  Integrating different representations from complementary sensing modalities
is crucial for robust scene interpretation in autonomous driving. While deep
learning architectures that fuse vision and range data for 2D object detection
have thrived in recent years, the corresponding modalities can degrade in
adverse weather or lighting conditions, ultimately leading to a drop in
performance. Although domain adaptation methods attempt to bridge the domain
gap between source and target domains, they do not readily extend to
heterogeneous data distributions. In this work, we propose an unsupervised
domain adaptation framework, which adapts a 2D object detector for RGB and
lidar sensors to one or more target domains featuring adverse weather
conditions. Our proposed approach consists of three components. First, a data
augmentation scheme that simulates weather distortions is devised to add domain
confusion and prevent overfitting on the source data. Second, to promote
cross-domain foreground object alignment, we leverage the complementary
features of multiple modalities through a multi-scale entropy-weighted domain
discriminator. Finally, we use carefully designed pretext tasks to learn a more
robust representation of the target domain data. Experiments performed on the
DENSE dataset show that our method can substantially alleviate the domain gap
under the single-target domain adaptation (STDA) setting and the less explored
yet more general multi-target domain adaptation (MTDA) setting.
