Lane detection for automotive sensors

The paper addresses the problem of detecting lane boundaries in color images of road scenes acquired from a car mounted visual sensor. It is shown that the lane boundaries in such images have to obey a set of global constraint equations. All images with such constrained lanes are modeled via deformable templates. The observed image is related to the underlying lane boundary features through a likelihood function which is based on the degree of match (in magnitude/direction) between the deformed template and the lane edges. The lane detection problem is formulated in a Bayesian setting, and it is posed as an equivalent problem of maximizing a posterior pdf which sits over a low-dimensional deformation space. This pdf is multi-modal hence a Metropolis algorithm is employed to obtain its maximum. Experimental results are shown to illustrate the performance of this algorithm