  Object detection in radar imagery with neural networks shows great potential
for improving autonomous driving. However, obtaining annotated datasets from
real radar images, crucial for training these networks, is challenging,
especially in scenarios with long-range detection and adverse weather and
lighting conditions where radar performance excels. To address this challenge,
we present RadSimReal, an innovative physical radar simulation capable of
generating synthetic radar images with accompanying annotations for various
radar types and environmental conditions, all without the need for real data
collection. Remarkably, our findings demonstrate that training object detection
models on RadSimReal data and subsequently evaluating them on real-world data
produce performance levels comparable to models trained and tested on real data
from the same dataset, and even achieves better performance when testing across
different real datasets. RadSimReal offers advantages over other physical radar
simulations that it does not necessitate knowledge of the radar design details,
which are often not disclosed by radar suppliers, and has faster run-time. This
innovative tool has the potential to advance the development of computer vision
algorithms for radar-based autonomous driving applications.
