  Autonomous radar has been an integral part of advanced driver assistance
systems due to its robustness to adverse weather and various lighting
conditions. Conventional automotive radars use digital signal processing (DSP)
algorithms to process raw data into sparse radar pins that do not provide
information regarding the size and orientation of the objects. In this paper,
we propose a deep-learning based algorithm for radar object detection. The
algorithm takes in radar data in its raw tensor representation and places
probabilistic oriented bounding boxes around the detected objects in
bird's-eye-view space. We created a new multimodal dataset with 102544 frames
of raw radar and synchronized LiDAR data. To reduce human annotation effort we
developed a scalable pipeline to automatically annotate ground truth using
LiDAR as reference. Based on this dataset we developed a vehicle detection
pipeline using raw radar data as the only input. Our best performing radar
detection model achieves 77.28\% AP under oriented IoU of 0.3. To the best of
our knowledge, this is the first attempt to investigate object detection with
raw radar data for conventional corner automotive radars.
