To develop a safe Intelligent Transportation System (ITS) while driving on unpredictable curves or road regions, high precision road segmentation and cover level of forward view estimation for drivers is necessary. Cover level of forward view is defined as the level of difficulty in predicting the dangerousness of road edge or incoming object near road edge especially at a curve due to the obstacles at the surrounding. We focus on road segmentation as it is one of the fundamental steps in developing ITS. According to the previous studies, road region is not segmented precisely; hence a new method of road segmentation is introduced. Input images had undergone illuminant invariant space conversion to remove shadow regions effectively. Next, the bottom part of illuminant invariant image (assumed to be road surface) is sampled to get a road model, which is then applied to obtain the road probability image. Lastly, dilation and erosion using mathematical morphology operation is applied to obtain road region. Our method of road segmentation shows precision of 0.73, recall as 0.82 and F measure as 0.81. High Precision road segmentation is very important for better cover level of forward view estimation. Segmented road region can be fully utilized for curve estimation and obstacles detection and hence lead to a better performance of cover level of forward view. Based on the results obtained, further improvement in several aspects, especially from the input image or illuminant invariant image to road probability image, has to be done in order to obtain perfectly segmented road region.