ML-based Fault Injection for Autonomous Vehicles: A Case for Bayesian Fault Injection

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.


Saurabh Jha (University of Illinois at Urbana-Champaign)
Subho Banerjee (University of Illinois at Urbana-Champaign)
Timothy Tsai (NVIDIA)
Zbigniew T. Kalbarczyk (University of Illinois at Urbana-Champaign)
Ravishankar K. Iyer (University of Illinois at Urbana-Champaign)

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