  Radar is usually more robust than the camera in severe driving scenarios,
e.g., weak/strong lighting and bad weather. However, unlike RGB images captured
by a camera, the semantic information from the radar signals is noticeably
difficult to extract. In this paper, we propose a deep radar object detection
network (RODNet), to effectively detect objects purely from the carefully
processed radar frequency data in the format of range-azimuth frequency
heatmaps (RAMaps). Three different 3D autoencoder based architectures are
introduced to predict object confidence distribution from each snippet of the
input RAMaps. The final detection results are then calculated using our
post-processing method, called location-based non-maximum suppression (L-NMS).
Instead of using burdensome human-labeled ground truth, we train the RODNet
using the annotations generated automatically by a novel 3D localization method
using a camera-radar fusion (CRF) strategy. To train and evaluate our method,
we build a new dataset -- CRUW, containing synchronized videos and RAMaps in
various driving scenarios. After intensive experiments, our RODNet shows
favorable object detection performance without the presence of the camera.
