  The performance of perception systems developed for autonomous driving
vehicles has seen significant improvements over the last few years. This
improvement was associated with the increasing use of LiDAR sensors and point
cloud data to facilitate the task of object detection and recognition in
autonomous driving. However, LiDAR and camera systems show deteriorating
performances when used in unfavorable conditions like dusty and rainy weather.
Radars on the other hand operate on relatively longer wavelengths which allows
for much more robust measurements in these conditions. Despite that,
radar-centric data sets do not get a lot of attention in the development of
deep learning techniques for radar perception. In this work, we consider the
radar object detection problem, in which the radar frequency data is the only
input into the detection framework. We further investigate the challenges of
using radar-only data in deep learning models. We propose a transformers-based
model, named RadarFormer, that utilizes state-of-the-art developments in vision
deep learning. Our model also introduces a channel-chirp-time merging module
that reduces the size and complexity of our models by more than 10 times
without compromising accuracy. Comprehensive experiments on the CRUW radar
dataset demonstrate the advantages of the proposed method. Our RadarFormer
performs favorably against the state-of-the-art methods while being 2x faster
during inference and requiring only one-tenth of their model parameters. The
code associated with this paper is available at
https://github.com/YahiDar/RadarFormer.
