  Autonomous vehicles utilize urban scene segmentation to understand the real
world like a human and react accordingly. Semantic segmentation of normal
scenes has experienced a remarkable rise in accuracy on conventional
benchmarks. However, a significant portion of real-life accidents features
abnormal scenes, such as those with object deformations, overturns, and
unexpected traffic behaviors. Since even small mis-segmentation of driving
scenes can lead to serious threats to human lives, the robustness of such
models in accident scenarios is an extremely important factor in ensuring
safety of intelligent transportation systems.
  In this paper, we propose a Multi-source Meta-learning Unsupervised Domain
Adaptation (MMUDA) framework, to improve the generalization of segmentation
transformers to extreme accident scenes. In MMUDA, we make use of Multi-Domain
Mixed Sampling to augment the images of multiple-source domains (normal scenes)
with the target data appearances (abnormal scenes). To train our model, we
intertwine and study a meta-learning strategy in the multi-source setting for
robustifying the segmentation results. We further enhance the segmentation
backbone (SegFormer) with a HybridASPP decoder design, featuring large window
attention spatial pyramid pooling and strip pooling, to efficiently aggregate
long-range contextual dependencies. Our approach achieves a mIoU score of
46.97% on the DADA-seg benchmark, surpassing the previous state-of-the-art
model by more than 7.50%. Code will be made publicly available at
https://github.com/xinyu-laura/MMUDA.
