Multiple Lanes Identification for Advanced Driver Assistance System (ADAS) 
Suvarna Shirke1, R. Udaya Kumar2
1Suvarna Shirke, Research Scholar, Assistant Professor, Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
2Dr. R. Udaya Kumar, Professor and Supervisor, Department of Information Technology, Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
Manuscript received on 27 August 2019 | Revised Manuscript received on 03 September 2019 | Manuscript Published on 14 September 2019 | PP: 533-538 | Volume-8 Issue-5S3, July 2019 | Retrieval Number: E11040785S319/19©BEIESP | DOI: 10.35940/ijeat.E1104.0785S319
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© 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: Now a days, a multi-lane recognition technique that uses the ridge features and the inverse perspective mapping (IPM) is generally used to distinguish lanes since it can evacuate the perspective distortion on lines that lie in parallel in reality. The lane detection is one of the approach to design the ADAS, if the vehicles follows the lane then there is less chance to get an accident. The detected information of lane path is used for controlling the vehicles and giving alerts to drivers. Therefore most of the researchers are attracted towards this field. But, due to the varying road conditions, it is very difficult to detect the lane. The computer vision and machine learning approaches are presents in most of the articles. In this paper, a survey of different method is presents for the road picture segmentation for the multi-lane detection. The Lane Departure Warning (LDW) system can help to reduce vehicle crashes that are caused by careless or drowsy driving. There has been much research on vision based lane detection for the LDW system. In these lane detection methods, color or edge information is utilized as a feature of the lane. The feature-based methods are usually applied to localize the lanes in the road images by extracting low-level features. On the other hand, the model-based methods use several geometrical elements to describe the lanes, including parabolic curves, hyperbola and straight lines. Feature-based methods require a dataset containing several thousand images of the roads with well-painted and prominent lane markings that are subsequently converted to features. Moreover, these methods may suffer from noise.
Keywords: Region Based Iterative Seed, Segmentation, Multilane Classification, Multilane Detection Advanced Driver Assistance System (ADAS).
Scope of the Article: Advanced Computer Networking