The Radar Ghost Dataset -- An Evaluation of Ghost Objects in Automotive
  Radar Data
Florian Kraus Nicolas Scheiner Werner Ritter Klaus Dietmayer
Radar sensors have a long tradition in advanced driver assistance systems
(ADAS) and also play a major role in current concepts for autonomous vehicles.
Their importance is reasoned by their high robustness against meteorological
effects, such as rain, snow, or fog, and the radar's ability to measure
relative radial velocity differences via the Doppler effect. The cause for
these advantages, namely the large wavelength, is also one of the drawbacks of
radar sensors. Compared to camera or lidar sensor, a lot more surfaces in a
typical traffic scenario appear flat relative to the radar's emitted signal.
This results in multi-path reflections or so called ghost detections in the
radar signal. Ghost objects pose a major source for potential false positive
detections in a vehicle's perception pipeline. Therefore, it is important to be
able to segregate multi-path reflections from direct ones. In this article, we
present a dataset with detailed manual annotations for different kinds of ghost
detections. Moreover, two different approaches for identifying these kinds of
objects are evaluated. We hope that our dataset encourages more researchers to
engage in the fields of multi-path object suppression or exploitation.