  Deep neural networks for scene perception in automated vehicles achieve
excellent results for the domains they were trained on. However, in real-world
conditions, the domain of operation and its underlying data distribution are
subject to change. Adverse weather conditions, in particular, can significantly
decrease model performance when such data are not available during
training.Additionally, when a model is incrementally adapted to a new domain,
it suffers from catastrophic forgetting, causing a significant drop in
performance on previously observed domains. Despite recent progress in reducing
catastrophic forgetting, its causes and effects remain obscure. Therefore, we
study how the representations of semantic segmentation models are affected
during domain-incremental learning in adverse weather conditions. Our
experiments and representational analyses indicate that catastrophic forgetting
is primarily caused by changes to low-level features in domain-incremental
learning and that learning more general features on the source domain using
pre-training and image augmentations leads to efficient feature reuse in
subsequent tasks, which drastically reduces catastrophic forgetting. These
findings highlight the importance of methods that facilitate generalized
features for effective continual learning algorithms.
