  We tackle the problem of exploiting Radar for perception in the context of
self-driving as Radar provides complementary information to other sensors such
as LiDAR or cameras in the form of Doppler velocity. The main challenges of
using Radar are the noise and measurement ambiguities which have been a
struggle for existing simple input or output fusion methods. To better address
this, we propose a new solution that exploits both LiDAR and Radar sensors for
perception. Our approach, dubbed RadarNet, features a voxel-based early fusion
and an attention-based late fusion, which learn from data to exploit both
geometric and dynamic information of Radar data. RadarNet achieves
state-of-the-art results on two large-scale real-world datasets in the tasks of
object detection and velocity estimation. We further show that exploiting Radar
improves the perception capabilities of detecting faraway objects and
understanding the motion of dynamic objects.

Computer Vision and Pattern Recognition