Computer Vision System for Detection of Passenger Sleeping State for Advanced Driver Assistance Systems
Keywords:
computer vision, passenger fatigue detection, Advanced Driver Assistance System
Abstract
The goal of this research is to develop an in-vehicle computerized system able to warn the driver, to assess the passenger’s state of sleeping in order to avoid affecting the driver psychologically and induce drowsiness. This new feature proposed for Advanced Driver Assistance System might increase car safety by mitigation or avoidance of accidents.
Downloads
Download data is not yet available.
References
[1]. Walter H. Moog, A. O. K. Baden-Württemberg, et al., Hellwach am Steuer - Bestandsaufnahme der Situation von Lastkraftwagenfahrern und Vorschläge zur Prävention von Sekundenschlaf am Steuer, 2011.
[2]. S. E. Shladover, Review of the state of development of advanced vehicle control systems, Vehicle System Dynamics, 24 (6–7), p. 551-595, 1995.
[3]. S. Tsugawa, M. Aoki, A. Hosaka, K. Seki, A survey of present IVHS activities in Japan, Control Engineering Practice, 5 (11), p. 1591-1597, November 1997.
[6]. A. Vahidi, A. Eskandarian, Research advances in intelligent collision avoidance and adaptive cruise control, IEEE Trans. on Intelligent Transportation Systems, 4 (3), p. 143-153, September 2003.
[7]. R. Bishop, Intelligent Vehicle Technology and Trends, Artech House, Norwood, MA, USA, 2005.
[8]. P. L. Zador, S. A. Krawchuk, R. B. Voas, Automotive collision avoidance system (ACAS) program. Final Report, DOT HS 809 080, National Highway Traffic Safety Administration, Washington, DC, USA, August 2000, http://wwwnrd.nhtsa.dot.gov/departments/nrd-12/pubs_rev.html.
[9]. H. M. Jagtman, V. A. W. J. Marchau, T. Heijer, Current knowledge on safety impacts of Collision Avoidance Systems (CAS), In P. M. Herder and W. A. H. Thissen, editors, Proc. of the 5th International Conference on Technology, Policy and Innovation, Delft, June 26-29, 2001.
[10]. ***, The 100-Car Naturalistic Driving Study, Phase II - Results of the 100 Car Field Experiment, Report No. DOT HS 810 593, April 2006.
[11]. Luke Sebastian Fletcher, An Automated Co-driver for Advanced Driver Assistance Systems: The next step in road safety, A thesis submitted for the degree of Doctor of Philosophy at the Australian National University.
[12]. Dong W., Wu X., Driver fatigue detection based on the distance of eyelid, In: IEEE Int. Workshop VLSI Design & Video Tech., Suzhou, China, 2005.
[13]. Fletcher L., Petersson L., ZelinskyA., Driver assistance systems based on vision in and out of vehicles, In: IEEE Proceedings of Intelligent Vehicles Symposium, p. 322-327, 2003.
[14]. Bergasa L. M., Nuevo J., Sotelo M. A., Barea R., Lopez M. E., Real-time system for monitoring driver vigilance, IEEE Trans. Intell. Transp. Syst., 7 (1), p. 63-77, 2006.
[15]. Cheng S. Y., Park S., Trivedi M. M., Multiperspective and multimodal video arrays for 3d body tracking and activity analysis, Comput. Vis. Image Underst., Special Issue on Advances in Vision Algorithms and Systems Beyond the Visible Spectrum, 106 (2-3), p. 245-257, 2007.
[16]. Cheng S. Y., Trivedi M. M., Turn-intent analysis using body pose for intelligent driver assistance, IEEE Pervasive Comput., 5 (4), p. 28-37, 2006.
[17]. Doshi A., Trivedi M. M., Investigating the relationships between gaze patterns, dynamic vehicle surround analysis, and driver intentions, In: IEEE Intelligent Vehicles Symposium, 2009.
[18]. Trivedi M. M., Cheng S. Y., Childers E., Krotosky S., Occupant posture analysis with stereo and thermal infrared video: Algorithms and experimental evaluation, IEEE Trans. Veh. Technol., Special Issue on In-Vehicle Vision Systems, 53 (6), p. 1698-1712, 2004.
[19]. Wu J., Trivedi M. M., An eye localization, tracking and blink pattern recognition system: Algorithm and evaluation, ACM Trans. Multimedia Comput. Commun. Appl., 6 (2), 2010.
[20]. Zhu Y., Fujimura K., Head pose estimation for driver monitoring, In Intelligent Vehicles Symposium, p. 501-506, June 2004.
[21]. Zhu Z., Fujimura K., Ji Q., Real-time eye detection and tracking under various light conditions, InETRA '02: Proceedings of the Symposium on Eye Tracking Research & Applications, p. 139-144, ACM Press, 2002.
[2]. S. E. Shladover, Review of the state of development of advanced vehicle control systems, Vehicle System Dynamics, 24 (6–7), p. 551-595, 1995.
[3]. S. Tsugawa, M. Aoki, A. Hosaka, K. Seki, A survey of present IVHS activities in Japan, Control Engineering Practice, 5 (11), p. 1591-1597, November 1997.
[6]. A. Vahidi, A. Eskandarian, Research advances in intelligent collision avoidance and adaptive cruise control, IEEE Trans. on Intelligent Transportation Systems, 4 (3), p. 143-153, September 2003.
[7]. R. Bishop, Intelligent Vehicle Technology and Trends, Artech House, Norwood, MA, USA, 2005.
[8]. P. L. Zador, S. A. Krawchuk, R. B. Voas, Automotive collision avoidance system (ACAS) program. Final Report, DOT HS 809 080, National Highway Traffic Safety Administration, Washington, DC, USA, August 2000, http://wwwnrd.nhtsa.dot.gov/departments/nrd-12/pubs_rev.html.
[9]. H. M. Jagtman, V. A. W. J. Marchau, T. Heijer, Current knowledge on safety impacts of Collision Avoidance Systems (CAS), In P. M. Herder and W. A. H. Thissen, editors, Proc. of the 5th International Conference on Technology, Policy and Innovation, Delft, June 26-29, 2001.
[10]. ***, The 100-Car Naturalistic Driving Study, Phase II - Results of the 100 Car Field Experiment, Report No. DOT HS 810 593, April 2006.
[11]. Luke Sebastian Fletcher, An Automated Co-driver for Advanced Driver Assistance Systems: The next step in road safety, A thesis submitted for the degree of Doctor of Philosophy at the Australian National University.
[12]. Dong W., Wu X., Driver fatigue detection based on the distance of eyelid, In: IEEE Int. Workshop VLSI Design & Video Tech., Suzhou, China, 2005.
[13]. Fletcher L., Petersson L., ZelinskyA., Driver assistance systems based on vision in and out of vehicles, In: IEEE Proceedings of Intelligent Vehicles Symposium, p. 322-327, 2003.
[14]. Bergasa L. M., Nuevo J., Sotelo M. A., Barea R., Lopez M. E., Real-time system for monitoring driver vigilance, IEEE Trans. Intell. Transp. Syst., 7 (1), p. 63-77, 2006.
[15]. Cheng S. Y., Park S., Trivedi M. M., Multiperspective and multimodal video arrays for 3d body tracking and activity analysis, Comput. Vis. Image Underst., Special Issue on Advances in Vision Algorithms and Systems Beyond the Visible Spectrum, 106 (2-3), p. 245-257, 2007.
[16]. Cheng S. Y., Trivedi M. M., Turn-intent analysis using body pose for intelligent driver assistance, IEEE Pervasive Comput., 5 (4), p. 28-37, 2006.
[17]. Doshi A., Trivedi M. M., Investigating the relationships between gaze patterns, dynamic vehicle surround analysis, and driver intentions, In: IEEE Intelligent Vehicles Symposium, 2009.
[18]. Trivedi M. M., Cheng S. Y., Childers E., Krotosky S., Occupant posture analysis with stereo and thermal infrared video: Algorithms and experimental evaluation, IEEE Trans. Veh. Technol., Special Issue on In-Vehicle Vision Systems, 53 (6), p. 1698-1712, 2004.
[19]. Wu J., Trivedi M. M., An eye localization, tracking and blink pattern recognition system: Algorithm and evaluation, ACM Trans. Multimedia Comput. Commun. Appl., 6 (2), 2010.
[20]. Zhu Y., Fujimura K., Head pose estimation for driver monitoring, In Intelligent Vehicles Symposium, p. 501-506, June 2004.
[21]. Zhu Z., Fujimura K., Ji Q., Real-time eye detection and tracking under various light conditions, InETRA '02: Proceedings of the Symposium on Eye Tracking Research & Applications, p. 139-144, ACM Press, 2002.
Published
2014-12-15
How to Cite
1.
PETREA I, MARIN FB. Computer Vision System for Detection of Passenger Sleeping State for Advanced Driver Assistance Systems. The Annals of “Dunarea de Jos” University of Galati. Fascicle IX, Metallurgy and Materials Science [Internet]. 15Dec.2014 [cited 26Dec.2024];37(4):35-8. Available from: https://gup.ugal.ro/ugaljournals/index.php/mms/article/view/2224
Issue
Section
Articles
