  Detecting traversable road areas ahead a moving vehicle is a key process for
modern autonomous driving systems. A common approach to road detection consists
of exploiting color features to classify pixels as road or background. These
algorithms reduce the effect of lighting variations and weather conditions by
exploiting the discriminant/invariant properties of different color
representations. Furthermore, the lack of labeled datasets has motivated the
development of algorithms performing on single images based on the assumption
that the bottom part of the image belongs to the road surface.
  In this paper, we first introduce a dataset of road images taken at different
times and in different scenarios using an onboard camera. Then, we devise a
simple online algorithm and conduct an exhaustive evaluation of different
classifiers and the effect of using different color representation to
characterize pixels.
