skip to main content
research-article

Towards a Reliable Ground Truth for Drowsiness: A Complexity Analysis on the Example of Driver Fatigue

Authors Info & Claims
Published:18 June 2020Publication History
Skip Abstract Section

Abstract

The increasing number and complexity of advanced driver assistance systems (ADAS) pave the way for fully automated driving. Automated vehicles are said to increase road safety and prevent human-made (fatal) accidents, amongst others. In the lower levels of automation, however, the driver is still responsible as a fallback authority. As a consequence, systems that reliably monitor the driver's state, especially the risk factor drowsiness, become increasingly essential to ensure the driver's ability to take over control from the vehicle on time. In research, the use of supervised machine learning for drowsiness detection is the prevalent method. As the ground truth for drowsiness is both application- and user-dependent, and no golden standard exists for its definition, measures are usually applied in the form of observer ratings. Also, in this work, observer ratings were investigated with regard to the required level of detail/complexity. To this end, video data, recorded within a simulator study (N = 30) comprised of each 45-minute manual and automated driving sessions, were evaluated by trained raters. Correlation analysis results show that - depending on the number of drowsiness levels - a comparable ground truth can be generated by reducing the rating frequency and thus the rating complexity by a factor of five. The knowledge gained can be used in future studies in this research area, the collection of a reliable and valid ground truth of drowsiness, as well as for improving the process in developing interactive drowsiness detection systems.

References

  1. Christer Ahlstrom, Carina Fors, Anna Anund, and David Hallvig. 2015. Video-based observer rated sleepiness versus self-reported subjective sleepiness in real road driving. European Transport Research Review, Vol. 7, 4 (23 November 2015), 38. https://doi.org/10.1007/s12544-015-0188-yGoogle ScholarGoogle ScholarCross RefCross Ref
  2. T. Åkerstedt and M. Gillberg. 1990. Subjective and objective sleepiness in the active individual. International Journal of Neuroscience, Vol. 52 (1990), 29--37. https://doi.org/10.3109/00207459008994241Google ScholarGoogle ScholarCross RefCross Ref
  3. Anna Anund, Carina Fors, David Hallvig, Torbjörn Åkerstedt, and Göran Kecklund. 2013. Observer Rated Sleepiness and Real Road Driving: An Explorative Study. PLOS ONE, Vol. 8, 5 (May 2013), 1--8. https://doi.org/10.1371/journal.pone.0064782Google ScholarGoogle ScholarCross RefCross Ref
  4. G. Borghini, G. Vecchiato, J. Toppi, L. Astolfi, A. Maglione, R. Isabella, C. Caltagirone, W. Kong, D. Wei, Z. Zhou, L. Polidori, S. Vitiello, and F. Babiloni. 2012. Assessment of mental fatigue during car driving by using high resolution EEG activity and neurophysiologic indices. In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 6442--6445. https://doi.org/10.1109/EMBC.2012.6347469Google ScholarGoogle ScholarCross RefCross Ref
  5. Karel Brookhuis, Dick de Waard, and Will Janssen. 2019. Behavioural impacts of Advanced Driver Assistance Systems-an overview. European Journal of Transport and Infrastructure Research, Vol. 1, 3 (2019). https://journals.open.tudelft.nl/index.php/ejtir/article/view/3667Google ScholarGoogle Scholar
  6. Zhuo Chen, Ruizhou Ding, Ting Wu Chin, and Diana Marculescu. 2019. Understanding the impact of label granularity on CNN-based image classification. Technical Report. 895--904 pages. https://doi.org/10.1109/ICDMW.2018.00131Google ScholarGoogle Scholar
  7. Charlotte Jacobé de Naurois, Christophe Bourdin, Anca Stratulat, Emmanuelle Diaz, and Jean-Louis Vercher. 2019. Detection and prediction of driver drowsiness using artificial neural network models. Accident Analysis & Prevention, Vol. 126 (2019), 95 -- 104. https://doi.org/10.1016/j.aap.2017.11.038Google ScholarGoogle ScholarCross RefCross Ref
  8. EuroNCAP. 2017. EuroNCAP 2025 Roadmap. (2017), 1--17.Google ScholarGoogle Scholar
  9. F. Friedrichs and B. Yang. 2010. Drowsiness monitoring by steering and lane data based features under real driving conditions. In 2010 18th European Signal Processing Conference. 209--213.Google ScholarGoogle Scholar
  10. Rongrong Fu, Hong Wang, and Wenbo Zhao. 2016. Dynamic driver fatigue detection using hidden Markov model in real driving condition. Expert Systems with Applications, Vol. 63 (2016), 397--411. https://doi.org/10.1016/j.eswa.2016.06.042Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Jasper Gielen and Jean-Marie Aerts. 2019. Feature Extraction and Evaluation for Driver Drowsiness Detection Based on Thermoregulation. Applied Sciences, Vol. 9, 17 (2019). https://doi.org/10.3390/app9173555Google ScholarGoogle ScholarCross RefCross Ref
  12. IPG Automotive GmbH. 2018. CarMaker: Virtual testing of automobiles and light-duty vehicles. https://ipg-automotive.com/products-services/simulation-software/carmaker/ (accesssed on 21 April 2020).Google ScholarGoogle Scholar
  13. National Highway Traffic Safety Administration and Us Department of Transportation. 2015. TRAFFIC SAFETY FACTS Critical Reasons for Crashes Investigated in the National Motor Vehicle Crash Causation Survey. (2015). https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812115Google ScholarGoogle Scholar
  14. E. Hoddes, V. Zarcone, H. Smythe, R. Phillips, and W. C. Dement. 1973. Quantification of Sleepiness: A New Approach. Psychophysiology, Vol. 10, 4 (1973), 431--436. https://doi.org/10.1111/j.1469--8986.1973.tb00801.xGoogle ScholarGoogle ScholarCross RefCross Ref
  15. Murray Johns. 1998. Rethinking the assessment of sleepiness. Sleep Medicine Reviews, Vol. 2, 1 (1998), 3 -- 15. https://doi.org/10.1016/S1087-0792(98)90050--8Google ScholarGoogle ScholarCross RefCross Ref
  16. Murray W. Johns. 1991. A New Method for Measuring Daytime Sleepiness: The Epworth Sleepiness Scale. Sleep, Vol. 14, 6 (1991), 540--545. https://doi.org/10.1093/sleep/14.6.540Google ScholarGoogle ScholarCross RefCross Ref
  17. Sang-Joong Jung, Heung-Sub Shin, and Wan-Young Chung. 2014. Driver fatigue and drowsiness monitoring system with embedded electrocardiogram sensor on steering wheel. IET Intelligent Transport Systems, Vol. 8, 1 (2014), 43--50. https://doi.org/10.1049/iet-its.2012.0032Google ScholarGoogle ScholarCross RefCross Ref
  18. Katja Karrer-Gauß. 20110. Prospektive Bewertung von Systemen zur Mü digkeitserkennung Ableitung von Gestaltungsempfehlungen zur Vermeidung von Risikokompensation aus empirischen Untersuchungen. Ph.D. Dissertation. Verkehrs- und Maschinensysteme, Technische Universitat Berlin, Berlin, Germany.Google ScholarGoogle Scholar
  19. Thomas Kundinger and Andreas Riener. 2020. The Potential of Wrist-Worn Wearables for Driver Drowsiness Detection: A Feasibility Analysis (UMAP '20). in press. https://doi.org/10.1145/3340631.3394852Google ScholarGoogle Scholar
  20. Thomas Kundinger, Andreas Riener, Nikoletta Sofra, and Klemens Weigl. 2018. Drowsiness Detection and Warning in Manual and Automated Driving: Results from Subjective Evaluation (AutomotiveUI '18). 229--236. https://doi.org/10.1145/3239060.3239073Google ScholarGoogle Scholar
  21. Thomas Kundinger, Nikoletta Sofra, and Andreas Riener. 2020 a. Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection. Sensors, Vol. 20, 4 (2020). https://doi.org/10.3390/s20041029Google ScholarGoogle Scholar
  22. Thomas Kundinger, Phani Krishna Yalavarthi, Andreas Riener, Philipp Wintersberger, and Clemens Schartmüller. 2020 b. Feasibility of smart wearables for driver drowsiness detection and its potential among different age groups. International Journal of Pervasive Computing and Communications, Vol. 16, 1 (2020), 1--23. https://doi.org/10.1108/IJPCC-03--2019-0017Google ScholarGoogle ScholarCross RefCross Ref
  23. Boon-leng Lee, Boon-giin Lee, G. Li, and W.-Y. Chung. 2014. Wearable Driver Drowsiness Detection System Based on Smartwatch. Korea Institute of Signal Processing and Systems, Vol. 15 (2014), 134--146.Google ScholarGoogle Scholar
  24. Hyeonjeong Lee, Jaewon Lee, and Miyoung Shin. 2019. Using Wearable ECG/PPG Sensors for Driver Drowsiness Detection Based on Distinguishable Pattern of Recurrence Plots. Electronics, Vol. 8, 2 (2019). https://doi.org/10.3390/electronics8020192Google ScholarGoogle Scholar
  25. L. B. Leng, L. B. Giin, and W. Chung. 2015. Wearable driver drowsiness detection system based on biomedical and motion sensors. In 2015 IEEE Sensors Journal. 1--4. https://doi.org/10.1109/ICSENS.2015.7370355Google ScholarGoogle Scholar
  26. Gang Li and Wan-Young Chung. 2015. A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness. Sensors, Vol. 15, 8 (2015), 20873--20893. https://doi.org/10.3390/s150820873Google ScholarGoogle ScholarCross RefCross Ref
  27. Gang Li, Boon Leng Lee, and Wan Young Chung. 2015a. Smartwatch-Based Wearable EEG System for Driver Drowsiness Detection. IEEE Sensors Journal, Vol. 15, 12 (2015), 7169--7180. https://doi.org/10.1109/JSEN.2015.2473679Google ScholarGoogle ScholarCross RefCross Ref
  28. Quanzhe Li, Juan Wu, Shin-Dug Kim, and Cheong-Ghil Kim. 2015b. Hybrid Driver Fatigue Detection System Based on Data Fusion with Wearable Sensor Devices.Google ScholarGoogle Scholar
  29. Zuojin Li, Shengbo Eben Li, Renjie Li, Bo Cheng, and Jinliang Shi. 2017. Online Detection of Driver Fatigue Using Steering Wheel Angles for Real Driving Conditions. Sensors, Vol. 17, 3 (2017). https://doi.org/10.3390/s17030495Google ScholarGoogle Scholar
  30. Todd Litman. 2017. Autonomous Vehicle Implementation Predictions. Technical Report.Google ScholarGoogle Scholar
  31. Alina Mashko. 2017. Subjective Methods for Assessment of Driver Drowsiness. Acta Polytechnica CTU Proceedings, Vol. 12, 64. https://doi.org/10.14311/APP.2017.12.0064Google ScholarGoogle ScholarCross RefCross Ref
  32. Anthony D. McDonald, Chris Schwarz, John D Lee, and Timothy L Brown. 2012. Real-time detection of drowsiness related lane departures using steering wheel angle. In Proceedings of the Human Factors and Ergonomics Society. 2201--2205. https://doi.org/10.1177/1071181312561464Google ScholarGoogle ScholarCross RefCross Ref
  33. Aqsa Mehreen, Syed Muhammad Anwar, Muhammad Haseeb, Muhammad Majid, and Muhammad Obaid Ullah. 2019. A Hybrid Scheme for Drowsiness Detection Using Wearable Sensors. IEEE Sensors Journal, Vol. 19, 13 (2019), 5119--5126. https://doi.org/10.1109/JSEN.2019.2904222Google ScholarGoogle ScholarCross RefCross Ref
  34. Lars Michael, Sven Passmann, and Ruth Becker. 2012. Electrodermal lability as an indicator for subjective sleepiness during total sleep deprivation. Journal of Sleep Research, Vol. 21, 4 (2012), 470--478. https://doi.org/10.1111/j.1365--2869.2011.00984.xGoogle ScholarGoogle ScholarCross RefCross Ref
  35. J. M. Owens, T. A. Dingus, F. Guo, Y. Fang, M. Perez, J. McClafferty, and B Tefft. 2018. Prevalence of Drowsy Driving Crashes: Estimates from a Large-Scale Naturalistic Driving Study. (Research Brief.). Washington, D.C.: AAA Foundation for Traffic Safety (February 2018).Google ScholarGoogle Scholar
  36. Mohsen Poursadeghiyan, Adel Mazloumi, Gebraeil Nasl Saraji, Ali Niknezhad, Arash Akbarzadeh, and Mohammad Hossein Ebrahimi. 2017. Determination the Levels of Subjective and Observer Rating of Drowsiness and Their Associations with Facial Dynamic Changes. Iranian Journal of Public Health, Vol. 46, 1 (2017), 93--102.Google ScholarGoogle Scholar
  37. Arun Sahayadhas, Kenneth Sundaraj, and Murugappan Murugappan. 2012. Detecting Driver Drowsiness Based on Sensors: A Review. Sensors, Vol. 12, 12 (2012), 16937--16953. https://doi.org/10.3390/s121216937Google ScholarGoogle ScholarCross RefCross Ref
  38. J. Schmidt, C. Braunagel, W. Stolzmann, and K. Karrer-Gauß. 2016. Driver drowsiness and behavior detection in prolonged conditionally automated drives. In 2016 IEEE Intelligent Vehicles Symposium (IV). 400--405. https://doi.org/10.1109/IVS.2016.7535417Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Azmeh Shahid, Kate Wilkinson, Shai Marcu, and Colin M. Shapiro. 2012. Stanford Sleepiness Scale (SSS). Springer New York, 369--370. https://doi.org/10.1007/978--1--4419--9893--4_91Google ScholarGoogle Scholar
  40. G. Sikander and S. Anwar. 2019. Driver Fatigue Detection Systems: A Review. IEEE Transactions on Intelligent Transportation Systems, Vol. 20, 6 (June 2019), 2339--2352. https://doi.org/10.1109/TITS.2018.2868499Google ScholarGoogle Scholar
  41. Jeremy D. Slater. 2008. A definition of drowsiness: One purpose for sleep? Medical Hypotheses, Vol. 71, 5 (November 2008), 641--644. https://doi.org/10.1016/j.mehy.2008.05.035Google ScholarGoogle ScholarCross RefCross Ref
  42. Sleep Health Foundation. 2015. Sleep Needs Across The Lifespan. http://www.sleephealthfoundation.org.au/files/pdfs/Sleep-Needs-Across-Lifespan.pdf (accesssed on 21 April 2020).Google ScholarGoogle Scholar
  43. Society of Automotive Engineers (SAE) International. 2018. Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles.Google ScholarGoogle Scholar
  44. Udo Trutschel, Bill Sirois, David Sommer, Martin Golz, and Dave Edwards. 2011. PERCLOS: An Alertness Measure of the Past. Proceedings of the 6th International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design (2011), 172--179. https://doi.org/10.17077/drivingassessment.1394Google ScholarGoogle ScholarCross RefCross Ref
  45. F. Wang, S. Wang, X. Wang, Y. Peng, and Y. Yang. 2014. Design of driving fatigue detection system based on hybrid measures using wavelet-packets transform. In 2014 IEEE International Conference on Robotics and Automation (ICRA). 4037--4042. https://doi.org/10.1109/ICRA.2014.6907445Google ScholarGoogle ScholarCross RefCross Ref
  46. Veronika Weinbeer, T. Muhr, Klaus Bengler, C. Baur, J. Radlmayr, and J. Bill. 2017. Highly automated driving: How to get the driver drowsy and how does drowsiness influence various take-over-aspects?. In 8. Tagung Fahrerassistenz. Lehrstuhl für Fahrzeugtechnik mit TÜ V SÜ D Akademie, München.Google ScholarGoogle Scholar
  47. Douglas M Wiegand, Julie Mcclafferty, Shelby E Mcdonald, and Richard J Hanowski. 2009. Development and Evaluation of a Naturalistic Observer Rating of Drowsiness Protocol Final Report. The National Surface Transportation Safety Center for Excellence (2009).Google ScholarGoogle Scholar
  48. Walter W. Wierwille and Lynne A. Ellsworth. 1994. Evaluation of driver drowsiness by trained raters. Accident Analysis and Prevention, Vol. 26, 5 (1994), 571--581.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Towards a Reliable Ground Truth for Drowsiness: A Complexity Analysis on the Example of Driver Fatigue

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in

          Full Access

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader
          About Cookies On This Site

          We use cookies to ensure that we give you the best experience on our website.

          Learn more

          Got it!