Intelligent corner synthesis via cycle-consistent generative adversarial networks for efficient validation of autonomous driving systems

Today's automotive vehicles are often equipped with powerful data processing systems for driver assistance and/or autonomous driving. To meet the rigorous safety standard, one critical task is to ensure extremely small failure rate over all possible operation conditions. Such a validation task requires a large amount of on-road testing data to cover all possible corners. In this paper, we describe a novel general-purpose methodology to synthetically and efficiently generate a broad spectrum of corner cases for validation purpose. Our proposed method is based upon cycle-consistent generative adversarial networks (CycleGANs) trained by a small set of image samples to mathematically map a nominal case to other corner cases. By taking STOP sign detection as an example, our numerical experiments demonstrate that the proposed approach is able to reduce the validation error by up to 100× given a limited data set for corner cases.