2D Car Detection in Radar Data with PointNets

Andreas Danzer1, Thomas Griebel1, Martin Bach1, Klaus Dietmayer2

  • 1Ulm University
  • 2University of Ulm



Regular Session


11:00 - 12:00 | Mon 28 Oct | The Great Room II | MoC-T3

Regular Session on Object Detection and Classification (I)

Full Text


For many automated driving functions, a highly accurate perception of the vehicle environment is a crucial prerequisite. Modern high-resolution radar sensors generate multiple radar targets per object, which makes these sensors particularly suitable for the 2D object detection task. This work presents an approach to detect 2D objects solely depending on sparse radar data using PointNets. In literature, only methods are presented so far which perform either object classification or bounding box estimation for objects. In contrast, this method facilitates a classification together with a bounding box estimation of objects using a single radar sensor. To this end, PointNets are adjusted for radar data performing 2D object classification with segmentation, and 2D bounding box regression in order to estimate an amodal 2D bounding box. The algorithm is evaluated using an automatically created dataset which consist of various realistic driving maneuvers. The results show the great potential of object detection in high-resolution radar data using PointNets.

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