Binocular Stereopsis and Lane Marker Flow for Vehicle Navigation: Lateral and Longitudinal Control

Dieter Koller, Quang-Tuan Luong, and Jitendra Malik

University of California at Berkeley, Technical Report UCB:CSD-94-804, March 24, 1994

Abstract

We propose a new approach for vision based longitudinal and lateral vehicle control which makes extensive use of binocular stereopsis. Longitudinal control --- i.e. maintaining a safe, constant distance from the vehicle in front --- is supported by detecting and measuring the distances to leading vehicles using binocular stereo. A known 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. Any significant residual image disparity then indicates an object not lying in the road plane and hence a potential obstacle. This approach allows us to separate image features into those lying in the road plane, e.g. lane markers, and those due to other objects. The features which lie on the road are stationary in the scene and appear to move only because of the egomotion of the vehicle. Measurements on these features are used for dynamic update of (a) the camera parameters in the presence of camera vibration and changes in road slope (b) the lateral position of the vehicle with respect to the lane markers. In the absence of this separation, image features due to vehicles which happen to lie in the search zone for lane markers would corrupt the estimation of the road boundary contours. This problem has not yet been addressed by any lane marker based vehicle guidance approach, but has to be taken very seriously, since usually one has to cope with crowded traffic scenes where lane markers are often obstructed by vehicles. Lane markers are detected and used for lateral control, i.e. following the road while maintaining a constant lateral distance to the road boundary. For that purpose we model the road and hence the shape of the lane markers as clothoidal curves, the curvatures of which we estimate recursively along the image sequence. These curvature estimates also provides desirable look-ahead information for a smooth ride in the car.

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Berkeley, May 26, 1994.

Last modified on Tuesday, November 20, 1996, Dieter Koller (koller@vision.caltech.edu)