In modern technical systems, ensuring feature availability and system reliability throughout the entire life cycle is crucial, particularly as embedded system elements degrade over time. This paper introduces a methodology for detecting feature degradation and maintaining feature availability in systems operating autonomously, focusing on camera systems within adaptive cruise control (ACC). Adverse weather conditions like rain and snow present significant challenges to camera sensors, degrading image quality and impairing system performance. We quantify this degradation by analysing the visibility range (VR), sharpness (SH), and blur (BL) of images (from SHIFT dataset) under varying intensities of rain and snow. Additionally, we assess Gaussian noise (GN) as a measure of image degradation, comparing the impact of weather conditions on sensor performance. Our study demonstrates that while both rain and snow degrade image quality, snow consistently has a greater impact on all performance indicators. The findings highlight the importance of robust sensor designs and adaptive software approaches to maintain system reliability under real-world weather conditions.