  Continual Test-Time Adaptation (CTTA) involves adapting a pre-trained source
model to continually changing unsupervised target domains. In this paper, we
systematically analyze the challenges of this task: online environment,
unsupervised nature, and the risks of error accumulation and catastrophic
forgetting under continual domain shifts. To address these challenges, we
reshape the online data buffering and organizing mechanism for CTTA. We propose
an uncertainty-aware buffering approach to identify and aggregate significant
samples with high certainty from the unsupervised, single-pass data stream.
Based on this, we propose a graph-based class relation preservation constraint
to overcome catastrophic forgetting. Furthermore, a pseudo-target replay
objective is used to mitigate error accumulation. Extensive experiments
demonstrate the superiority of our method in both segmentation and
classification CTTA tasks. Code is available at https://github.com/z1358/OBAO.
