  Accurate road surface classification is crucial for autonomous vehicles (AVs)
to optimize driving conditions, enhance safety, and enable advanced road
mapping. However, deep learning models for road surface classification suffer
from poor generalization when tested on unseen datasets. To update these models
with new information, also the original training dataset must be taken into
account, in order to avoid catastrophic forgetting. This is, however,
inefficient if not impossible, e.g., when the data is collected in streams or
large amounts. To overcome this limitation and enable fast and efficient
cross-dataset adaptation, we propose to employ continual learning finetuning
methods designed to retain past knowledge while adapting to new data, thus
effectively avoiding forgetting. Experimental results demonstrate the
superiority of this approach over naive finetuning, achieving performance close
to fresh retraining. While solving this known problem, we also provide a
general description of how the same technique can be adopted in other AV
scenarios. We highlight the potential computational and economic benefits that
a continual-based adaptation can bring to the AV industry, while also reducing
greenhouse emissions due to unnecessary joint retraining.
