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The detection and tracking of moving vehicles is a necessity for collision-free navigation. In natural unstructured environments, motion-based detection is challenging due to low signal to noise ratio. This paper describes our approach for a 14 km/h fast autonomous outdoor robot that is equipped with a Velodyne HDL-64E S2 for environment perception. We extend existing work that has proven reliable in urban environments. To overcome the unavailability of road network information for background separation, we introduce a foreground model that incorporates geometric as well as temporal cues. Local shape estimates successfully guide vehicle localization. Extensive evaluation shows that the system works reliable and efficient in various outdoor scenarios without any prior knowledge about the road network. Experiments with our own sensor as well as on publicly available data from the DARPA Urban Challenge revealed more than 96 % correctly identified vehicles.

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