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Color can be a useful feature in autonomous vehicle systems that are based on machine vision, for tasks such as obstacle detection, lane/road following, and recognition of miscellaneous scene objects. Unfortunately, few existing autonomous vehicle systems use color to its full extent, largely because color-based recognition in outdoor scenes is complicated, and existing color machine-vision techniques have not been shown to be effective in realistic outdoor images. This paper presents a technique for achieving effective real-time color recognition in outdoor scenes. The technique uses multivariate decision trees for piecewise linear non-parametric function approximation to learn the color of a target object from training samples, and then detects targets by classifying pixels based on the approximated function. The method has been successfully tested in several domains, such as autonomous highway navigation …