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http://dx.doi.org/10.9708/jksci.2017.22.03.027

Lane Detection Using Biased Discriminant Analysis  

Kim, Tae Kyung (Dept. of Computer Science and Engineering, Dankook University)
Kwak, Nojun (Dept. of Transdisciplinary Studies, Seoul National University)
Choi, Sang-Il (Dept. of Computer Science and Engineering, Dankook University)
Publication Information
Journal of the Korea Society of Computer and Information / v.22, no.3, 2017 , pp. 27-34 More about this Journal
Abstract
We propose a cascade lane detector that uses biased discriminant analysis (BDA) to work effectively even when there are various external factors on the road. The proposed cascade detector was designed with an existing lane detector and the detection module using BDA. By placing the BDA module in the latter stage of the cascade detector, the erroneously detected results by the existing detector due to sunlight or road fraction were filtered out at the final lane detection results. Experimental results on road images taken under various environmental conditions showed that the proposed method gave more robust lane detection results than conventional methods alone.
Keywords
Lane detection; Advanced driver assistance systems; Biased discriminant analysis;
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