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an automatic parking system relies on precise estimation of parking space geometry. This paper proposes the use of a hierarchical threedimensional occupancy grid for the detection of parking spaces. The occupancy grid covers the environment representation of the static world. A hierarchical design allows dynamic selection of the level of detail. Applying a three-dimensional grid provides the additional benefit of supporting a variety of other functions including height estimation using a single environment representation type [7].The presented approach derives the distance to obstacles and walls and thus is able to represent the free space that forms parking spaces. In a second step, the dimensions of the parking space are calculated. For evaluation, real parking spaces are detected and estimated using short range radar sensors. The calculated dimensions are compared to the ground truth.

This paper presents a novel approach to environment perception providing detailed information for dynamic objects using occupancy grid maps. The shape representation of dynamic objects is derived from dedicated local grid maps. This allows for a precise contour estimation over time in terms of polylines. In addition to that, the local grid map is used to improve the object tracking by formulating measurements for the Kalman filter update overcoming partial occlusion and oversegmentation of raw data. The algorithm is tested on different data sets and initial results are presented and discussed.

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