Radar-based Dynamic Occupancy Grid Mapping and Object Detection
This work addresses the need for robust sensor fusion in automated driving, but it is incremental as it extends a previous approach to use only radar data.
The paper tackles the problem of dynamic environment modeling for automated driving by developing a radar-based dynamic occupancy grid mapping and object detection method, achieving advantages over lidar-based methods in urban environments as shown by qualitative and quantitative evaluation.
Environment modeling utilizing sensor data fusion and object tracking is crucial for safe automated driving. In recent years, the classical occupancy grid map approach, which assumes a static environment, has been extended to dynamic occupancy grid maps, which maintain the possibility of a low-level data fusion while also estimating the position and velocity distribution of the dynamic local environment. This paper presents the further development of a previous approach. To the best of the author's knowledge, there is no publication about dynamic occupancy grid mapping with subsequent analysis based only on radar data. Therefore in this work, the data of multiple radar sensors are fused, and a grid-based object tracking and mapping method is applied. Subsequently, the clustering of dynamic areas provides high-level object information. For comparison, also a lidar-based method is developed. The approach is evaluated qualitatively and quantitatively with real-world data from a moving vehicle in urban environments. The evaluation illustrates the advantages of the radar-based dynamic occupancy grid map, considering different comparison metrics.