  Current LiDAR-based Vehicle-to-Everything (V2X) multi-agent perception
systems have shown the significant success on 3D object detection. While these
models perform well in the trained clean weather, they struggle in unseen
adverse weather conditions with the real-world domain gap. In this paper, we
propose a domain generalization approach, named V2X-DGW, for LiDAR-based 3D
object detection on multi-agent perception system under adverse weather
conditions. Not only in the clean weather does our research aim to ensure
favorable multi-agent performance, but also in the unseen adverse weather
conditions by learning only on the clean weather data. To advance research in
this area, we have simulated the impact of three prevalent adverse weather
conditions on two widely-used multi-agent datasets, resulting in the creation
of two novel benchmark datasets: OPV2V-w and V2XSet-w.
  To this end, we first introduce the Adaptive Weather Augmentation (AWA) to
mimic the unseen adverse weather conditions, and then propose two alignments
for generalizable representation learning: Trust-region Weather-invariant
Alignment (TWA) and Agent-aware Contrastive Alignment (ACA). Extensive
experimental results demonstrate that our V2X-DGW achieved improvements in the
unseen adverse weather conditions.
