  Sensor fusion is a crucial augmentation technique for improving the accuracy
and reliability of perception systems for automated vehicles under diverse
driving conditions. However, adverse weather and low-light conditions remain
challenging, where sensor performance degrades significantly, exposing vehicle
safety to potential risks. Advanced sensors such as LiDARs can help mitigate
the issue but with extremely high marginal costs. In this paper, we propose a
novel transformer-based 3D object detection model "REDFormer" to tackle low
visibility conditions, exploiting the power of a more practical and
cost-effective solution by leveraging bird's-eye-view camera-radar fusion.
Using the nuScenes dataset with multi-radar point clouds, weather information,
and time-of-day data, our model outperforms state-of-the-art (SOTA) models on
classification and detection accuracy. Finally, we provide extensive ablation
studies of each model component on their contributions to address the
above-mentioned challenges. Particularly, it is shown in the experiments that
our model achieves a significant performance improvement over the baseline
model in low-visibility scenarios, specifically exhibiting a 31.31% increase in
rainy scenes and a 46.99% enhancement in nighttime scenes.The source code of
this study is publicly available.
