  Recently, as many studies of autonomous vehicles have been achieved for
levels 4 and 5, there has been also increasing interest in the advancement of
perception, decision, and control technologies, which are the three major
aspects of autonomous vehicles. As for the perception technologies achieving
reliable maneuvering of autonomous vehicles, object detection by using diverse
sensors (e.g., LiDAR, radar, and camera) should be prioritized. These sensors
require to detect objects accurately and quickly in diverse weather conditions,
but they tend to have challenges to consistently detect objects in bad weather
conditions with rain, snow, or fog. Thus, in this study, based on the
experimentally obtained raindrop data from precipitation conditions, we
constructed a novel dataset that could test diverse network model in various
precipitation conditions through the CARLA simulator. Consequently, based on
our novel dataset, YOLO series, a one-stage-detector, was used to
quantitatively verify how much object detection performance could be decreased
under various precipitation conditions from normal to extreme heavy rain
situations.
