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

Saurabh Jha, Subho Banerjee, Timothy Tsai, Siva K.S. Hari, Michael B. Sullivan, Zbigniew T. Kalbarczyk, Stephen W. Keckler, Ravishankar K. Iyer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings - 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages112-124
Number of pages13
ISBN (Electronic)9781728100562
DOIs
StatePublished - Jun 2019
Event49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2019 - Portland, United States
Duration: Jun 24 2019Jun 27 2019

Publication series

NameProceedings - 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2019

Conference

Conference49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2019
CountryUnited States
CityPortland
Period6/24/196/27/19

Keywords

  • Autonomous Vehicles
  • Fault Injection
  • Machine Learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Safety, Risk, Reliability and Quality
  • Hardware and Architecture

Fingerprint Dive into the research topics of 'ML-Based Fault Injection for Autonomous Vehicles: A Case for Bayesian Fault Injection'. Together they form a unique fingerprint.

  • Cite this

    Jha, S., Banerjee, S., Tsai, T., Hari, S. K. S., Sullivan, M. B., Kalbarczyk, Z. T., Keckler, S. W., & Iyer, R. K. (2019). ML-Based Fault Injection for Autonomous Vehicles: A Case for Bayesian Fault Injection. In Proceedings - 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2019 (pp. 112-124). [8809495] (Proceedings - 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DSN.2019.00025