Road and Lane Edge Detection with Multisensor Fusion Methods

This paper treats automated detection of road and lane boundaries by fusing information from forwardlooking optical and active W-band radar imaging sensors mounted on a motor vehicle. A deformable template model is used to globally describe the boundary shapes. The optical and radar imaging processes are characterized with random field likelihoods. The multisensor fusion edge detection problem is posed in a Bayesian framework and a joint MAP estimate is employed to locate the road and lane boundaries. Three optimization approaches, multi-resolution pseudo-exhaustive search, Metropolis algorithm, and Metropolis algorithm with pre-tuned curvature, are proposed to implement the joint MAT estimate. Experimental results are shown to demonstrate that the joint MAP algorithm operates robustly and efficiently in a variety of road scenarios.