Abstract-Future Advanced Driver Assistance Systems (ADAS) require detailed information about occupancy states in the vehicle's local environment. In contrast to widespread occupancy grids, this information should be represented in a compact, scalable and easy-to-interpret data structure. In this paper, we show how occupancy probabilities can efficiently be represented in our 2D Interval Map framework. The basic idea of this approach is to discretize the vehicle's environment only in longitudinal direction and to avoid quantization errors in lateral direction by storing continuous values. In order to correctly deal with dynamic obstacles in ADAS scenarios, the map also interacts with a model based object tracking.The comparison of our experimental results to a ground truth illustrates the differences of grid and interval based environment representations. A tested collision avoidance function yields similar results for both representations, while computation times and memory requirements are substantially improved by the application of the 2D Interval Map.

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