Roadway departure mitigation systems for helping to avoid and/or mitigate roadway departure collisions have been introduced by several vehicle manufactures in recent years. To support the development and performance evaluation of the roadway departure mitigation systems, a set of commonly seen roadside surrogate objects need to be developed. These objects include grass, curbs, metal guardrail, concrete divider, and traffic barrel/cones. This paper describes how to determine the representative color of these roadside surrogates. 24,762 locations with Google street view images were selected for the color determination of roadside objects. To mitigate the effect of the brightness to the color determination, the images not in good weather, not in bright daylight and under shade were manually eliminated. Then, the RGB values of the roadside objects in the remaining images were extracted. To obtain the representative color of the roadside objects, the K-means clustering algorithm was applied to find the color clusters of each type of roadside objects in the modified CIE LUV color space. The Silhouette index was applied to determine the optimal number of clusters. The clustered colors that cover the highest percentage of sampled locations were chosen as the representative colors of the roadside object type.