A deformable-template approach to lane detection

Vision-based algorithms for locating lane boundaries without a prior model of the road being viewed need to be able to operate robustly under a wide variety of conditions, including cases where there are large amounts of clutter in the image. This clutter can be due to shadows, puddles, oil stains, tire skid marks, etc. This poses a challenge for edge-based lane detection schemes, as it is often impossible to select a gradient magnitude threshold which doesn't either remove edges of interest corresponding to road markings and edges or include edges corresponding to irrelevant clutter. The approach taken in this work is to use a deformable template model of lane structure to locate lane boundaries without thresholding the intensity gradient information. The Metropolis algorithm is used to maximize a function which evaluates how well the image gradient data supports a given set of template deformation parameters. The result, the LOIS lane detection algorithm (likelihood of image shape), is able to detect lane markings in situations with strong mottled shadows and broken or interrupted lane markings which would pose problems for algorithms which use local, thresholded edge information.