Environment perception for inner-city driver assistance and highly-automated driving

While driver assistance systems mainly targeted highway or parking scenarios in the past, systems assisting in inner-city traffic increasingly get into focus today. Since driving in urban areas is characterized by a larger variety of situations that have to be covered, finding an adequate representation of the vehicle surrounding is a challenging task for the perception of these systems. In this paper we present our perception system that has been specifically designed for the demands of inner-city driving. It first features a plugin-based architecture by which multiple sensor setups as well as different driver assistance functions can be supported. Second, it is characterized by a hybrid modeling approach that combines the well known model-based object tracking technique with model-free representations in terms of grids. We will present details on a specific implementation of the system using a 3D lidar sensor. Finally, it is shown how the system is used in the Narrow Road Assistant - a next-generation driver assistance system supporting the driver in safely passing narrow road passages in inner-city.