Abstract
Lane detection is an essential visual capability for the autonomous vehicle navigation used in the intelligent transportation system (ITS). Several approaches for lane detection have been suggested in the past. However, there is still one issue about robustness. This paper presents a robust and real-time multi-lane detection method based on road marking feature points (RMFP) for autonomous vehicle navigation in an urban environment. The key idea in this paper is to apply methods from extracting RMFP and the target tracking domain to identify lane information. Then the RMFP is extracted from the gray-scale image and the IPM image. Additionally, the lane line color and structure features are used to sift through the RMFP that meet lane lines. Lastly, the clustering method is used to generate lane lines, and these lines are tracked by frame association and Kalman Filter. The experiment results based on image sequences taken under various visual conditions showed that the proposed system can identify the lane line markings with 98% accuracy. The method shows much robustness under various complicated scenarios and meets the real-time requirement.
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