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A Robust Approach for Lane Detection in Challenging Illumination Scenarios

Automated lane identification is an essential component of perception based driver assistance system. These systems employed in intelligent vehicles minimize the fatal accidents and improve safety of driver as well as passenger and enhance the traffic scenarios. In this article, a lane detection approach based on image processing that determines the painted lanes on road in challenging scenarios is proposed. In the preprocessing stage, unique technique is used to detect and minimize the shadow and other illumination effects on the road which impose a crucial problem while detecting the lane lines. Two different thresholds are utilized to identify the probable lane boundaries and the outliers are removed in the post processing stage. The credible lane edges are obtained and superimposed on the original image. The tested results show the efficacy of the presented algorithm and it is evident from the detected response that this technique is effective in detecting straight and curved lanes in challenging hilly road scenarios.