We propose a method to recognize the traffic scene in front of a moving vehicle with respect to the road topology and the existence of objects. To this end, we use a two-stage system, where the first stage abstracts from the underlying image by means of a rough super-pixel segmentation of the scene. In a second stage, this meta representation is then used to construct a feature set for a classifier that is able to distinguish between different road types as well as detect the existence of commonly encountered objects, such as cars or pedestrian crossings. We show that by relying on an intermediate stage, we can effectively abstract from any peculiarities of the underlying image data due to \eg color abberrations. The method is tested on two long, challenging urban data sets, covering both day light and dusk conditions. Compared to a state-of-the-art descriptor, we show improved classification performance, especially for object classes.