<strong>Paper Title</strong><br> Weather-Informed Vision Enhancement for Autonomous Vehicles in Adverse Conditions<br> <br> <strong>Abstract</strong><br> Providing Advanced Driver Assistance Systems (ADAS) features requires high-quality image data collected by vehicles. However, adverse weather conditions and nighttime significantly degrade image quality, negatively impacting object detection accuracy and model performance for ADAS function- alities. This paper addresses this critical issue by referencing relevant works that have encountered similar challenges. We propose a novel solution that utilizes the vehicle鈥檚 GPS location and data collection timestamp to query weather forecast via a weather API. By obtaining precise weather details at the timeandlocationofdatacollection,weenhanceimagequalitythrough a pre-processing step tailored to the specific weather conditions. UsingtheDAWN(DetectioninAdverseWeatherNature)dataset, our approach demonstrates substantial improvements in image clarity and object detection accuracy across various weather scenarios, significantly enhancing the robustness and reliabilityof object detection models for ADAS systems. Keywords - ADAS Features, Image Enhancement, Adverse Weather Conditions, Object Detection, Weather Api