  To enable self-driving vehicles accurate detection and tracking of
surrounding objects is essential. While Light Detection and Ranging (LiDAR)
sensors have set the benchmark for high-performance systems, the appeal of
camera-only solutions lies in their cost-effectiveness. Notably, despite the
prevalent use of Radio Detection and Ranging (RADAR) sensors in automotive
systems, their potential in 3D detection and tracking has been largely
disregarded due to data sparsity and measurement noise. As a recent
development, the combination of RADARs and cameras is emerging as a promising
solution. This paper presents Camera-RADAR 3D Detection and Tracking (CR3DT), a
camera-RADAR fusion model for 3D object detection, and Multi-Object Tracking
(MOT). Building upon the foundations of the State-of-the-Art (SotA) camera-only
BEVDet architecture, CR3DT demonstrates substantial improvements in both
detection and tracking capabilities, by incorporating the spatial and velocity
information of the RADAR sensor. Experimental results demonstrate an absolute
improvement in detection performance of 5.3% in mean Average Precision (mAP)
and a 14.9% increase in Average Multi-Object Tracking Accuracy (AMOTA) on the
nuScenes dataset when leveraging both modalities. CR3DT bridges the gap between
high-performance and cost-effective perception systems in autonomous driving,
by capitalizing on the ubiquitous presence of RADAR in automotive applications.
The code is available at: https://github.com/ETH-PBL/CR3DT.
