TY - GEN
T1 - ML-Based Fault Injection for Autonomous Vehicles
T2 - 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2019
AU - Jha, Saurabh
AU - Banerjee, Subho
AU - Tsai, Timothy
AU - Hari, Siva K.S.
AU - Sullivan, Michael B.
AU - Kalbarczyk, Zbigniew T.
AU - Keckler, Stephen W.
AU - Iyer, Ravishankar K.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - 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.
AB - 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.
KW - Autonomous Vehicles
KW - Fault Injection
KW - Machine Learning
UR - http://www.scopus.com/inward/record.url?scp=85072099788&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85072099788&partnerID=8YFLogxK
U2 - 10.1109/DSN.2019.00025
DO - 10.1109/DSN.2019.00025
M3 - Conference contribution
AN - SCOPUS:85072099788
T3 - Proceedings - 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2019
SP - 112
EP - 124
BT - Proceedings - 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 24 June 2019 through 27 June 2019
ER -