An Effective Sharp Curve Lane Detector 
A.V.Paramkusam1, A.Bharath2, B.Ramya3, G.Lavanya4, B.Karthik5
1A.V.Paramkusam*, Lendi Institute of Engineering & Technology, JNTUK, Vizianagaram, (A.P), India.
2A. Bharath, Lendi Institute of Engineering & Technology, JNTUK, Vizianagaram, (A.P), India.
3B. Ramya, Lendi Institute of Engineering & Technology, JNTUK, Vizianagaram, (A.P), India.
4G. Lavanya, Lendi Institute of Engineering & Technology, JNTUK, Vizianagaram, (A.P), India.
5B. Karthik, Lendi Institute of Engineering & Technology, JNTUK, Vizianagaram, (A.P), India.
Manuscript received on June 16, 2020. | Revised Manuscript received on June 25, 2020. | Manuscript published on July 10, 2020. | PP: 359-363 | Volume-9 Issue-9, July 2020 | Retrieval Number: 100.1/ijitee.I6973079920 | DOI: 10.35940/ijitee.I6973.079920
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. 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: Sharp bend path location is one of the difficulties of visual condition discernment innovation for self-governing driving. Right now, new hyperbola fitting based technique for bend path recognition is proposed. The strategy for the most part incorporates three sections: extraction, bunching, and hyperbola fitting of path highlight focuses. We analysed our technique with the Bezier bend fitting based, the least squares bend fitting based, the spline fitting based techniques, and a current hyperbola fitting based strategy. Examinations show that our strategy performs superior to these technique.
Keywords: Clustering, Hyperbola fitting Structural feature constraint.
Scope of the Article: Clustering