  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
