Robust lane marking detection under different road conditions

In this paper a new lane marking detection algorithm in different road conditions for monocular vision was proposed. Traditional detection algorithms implement the same operation for different road conditions. It is difficult to simultaneously satisfy the requirements of timesaving and robustness in different road conditions. Our algorithm divides the road conditions into two classes. One class is for the clean road, and the other one is for the road with disturbances such as shadows, non-lane markings and vehicles. Our algorithm has its advantages in clean road while has a robust detection of lane markings in complex road. On the remapping image obtained from inverse perspective transformation, a search strategy is used to judge whether pixels belong to the same lane marking. When disturbances appear on the road, this paper uses probabilistic Hough transform to detect lines, and finds out the true lane markings by use of their geometrical features. The experimental results have shown the robustness and accuracy of our algorithm with respect to shadows, changing illumination and non-lane markings.