Occupancy grids are a very convenient tool for environment representation in robotics. This paper will detail a novel approach to compute occupancy grids from stereo-vision, and shows its application for the field of intelligent vehicles. In the proposed approach, occupancy is initially computed directly in the stereoscopic sensor's disparity space. The calculation formally accounts for the detection of obstacles and road pixels in disparity space, as well as partial occlusions in the scene. In a second stage, this disparity-space occupancy grid is transformed into a Cartesian space occupancy grid to be used by subsequent applications. This transformation includes spatial and temporal filtering. The proposed method is designed to be easily processed in parallel. Consequently, we chose to implement it on GPU, which allows real-time processing for the demanding application. In this paper, we present this method and we propose an application to the problem of perception in a road environment. Results are presented with real road data, comparing qualitatively this approach with others.