A lane sensing algorithm using vision sensors is developed based on lane geometry models. The parameters of the lane geometry models are estimated by a Kalman filter and utilized to reconstruct the lane geometry in the global coordinate. The inverse perspective mapping from image plane to global coordinate assumes earth to be flat, but roll and pitch motions of a vehicle are considered from the perspective of the lane sensing. The proposed algorithm shows robust lane sensing performance compared to the conventional algorithms.