 60 min of CAD did not deteriorate in the fatigue condition compared to the alertness condition.

Keywords

Conditionally automated driving Driver state Fatigue Take-over performance Naturalistic non-driving-related tasks 

Notes

Acknowledgements

This report is based on parts of the research project carried out at the request of the Federal Ministry of Transport and Digital Infrastructure, represented by the Federal Highway Research Institute, under research project No. 82.0628/2015. The author is solely responsible for the content.

References

  1. Damböck D (2013) Automationseffekte im Fahrzeug – von der Reaktion zur Übernahme (Dissertation). Technische Universität München, MünchenGoogle Scholar
  2. Feldhütter A, Gold C, Schneider S, Bengler K (2017) How the duration of automated driving influences take-over performance and gaze behavior. In: Schlick C, Duckwitz S, Flemisch F, Frenz M, Kuz S, Mertens A, Mütze-Niewöhner S (Eds.) Advances in ergonomic design of systems, products and processes: Proceedings of the Annual Meeting of GfA 2016, 1st ed. Berlin: Springer. pp. 309–318.  https://doi.org/10.1007/978-3-662-53305-5_22 Google Scholar
  3. Gershon P, Ronen A, Oron-Gilad T, Shinar D (2009) The effects of an interactive cognitive task (ICT) in suppressing fatigue symptoms in driving. Transp Res Part F Traffic Psychol Behav 12(1):21–28.  https://doi.org/10.1016/j.trf.2008.06.004 CrossRefGoogle Scholar
  4. Gold C (2016) Modeling of take-over performance in highly automated vehicle guidance (Dissertation). Technische Universität München, Garching. Retrieved from https://mediatum.ub.tum.de/1296132
  5. Gold C, Bengler K (2014) Taking over control from highly automated vehicles. In: Ahram T, Karwowski W, Marek T (Eds.) Proceedings of the 5th International Conference on Applied Human Factors and Ergonomics AHFE 2014. 19–23 July 2014 (pp. 3662–3667). Kraków, PolandGoogle Scholar
  6. Gold C, Damböck D, Lorenz L, Bengler K (2013) “Take over!”: How long does it take to get the driver back into the loop? Proc Hum Factors Ergon Soc Annu Meet 57(1):1938–1942.  https://doi.org/10.1177/1541931213571433 CrossRefGoogle Scholar
  7. Gold C, Berisha I, Bengler K (2015) Utilization of drivetime—performing non-driving related tasks while driving highly automated. Proc Hum Factors Ergon Soc Annu Meet 59(1):1666–1670.  https://doi.org/10.1177/1541931215591360 CrossRefGoogle Scholar
  8. Gold C, Körber M, Lechner D, Bengler K (2016) Taking over control from highly automated vehicles in complex traffic situations: the role of traffic density. Hum Factors 58(4):642–652.  https://doi.org/10.1177/0018720816634226 CrossRefGoogle Scholar
  9. Hanowski RJ, Bowman D, Alden A, Wierwille WW, Carroll R (2008) PERCLOS+: moving beyond single-metric drowsiness monitors. In International SAE (ed) SAE technical paper series.  https://doi.org/10.4271/2008-01-2692
  10. Hargutt V (2003) Das Lidschlussverhalten als Indikator für Aufmerksamkeits- und Müdigkeitsprozesse bei Arbeitshandlungen (Dissertation). Julius-Maximilians-Universität Würzburg, Würzburg. Retrieved from http://www.psychologie.uni-wuerzburg.de/izvw/texte/2003_hargutt_Das_Lidschlussverhalten.pdf
  11. International Organization for Standardization (2012) Road Vehicles—Ergonomic aspects of transport information and control systems—Calibration tasks for methods which assess driver demand due to the use of in-vehicle systems. (Norm, ISO/TS 14198:2012)Google Scholar
  12. Körber M, Cingel A, Zimmermann M, Bengler K (2015) Vigilance decrement and passive fatigue caused by monotony in automated driving. Proc Manuf 3:2403–2409.  https://doi.org/10.1016/j.promfg.2015.07.499 Google Scholar
  13. Körber M, Gold C, Lechner D, Bengler K (2016) The influence of age on the take-over of vehicle control in highly automated driving. Transp Res Part F Traffic Psychol Behav 39:19–32.  https://doi.org/10.1016/j.trf.2016.03.002 CrossRefGoogle Scholar
  14. Lovato N, Lack L (2010) The effects of napping on cognitive functioning. Prog Brain Res 185:155–166.  https://doi.org/10.1016/B978-0-444-53702-7.00009-9 CrossRefGoogle Scholar
  15. 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 12(3):218–224.  https://doi.org/10.1016/j.trf.2008.11.005 CrossRefGoogle Scholar
  16. McIntire LK, McKinley RA, Goodyear C, McIntire JP (2014) Detection of vigilance performance using eye blinks. Appl Ergon 45(2):354–362.  https://doi.org/10.1016/j.apergo.2013.04.020 CrossRefGoogle Scholar
  17. Neubauer C, Matthews G, Langheim L, Saxby D (2012a) Fatigue and voluntary utilization of automation in simulated driving. Hum Factors J Hum Factors Ergon Soc 54(5):734–746.  https://doi.org/10.1177/0018720811423261 CrossRefGoogle Scholar
  18. Neubauer C, Matthews G, Saxby D (2012b) The Effects of cell phone use and automation on driver performance and subjective state in simulated driving. Proc Hum Factors Ergon Soc Annu Meet 56(1):1987–1991.  https://doi.org/10.1177/1071181312561415 CrossRefGoogle Scholar
  19. Neubauer C, Matthews G, Saxby D (2014) Fatigue in the automated vehicle: do games and conversation distract or energize the driver? Proc Hum Factors Ergon Soc Annu Meet 58(1):2053–2057.  https://doi.org/10.1177/1541931214581432 CrossRefGoogle Scholar
  20. Omae M, Fujioka T, Hashimoto N, Shimizu H (2006) The application of RTK-GPS and steer-by-wire technology to the automatic driving of vehicles and an evaluation of driver behavior. IATSS Res 30(2):29–38.  https://doi.org/10.1016/S0386-1112(14)60167-9 CrossRefGoogle Scholar
  21. Petermann-Stock I, Hackenberg L, Muhr T, Mergl C (2013) Wie lange braucht der Fahrer? Eine Analyse zu Übernahmezeiten aus verschiedenen Nebentätigkeiten während einer hochautomatisierten Staufahrt. 6. Tagung Fahrerassistenzsysteme. Der Weg zum automatischen Fahren Google Scholar
  22. Radlmayr J, Gold C, Lorenz L, Farid M, Bengler K (2014) How traffic situations and non-driving related tasks affect the take-over quality in highly automated driving. Proc Hum Factors Ergon Soc Annu Meet 58(1):2063–2067.  https://doi.org/10.1177/1541931214581434 CrossRefGoogle Scholar
  23. Rosario H de, Solaz JS, Rodríguez N, Bergasa LM (2010) Controlled inducement and measurement of drowsiness in a driving simulator. IET Intel Transport Syst 4(4):280.  https://doi.org/10.1049/iet-its.2009.0110 CrossRefGoogle Scholar
  24. SAE International (2016). Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicle. (SAE Standard, J 3016_201609)Google Scholar
  25. Saxby DJ, Matthews G, Warm JS, Hitchcock EM, Neubauer C (2013) Active and passive fatigue in simulated driving: discriminating styles of workload regulation and their safety impacts. J Exp Psychol Appl 19(4):287–300.  https://doi.org/10.1037/a0034386 CrossRefGoogle Scholar
  26. Schleicher R, Galley N, Briest S, Galley L (2008) Blinks and saccades as indicators of fatigue in sleepiness warnings: looking tired? Ergonomics 51(7):982–1010.  https://doi.org/10.1080/00140130701817062 CrossRefGoogle Scholar
  27. Schmidt EA, Schrauf M, Simon M, Fritzsche M, Buchner A, Kincses WE (2009) Drivers’ misjudgement of vigilance state during prolonged monotonous daytime driving. Accid Anal Prev 41(5):1087–1093.  https://doi.org/10.1016/j.aap.2009.06.007 CrossRefGoogle Scholar
  28. 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): 19–22 June 2016 (pp. 400–405). Piscataway, NJ: IEEE.  https://doi.org/10.1109/IVS.2016.7535417
  29. Schömig N, Hargutt V, Neukum A, Petermann-Stock I, Othersen I (2015) The interaction between highly automated driving and the development of drowsiness. Proc Manuf 3:6652–6659.  https://doi.org/10.1016/j.promfg.2015.11.005 Google Scholar
  30. Wierwille W, Ellsworth L, Wreggit S, Fairbanks R, Kirn C (1994) Research on vehicle-based driverstatus/performance monitoring: development, validation, and refinementof algorithms for detection of driver drowsiness. DOT HS 808:247Google Scholar
  31. Wu G, Liu Z, Pan X, Chen F, Xu M, Feng D, Xia Z (2017) Fatigue driving influence research and assessment. In: Stanton NA, Landry S, Di Bucchianico G, Vallicelli A (Eds.) Advances in intelligent systems and computing: Vol. 484. Advances in human aspects of transportation: Proceedings of the AHFE 2016 International Conference on human factors in transportation, July 27–31, 2016, Walt Disney World®, Florida, USA (pp. 677–688). Cham: Springer International Publishing.  https://doi.org/10.1007/978-3-319-41682-3_57

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Technical University of MunichMunichGermany

Personalised recommendations