This paper presents a non-intrusive approach for monitoring driver drowsiness using the fusion of several optimized indicators based on driver physical and driving performance measures, obtained from ADAS (Advanced Driver Assistant Systems) in simulated conditions. The paper is focused on real-time drowsiness detection technology rather than on long-term sleep/awake regulation prediction technology. We have developed our own vision system in order to obtain robust and optimized driver indicators able to be used in simulators and future real environments. These indicators are principally based on driver physical and driving performance skills. The fusion of several indicators, proposed in the literature, is evaluated using a neural network and a stochastic optimization method to obtain the best combination. We propose a new method for ground-truth generation based on a supervised Karolinska Sleepiness Scale (KSS). An extensive evaluation of indicators, derived from trials over a third generation simulator with several test subjects during different driving sessions, was performed. The main conclusions about the performance of single indicators and the best combinations of them are included, as well as the future works derived from this study.
References
[1]
Commission of the European Communities. White Paper—European transport policy for 2010: Time to decide, 2001. Available online: http://ec.europa.eu/transport/themes/strategies/2001_white_paper_en.htm (accessed on 30 October 2013).
[2]
European Commission. ommunication on the Future of Transport on 17 June 2009. Available online: http://ec.europa.eu/transport/strategies/2009_future_of_transport (accessed on 30 October 2013).
[3]
European Commission. White Paper: Roadmap to a Single European Transport Area—Towards a competitive and resource efficient transport system, 2011. Available online: http://ec.europa.eu/transport/themes/strategies/2011_white_paper_en.htm (accessed on 30 October 2013).
[4]
Ranney, T.A.; Mazzai, E.; Garrott, R.; Goodman, M.J. NHTSA Driver Distraction Research: Past, Present, and Future; Technical Report; NHTSA: Transportation Research Center Inc.: East Liberty, OH, USA, 2000.
[5]
Lee, J.; Regan, M.; Young, K. Defining Driver Distraction. In Driver Distraction: Theory, Effects, and Mitigation. Technical Report; CRC Press: Boca Raton, FL, USA, 2009.
[6]
Brill, J.C.; Hancock, P.A.; Gilson, R.D. Driver Fatigue: Is Something Missing? Proceedings of the Second International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, Park City, UT, USA, 21–24 July 2003; pp. 138–142.
[7]
Croo, H.D.; Bandmann, M.; Mackay, G.M.; Rumar, K.; Vollenhoven, P. The Role of Driver Fatigue in Commercial Road Transport Crashes. Technical Report; European Transport Safety Council: Brussels, Belgium, 2001.
[8]
Klauer, S.G.; Dingus, T.A.; Neale, V.L.; Sudweeks, J.D.; Ramsey, D.J. The Impact of Driver Inattention on Near-Crash/Crash Risk: An Analysis Using the 100-Car Naturalistic Driving Study Data; National Highway Traffic Safety Administration: Transportation Research Center Inc.: East Liberty, OH, USA, 2006.
[9]
ROSPA. Driver Fatigue and Road Accidents. A Literature Review and Position Paper. Technical Report; Royal Society for the Prevention of Accidents: Birmingham, UK, 2001.
[10]
Dong, Y.; Hu, Z.; Uchimura, K.; Murayama, N. Driver Inattention Monitoring System for Intelligent Vehicles: A Review. IEEE Trans. Intell. Transp. Syst. 2011, 12, 596–614.
[11]
Berka, C.; Levendowski, D.J.; Lumicao, M.N.; Yau, A.; Davis, G.; Zivkovic, V.T.; Olmstead, R.E.; Tremoulet, P.D.; Craven, P.L. EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. Aviat. Space Environ. Med. 2007, 78, 231–244.
[12]
Oron-Gilad, T.; Ronen, A.; Shinar, D. Alertness maintaining tasks (AMTs) while driving. Accid. Anal. Prev. 2008, 40, 851–860.
[13]
De Rosario, H.; Solaz, J.; Rodrguez, N.; Bergasa, L. Controlled inducement and measurement of drowsiness in a driving simulator. Intell. Transp. Syst. IET 2010, 4, 280–288.
[14]
Baek, H.J.; Lee, H.B.; Kim, J.S.; Choi, J.M.; Kim, K.K.; Park, K.S. Nonintrusive biological signal monitoring in a car to evaluate a driver's stress and health state. Telemed. e-Health 2009, 15, 182–189.
[15]
Bal, I.; Klonowski, W. SENSATION—New Nanosensors and Application of Nonlinear Dynamics for Analysis of Biosignals Measured by These Sensors. In World Congress on Medical Physics and Biomedical Engineering 2006; Magjarevic, R., Nagel, J.H., Eds.; Springer: Berlin/Heidelberg, Germany, 2007; Volume 14, pp. 735–736.
[16]
Hoddes, E.; Zarcone, V.; Smythe, H.; Phillips, R.; Dement, W.C. Quantification of sleepiness: A new approach. Psychophysiology 1973, 10, 431–436.
[17]
Akerstedt, T.; Gillberg, M. Subjective and objective sleepiness in the active individual. Int. J. Neurosci. 1990, 52, 29–37.
[18]
Kaida, K.; Takahashi, M.; ？kerstedt, T.; Nakata, A.; Otsuka, Y.; Haratani, T.; Fukasawa, K. Validation of the Karolinska sleepiness scale against performance and EEG variables. Clin. Neurophysiol. 2006, 117, 1574–1581.
[19]
Ingre, M.; Akerstedt, T.; Peters, B.; Anund, A.; Kecklund, G. Subjective sleepiness, simulated driving performance and blink duration: Examining individual differences. J. Sleep Res. 2006, 15, 47–53.
[20]
Wakita, T.; Ozawa, K.; Miyajima, C.; Igarashi, K.; Itou, K.; Takeda, K.; Itakura, F. Driver Identification Using Driving Behavior Signals. Proceedings of the 2005 IEEE Intelligent Transportation Systems, Vienna, Austria, 13–16 September 2005; pp. 396–401.
[21]
McCall, J.; Wipf, D.; Trivedi, M.; Rao, B. Lane change intent analysis using robust operators and sparse bayesian learning. IEEE Trans. Intell. Transp. Syst. 2007, 8, 431–440.
[22]
Eskandarian, A.; Mortazavi, A. Evaluation of a Smart Algorithm for Commercial Vehicle Driver Drowsiness Detection. Proceedings of the 2007 IEEE Intelligent Vehicles Symposium, Istanbul, Turkey, 13–15 June 2007; pp. 553–559.
[23]
Sandberg, D.; Akerstedt, T.; Anund, A.; Kecklund, G.; Wahde, M. Detecting driver sleepiness using optimized nonlinear combinations of sleepiness indicators. IEEE Trans. Intell. Transp. Syst. 2011, 12, 97–108.
[24]
Volvo. Volvo Driver Alert Control. 2012. Available online: http://www.volvocars.com/us/top/yourvolvo/volvoownersinstructionalvideos/pages/volvo-driveralertcontrol.aspx (accessed on 30 October 2013).
[25]
Mercedes-Benz. Attention Assist. Early warning for driver drowsiness. 2012. Available online: http://www.mbusa.com/mercedes/benz/safety#module-3 (accessed on 30 October 2013).
[26]
Lexus. LS 600h. 2012. Available online: http://www.testdriven.co.uk/lexus-ls-600h (accessed on 30 October 2013).
[27]
Saab. Saab Driver Attention Warning Sytem. 2012. Available online: http://www.zercustoms.com/news/Saab-Driver-Attention-Warning-System.html (accessed on 30 October 2013).
[28]
Ueno, H.; Kaneda, M.; Tsukino, M. Development of Drowsiness Detection System. Proceedings of the Vehicle Navigation and Information Systems Conference, Yokohama, Japan, 1–2 September 1994; pp. 15–20.
[29]
Dinges, D. PERCLOS: A Valid Psychophysiology Measure of Alertness as Assessed by Psychomotor Vigilance. Technical Report; Federal Highway Administration: Washington, DC, USA, 1998.
[30]
Vural, E.; Cetin, M.; Ercil, A.; Littlewort, G.; Bartlett, M.; Movellan, J. Drowsy Driver Detection Through Facial Movement Analysis. In Human-Computer Interaction; Springer: New York, NY, USA, 2007; pp. 6–18.
[31]
Highway Safety Group. Driver Fatigue, Lane Management & Warning Systems. 2012. Available online: http://www.driverfatiguemonitor.com/dfm/dfm.html (accessed on 30 October 2013).
[32]
Flores, M.J.; Armingol, J.M.; de la Escalera, A. Driver drowsiness warning system using visual information for both diurnal and nocturnal illumination conditions. EURASIP 2010, 2010, doi:10.1155/2010/438205.
[33]
Ji, Q.; Yang, X. Real-time eye, gaze, and face pose tracking for monitoring driver vigilance. Real-Time Imaging 2002, 8, 357–377.
[34]
Bergasa, L.; Nuevo, J.; Sotelo, M.; Barea, R.; Lopez, M. Real-time system for monitoring driver vigilance. IEEE Trans. Intell. Transp. Syst. 2006, 7, 63–77.
[35]
Heinzmann, J.; Tate, D.; Scott, R. Using Technology to Eliminate Drowsy Driving. Proceedings of the SPE International Conference on Health, Safety, and Environment in Oil and Gas Exploration and Production, Society of Petroleum Engineers, Nice, France, 15–17 April 2008; pp. 1–8.
[36]
Jimenez, P.; Bergasa, L.M.; Nuevo, J.; Hernandez, N.; Daza, I.G. Gaze Fixation System for the Evaluation of Driver Distractions Induced by IVIS. IEEE Trans. Intell. Transp. Syst. 2012, 13, 1167–1178.
[37]
Smart Eye AB. Smart Eye Pro. 2012. Available online: http://www.smarteye.se (accessed on 30 October 2013).
[38]
Seeing Machines. FaceLAB and DSS. 2012. Available online: http://www.seeingmachines.com (accessed on 30 October 2013).
[39]
Bowman, D.S.; Schaudt, W.A.; Hanowski, R.J. Advances in drowsy driver assistance systems through data fusion. Handb. Intell. Veh. 2012, 2012, 895–912.
[40]
Hanowski, R.; Bowman, D.; Alden, A.; Wierwille, W.; Carroll, R. PERCLOS+: Moving Beyond Single-Metric Drowsiness Monitors. SAE Technical Paper 2008-01-2692; SAE: Atlanta, GA, USA, 2008.
[41]
CEIT. Centro de estudios e investigaciones técnicas de Gipuzkoa. 2012. Available online: http://www.ceit.es (accesed on 30 October 2013).
[42]
Daza, I.; Hernandez, N.; Bergasa, L.; Parra, I.; Yebes, J.; Gavilan, M.; Quintero, R.; Llorca, D.; Sotelo, M. Drowsiness Monitoring Based on Driver and Driving Data Fusion. Proceedings of the 14th International IEEE Conference on Intelligent Transportation Systems (ITSC'11), Washington, DC, USA, 5–7 October 2011; pp. 1199–1204.
[43]
Schmidt, E.A.; Schrauf, M.; Simon, M.; Fritzsche, M.; Buchner, A.; Kincses, W.E. Drivers' misjudgement of vigilance state during prolonged monotonous daytime driving. Accid. Anal. Prev. 2009, 41, 1087–1093.
[44]
Friedrichs, F.; Yang, B. Drowsiness Monitoring by Steering and Lane Data based Features under Real Driving Conditions. Proceedings of the European Signal Processing Conference, Aalborg, Denmark, 23–27 August 2010; pp. 23–27.
[45]
Friedrichs, F.; Yang, B. Camera-Based Drowsiness Reference for Driver State Classification under Real Driving Conditions. Proceedings of the 2010 IEEE Intelligent Vehicles Symposium (IV'10), San Diego, CA, USA, 21–24 June 2010; pp. 101–106.
[46]
Boyraz, P.; Acar, M.; Kerr, D. Multi-sensor driver drowsiness monitoring. Proc. Inst. Mech. Eng. Part D J. Automob. Eng. 2008, 222, 2041–2062.
[47]
Sandberg, D.; Wahde, M. Particle Swarm Optimization of Feedforward Neural Networks for the Detection of Drowsy Driving. Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN 2008), Hong Kong, China, 1–8 June 2008; pp. 788–793.
[48]
Garcia, I.; Bronte, S.; Bergasa, L.; Hernandez, N.; Delgado, B.; Sevillano, M. Vision-Based Drowsiness Detector for a Realistic Driving Simulator. Proceedings of the 13th International IEEE Conference on Intelligent Transportation Systems (ITSC'10), Madeira, Portugal, 19–22 September 2010; pp. 887–894.
[49]
Garcia, I.; Bronte, S.; Bergasa, L.; Almazan, J.; Yebes, J. Vision-Based Drowsiness Detector for Real Driving Conditions. Proceedings of the 2012 IEEE Intelligent Vehicles Symposium (IV'12), Alcala de Henares, Madrid, Spain, 3–7 June 2012; pp. 618–623.
[50]
Wierwille, W.; Lewin, M.; Fairbanks, R. Research on Vehicle-Based Driver Status/Performance Monitoring: Part III; U.S. Department of Transportation, National Highway Traffic Safety Administration: East Liberty, OH, USA, 1996.
[51]
Godthelp, H.; Milgram, P.; Blaauw, G.J. The development of a time-related measure to describe driving strategy. Hum. Factors J. Hum. Factors Ergon. Soc. 1984, 26, 257–268.
[52]
Fairclough, S.H.; Graham, R. Impairment of driving performance caused by sleep deprivation or alcohol: A comparative study. Hum. Factors: J. Hum. Factors Ergon. Soc. 1999, 41, 118–128.
[53]
Holland, J.H. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence; MIT Press: Cambridge, MA, USA, 1992.
[54]
Dong, Y.; Hu, Z.; Uchimura, K.; Murayama, N. Driver inattention monitoring system for intelligent vehicles: A review. IEEE Trans. Intell. Transp. Syst. 2011, 12, 596–614.
[55]
Caterpillar. Operator Fatigue Detection Technology Review. Technical Report; Caterpillar Inc.: Peoria, IL, USA, 2008.
