The safety and resilience of fully autonomous vehicles (AVs) are of signiﬁcant concern, as exempliﬁed by several headline-making accidents. While AV development today involves veriﬁcation, validation, and testing, end-to-end assessment of AV systems under accidental faults in realistic driving scenarios has been largely unexplored. This paper presents DriveFI, a machine learning-based fault injection engine, which can mine situations and faults that maximally impact AV safety, as demonstrated on two industry-grade AV technology stacks (from NVIDIA and Baidu). For example, DriveFI found 561 safety-critical faults in less than 4 hours. In comparison, random injection experiments executed over several weeks could not ﬁnd any safety-critical faults.
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