Ensuring the safety of all traffic participants is a prerequisite for
bringing intelligent vehicles closer to practical applications. The assistance
system should not only achieve high accuracy under normal conditions, but
obtain robust perception against extreme situations. However, traffic accidents
that involve object collisions, deformations, overturns, etc., yet unseen in
most training sets, will largely harm the performance of existing semantic
segmentation models. To tackle this issue, we present a rarely addressed task
regarding semantic segmentation in accidental scenarios, along with an accident
dataset DADA-seg. It contains 313 various accident sequences with 40 frames
each, of which the time windows are located before and during a traffic
accident. Every 11th frame is manually annotated for benchmarking the
segmentation performance. Furthermore, we propose a novel event-based
multi-modal segmentation architecture ISSAFE. Our experiments indicate that
event-based data can provide complementary information to stabilize semantic
segmentation under adverse conditions by preserving fine-grain motion of
fast-moving foreground (crash objects) in accidents. Our approach achieves
+8.2% mIoU performance gain on the proposed evaluation set, exceeding more than
10 state-of-the-art segmentation methods. The proposed ISSAFE architecture is
demonstrated to be consistently effective for models learned on multiple source
databases including Cityscapes, KITTI-360, BDD and ApolloScape.

提出了一个关于意外情况下语义分割的任务，以及一个意外情况数据集 DADA-seg，同时提出了一种新的基于事件的多模态分割架构 ISSAFE，实验证明该方法在意外情况下可以稳定语义分割并获得与多个领先的分割方法相比的性能提升。