Design and Implementation of Intelligent Systems Detect the Driver’s Lack of Concentration

Document Type : Research Article

Authors

1 Department of Mechanical Engineering, K.N.Toosi University Of Technology, Tehran, Iran

2 Department of Mechanical Engineering, Pardis Branch/ Islamic Azad University, Tehran, Iran

3 Department of Mechatronics Engineering, South Branch/ Islamic Azad University, Tehran, Iran

Abstract

Today one of the serious challenges which the world faces is the cars accident. Accident poses irreparable damages to humans all across the globe. Many factors like technical bugs, disregarding of driving rules and loss of concentration contribute most car accidents. An experimental perspective over losing concentration proves its vital role in accidents. In this context, it is very important to monitor the driver’s lack of concentration. This article tries to recommend an intelligent algorithm in order to determine driver consciousness based on visual processing, eye state is one of the most important features to detecting driver’s lack of concentration. The algorithm contains two phases: 1- Face components detection, 2- Driver consciousness detection, The algorithm provides with a full-scale database so as to recover the algorithm instantly. Research findings confirm that our recommended intelligent Algorithm is 96% Successful to predict the driver consciousness. Moreover, we invent a concentration lost cautionary that was tested on a prototype that satisfied our expectations. Finally, we conclude that our recommended Algorithm can act as a deterrent against most terrible accidents successfully. We hope this algorithm reduces accident rate and create an advancement in Smart Cars knowledge.

Keywords

Main Subjects

References

[1] K. Hayashi, K. Ishihara, H. Hashimoto, K. Oguri, Individualized drowsiness detection during driving by pulse wave analysis with neural network, in: Intelligent Transportation Systems, 2005. Proceedings. 2005 IEEE, IEEE, 2005, pp. 901-906.
[2] P. Smith, M. Shah, N.J.I.t.o.i.t.s. da Vitoria Lobo, Determining driver visual attention with one camera, 4(4) (2003) 205-218.
[3] C.-T. Lin, L.-W. Ko, I.-F. Chung, T.-Y. Huang, Y.-C. Chen, T.-P. Jung, S.-F.J.I.T.o.C. Liang, S.I.R. Papers, Adaptive EEG-based alertness estimation system by using ICA-based fuzzy neural networks, 53(11) (2006) 2469-2476.
[4] A. Dasgupta, A. George, S. Happy, A.J.I.T.o.I.T.S. Routray, A vision-based system for monitoring the loss of attention in automotive drivers, 14(4) (2013) 1825-1838.
[5] R.N. Khushaba, S. Kodagoda, S. Lal, G.J.I.T.o.B.E. Dissanayake, Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm, 58(1) (2011) 121-131.
[6] M. Matousek, I.J.C.N. Petersén, A method for assessing alertness fluctuations from EEG spectra, 55(1) (1983) 108-113.
[7] S. Gupta, S. Kar, S. Gupta, A. Routray, Fatigue in human drivers: A study using ocular, Psychometric, physiological signals, in: Students' Technology Symposium (TechSym), 2010 IEEE, IEEE, 2010, pp. 234-240.
[8] M. Sigari, Fathi,M., Designing and testing a system to monitor the driver's face In order to detect fatigue and lack of concentration, Iran, The first national conference On Traffic Safety And Promoting Solutions., (2010).
[9] M. Sigari, M. Fathi, M. Soryani, Designing A Driver’s Face Monitoring System For Driver’s Fatigue And Distraction Detection, (2012).
[10] P. Viola, M.J.J.I.j.o.c.v. Jones, Robust real-time face detection, 57(2) (2004) 137-154.
[11] Z. Bian, Y.J.F.p.r.f.E.i.p. Zhang, reconstruction, Retinex image enhancement techniques: algorithm, application and advantages, (2002).
[12] R.O. Mbouna, S.G. Kong, M.-G.J.I.t.o.i.t.s. Chun, Visual analysis of eye state and head pose for driver alertness monitoring, 14(3) (2013) 1462-1469.
[13] T. Nguyen, M.-T. Chew, S. Demidenko, Eye tracking system to detect driver drowsiness, in: Automation, Robotics and Applications (ICARA), 2015 6th International Conference on, IEEE, 2015, pp. 472-477.
[14] B.D. Lucas, T. Kanade, An iterative image registration technique with an application to stereo vision, (1981).
[15] C. Tomasi, T. Kanade, Detection and Tracking of Point Features. Carnegie Mellon University Technical Report CMU-CS-91-132, (1991).
[16] A. Królak, P.J.U.A.i.t.I.S. Strumiłło, Eye-blink detection system for human–computer interaction, 11(4) (2012) 409-419.
[17] B. Cyganek, S.J.N. Gruszczyński, Hybrid computer vision system for drivers' eye recognition and fatigue monitoring, 126 (2014) 78-94.
Amirkabir Journal of Mechanical Engineering
Volume 50, Issue 5
January and February 2019
Pages 1145-1154

Files

Share

How to cite

Statistics