  We present a data-driven car occupancy detection algorithm using
ultra-wideband radar based on the ResNet architecture. The algorithm is trained
on a dataset of channel impulse responses obtained from measurements at three
different activity levels of the occupants (i.e. breathing, talking, moving).
We compare the presented algorithm against a state-of-the-art car occupancy
detection algorithm based on variational message passing (VMP). Our presented
ResNet architecture is able to outperform the VMP algorithm in terms of the
area under the receiver operating curve (AUC) at low signal-to-noise ratios
(SNRs) for all three activity levels of the target. Specifically, for an SNR of
-20 dB the VMP detector achieves an AUC of 0.87 while the ResNet architecture
achieves an AUC of 0.91 if the target is sitting still and breathing naturally.
The difference in performance for the other activities is similar. To
facilitate the implementation in the onboard computer of a car we perform an
ablation study to optimize the tradeoff between performance and computational
complexity for several ResNet architectures. The dataset used to train and
evaluate the algorithm is openly accessible. This facilitates an easy
comparison in future works.
