Road segmentation is one of the most preliminary and important tasks for the road following and planning of the Autonomous Land Vehicle (ALV), since the efficiency of road segmentation has direct effect on the reliability of road following and planning, and consequently the speed of ALV. Therefore, road segmentation has been extensively studied, and a variety of methods for color road segmentation have been proposed, since color images contain more information of road than gray level images do. In most of the existing color road segmentation approaches, a best discriminant vector, which is a linear transformation of color vector (r,g,b), was used to project and classify a point in color space, and only one such projection was used in the segmentation, which may lead to instability of segmentation under variant circumstances. This presentation proposed a new color road segmentation method in which a pyramid based data structure and the corresponding region splitting and combination techniques for the classification of sensed areas are adopted. At the same time, two transformations of the (R,G,B) color space, and data fusion technique are used to increase the efficiency of the road segmentation. Experiment results are presented to illustrate the performance of this approach.