近年来,基于深度学习的视觉感知技术的发展极大地促进了车联网领域中自动驾驶的繁荣,然而自动驾驶系统的安全问题频出引发了人们对自动驾驶未来的担忧.由于深度学习系统的行为缺乏可解释性,测试基于深度学习的自动驾驶系统的安全性极具挑战.目前,已有针对自动驾驶场景的安全性测试工作被提出,但这些方法在测试场景生成、安全问题检测和安全问题解释等方面仍存在不足之处.针对基于视觉感知的自动驾驶系统,设计开发了一种场景驱动的、可解释性强的、运行高效的安全性测试系统.提出了一种能够平衡真实性与丰富度的场景描述方法,并利用实时渲染引擎生成可以用于驾驶系统安全性测试的场景;设计了一种高效的针对非线性系统的场景搜索算法,其可以针对不同的待测试系统动态调整搜索方案;同时,还设计了一个故障分析器,自动化分析定位待测试系统的安全性缺陷成因.复现了现有基于实时渲染引擎的动态自动驾驶测试系统,并同时使用本系统和复现系统对CILRS系统和CIL系统进行安全测试,实验结果表明相同时间下该工作的安全问题发现率是复现的场景驱动的动态测试方法的1.4倍.进一步的实验表明:可以分别为具有代表性的深度学习 自动驾驶系统CIL和CILRS,从旷野、乡村与城市的3类环境中动态生成的共3 000个场景中,搜索到1 939个和1671个造成故障的场景,并且每个故障场景的搜索时间平均为16.86s.分析器从统计的角度判断出CILRS系统容易导致故障的区域在道路两侧,雨天和红色或黄色物体更易导致该自动驾驶系统发生故障.

The data abuse issue has risen along with the widespread development of the deep learning inference service (DLIS). Specifically, mobile users worry about their input data being labeled to secretly train new deep learning models that are unrelated to the DLIS they subscribe to. This unique issue, unlike the privacy problem, is about the rights of data owners in the context of deep learning. However, preventing data abuse is demanding when considering the usability and generality in the mobile scenario. In this work, we propose, to our best knowledge, the first data abuse prevention mechanism called DAPter. DAPter is a user-side DLIS-input converter, which removes unnecessary information with respect to the targeted DLIS. The converted input data by DAPter maintains good inference accuracy and is difficult to be labeled manually or automatically for the new model training. DAPter’s conversion is empowered by our lightweight generative model trained with a novel loss function to minimize abusable information in the input data. Furthermore, adapting DAPter requires no change in the existing DLIS backend and models. We conduct comprehensive experiments with our DAPter prototype on mobile devices and demonstrate that DAPter can substantially raise the bar of the data abuse difficulty with little impact on the service quality and overhead.