Abstract
Advanced sensing systems, sophisticated algorithms and increasing computational resources continuously enhance active safety technology for vehicles. Driver status monitoring belongs to the key components of advanced driver assistance system which is capable of improving car and road safety without compromising driving experience. This paper presents a novel approach to driver status monitoring aimed at drowsiness detection based on depth camera, pulse rate sensor and steering angle sensor. Due to NIR active illumination depth camera can provide reliable head movement information in 3D alongside eye gaze estimation and blink detection in a non-intrusive manner. Multi-sensor data fusion on feature level and multilayer neural network facilitate the classification of driver drowsiness level based on which a warning can be issued to prevent traffic accidents. The presented approach is implemented on an integrated soft-computing system for driving simulation (DeCaDrive) with multi-sensing interfaces. The classification accuracy of \(98.9\,\%\) for up to three drowsiness levels has been achieved based on data sets of five test subjects with 588-min driving sequence.
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References
von Seelen, W.: Elektronisches Auge OPEL, Multimodaler Sensor zur Fahrzeugführung: Teilprojekt: Architektur, Rundumsicht und Objekterkennung; Abschlubericht zum 30. Juni 1997. Univ., Inst. für Neuroinformatik (1997)
Skribanowitz, J., Knobloch, T., Schreiter, J., König, A.: VLSI Implementation of an application-specific vision chip for overtake monitoring, real time eye tracking, and visual inspection. In: MicroNeuro, vol. 99, pp. 4552 (1999)
Ford Motor Company: http://www.ford.com. last visited: 02.11.2012
Daimler, A.G.: http://www.daimler.com. last visited: 02.11.2012
Volkswagen, A.G.: http://www.volkswagen.com. last visited: 02.11.2012
Toyota Motor Corporation: http://www.toyota.com. last visited: 02.11.2012
Volvo Car Corporation: http://www.volvocars.com. last visited: 02.11.2012
Li, L., Xu, Y., König, A.: Robust depth camera based multi-user eye tracking for autostereoscopic displays. In: 9th International Multi-Conference on SSD, pp. 1–6 (2012)
Werber, K.: Untersuchung von Fahrerassistenzsystemen zur Fahrer- Zustands- und Absichtserkennung mit Multisensorik. In: Diplomarbeit, ISE, TU Kaiserslautern (2012)
Li, L., Xu, Y., König, A.: Robust depth camera based eye localization for human-machine interactions. In: KES’2011, vol. 6881, p. 424 (2011)
Li, H., Roivainen, P., Forchheimer, R.: 3-D motion estimation in model-based facial image coding. IEEE Trans. PAMI 15(6), 545–555 (1993)
Face Tracking: http://msdn.microsoft.com. last visited: 28.10.2012
Guarde, A.: Kinect based eye tracking for driver drowsiness detection. In: Studienarbeit, Institute of Integrated Sensor Systems (ISE), TU Kaiserslautern (2012)
Matsumoto, Y., Zelinsky, A.: An algorithm for real-time stereo vision implementation of head pose and gaze direction measurement. In: 4th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 499–504 (2000)
Sherman, P.: The potential of steering wheel information to detect driver drowsiness and associated lane departure. Technical Report, Iowa State University (1996)
Yu, X.: Real-time nonintrusive detection of driver drowsiness: Final Report. Technical Report, University of Minnesota (2009)
Møller, M. F.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw. 6(4), 525–533 (1993)
Moré, J.J.: The Levenberg-Marquardt algorithm: implementation and theory. Numerical Anal. Dundee (1977)
Feng R., Zhang, G., Cheng, B.: An on-board system for detecting driver drowsiness based on multi-sensor data fusion using Dempster-Shafer theory, In: ICNSC, pp. 897–902 (2009)
Acknowledgments
The authors would like to thank Abhaya C. Kammara for giving support to construct the DeCaDrive system. The help from students in ISE are gratefully appreciated.
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Li, L., Werber, K., Calvillo, C.F., Dinh, K.D., Guarde, A., König, A. (2014). Multi-Sensor Soft-Computing System for Driver Drowsiness Detection. In: Snášel, V., Krömer, P., Köppen, M., Schaefer, G. (eds) Soft Computing in Industrial Applications. Advances in Intelligent Systems and Computing, vol 223. Springer, Cham. https://doi.org/10.1007/978-3-319-00930-8_12
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DOI: https://doi.org/10.1007/978-3-319-00930-8_12
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