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Lane detection can provide important information for safety driving. In this paper, a real time vision-based lane detection method is presented to find the position and type of lanes in each video frame. In the proposed lane detection method, lane hypothesis is generated and verified based on an effective combination of lane-mark edge-link features. First, lane-mark candidates are searched inside region of interest (ROI). During this searching process, an extended edge-linking algorithm with directional edge-gap closing is used to produce more complete edge-links, and features like lane-mark edge orientation and lane-mark width are used to select candidate lane-mark edge-link pairs. For the verification of lane-mark candidates, color is checked inside the region enclosed by candidate edge-link pairs in YUV color space. Additionally, the continuity of the lane is estimated employing a Bayesian probability model based on lane-mark color and edge-link length ratio. Finally, a simple lane departure model is built to detect lane departures based on lane locations in the image. Experiment results show that the proposed lane detection method can work robustly in real-time, and can achieve an average speed of 30~50ms per frame for 180×120 image size, with a correct detection rate over 92%.