This paper proposes a novel approach for vehicle orientation detection using "vehicle color" and edge information based on clustering framework. To extract the "vehicle color", this paper proposes a novel color transform model which is global and does not need to be re-estimated for any new vehicles or new images. This model is invariant to various situations like contrast changes, background and lighting. Compared with traditional methods which use motion feature to determine vehicle orientations, this paper uses only one still image to finish this task. After feature extraction, the normalized cut spectral clustering (N-cut) is used for vehicle orientation clustering. The N-cut criterion tries to minimize the ratio of the total dissimilarity between groups to the total similarity within the groups. Then, the vehicle orientation can be detected using the eigenvector derived from the N-cut result. Experimental results reveal the superior performances in vehicle orientation estimation.