Self-driving cars have the potential to change the paradigm of transportation. According to the U.S. Department of Transportation National Motor Vehicle Crash Causation Survey, 93% of all vehicle accidents are influenced by human error. Eliminating those accidents would be a giant leap forward, a safer means of transportation. However, developing autonomous driving systems requires a tremendous amount of training images, usually collected and labelled by human labor, which is costly and error-prone. To make things worse, gathering such a vast amount of real driving images is challenging because we cannot artificially make unusual corner cases or peculiar weather and lighting conditions.