一句话：提出基于GAN的蜕变测试与输入验证框架，生成多样驾驶场景并验证DNN输入以提升自动驾驶系统可靠性。
基于图像的自动驾驶系统依赖 DNN，但 DNN 可能产生错误行为并导致致命事故。现有测试技术通过生成人工驾驶场景来扩充测试集，例如对原始图像进行变换，但存在两个不足：生成的合成图像往往缺乏驾驶场景多样性，影响测试效果与可靠性；同时，训练域与应用域不匹配会显著降低机器学习系统精度，因此需要对输入进行验证以提升鲁棒性。为此，论文提出 DeepRoad，一个基于 GAN 的蜕变测试与输入验证框架，利用 GAN 生成/变换驾驶场景用于蜕变测试，并对输入进行验证，以缓解域不匹配问题，从而增强自动驾驶系统的测试充分性与可靠性。
While Deep Neural Networks (DNNs) have established the fundamentals of image-based autonomous driving systems, they may exhibit erroneous behaviors and cause fatal accidents. To address the safety issues in autonomous driving systems, a recent set of testing techniques have been designed to automatically generate artificial driving scenes to enrich test suite, e.g., generating new input images transformed from the original ones. However, these techniques are insufficient due to two limitations: first, many such synthetic images often lack diversity of driving scenes, and hence compromise the resulting efficacy and reliability. Second, for machine-learning-based systems, a mismatch between training and application domain can dramatically degrade system accuracy, such that it is necessary to validate inputs for improving system robustness.
@inproceedings{paperbot4704,
title = {DeepRoad: GAN-based metamorphic testing and input validation framework for autonomous driving systems},
author = {Mengshi Zhang and Yuqun Zhang and Lingming Zhang and Cong Liu and Sarfraz Khurshid},
booktitle = {ASE},
year = {2018},
doi = {10.1145/3238147.3238187}
}