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     Research Journal of Applied Sciences, Engineering and Technology


Real Time Lane Detection in Autonomous Vehicles Using Image Processing

1Jasmine Wadhwa, 1G.S. Kalra and 2B.V. Kranthi
1School of Electronics Engineering, Lovely Professional University, Phagwara, Punjab, India
2Gitam University, Bangalore, India
Research Journal of Applied Sciences, Engineering and Technology  2015  4:429-433
http://dx.doi.org/10.19026/rjaset.11.1798  |  © The Author(s) 2015
Received: April ‎19, ‎2015  |  Accepted: May ‎28, ‎2015  |  Published: October 05, 2015

Abstract

The aim of this research is to detect and analyze the drivable path ahead of the autonomous vehicles by implementing path detection algorithms in MATLAB. This study presents a technique for real time detection of lanes marked on the road that can be used in autonomous vehicles. Since, for safe navigation of autonomous vehicles, detection of drivable path is a key requirement. Painted road markings on rural, urban roads and highways help to detect precisely the road area for the vehicle to move. Lane detection algorithms aim at finding the edges or boundaries of road within which vehicle can move. This target is accomplished by the use of camera set up on the vehicle that tracks road ahead of the vehicle. Lanes are detected through processing of each frame of video by edge detection and Hough transform techniques. This system can be further make compatible with the hardware of the vehicle to take certain actions depending on the detections made. This study presents potential approaches and requirements that are needed for detection of lanes, its algorithm and result.

Keywords:

Autonomous vehicles, boundaries, lane detection, masking,


References

  1. Aharon Bar, H., L. Ronen, L. Dan and R. Guy, 2014. Recent progress in road and lane detection: A survey. Mach. Vision Appl., 25: 727-745.
    CrossRef    
  2. Amit, B., 2007. Lane detection system for autonomous vehicle navigation. Proc. SPIE, Vol. 6764.
  3. Guowei, T., L. Xiande and Y. Hui, 2002. A road boundary detection method for autonomous land vehicle. Proceeding of the 4th World Conference on Intelligent Control and Automation, 4: 2949-2951.
  4. Qingquan, L., C. Long and L. Ming, 2014. A sensor-fusion drivable-region and lane-detection system for autonomous vehicle navigation in challenging road scenarios. IEEE T. Veh. Technol., 63: 540-555.
    CrossRef    
  5. Samadzadegan, F., A. Sarafraz and M. Tabibi, 2007. Automatic lane detection in image sequences for vision-based navigation purposes. Proceeding of 7th WSEAS International Conference on Signal, Speech and Image Processing.
  6. Sowers, J.P. and R. Mehrotra, 1989. Road boundary detection for autonomous navigation. Proceeding of the 3rd International Conference on Image Processing and its Applications, pp: 68-72.
  7. Stefan, V., S. Constantin and D. Rudiger, 2007. Road-marking analysis for autonomous vehicle guidance. Proceeding of the 3rd European Conference on Mobile Robots (EMCR).
  8. Tamer, S. and M. Bayoumy, 2012. Lane tracking and obstacle avoidance for autonomous ground vehicle. Proceeding of the IEEE 9th France-Japan and 7th Europe-Asia Congress on and Research and Education in Mechatronics, pp: 264-271.
    PMid:22687304    
  9. Tolga, B. and E. Aytül, 2007. Real-time automated road, lane and car detection for autonomous driving. Faculty of Engineering and Natural Sciences, Sabanci University, researchgate.net.
  10. Yuan, S. and T. Zheng, 2004. Vision based lane detection in autonomous vehicle. Proceeding of the 5th World Conference on Intelligent Control and Automation, 6: 5258-5260.
    CrossRef    

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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