In this work, the authors address the problem of road interpretation for driver assistance based on an early cognitive vision system. The structure of a road and the relevant traffic are interpreted in terms of ego-motion estimation of the car, independently moving objects on the road, lane markers and large scale maps of the road. The authors make use of temporal and spatial disambiguation mechanisms to increase the reliability of visually extracted 2D and 3D information. This information is then used to interpret the layout of the road by using lane markers that are detected via Bayesian reasoning. The authors also estimate the ego-motion of the car which is used to create large scale maps of the road and also to detect independently moving objects. Sample results for the presented algorithms are shown on a stereo image sequence, that has been collected from a structured road.
Road interpretation for driver assistance based on an early cognitive vision system
Straßeninterpretation zur Fahrerassistenz basierend auf einem früherkennenden Vision-System
2009
10 Seiten, 13 Bilder, 21 Quellen
Aufsatz (Konferenz)
Englisch
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