Unstructured road region detection forms a main problem of environment sensing for intelligent vehicle. In this paper

two learers are proposed to solve this problem. One is a support vector machine (SVM) classifier which utilizes multi-orientation Gabor texture histogram

and the other is a color histogram back-projection model. Both learners are combined in a co-learning framework. In practical running

the two learners can provide “labeled” samples for each other. This approach can improve the online learning capability and avoid the model drifting problem which often occurs in self-learning approach. Experimental results show the advantages of the proposed co-learning approach.