Road detection is a key component of Advanced Driving Assistance Systems, which provides valid space and candidate regions of objects for vehicles. Mainstream road detection methods have focused on extracting discriminative features. In this paper, we propose a robust feature fusion framework, called “Feature++”, which is combined with superpixel feature and 3D feature extracted from stereo images. Then a neural network classifier is been trained to decide whether a superpixel is road region or not. Finally, the classified results are further refined by conditional random field. Experiments conducted on the KITTI ROAD benchmark show that the proposed “Feature++” method outperforms most manually designed features, and are comparable with state-of-the-art methods that based on deep learning architecture.