  Unlike RGB cameras that use visible light bands (384$\sim$769 THz) and Lidars
that use infrared bands (361$\sim$331 THz), Radars use relatively longer
wavelength radio bands (77$\sim$81 GHz), resulting in robust measurements in
adverse weathers. Unfortunately, existing Radar datasets only contain a
relatively small number of samples compared to the existing camera and Lidar
datasets. This may hinder the development of sophisticated data-driven deep
learning techniques for Radar-based perception. Moreover, most of the existing
Radar datasets only provide 3D Radar tensor (3DRT) data that contain power
measurements along the Doppler, range, and azimuth dimensions. As there is no
elevation information, it is challenging to estimate the 3D bounding box of an
object from 3DRT. In this work, we introduce KAIST-Radar (K-Radar), a novel
large-scale object detection dataset and benchmark that contains 35K frames of
4D Radar tensor (4DRT) data with power measurements along the Doppler, range,
azimuth, and elevation dimensions, together with carefully annotated 3D
bounding box labels of objects on the roads. K-Radar includes challenging
driving conditions such as adverse weathers (fog, rain, and snow) on various
road structures (urban, suburban roads, alleyways, and highways). In addition
to the 4DRT, we provide auxiliary measurements from carefully calibrated
high-resolution Lidars, surround stereo cameras, and RTK-GPS. We also provide
4DRT-based object detection baseline neural networks (baseline NNs) and show
that the height information is crucial for 3D object detection. And by
comparing the baseline NN with a similarly-structured Lidar-based neural
network, we demonstrate that 4D Radar is a more robust sensor for adverse
weather conditions. All codes are available at
https://github.com/kaist-avelab/k-radar.
