  While Deep Neural Networks (DNNs) have established the fundamentals of
DNN-based autonomous driving systems, they may exhibit erroneous behaviors and
cause fatal accidents. To resolve the safety issues of autonomous driving
systems, a recent set of testing techniques have been designed to automatically
generate test cases, e.g., new input images transformed from the original ones.
Unfortunately, many such generated input images often render inferior
authenticity, lacking accurate semantic information of the driving scenes and
hence compromising the resulting efficacy and reliability.
  In this paper, we propose DeepRoad, an unsupervised framework to
automatically generate large amounts of accurate driving scenes to test the
consistency of DNN-based autonomous driving systems across different scenes. In
particular, DeepRoad delivers driving scenes with various weather conditions
(including those with rather extreme conditions) by applying the Generative
Adversarial Networks (GANs) along with the corresponding real-world weather
scenes. Moreover, we have implemented DeepRoad to test three well-recognized
DNN-based autonomous driving systems. Experimental results demonstrate that
DeepRoad can detect thousands of behavioral inconsistencies in these systems.
