Test-Time Adaptive Segmentation (TTA-Seg) aims to adapt trained models to unlabeled test data with shifted semantic distributions, which is critical for reliable scene understanding in autonomous driving under varying weather, illumination, and geographic conditions. Existing methods typically rely on momentum updates driven by model predictions to estimate target-domain distributions. However, such prediction-dependent strategies often fail to capture accurate semantic structures, leading to biased representations and error accumulation under large domain gaps in safety-critical scenarios. To address these challenges, we propose a semantic-aware prototype optimization framework for TTA-Seg. Specifically, we first extract instance-level mixture-of-prototypes from source data as frozen semantic priors, providing stable and transferable knowledge without requiring retraining. Based on this, target-domain semantics are dynamically modeled through feature–prototype alignment, enabling adaptive perception in continuously changing environments. Furthermore, we introduce learnable global prototypes to capture class-level relationships and model both intra-class variations and inter-class dependencies, thereby enhancing semantic consistency. Finally, we develop a consensus-guided weighting mechanism to adaptively balance local and global knowledge for stable and efficient deployment. Extensive experiments demonstrate that our method achieves state-of-the-art performance, improving the average mIoU by + 1.48%, +0.9%, and + 0.5% under Sim-to-Real TTA, Cityscapes-to-ACDC TTA, and Cityscapes-to-ACDC CTTA settings, respectively.
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