This paper addresses the problem of extracting the road region in different driving environments with dynamic lighting changes, for driver-assistance applications. In this paper, we propose a stereo visual sensor system and a vision-based road extraction method in a new color space. The color space is designed such that it is representative of intrinsic reflectance of the road surface, and independent of illumination source. Our basic model of road color is a mixture of Gaussians in that color space, constructed from road sample pixels. Those color samples are reliably collected from stereo-verified ground patches inside a pre-defined trapezoidal learning region. The advantages of this system with respect to other systems are that it is more economical for driver-assistance applications while giving robust results and, in particular, recognizing shadows on road as drivable road surface instead of non-road.
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