MRF-based road detection with unsupervised learning for autonomous driving in changing environments

This paper presents a vision-based approach with unsupervised learning for robust, accurate and stable detection of the drivable road to deal with autonomous driving in changing environments. This approach is based on a formulation of stereo with homography as a Maximum A Posteriori (MAP) problem in a Markov Random Field (MRF). Under this formulation, we develop an alternating optimization algorithm that alternates between computing the binary labeling and learning the optimal parameters from the stereo pair itself. The labeling is optimized by minimizing a well-defined energy function that consists of matching energy, smoothness energy and tracking energy. The parameters, including nine homography parameters and four MRF parameters, are learned online by applying a hard Expectation Maximization (EM) algorithm to maximize conditional likelihood. The proposed automatic parameter tuning procedure not only improves the accuracy of road detection but also makes the approach adaptive to changing environments without any a priori knowledge of the road. Experimental results show the optimality as well as adaptability of the proposed approach on a wide variety of challenging roads with changing environments.