Abstract： In view of the problems that the real-time, robustness and efficient of the existing lane detection algorithm are low, an improved and fast vision lane detection algorithm based on RANSAC (random sample consensus) was proposed. First, the inverse perspective mapping was conducted. Then, the image was filtered using anisotropic Gasssian filters. The quantile threshold method which has a strong adaptability to different illumination brightness image was used to the filtered image. The initial lines were detected using the histogram statistics method because almost all the lanes in the transform image were vertical. After that, an improved and fast RANSAC curve fitting step was performed to refine the detected initial lines and correctly detect curved lanes. Finally, a postprocessing was conducted to further improve the accuracy of algorithm. The results show that the improved algorithm has a great robustness, strong stability and high efficiency, which can meet the requirements of intelligent vehicle realtime detection.
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