An Optimal Approach for Use of Lane Detection Algorithm Using Reliable Lane Markings
Kanishk1, Chandan Kumar2, Kundan Singh3, R.B. Sarooraj4
1Kanishk, Department of Computer Science Engineering, SRM Institute of Science and Technology, Chennai, India
2Chandan Kumar, Department of Computer Science Engineering, SRM Institute of Science and Technology, Chennai, India
3Kundan Singh, Department of Computer Science Engineering, SRM Institute of Science and Technology, Chennai, India
4R. B. Sarooraj, Department of Computer Science Engineering, SRM Institute of Science and Technology, Chennai, India
Manuscript received on May 02, 2019. | Revised Manuscript received on May 05, 2019. | Manuscript published on May 30, 2019. | PP: 6-10 | VVolume-9 Issue-1, May 2019. | Retrieval Number: A3211059119/19©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper proposes efficient and innovative way of Lane Detection Algorithm implementation for developing nations like India, Sri-Lanka, Pakistan and Urban Nation as China, USA. The paper consists of analytical approach for the use of algorithm and implementation of the same. The approach for the implementation of the algorithm is robust and effective. We would be using Active Safety System first; the collective images of the objects (nodes) are captured by the front – end camera as binary image from the Region of Interest. The Binary images are then converted to Grey – Scale Images. The Region of Interest (ROI) is determined and Lane Detection Algorithm is implemented to get the matched value with the real time object. An Active Safety System is designed and embedded with the micro controllers and Adaptive Cruise Control and further, Sensor Fusion is done. The Experimental results show that the algorithm was implemented successfully and identifies lanes in complex situations and worn out areas and curved lanes.
Keywords: Objects, Binary Images, Grey Scale Images, Bird’s eye view, Inverse Perspective Mapping, Gaussian’s density function, Histogram images, Region of Interest.