An integrated stereo-based approach to automatic vehicle guidance

Proposes a new approach for vision-based longitudinal and lateral vehicle control. The novel feature of this approach is the use of binocular vision. We integrate two modules consisting of a new, domain-specific, efficient binocular stereo algorithm, and a lane marker detection algorithm, and show that the integration results in a improved performance for each of the modules. Longitudinal control is supported by detecting and measuring the distances to leading vehicles using binocular stereo. The knowledge of the camera geometry with respect to the locally planar road is used to map the images of the road plane in the two camera views into alignment. This allows us to separate image features into those lying in the road plane, e.g. lane markers, and those due to other objects which are dynamically integrated into an obstacle map. Therefore, in contrast with the previous work, we can cope with the difficulties arising from occlusion of lane markers by other vehicles. The detection and measurement of the lane markers provides us with the positional parameters and the road curvature which are needed for lateral vehicle control. Moreover, this information is also used to update the camera geometry with respect to the road, therefore allowing us to cope with the problem of vibrations and road inclination to obtain consistent results from binocular stereo