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Recent progress in advanced driver assistance systems and the race towardsautonomous vehicles is mainly driven by two factors: (1) increasinglysophisticated algorithms that interpret the environment around the vehicle andreact accordingly, and (2) the continuous improvements of sensor technologyitself. In terms of cameras, these improvements typically include higherspatial resolution, which as a consequence requires more data to be processed.The trend to add multiple cameras to cover the entire surrounding of thevehicle is not conducive in that matter. At the same time, an increasing numberof special purpose algorithms need access to the sensor input data to correctlyinterpret the various complex situations that can occur, particularly in urbantraffic.  By observing those trends, it becomes clear that a key challenge for visionarchitectures in intelligent vehicles is to share computational resources. Webelieve this challenge should be faced by introducing a representation of thesensory data that provides compressed and structured access to all relevantvisual content of the scene. The Stixel World discussed in this paper is such arepresentation. It is a medium-level model of the environment that isspecifically designed to compress information about obstacles by leveraging thetypical layout of outdoor traffic scenes. It has proven useful for a multitudeof automotive vision applications, including object detection, tracking,segmentation, and mapping.  In this paper, we summarize the ideas behind the model and generalize it totake into account multiple dense input streams: the image itself, stereo depthmaps, and semantic class probability maps that can be generated, e.g., by CNNs.Our generalization is embedded into a novel mathematical formulation for theStixel model. We further sketch how the free parameters of the model can belearned using structured SVMs.

The Stixel world: A medium-level representation of traffic scenes