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Road terrain identification is one of the important tasks for driving assistant systems or autonomous land vehicles. It plays a key role in improving driving strategy and enhancing fuel efficiency. In this paper, a two-stage approach using multiple sensors is presented. In the first stage, a feature-based identification approach is performed using an accelerometer, a camera, and downward-looking and forward-looking laser range finders (LRFs). This produces four classification label sequences. In the second stage, a majority vote is implemented for each label sequences to match them into synchronized road patches. Then a Markov Random Field (MRF) model is designed to generate the final optimized identification results to improve the forward-looking LRF. This approach enables the vehicle to observe the upcoming road terrain before moving onto it by fusing all the classification results using an MRF algorithm. The …