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The high-resolution radar sensors have the ability to detect thousands of reflection points per cycle, which promotes the perception capability on a pixel level similar to video systems. In this paper, an occupancy grid map is created to model the static environment. The reflection amplitudes of all detection points are compensated, normalized, and then converted to the detection probability based on a radar sensor model. According to the movement of the ego vehicle, the a posteriori occupancy probability is computed to build the occupancy grid map. Thereafter the occupancy grid map is converted to the binary grid map, where the grids in the obstacle areas are defined as occupied. In order to eliminate the outliers, the connected occupied grids are clustered using the Connected-Component Labelling algorithm. Through the Moore-Neighbour Tracing algorithm the boundaries of the clustered occupied grids are recognized. Based on the boundaries, the interval-based free space detection is performed using the Bresenham's line algorithm. As mentioned, the occupancy grid map and the free space detection results obtained from radar road measurements match with the real scenarios.

High Resolution Radar-based Occupancy Grid Mapping and Free Space Detection