Automatic driving on ill-defined roads: an adaptive, shape-constrained, color-based method

Autonomous following of ill-defined roads is an important part of visual navigation systems. This paper presents an adaptive method that uses a statistical model of the color of the road surface within a trapezoidal shape that approximately corresponds to the projection of the road on the image plane. The method does not perform an explicit segmentation of the images but instead expands the shape sideways until the match between shape and road worsens, simultaneously computing the color statistics. Results show that the method is capable of reactively following roads, at driving speeds typical of the robots used, in a variety of situations while coping with variable conditions of the road such as surface type, puddles, and shadows. We extensively evaluate the proposed method using a large number of datasets with ground truth (available from http://www.aber.ac.uk/en/cs/research/ir/dss/). We moreover evaluate many color spaces in the context of road following, and we find that the color spaces that separate luminance from color information perform best, especially if the luminance information is discarded.