Back
 CS  Vol.5 No.8 , August 2014
Real-Time Lane Detection for Driver Assistance System
Abstract: Traffic problem is more serious, as the number of vehicles is growing. Most of the road accidents were caused by carelessness of drivers. To reduce the number of traffic accidents and improve the safety and efficiency of traffic for many years around the world and company studies have been conducted on intelligent transport systems (ITS). Intelligent vehicle, (IV) the system is part of a system which is designed to assist drivers in the perception of any dangerous situations before, to avoid accidents after sensing and understanding the environment around itself. In this paper, it proposes architecture for driver assistance system based on image processing technology. To predict possible Lane departure, camera is mounted on the windshield of the car to determine the layout of roads and determines the position of the vehicle on line Lane. The resulting sequence of images is analyzed and processed by the proposed system, which automatically detects the Lane lines. The results showed of the proposed system to work well in a variety of settings, In addition computer response system is inexpensive and almost real time.
Cite this paper: Smadi, T. (2014) Real-Time Lane Detection for Driver Assistance System. Circuits and Systems, 5, 201-207. doi: 10.4236/cs.2014.58022.
References

[1]   Miao, X.D., Li, S.M. and Shen, H. (2012) On-Board Lane Detection System for Intelligent Vehicle Based On Monocular Vision. International Journal on Smart Sensing and Intelligent Systems, 5.
http://www.s2is.org/Issues/v5/n4/papers/paper13.pdf

[2]   Houser, A., Pierowicz, J. and Fuglewicz, D. (2005) Concept of Operations and Voluntary Operational Requirements for Lane Departure Warning Systems (LDWS) On-Board Commercial Motor Vehicles. Federal Motor Carrier Safety Administration, Washington DC, Tech. Rep.
http://dl.acm.org/citation.cfm?id=1656549

[3]   Alessandretti, G., Broggi, A. and Cerri, P. (2007) Vehicle and Guard Rail Detection Using Radar and Vision Data Fusion. IEEE Transactions on Intelligent Transportation Systems, 8, 95-105.
http://www.ce.unipr.it/people/bertozzi/pap/cr/cerridimmelolaprossimavolta.pdf

[4]   Lalimi, M.A., Ghofrani, S. and McLernon, D. (2013) A Vehicle License Plate Detection Method Using Region and Edge Based Methods. Computers & Electrical Engineering, 39, 834-845.
http://dx.doi.org/10.1016/j.compeleceng.2012.09.015

[5]   Satzoda, R.K., Suchitra, S. and Srikanthan, T. (2008) Parallelizing Hough Transform Computation. IEEE Signal Processing Letters, 15, 297-300.
http://dl.acm.org/citation.cfm?id=1657389
http://dx.doi.org/10.1109/LSP.2008.917804


[6]   Hardzeyeu, V. and Klefenz, F. (2008) On Using the Hough Transform for Driving Assistance Applications. 4th International Conference on Intelligent Computer Communication and Processing, Shanghai, 28-30 August 2008, 91-98.

[7]   Duda, R.O. and Hart, P.E. (1972) Use of The Hough Transform to Detect Lines and Curves in Pictures. ACM Community Management, 15, 11-15,

[8]   Olmos, K., Pierre, S. and Boudreault, Y. (2003) Traffic Simulation in Urban Cellular Networks of Manhattan Type. Computers and Electrical Engineering, 29, 443-461.
http://dx.doi.org/10.1109/LSP.2008.917804

[9]   Gonzalez, R.C. and Woods, R.E. (2002) Digital Image Processing. 2nd Edition, New York: Prentice Hall.

[10]   Suchitra, S., Satzoda, R.K. and Srikanthan, T. (2009) Exploiting Inherent Parallelisms for Accelerating Linear Hough Transform Computation. IEEE Transactions on Image Processing, 18, 2255-2264.

[11]   Yoo, H., Yang, U. and Sohn, K. (2013) Gradient-Enhancing Conversion for Illumination-Robust Lane Detection. IEEE Transactions on Intelligent Transportation Systems, 14, 1083-1094.

[12]   Farrell, J.A., Givargis, T.D. and Barth, M.J. (2000) Realtime Differential Carrier Phase GPS-Aided INS. IEEE Transactions on Control Systems Technology, 8, 709-720.
http://dx.doi.org/10.1109/87.852915

[13]   Lopez, A., Canero, C., Serrat, J., Saludes, J., Lumbreras, F. and Graf, T. (2005) Detection of Lane Markings Based on Ridgeness and RANSAC. IEEE Conference on Intelligent Transportation Systems, 733-738.

[14]   Wu, B.F., Chen, C.J., Hsu, Y.P. and Chung, M.W. (2006) A DSP-Based Lane Departure Warning System. Mathematical Methods and Computational Techniques in Electrical Engineering, Proc. 8th WSEAS Int. Conf., Bucharest, 240-245.

[15]   Al Smadi, T. (2011) Computing Simulation for Traffic Control over Two Intersections. Journal of Advanced Computer Science and Technology Research, 1, 10-24.
http://www.sign-ific-ance.co.uk/dsr/index.php/JACSTR/article/view/38

[16]   Chen, L., Li, Q., Li, M., Zhang, L. and Mao, Q. (2012) Design of a Multi-Sensor Cooperation Travel Environment Perception System for Autonomous Vehicle. Sensors, 12, 12386-12404.
http://www.mdpi.com/1424-8220/12/9/12386
http://dx.doi.org/10.3390/s120912386


 
 
Top