RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects
It addresses perception challenges in autonomous driving by improving detection of faraway objects and motion understanding, though it is incremental in sensor fusion methods.
The paper tackles the problem of robust perception for dynamic objects in self-driving by exploiting Radar data, achieving state-of-the-art results on object detection and velocity estimation on two large-scale datasets.
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.