  We present an approach to automatically generate semantic labels for real
recordings of automotive range-Doppler (RD) radar spectra. Such labels are
required when training a neural network for object recognition from radar data.
The automatic labeling approach rests on the simultaneous recording of camera
and lidar data in addition to the radar spectrum. By warping radar spectra into
the camera image, state-of-the-art object recognition algorithms can be applied
to label relevant objects, such as cars, in the camera image. The warping
operation is designed to be fully differentiable, which allows backpropagating
the gradient computed on the camera image through the warping operation to the
neural network operating on the radar data. As the warping operation relies on
accurate scene flow estimation, we further propose a novel scene flow
estimation algorithm which exploits information from camera, lidar and radar
sensors. The proposed scene flow estimation approach is compared against a
state-of-the-art scene flow algorithm, and it outperforms it by approximately
30% w.r.t. mean average error. The feasibility of the overall framework for
automatic label generation for RD spectra is verified by evaluating the
performance of neural networks trained with the proposed framework for
Direction-of-Arrival estimation.
