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SECTION C: ENGINEERING

Vol. 6 No. 2 (2014)

A new system to detect distraction and drowsiness using time of flight technology forintelligent vehicles

DOI
https://doi.org/10.18272/aci.v6i2.182
Submitted
September 30, 2015
Published
2014-12-19

Abstract

Nowadays, most countries in the world suffer several traffic issues which generate public health problems such as deaths and injuries of drivers and pedestrians. In order to reduce these fatalities, a system for automatic detection of both distraction and drowsiness is presented in this research. Artificial intelligence, computer vision and time of flight (TOF) technologies are used to compute both distraction and drowsiness indexes, in real time. Several experiments have been developed in real conditions during the day, inside a real vehicle and in laboratory conditions, to prove the efficiency of the system.

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References

  1. Bergasa, L.; Nuevo, J.; Sotelo, M.; Vazquez, M. 2004. "Real Time System for Monitoring Driver Vigilance". IEEE Intelligent Vehicles Symposium.
  2. Brandt, T.; Stemmer, R.; Mertsching, B.; Rakotomirainy, A. 2004. "Affordable Visual Driver Monitoring System for Fatigue and Monotony". IEEE International Conference on Systems, Man and Cybernetics, 7: 6451-6456.
  3. Friedrichs, F.; Yang, B. 2010. "Camera-based drowsiness reference for driver state classification under real driving conditions". IEEE Intelligent Vehicles Symposium, 4.
  4. Ji, Q.; Yang, X. 2002. "Real-Time Eye, Gaze, and Face Pose Tracking for Monitoring Driver Vigilance". Real Time Imaging, Elsevier Science Ltd, 8: 357-377.
  5. NHTSA. 1998. "Evaluation of techniques for ocular measurement as an index of fatigue and the basis for alertness management". Final report DOT HS 808762, National Highway Traffic Safety Administration, Virginia 22161, USA.
  6. Wang, Q.; Yang, J.; Ren, M.; Zheng, Y. 2006. "Driver Fatigue Detection: A Survey". IEEE Proceedings of the 6th World Congress on Intelligent Control, 2: 8587-8591.
  7. El Comercio. 2010. "El arrollamiento de 31 personas se juzga desde ayer". http://www4.elcomercio.com/Judicial/el_arrollamiento_de_31_personas_se_juzga_desde_ayer.aspx.
  8. El Comercio. 2010. "Los peatones y los conductores no respetan los semáforos". http://www.elcomercio.com/2010-08-26/Noticias/Quito/Noticia-Principal/EC100826P13SEMAFOROS.aspx.
  9. Secretaría General de la Comunidad Andina. 2011. "Accidentes de tránsito en la Comunidad Andina 2010". http://estadisticas.comunidadandina.org/eportal/contenidos/1624_8.pdf.
  10. Armingol, J.; de la Escalera, A.; Hilario, C.; Collado, J.; Carrasco, J.; Flores, M.; Pastor, J.; Rodríguez, F. 2007. "IVVI: Intelligent Vehicle based on Visual Information". Robotics and Autonomous Systems, 55 (12): 904-916.
  11. Sabet, M.; Zoroofi, R.; Sadeghniiat-Haghighi, K.; Sabbaghian, M. 2012. "A new system for driver drowsiness and distraction detection". Conference on Electrical Engineering (ICEE): 1247-1251.
  12. Microsoft. 2014. "Kinetic". http://www.xbox.com/kinect.
  13. La Hora. 2013. "Ecuador es el segundo país en muertes por accidentes de tránsito". http://www.lahora.com.ec/index.php/noticias/show/1101523310#.UnJwOhCtXMs.
  14. Yekhshatyan, L.; Lee, J. 2013. "Changes in the Correlation Between Eye and Steering Movements Indicate Driver Distraction". IEEE Transactions on Intelligent Transport Systems, 14 (1): 136-145.
  15. Flores, M.; Armingol, J.; Escalera, A. 2011. "Driver drowsiness detection system under infrared illumination for an intelligent vehicle". Intelligent Transport Systems, IET, 5 (4): 241-251.
  16. Gallahan, S.; Golzar, G.; Jain, A.; Samay, A.; Trerotola, T.; Weisskopf, J.; Lau, N. 2013. "Detecting and mitigating driver distraction with motion capture technology: Distracted driving warning system". IEEE Systems and Information Engineering Design Symposium (SIEDS): 76-81.
  17. Agencia Nacional de Tránsito. 2013. http://www.ant.gob.ec/.
  18. Azman, A.; Meng, Q.; Edirisinghe, E. 2010. "Non intrusive physiological measurement for driver cognitive distraction detection: Eye and mouth movements". IEEE International Conference on Advanced Computer Theory and Engineering (ICACTE), 3: 595-599.
  19. Khoshelham, K.; Oude Elberink, S. 2012. "Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications". Sensors 2012: 1437-1454.
  20. Webb, J.; Ashley, J. 2012. "Beginning Kinect programming with the Microsoft Kinect SDK". Friends of Apress.
  21. Abtahi, S.; Hariri, B.; Shirmohammadi, S. 2011. "Driver drowsiness monitoring based on yawning detection". IEEE Conference on Instrumentation and Measurement Technology (I2MTC): 1-4.