CFP last date
20 December 2024
Reseach Article

Evident based Case Study and Analysis on Lane - Change Detection

by Harleen Kaur
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 130 - Number 4
Year of Publication: 2015
Authors: Harleen Kaur
10.5120/ijca2015906504

Harleen Kaur . Evident based Case Study and Analysis on Lane - Change Detection. International Journal of Computer Applications. 130, 4 ( November 2015), 7-11. DOI=10.5120/ijca2015906504

@article{ 10.5120/ijca2015906504,
author = { Harleen Kaur },
title = { Evident based Case Study and Analysis on Lane - Change Detection },
journal = { International Journal of Computer Applications },
issue_date = { November 2015 },
volume = { 130 },
number = { 4 },
month = { November },
year = { 2015 },
issn = { 0975-8887 },
pages = { 7-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume130/number4/23195-2015906504/ },
doi = { 10.5120/ijca2015906504 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:24:16.233991+05:30
%A Harleen Kaur
%T Evident based Case Study and Analysis on Lane - Change Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 130
%N 4
%P 7-11
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The main goal of this article is to offer various insights in the domain of lane detection system of ITS. More specifically, we provide an overview of various lane detection techniques by establishing several ways of categorization in tabular form. Finally, we spot the left over gaps both in research and in system level evaluation and propose research directions for bridging these gaps. A procedure of lane detection has also been discussed in this paper. Moreover analysis of accidents for last decade has been discussed.

References
  1. Malaysia Road Safety Department, 2010, "Accidents cost Malaysia RM9.3bil." from http://thestar.com.my/news/
  2. MUFORS; (2011). "Malaysia Sees Increased Road Fatalities."
  3. E.D. Dickmanns and B.D. Mysliwetz, “Recursive 3-D Road and relative Ego-State Recognizition”, IEEE Transaction on PAMI, vol 14, no. 2, pp: 199-213, 1992.
  4. A. Broggi and S. Berte, “Vision-based Road Detection in Automotive Systems: a Real-time Expectation-driven Approach”, Journal of Artificial Intelligence Research, vol. 3, pp. 325-348, 1995.
  5. A. Kaske, R. Husson and D.Wolf, “Chi-Square Fitting Of Deformable Templates For Lane Boundary Detection”, 1995.
  6. D.J Kang, J.W. Choi and I.S. Kweon, “Finding and Tracking Road Lanes Using Line-snakes”, Proceedings of Conference on Intelligent Vehicle, pp. 189-194, 1996.
  7. M. Bertozzi and A. Broggi, “GOLD: A Parallel Real-time Stereo Vision System for Generic Obstacle and Lane Detection”, IEEE Transactions of Image Processing, pp:62-81, 1998.
  8. Y. Wang, D. Shen and E.K. Teoh. “Lane Detection Using Catmull-Rom Spline”. In Proc. of the IEEE Intelligent Vehicles, 1998.
  9. C. Kreucher and S. Lakshmanan, “LANA: A lane extraction algorithm that uses frequency domain features,” IEEE Trans. Robot. Autom., vol. 15, no. 2, pp. 343–350, Apr. 1999.
  10. G. Loy, L. Fletcher, N. Apostoloff and A. Zelinsky. “An adaptive Fusion Architecture for Target Tracking”. Proc. 5th Int’l Conf. Automatic Face and Gesture Recognition, IEEE CS Press, pp: 261–266, 2002.
  11. C. Rasmussen, “Combining Laser Range, Color, and Texture Cues for Autonomous Road Following”, In Proc. IEEE Inter. Conf. on Robotics & Automation, Washington, DC, May, 2002.
  12. J.C. McCall, M.M. Trivedi, “Video based lane estimation and tracking for driver assistance: survey, system and evaluation”, IEEE Transact. Intelligent Transport. System, pp: 20–37, 2006.
  13. C. Rasmussen, “Texture-based vanishing point voting for road shape estimation”, BMVC, 2004.
  14. Y. Wang, E.K. Teoh and D. Shen, “Lane detection and tracking using B-Snake”, Image Vision Comput. 22, pp: 269–280, 2004.
  15. J.C. McCall, M.M. Trivedi, “Video based lane estimation and tracking for driver assistance: survey, system and evaluation”, IEEE Transact. Intelligent Transport. System, pp: 20–37, 2006.
  16. Z. Kim, “Robust Lane Detection and Tracking in Challenging Scenarios”, In IEEE Transactions on Intelligent Transportation Systems, vol. 9, no. 1, pp. 16 - 26, 2008.
  17. J.M. Alvarez and A. Lopez, “Novel Index for Objective Evaluation of Road Detection Algorithms”, Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems, Beijing, China, pp: 12-15, 2008.
  18. M. Aly, “Real time Detection of Lane Markers in Urban Streets”, In IEEE Intelligent Vehicles Symposium, pp. 7 - 12, 2008.
  19. Z. Teng, J.H. Kin and D.J. Kang, “Real-time Lane detection by using multiple cues”, In IEEE International Conference on Control Automation and Systems, pp. 2334 - 2337, 2010.
  20. S. Zhou, Y. Ziang, J. Xi, J. Gong, G. Xiong and H. Chen, “A novel lane detection based on geometrical model and gabor filter”, in IEEE Intelligent Vehicles Symposium, pp. 59-64, 2010.
  21. K. Ghazali, R. Xiao and J. Ma, “ Road Lane Detection Using H-Maxima And Improved Hough Transform”, Fourth International Conference on Computational Intelligence, Modelling and Simulation, pp: 2166-8531, 2012.
  22. R. K. Satzoda and Mohan M. Trivedi, “Vision-based Lane Analysis: Exploration of Issues and Approaches for Embedded Realization”, IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2013.
  23. S. Fernando, L. Udawatta, B. Horan and P. Pathirana, “Real-time Lane Detection on Suburban Streets using Visual Cue Integration”, International Journal of Advanced Robotic Systems, 2014.
Index Terms

Computer Science
Information Sciences

Keywords

ITS Lane Detection Accident