  Object detection using automotive radars has not been explored with deep
learning models in comparison to the camera based approaches. This can be
attributed to the lack of public radar datasets. In this paper, we collect a
novel radar dataset that contains radar data in the form of
Range-Azimuth-Doppler tensors along with the bounding boxes on the tensor for
dynamic road users, category labels, and 2D bounding boxes on the Cartesian
Bird-Eye-View range map. To build the dataset, we propose an instance-wise
auto-annotation method. Furthermore, a novel Range-Azimuth-Doppler based
multi-class object detection deep learning model is proposed. The algorithm is
a one-stage anchor-based detector that generates both 3D bounding boxes and 2D
bounding boxes on Range-Azimuth-Doppler and Cartesian domains, respectively.
Our proposed algorithm achieves 56.3% AP with IOU of 0.3 on 3D bounding box
predictions, and 51.6% with IOU of 0.5 on 2D bounding box prediction. Our
dataset and the code can be found at
https://github.com/ZhangAoCanada/RADDet.git.
