  Since autonomous driving systems usually face dynamic and ever-changing
environments, continual test-time adaptation (CTTA) has been proposed as a
strategy for transferring deployed models to continually changing target
domains. However, the pursuit of long-term adaptation often introduces
catastrophic forgetting and error accumulation problems, which impede the
practical implementation of CTTA in the real world. Recently, existing CTTA
methods mainly focus on utilizing a majority of parameters to fit target domain
knowledge through self-training. Unfortunately, these approaches often amplify
the challenge of error accumulation due to noisy pseudo-labels, and pose
practical limitations stemming from the heavy computational costs associated
with entire model updates. In this paper, we propose a distribution-aware
tuning (DAT) method to make the semantic segmentation CTTA efficient and
practical in real-world applications. DAT adaptively selects and updates two
small groups of trainable parameters based on data distribution during the
continual adaptation process, including domain-specific parameters (DSP) and
task-relevant parameters (TRP). Specifically, DSP exhibits sensitivity to
outputs with substantial distribution shifts, effectively mitigating the
problem of error accumulation. In contrast, TRP are allocated to positions that
are responsive to outputs with minor distribution shifts, which are fine-tuned
to avoid the catastrophic forgetting problem. In addition, since CTTA is a
temporal task, we introduce the Parameter Accumulation Update (PAU) strategy to
collect the updated DSP and TRP in target domain sequences. We conduct
extensive experiments on two widely-used semantic segmentation CTTA benchmarks,
achieving promising performance compared to previous state-of-the-art methods.
