DeepBillboard: Systematic Physical-World Testing of Autonomous Driving Systems
DeepBillboard: Systematic Physical-World Testing of Autonomous Driving Systems
复制标题
DOI：
10.1145/3377811.3380422
复制
发表时间：
2018-12
期刊：
影响因子：
--
通讯作者：
Husheng Zhou;Wei Li-;Yuankun Zhu;Yuqun Zhang;Bei Yu;Lingming Zhang;Cong Liu
中科院分区：
文献类型：
--
作者：
Husheng Zhou;Wei Li-;Yuankun Zhu;Yuqun Zhang;Bei Yu;Lingming Zhang;Cong Liu
深度神经网络（DNN）已广泛应用于自动驾驶汽车等自动驾驶系统。最近，DNN测试已经被广泛研究，以自动生成对抗性示例，这些示例将小幅度的扰动注入到输入中，以在极端情况下测试DNN。虽然现有的测试技术被证明是有效的，特别是对于自动驾驶，但它们主要集中在生成数字对抗扰动，例如，改变图像像素，这在物理世界中可能永远不会发生。因此，在有关自动驾驶测试的文献中有一个关键的缺失：理解和利用数字和物理对抗扰动生成来影响转向决策。在本文中，我们提出了一种系统的物理世界测试方法，即DeepBillboard，针对一个非常常见和实用的驾驶场景：驾车广告牌。DeepBillboard能够生成一个强大且有弹性的可打印对抗性广告牌测试，该测试可在动态变化的驾驶条件下工作，包括视角、距离和照明。我们的目标是通过我们生成的对抗性广告牌最大限度地提高自动驾驶汽车驾驶的转向角错误的可能性，程度和持续时间。我们通过数字扰动实验和物理世界案例研究，广泛评估了DeepBillboard的有效性和鲁棒性。数字实验结果表明，DeepBillboard对各种转向模型和场景都是有效的。此外，物理案例研究表明，DeepBillboard具有足够的鲁棒性和弹性，可以在各种天气条件下为真实驾驶生成物理世界对抗性广告牌测试，能够误导高达26.44度的平均转向角误差。据我们所知，这是第一项证明为实际自动驾驶系统生成真实和连续的物理世界测试的可能性的研究;此外，DeepBillboard可以直接推广到路边沿着的各种其他物理实体/表面，例如，墙上的涂鸦
Deep Neural Networks (DNNs) have been widely applied in autonomous systems such as self-driving vehicles. Recently, DNN testing has been intensively studied to automatically generate adversarial examples, which inject small-magnitude perturbations into inputs to test DNNs under extreme situations. While existing testing techniques prove to be effective, particularly for autonomous driving, they mostly focus on generating digital adversarial perturbations, e.g., changing image pixels, which may never happen in the physical world. Thus, there is a critical missing piece in the literature on autonomous driving testing: understanding and exploiting both digital and physical adversarial perturbation generation for impacting steering decisions. In this paper, we propose a systematic physical-world testing approach, namely DeepBillboard, targeting at a quite common and practical driving scenario: drive-by billboards. DeepBillboard is capable of generating a robust and resilient printable adversarial billboard test, which works under dynamic changing driving conditions including viewing angle, distance, and lighting. The objective is to maximize the possibility, degree, and duration of the steering-angle errors of an autonomous vehicle driving by our generated adversarial billboard. We have extensively evaluated the efficacy and robustness of DeepBillboard by conducting both experiments with digital perturbations and physical-world case studies. The digital experimental results show that DeepBillboard is effective for various steering models and scenes. Furthermore, the physical case studies demonstrate that DeepBillboard is sufficiently robust and resilient for generating physical-world adversarial billboard tests for real-world driving under various weather conditions, being able to mislead the average steering angle error up to 26.44 degrees. To the best of our knowledge, this is the first study demonstrating the possibility of generating realistic and continuous physical-world tests for practical autonomous driving systems; moreover, DeepBillboard can be directly generalized to a variety of other physical entities/surfaces along the curbside, e.g., a graffiti painted on a wall.