Color model-based real-time learning for road following

Road following is an important skill vital to the development and deployment of autonomous vehicles. Over the past few decades, a large number of road following computer vision systems have been developed. All of these systems have limitations in their capabilities, arising from assumptions of idealized conditions. The systems show dependency on highly structured roads, road homogeneity, simplified road shapes, and idealized lighting conditions. In the real world, the systems are only effective in specialized cases. This paper proposes a vision system that is capable of dealing with many of these limitations, accurately segmenting unstructured, nonhomogeneous roads of arbitrary shape under various lighting conditions. The system uses color classification and learning to construct and use multiple road and background models. Color models are constructed on a frame by frame basis and used to segment each color image into road and background by estimating the probability that a pixel belongs to a particular model. The models are constructed and learned independently of road shape, allowing the segmentation of arbitrary road shapes. Temporal fusion is used in the stabilization of the results. Preliminary testing demonstrates the system's effectiveness on roads not handled by previous systems