  We present our approach to unsupervised domain adaptation for single-stage
object detectors on top-view grid maps in automated driving scenarios. Our goal
is to train a robust object detector on grid maps generated from custom sensor
data and setups. We first introduce a single-stage object detector for grid
maps based on RetinaNet. We then extend our model by image- and instance-level
domain classifiers at different feature pyramid levels which are trained in an
adversarial manner. This allows us to train robust object detectors for
unlabeled domains. We evaluate our approach quantitatively on the nuScenes and
KITTI benchmarks and present qualitative domain adaptation results for
unlabeled measurements recorded by our experimental vehicle. Our results
demonstrate that object detection accuracy for unlabeled domains can be
improved by applying our domain adaptation strategy.
