Block-constraint line scanning method for lane detection

Considering the plentiful road markings in China, we present a Block-Constraint Line scanning (BCLS) method for lane detection in this paper. In this method, images are firstly pre-processed by a morphological top-hat transform, and then an imaging model is created for building relationship between lane parameters of the image coordinate and the WGS coordinate, from which target points on lane lines could be retained by a block-constraint line scanning algorithm. Finally, lanes could be extracted by a Progressive Probabilistic Hough Transform (PPHT) and the number of lanes is figured out through clustering. Our method is fast enough to meet real-time requirement. Experiments were carried out on the intelligent vehicle SmartV (Fig.1) on the Wuhan urban roads in China and the results show that this method can efficiently and accurately extract lanes in complex environments, even with the presence of non-lane road markings.