  State-of-the-art object detection systems for autonomous driving achieve
promising results in clear weather conditions. However, such autonomous safety
critical systems also need to work in degrading weather conditions, such as
rain, fog and snow. Unfortunately, most approaches evaluate only on the KITTI
dataset, which consists only of clear weather scenes. In this paper we address
this issue and perform one of the most detailed evaluation on single and dual
modality architectures on data captured in real weather conditions. We analyse
the performance degradation of these architectures in degrading weather
conditions. We demonstrate that an object detection architecture performing
good in clear weather might not be able to handle degrading weather conditions.
We also perform ablation studies on the dual modality architectures and show
their limitations.
