Schmid 1992: A computer vision system in an autonomous vehicle guidance application is presented for interpreting image sequences acquired by a camera moving relative to the environment. Objects with different shapes and changing positions as well as motion parameters in the perceived scene have to be recognized even if they occlude each other. The approach described is based on checking hypotheses by a combination of methods from knowledge representation and from control theory, e.g. recursive estimation. Hypothesis verification is done by analyzing the estimated motion parameters using methods from statistics. These algorithms have been implemented and tested on synthetic images. Tests using noise corrupted measurements from a CCD-camera are currently performed.