  Object detection is essential to safe autonomous or assisted driving.
Previous works usually utilize RGB images or LiDAR point clouds to identify and
localize multiple objects in self-driving. However, cameras tend to fail in bad
driving conditions, e.g. bad weather or weak lighting, while LiDAR scanners are
too expensive to get widely deployed in commercial applications. Radar has been
drawing more and more attention due to its robustness and low cost. In this
paper, we propose a scene-aware radar learning framework for accurate and
robust object detection. First, the learning framework contains branches
conditioning on the scene category of the radar sequence; with each branch
optimized for a specific type of scene. Second, three different 3D
autoencoder-based architectures are proposed for radar object detection and
ensemble learning is performed over the different architectures to further
boost the final performance. Third, we propose novel scene-aware sequence mix
augmentation (SceneMix) and scene-specific post-processing to generate more
robust detection results. In the ROD2021 Challenge, we achieved a final result
of average precision of 75.0% and an average recall of 81.0%. Moreover, in the
parking lot scene, our framework ranks first with an average precision of 97.8%
and an average recall of 98.6%, which demonstrates the effectiveness of our
framework.
