  Image deraining is a new challenging problem in applications of autonomous
vehicles. In a bad weather condition of heavy rainfall, raindrops, mainly
hitting the vehicle's windshield, can significantly reduce observation ability
even though the windshield wipers might be able to remove part of it. Moreover,
rain flows spreading over the windshield can yield the physical effect of
refraction, which seriously impede the sightline or undermine the machine
learning system equipped in the vehicle. In this paper, we propose a new
multi-stage multi-task recurrent generative adversarial network (M2GAN) to deal
with challenging problems of raindrops hitting the car's windshield. This
method is also applicable for removing raindrops appearing on a glass window or
lens. M2GAN is a multi-stage multi-task generative adversarial network that can
utilize prior high-level information, such as semantic segmentation, to boost
deraining performance. To demonstrate M2GAN, we introduce the first real-world
dataset for rain removal on autonomous vehicles. The experimental results show
that our proposed method is superior to other state-of-the-art approaches of
deraining raindrops in respect of quantitative metrics and visual quality.
M2GAN is considered the first method to deal with challenging problems of
real-world rains under unconstrained environments such as autonomous vehicles.
