Modern road safety systems, in general, and autonomous vehicles in particular, rely heavily on visual data to detect and classify objects such as pedestrians, traffic signs and lights, and other cars nearby to aid in good vehicle operation safely in their surroundings. However, the performance of object detection algorithms may degraded in difficult weather conditions. Despite tremendous advances in the development of drainage systems, the impact of rain on object perception has received little attention, particularly in the context of autonomous computing. The thesis' primary goal is to present a study on the most recent and novel ways for reducing the impact of wet weather on an autonomous vehicle's ability to detect objects