In this paper, we present a method for drivable road detection by extracting its specular intrinsic feature from an image. The resulting detection is then used in a stereo vision-based 3D road parameters extraction algorithm. A substantial representation of the road surface, called axis-calibration, is represented as an angle in log- chromaticity space. This feature provides an invariance to road surface under illuminant conditions with shadow or not. We also add a sky removal function in order to eliminate the negative effects of sky light on axis-calibration result. Then, a confidence interval calculation helps the pixels' classification to speed up the detection processing. At last, the approach is combined with a stereo- vision based method to filter out false detected pixels and to obtain precise 3D road parameters. The experimental results show that the proposed approach can be adapted for real-time ADAS system in various driving conditions.