Robot road following by adaptive color classification and shape tracking

This paper present a color vision based robot road following program. The program uses an adaptive color model to classify pixels as road or shoulder features. To update the color statistics, the program tracks the shape of the road through the image sequence. As long as the program has either a reasonable color model from the previous image or a reasonable estimation of the expected road position it can steer a robot vehicle along a road reliably even in the presence of surface color variation, fluctuation of illumination conditions and deviation of sensor response. We present results from several test runs of a robot vehicle in a natural outdoor environment. The paper includes a discussion of failure modes of the program that have been catalogued and analyzed in order to guide future developments.