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Radar has long been a common sensor on autonomous vehicles for obstacleranging and speed estimation. However, as a robust sensor to all-weatherconditions, radar's capability has not been well-exploited, compared withcamera or LiDAR. Instead of just serving as a supplementary sensor, radar'srich information hidden in the radio frequencies can potentially provide usefulclues to achieve more complicated tasks, like object classification anddetection. In this paper, we propose a new dataset, named CRUW, with asystematic annotator and performance evaluation system to address the radarobject detection (ROD) task, which aims to classify and localize the objects in3D purely from radar's radio frequency (RF) images. To the best of ourknowledge, CRUW is the first public large-scale dataset with a systematicannotation and evaluation system, which involves camera RGB images and radar RFimages, collected in various driving scenarios.

Rethinking of Radar's Role: A Camera-Radar Dataset and Systematic Annotator via Coordinate Alignment