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http://dx.doi.org/10.9717/kmms.2014.17.6.655

Model-Based Robust Lane Detection for Driver Assistance  

Duong, Tan-Hung (School of Electronic Engineering, Soongsil University)
Chung, Sun-Tae (School of Electronic Engineering, Soongsil University)
Cho, Seongwon (Dept. of Electrical Engineering, Hongik University)
Publication Information
Journal of Korea Multimedia Society / v.17, no.6, 2014 , pp. 655-670 More about this Journal
Abstract
In this paper, we propose an efficient and robust lane detection method for detecting immediate left and right lane boundaries of the lane in the roads. The proposed method are based on hyperbolic lane model and the reliable line segment clustering. The reliable line segment cluster is determined from the most probable cluster obtained from clustering line segments extracted by the efficient LSD algorithm. Experiments show that the proposed method works robustly against lanes with difficult environments such as ones with occlusions or with cast shadows in addition to ones with dashed lane marks, and that the proposed method performs better compared with other lane detection methods on an CMU/VASC lane dataset.
Keywords
Lane detection; Line segment extraction; Line segment clustering; Lane model fitting;
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