  In recent years, LiDAR-camera fusion models have markedly advanced 3D object
detection tasks in autonomous driving. However, their robustness against common
weather corruption such as fog, rain, snow, and sunlight in the intricate
physical world remains underexplored. In this paper, we evaluate the robustness
of fusion models from the perspective of fusion strategies on the corrupted
dataset. Based on the evaluation, we further propose a concise yet practical
fusion strategy to enhance the robustness of the fusion models, namely flexibly
weighted fusing features from LiDAR and camera sources to adapt to varying
weather scenarios. Experiments conducted on four types of fusion models, each
with two distinct lightweight implementations, confirm the broad applicability
and effectiveness of the approach.
