Robust extraction of lane markings using gradient angle histograms and directional signed edges

In this paper, we propose novel block-based techniques for robust extraction of lane marking edges in complex scenarios, such as in the presence of shadows, vehicles, other road markings etc. The techniques are based on the properties of lane markings and involve a two-stage processing: (1) generation of customized edge maps using histograms of gradient angles, and (2) directional signed edges in combination with Hough Transform to identify lane markings. It is shown that the proposed techniques show a detection accuracy of as high as 98% on test data collected on real road scenarios, representing the various complex cases.