Multiple Lane Detection Algorithm Based on Novel Dense Vanishing Point Estimation

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Multiple Lane Detection Algorithm Based on Novel Dense Vanishing Point Estimation

Ozgunalp, Umar

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