PreCFAR Gridmaps for Automotive Radar

European Radar Conference (EuRAD), pp. 161–164

Abstract

For higher levels of driving automation, a vehicle has to perceive the static environment in addition to other road users to ensure safe and reliable operation. This paper presents a radar-based method utilizing a lower data level than typical peak detection point clouds (e.g. Constant False Alarm Rate (CFAR) detection) to address some challenges in static environment perception. Using dense, image-like radar data overcomes CFAR's disadvantage of masking on extended objects as well as the suppression of low reflection amplitude targets. The improved performance of the proposed technique is demonstrated in a direct comparison to a CFAR approach based on real-world experiments.

Authors 5

1. Fabio Weishaupt

   Mercedes-Benz (Germany) · Daimler (Germany)

   Affiliation as printed

   Environment Perception, Research & Development,Mercedes-Benz AG,Stuttgart,Germany

2. Nils Appenrodt

   Mercedes-Benz (Germany) · Daimler (Germany)

   Affiliation as printed

   Environment Perception, Research & Development,Mercedes-Benz AG,Stuttgart,Germany

3. Julius F. Tilly

   Mercedes-Benz (Germany) · Daimler (Germany)

   Affiliation as printed

   Environment Perception, Research & Development,Mercedes-Benz AG,Stuttgart,Germany

4. Jürgen Dickmann

   Mercedes-Benz (Germany) · Daimler (Germany)

   Affiliation as printed

   Environment Perception, Research & Development,Mercedes-Benz AG,Stuttgart,Germany

5. Dirk Heberling Aachen Institute of High Frequency Technology

   Fraunhofer Institute for High Frequency Physics and Radar Techniques · RWTH Aachen University

   Affiliation as printed

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

   Fraunhofer Institute for High Frequency Physics and Radar Techniques FHR, Wachtberg, Germany

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