Generative Adversarial Synthesis of Radar Point Cloud Scenes

arXiv (Cornell University)

Abstract

For the validation and verification of automotive radars, datasets of realistic traffic scenarios are required, which, how ever, are laborious to acquire. In this paper, we introduce radar scene synthesis using GANs as an alternative to the real dataset acquisition and simulation-based approaches. We train a PointNet++ based GAN model to generate realistic radar point cloud scenes and use a binary classifier to evaluate the performance of scenes generated using this model against a test set of real scenes. We demonstrate that our GAN model achieves similar performance (~87%) to the real scenes test set.

Authors 3

1. Thomas Dallmann

   Technische Universität Ilmenau

   Affiliation as printed

   Radio Technologies for Automated and Connected Vehicles Research Group , Technische Universität Ilmenau , Helmholtzplatz 2 , 98693 Ilmenau , Germany

2. Torsten Schoen
3. Dirk Heberling Aachen Institute of High Frequency Technology

   RWTH Aachen University

   Affiliation as printed

   Institute of High Frequency Technology , RWTH Aachen University , 52056 Aachen , Germany