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
@article{arxiv.1907.01051,
title = {ML-based Fault Injection for Autonomous Vehicles: A Case for Bayesian Fault Injection},
author = {Saurabh Jha and Subho S. Banerjee and Timothy Tsai and Siva K. S. Hari and Michael B. Sullivan and Zbigniew T. Kalbarczyk and Stephen W. Keckler and Ravishankar K. Iyer},
journal= {arXiv preprint arXiv:1907.01051},
year = {2019}
}
Comments
Accepted at 2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks