  In the realm of deploying Machine Learning-based Advanced Driver Assistance
Systems (ML-ADAS) into real-world scenarios, adverse weather conditions pose a
significant challenge. Conventional ML models trained on clear weather data
falter when faced with scenarios like extreme fog or heavy rain, potentially
leading to accidents and safety hazards. This paper addresses this issue by
proposing a novel approach: employing a Denoising Deep Neural Network as a
preprocessing step to transform adverse weather images into clear weather
images, thereby enhancing the robustness of ML-ADAS systems. The proposed
method eliminates the need for retraining all subsequent Depp Neural Networks
(DNN) in the ML-ADAS pipeline, thus saving computational resources and time.
Moreover, it improves driver visualization, which is critical for safe
navigation in adverse weather conditions. By leveraging the UNet architecture
trained on an augmented KITTI dataset with synthetic adverse weather images, we
develop the Weather UNet (WUNet) DNN to remove weather artifacts. Our study
demonstrates substantial performance improvements in object detection with
WUNet preprocessing under adverse weather conditions. Notably, in scenarios
involving extreme fog, our proposed solution improves the mean Average
Precision (mAP) score of the YOLOv8n from 4% to 70%.
