The paper discusses various options for testing correspondence algorithms in stereo or motion analysis, designed or considered for vision-based driver assistance. The main focus is on testing on video sequences of real-world data. The authors suggest the classification of recorded video data into situations, defined by a co-occurrence of some events in recorded traffic scenes. About 100 to 200 frames (or 4 to 8 seconds of recording) are considered to be a basic sequence, to be identified with one particular situation. Future testing is expected to be on data that is reporting on hours of driving; multiple hours long video data may be segmented into basic sequences, and classified into situations. The paper prepares for this expected development. The paper explains the use of currently already available EISATS test data, aiming for such “wide-angle ” tests. The paper uses three different evaluation approaches for demonstrating (by means of EISATS examples) ideas, difficulties, and possible ways in this future field of extensive performance tests in vision-based driver assistance. The paper also contributes with proposals of evaluation techniques, especially in the case of lack of ground truth. The study shows that the complexity of real-world data often does not support an identification of absolute rankings of correspondence techniques; this is true already for a small set of selected situations. It is suggested that correspondence techniques need to be adaptively chosen in real time using fast situation classifiers (e.g., based on a few feature distributions).