The objective of this paper is to develop a robust and fast algorithm for vision-based road boundary detection. This paper proposes a flexible scenario to integrate two algorithms developed by our previous work for improving the precision and robustness of the lane boundary detection, and applied on the vision-based automated guided vehicle (AGV) system. In our previous study on vision-based AGV, the road boundary detection was used to measure the attitude of vehicle in order to guide along the lane center and keeps correct attitude. The traditional edge detection methods were being substituted for the histogram-based color difference fuzzy cluster analysis (HCDFCM) to fast recognize the lane boundary. Although HCDFCM held faster and more precise features than traditional methods, the shadowy road interfered in the precision of lane boundary detection. In this paper, we use fuzzy inference system (FIS) to enhance the contrast of shadowy pixels, and find the similarity with the lane model to solve the fault of detection problem in the case of shadowy situation. For the sake of reducing computational times adaptively, the enhanced algorithm provides a scene for incorporating HCDFCM with shadow removing algorithm. If the lane center variation on the image plane is larger than a certain threshold initialized by HCDFCM, the adjustable scan region on image plane uses to reinforce the robustness of lane boundary detection. The proposed method developed a feasible way to detect the lane boundary with high quality and reduced computational times.
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