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    吴昊, 王浩, 苏醒, 李明昊, 许封元, 仲盛. 自动驾驶系统中视觉感知模块的安全测试[J]. 计算机研究与发展, 2022, 59(5): 1133-1147. DOI: 10.7544/issn1000-1239.20211139
    引用本文: 吴昊, 王浩, 苏醒, 李明昊, 许封元, 仲盛. 自动驾驶系统中视觉感知模块的安全测试[J]. 计算机研究与发展, 2022, 59(5): 1133-1147. DOI: 10.7544/issn1000-1239.20211139
    Wu Hao, Wang Hao, Su Xing, Li Minghao, Xu Fengyuan, Zhong Sheng. Security Testing of Visual Perception Module in Autonomous Driving System[J]. Journal of Computer Research and Development, 2022, 59(5): 1133-1147. DOI: 10.7544/issn1000-1239.20211139
    Citation: Wu Hao, Wang Hao, Su Xing, Li Minghao, Xu Fengyuan, Zhong Sheng. Security Testing of Visual Perception Module in Autonomous Driving System[J]. Journal of Computer Research and Development, 2022, 59(5): 1133-1147. DOI: 10.7544/issn1000-1239.20211139

    自动驾驶系统中视觉感知模块的安全测试

    Security Testing of Visual Perception Module in Autonomous Driving System

    • 摘要: 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1 671个造成故障的场景,并且每个故障场景的搜索时间平均为16.86 s.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.

       

      Abstract: In recent years, developments of visual perception techniques based on deep learning have significantly promoted the prosperity of autonomous driving in Internet of vehicles scenarios. However, frequent security issues of autonomous driving systems have raised concerns about the future of autonomous driving. Since the behaviors of deep learning systems lack interpretability, testing the robustness of autonomous driving systems based on deep learning is challenging. The existing efforts on security testing for autonomous driving have limitations in scene description, security defect detection, and defect interpretation. Aiming at testing the security of the visual perception module of autonomous driving, we design and implement a scene-driven security testing system. A flexible scene description method that balances authenticity and richness is proposed. We utilize the real-time rendering engine to generate scenes for autonomous driving security testing. We design an efficient scene search algorithm for nonlinear systems that dynamically schedules search plans based on the testing feedback. We also design a failure analyzer to profile the cause of security issues automatically. We reproduce the latest dynamic automatic driving testing system, which is based on the real-time rendering engine, and test the CILRS system and CIL system with our system and the state-of-the-art system. The experimental results show that our system’s failure discovery rate is 1.4 times that of the state-of-the-art scene-driven dynamic testing system in the same amount of time. Further experiments show that our system can find 1 939 and 1 671 scenes through 3 000 dynamically-searched scenes, respectively, which cause security issues in the CIL and CILRS system’s visual perception module. The searched scenes are in three environments: the fields, the country, and the city, and the average search time for each failure-causing scene is 16.86s. From a statistical perspective, our analyzer finds that objects on both sides of the road, rainy weather and red or yellow objects are more likely to cause the CILRS system to fail.

       

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