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has been cited by the following article:

• TITLE: Robust Lane Detection in Shadows and Low Illumination Conditions using Local Gradient Features

  AUTHORS: Avishek Parajuli, Mehmet Celenk, H. Bryan Riley

  KEYWORDS: Local Gradient Features; Lane Detection; Linear Prediction; Characteristic Spectrum

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.3 No.1B, January 22, 2013

  ABSTRACT: This paper presents a method for lane boundaries detection which is not affected by the shadows, illumination and un-even road conditions. This method is based upon processing grayscale images using local gradient features, characteris-tic spectrum of lanes, and linear prediction. Firstly, points on the adjacent right and left lane are recognized using the local gradient descriptors. A simple linear prediction model is deployed to predict the direction of lane markers. The contribution of this paper is the use of vertical gradient image without converting into binary image(using suitable thre-shold), and introduction of characteristic lane gradient spectrum within the local window to locate the preciselane marking points along the horizontal scan line over the image. Experimental results show that this method has greater tolerance to shadows and low illumination conditions. A comparison is drawn between this method and recent methods reported in the literature.