Sistema Avanzado de Asistencia a la Conducción para la Detección de la Somnolencia


  • Marco J. Flores Universidad Carlos III de Madrid
  • José M. Armingol Universidad Carlos III de Madrid
  • Arturo de la Escalera Universidad Carlos III de Madrid


Palabras clave:

Inteligencia Artificial, Visión por Computador, somnolencia, distracción, conductor, accidentes de tráfico, iluminación infrarroja


En este artículo se presenta un sistema avanzado de asistencia a la conducción (SAAC) diseñado para detectar automáticamente a somnolencia y la distracción del conductor. Este sistema se compone de dos partes: una para trabajar durante el día con luminación natural, y otra para funcionar en la noche utilizando iluminación infrarroja. Los principales objetivos son localizar l rostro y los ojos del conductor para analizarlos a través del tiempo y generar un índice de somnolencia y uno de distracción. Para llo se han utilizado técnicas de Visión por Computador e Inteligencia Artificial. Finalmente, el sistema ha sido probado con varios onductores sobre un vehículo en condiciones reales de conducción, en el día y en la noche.


Los datos de descargas todavía no están disponibles.


ASFA, 2008. Driver fatigue is the number one cause of catastrophic truck accidents. Website,

Bergasa, L., Nuevo, J., Sotelo, M., Barea, R., Lopez, E., March 2006. Real-time system for monitoring driver vigilance. IEEE, Transactions on Intelligent Transportation Systems 7 (1), 63–77.

Bergasa, L., Nuevo, J., Sotelo, M., Vásquez, M., Junio 14-17 2004. Real-time system for monitoring driver vigilance. IEEE, Intelligent Vehicles Symposium 1 (2).

Bloemkolk, F., de Lijster, J., van Gelderen, M., July 2007. ITS strategy: the japanese formula for success. Study to promote ITS implementation in the Netherlands. Technical report, International Affaris Office, Ministry of Transportation, Public Works and Water Management.

Branzan, A., Widsten, B., Wang, T., Lan, J., Mah, J., June 2008. A computer vision-based system for real-time detection of sleep onset in fatigued drivers. IEEE, Intelligent Vehicles Symposium, 25–30.

Brookshear, J., 1983. Theory of computation: Formal Languages; Automata and Complexity. Vol. 1. Addison Wesley Iberoamericana.

Chang, B., Lim, J., Kim, H., Seo, B., September 2007. A study of classification of the level of sleepiness for the drowsy driving prevention. IEEE, SICE Annual Conference, 3084–3089.

Cristianini, N., Shawe-Taylor, J., 2006. An introduction to Support Vector Machines and other kernel-based learning methods. Cambrige University Press.

Daugman, J., 1985. Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional cortial filters. J. Optical Soc. Am. 2 (7), 1160–1169.

de la Escalera, A., 2001. Visión por Computador, Fundamentos y Métodos. Vol. 1. Prentice Hall, Pearson Educación, Madrid.

Dong, W., Wu, X., 2005. Driver fatigue detection based on distant eyelid. IEEE, Int. Workshop VLSI Design & Video Tech.

Doucet, A., N. Freitas de, Gordon, N., 2001. Sequential Monte Carlo Methods in Practice. Vol. 1. Springer-Verlag.

D`Orazio, T., Leo, M., Distante, A., June 2004. Eye detection in face images for a driver vigilance system. IEEE, Intelligent Vehicle Symposium, 95–98.

Durrett, R., 1991. Probability: Theory and Examples. Vol. 1. Library of Congress Catalogingin-Publication Data.

Evgeniou, T., Pontil, M., Papageorgiou, C., Poggio, T., 2000. Image representations for object detection using kernel classifiers. In Asian Conference on Computer Vision.

Fletcher, L., Petersson, L., Zelinsky, A., 2003. Driver assistance systems based on vision in and out of vehicles. IEEE, Proceedings of Intelligent Vehicle Symposium, 322–327.

Freund, Y., Schapire, R., 1995. A decision-theorical generalization of online learning and an application to boosting. In Second European Conference on Computational Learning Theory.

Gejgus, P., Sperka, M., 2003. Face tracking in color video sequences. Association for Computing Machinery, 245–249.

Grace, R., Byrne, V., Bierman, D., Legrand, J., Grcourt, D., Davis, R., Staszewski, J., Carnahan, B., Octuber 1998. A drowsy driver detection system for heavy vehicles. IEEE, Proceedings of Digital Avionics System Conference 2, 1–8.

Guo, J., Guo, X., July 2009. Eye state recognition based on shape analysis and fuzzy logic. IEEE Intelligent Vehicle Symposium, 78–82.

Hagenmeyer, L., August 2007. Development of a multimodal, universal human-machine-interface for hypovigilance-management-systems. Ph.D. thesis, Mechanical Engineering, University of Stuttgart, Institute for Human Factors and Technology Management.

Hanmi, I., 2005. Drowsy truck drivers. Website,

Hansen, D., Ji, Q., March 2010. In the eye of the beholder: A survey of models for eyes and gaze. IEEE Transactions on Pattern Analysis and Machine Intelligence (3), 478–500.

Hayami, T., Matsunaga, K., Shidoji, K., Matsuki, Y., September 2002. Detecting drowsiness while driving by measuring eye movement - a pilot study. IEEE International Conference on Intelligent Transportation Systems, 156–161.

Hilario, C., Octubre 2008. Detección de peatones en el espectro visible e infrarrojo para un sistema avanzado de asistencia a la conducción. Ph.D. thesis, Departamento de Ingeniería de Sistemas y Automática, Universidad Carlos III de Madrid.

Horng, W., Chen, C., Chang, Y., 2004. Driver fatigue detection based on eye tracking and dynamic template matching. IEEE Proceedings of, International Conference on Networking, Sensing and Control.

Isard, M., Blake, A., 1998. Condensation - conditional density propagation for visual tracking. International Journal of Computer Vision 29 (1), 5–28.

Isard, M. A., September 1998. Visual motion analysis by probabilistic propagation of conditional density. Ph.D. thesis, Department of Engineering Science, University of Oxford.

Ji, Q., Yang, X., 2001. Real-time visual cues extraction for monitoring driver vigilance. Lectures Notes in Computer Science, Proceedings of the Second International Workshop on Computer Vision Systems 2095, 107–124.

Ji, Q., Yang, X., 2002. Real-time eye, gaze and face pose tracking for monitoring driver vigilance. Elsevier Science Ltd., Real Time Imaging 1 (8), 357–377.

Ji, Q., Zhu, Z., Lan, P., Junio 2004. Real time nonintrusive monitoring and prediction of driver fatigue. IEEE, Transaction on Vehicular Technology 53 (4).

Jiangwei, C., Lisheng, J., Lie, G., Keyou, G., Rongben, W., June 2004a. Driver’s eye state detecting method design based on eye geometry feature. IEEE, Intelligent Vehicles Symposium, 357–362.

Jiangwei, C., Lisheng, J., Lie, G., Keyou, G., Rongben, W., June 2004b. A monitoring method of driver mouth behaviour based on machine vision. IEEE, Intelligent Vehicles Symposium, 351–356.

Knipling, R., Wierwille, W., 1994. Vehicle-based drowsy driver detection: Current status and future prospects. IVSH America Fourth Annual Meeting.

Koller-Meier, E., Ade, F., ???? Tracking multiple objects using the condensation algorithm.

Kücükay, F., Bergholz, J., 2005. Driver assistant systems. Lectures of Institute of Automatic Engineering

Kutila, M., Dicember 2006. Methods for machine vision based driver monitoring applications. Ph.D. thesis, Tietotalo Building, Auditorium TB104.

Lisheng, J., Xuan, S., Yuying, J., Haijing, H., Yuqin, S., June 2009. Study on driver’s mouth segmentation and location based on color space. IEEE Intelligent Vehicles Symposium, 500–506.

Liu, C., May 2004. Gabor-based kernel pca with fractional power polynomial models for face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligent 26 (5), 572–582.

Longhurst, G., ???? Understanding driver visual behaviour. Seeing Machine Pty Limited.

Looney, C., 1997. Pattern Recognition Using Neural Networks: theory and algorithms for engineers and scientists. Oxford University Press Inc.

Loy, G., January 2003. Computer vision to see people: a basis for enhanced human computer interaction. Ph.D. thesis, Robotics Systems Laboratory, Department of Systems Engineering, Research School of Information Sciences and Engineering, Australian National University.

Loy, G., Barnes, N., September 2004. Fast shape-based road sign detection for a driver assistance system. IEEE, International Conference on Intelligent Robots and Systems (IROS’04) 1, 70–75.

Loy, G., Zelinsky, A., August 2003. Fast radial symmetry for detecting points of interest. IEEE, Transactions on Pattern Analysis and Machine Intelligence 25 (8), 959–973.

Martinez, W., Martinez, A., 2002. Computational Statistics Handbook with Matlab. Chapman & Hall/CRC.

NHTSA, April 1998. Evaluation of techniques for ocular measurement as an index of fatigue and the basis for alertness management. Final Report DOT HS 808 762, National Highway Traffic Safety Administration, Virginia 22161, USA.

Otsu, N., 1979. A threshold selection method from gray-level histograms. IEEE Trans. Systems, Man and Cybernetics, 62–66.

Pitas, I., 2000. Digital Image Processing Algorithms and Applications. A Wiley-Interscience Publication. John Wiley & Sons, Inc.

Ristic, B., Arulampalam, S., Gordon, N., 2004. Beyond the Kalman Filter: Particle Filters for Tracking Applications. Vol. 1. Artech House.

Rogers, C., 1998. Hausdorff measures. Vol. 1. Cambridge: Cambridge University Press.

Rongben, W., Keyou, G., Shuming, S., Jiangwei, C., June 2003. A monitoring method of driver fatigue behavior based on machine vision. IEEE, Procedings on Intelligent Vehicles Symposium, 110–113.

Tian, Z., Qin, H., Octuber 2005. Real-time driver’s eye state detection. IEEE, International Conference on Vehicular Electronics and Safety, 285–289.

Viola, P., Jones, M., 2001. Rapid object detection using a boosted cascade of simple features. Computer Vision and Pattern Recognition, Proceedings of the 2001 IEEE Computer Society Conference on 1, 1–511–1–518.

Viola, P., Jones, M., 2002a. Fast and robust classification using asymmetric adaboost and a detector cascade. Advances in Neural Information Processing System, MIT Press, Cambrige, M. A. (14).

Viola, P., Jones, M., 2002b. Robust real-time object detection. International Journal of Computer Vision - to appear.

Vlacic, L., Parent, M., Harashima, F., 2001. Intelligent Vehicle Technologies. A division of Reed Educational and Professional Publishing Ltda. Library of Congress Cataloguing in Publication Data.

Wang, Q., Yang, J., Ren, M., Zheng, Y., June 2006. Driver fatigue detection: A survey. IEEE, Proceedings of the 6th World Congress on Intelligent Control and Automation, 8587 – 8591.

Wu, Y., Liu, H., Zha, H., June 2004. A new method of detection humand eyelids based on deformable templates. IEEE International Conference on Systems, Man and Cybernectics, 604–609.

Zhou, M., Wei, H., 2006. Face verification using gabor wavelets and adaboost. IEEE, 18th. International Conference on Pattern Recognition ICPR06 1, 404–407.

Zhu, Z., Fujimura, K., Ji, Q., 2002a. Real-time eye detection and tracking under various light conditions. Proceedings of the 2002 Symposium of Eye tracking research & applications, 139–144.

Zhu, Z., Ji, Q., Fujimura, K., Lee, K., 2002b. Combining Kalman filtering and mean shift for real time eye tracking under active ir illumination. Proceedings of the 16 th International Conference on Pattern Recognition (ICPR’02) 4, 318–321.


Cómo citar

Flores, M. J., Armingol, J. M. y de la Escalera, A. (2011) «Sistema Avanzado de Asistencia a la Conducción para la Detección de la Somnolencia», Revista Iberoamericana de Automática e Informática industrial, 8(3), pp. 216–228. doi: 10.1016/j.riai.2011.06.009.