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 Vp contains the global information of the road image. Hence, Vp-based lane detection algorithms are quite insensitive to interference. When curved lanes are assumed, Vp shifts with respect to the rows of the image. In this paper, a Vp for each individual row of the image is estimated by first extracting a Vpy (vertical position of the Vp) for each individual row of the image from the v-disparity. Then, based on the estimated Vpy's, a 2-D Vpx (horizontal position of the Vp) accumulator is efficiently formed. Thus, by globally optimizing this 2-D Vpx accumulator, globally optimum Vp s for the road image are extracted. Then, estimated Vp 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.
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