  Radar has long been a common sensor on autonomous vehicles for obstacle
ranging and speed estimation. However, as a robust sensor to all-weather
conditions, radar's capability has not been well-exploited, compared with
camera or LiDAR. Instead of just serving as a supplementary sensor, radar's
rich information hidden in the radio frequencies can potentially provide useful
clues to achieve more complicated tasks, like object classification and
detection. In this paper, we propose a new dataset, named CRUW, with a
systematic annotator and performance evaluation system to address the radar
object detection (ROD) task, which aims to classify and localize the objects in
3D purely from radar's radio frequency (RF) images. To the best of our
knowledge, CRUW is the first public large-scale dataset with a systematic
annotation and evaluation system, which involves camera RGB images and radar RF
images, collected in various driving scenarios.
