Multiple Lane Detection Algorithm Based on Novel Dense Vanishing Point Estimation

The detection of multiple curved lane markings is still a challenge for advanced driver assistance systems today, due to interference such as road markings and shadows cast by roadside structures and vehicles. The vanishing point V-p contains the global information of the road image. Hence, V-p-based lane detection algorithms are quite insensitive to interference. When curved lanes are assumed, V-p shifts with respect to the rows of the image. In this paper, a V-p for each individual row of the image is estimated by first extracting a V-py (vertical position of the Vp) for each individual row of the image from the v-disparity. Then, based on the estimated V(py')s, a 2-D V-px (horizontal position of the V-p) accumulator is efficiently formed. Thus, by globally optimizing this 2-D Vpx accumulator, globally optimum V-p s for the road image are extracted. Then, estimated V-p s are utilized for multiple curved lane marking detection on nonflat road surfaces. The resultant system achieves a detection rate of 99% in 1862 frames of six stereo vision test sequences.