AVFI: Fault Injection for Autonomous Vehicles

Autonomous vehicle (AV) technology is rapidly becoming a reality on U.S. roads, offering the promise of improvements in traffic management, safety, and the comfort and efficiency of vehicular travel. With this increasing popularity and ubiquitous deployment, resilience has become a critical requirement for public acceptance and adoption. Recent studies into the resilience of AVs have shown that though the AV systems are improving over time, they have not reached human levels of automation. Prior work in this area has studied the safety and resilience of individual components of the AV system (e.g., testing of neural networks powering the perception function). However, methods for holistic end-to-end resilience assessment of AV systems are still non-existent.Comment: Published in: 2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W

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 faultsComment: Accepted at 2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Network

Decomposing Genomics Algorithms: Core Computations for Accelerating Genomics

Technological advances in genomic analyses and computing sciences has led to a burst in genomics data. With those advances, there has also been parallel growth in dedicated accelerators for specific genomic analyses. However, biologists are in need of a reconfigurable machine that can allow them to perform multiple analyses without needing to go for dedicated compute platforms for each analysis. This work addresses the first steps in the design of such a reconfigurable machine. We hypothesize that this machine design can consist of some accelerators of computations common across various genomic analyses. This work studies a subset of genomic analyses and identifies such core computations. We further investigate the possibility of further accelerating through a deeper analysis of the computation primitives.National Science Foundation (NSF CNS 13-37732); Infosys; IBM Faculty Award; Office of the Vice Chancellor for Research, University of Illinois at Urbana-ChampaignOpe

Analyzing Security Vulnerabilities and Attacks

Coordinated Science Laboratory was formerly known as Control Systems Laborator