CAREER: Enabling Trustworthy Upgrades of Machine-Learning Intensive Cyber-Physical Systems

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This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Cyber-Physical Systems (CPS) sustainably benefit from software upgrades throughout their life cycles. However, as CPS become machine-learning-intensive due to rapidly increasing interactions between CPS and machine learning technologies, two major distinguishing factors associated with machine learning techniques raise significant safety concerns about CPS upgrades which play a critical role in enabling lifetime safety assurance. First, upgrades of machine learning components, which inherently result in system changes, come at significant safety risk for safety-critical CPS due to the vulnerabilities of machine learning techniques. Second, the traditional safe-by-verification upgrade framework, in which upgrades and verification have to be two separate procedures, is no longer valid for machine learning processes that update instantaneously during system operations. This project targets these unique challenges by developing scalable verification and monitoring methods for upgrades as well as safe upgrade procedures to enable trustworthy upgrades and achieve lifetime safety assurance in machine-learning-intensive CPS.This project will advance the state-of-the-art in the research of safety in machine-learning-intensive CPS from local time windows to global life cycles. With the expected research results, machine learning components in CPS can upgrade with desired safety assurance for lifetime safety purposes. In particular, this project will develop a novel scalable incremental verification framework as well as self-adaptive runtime monitoring methods for upgrades of machine-learning-intensive CPS. The proposed approach will also design safety-assured upgrade procedures by developing novel upgrade renewal procedures, safety-aware upgrades, and safety backup co-design methods. The project will develop an indoor vision-based autonomous vehicle testbed with upgradable neural networks for a variety of upgrade scenarios to perform rigorous evaluations. The integration of research and education plans will address CPS workforce shortage gaps, develop CPS curriculum, and design hands-on training for students. Activities such as engagement in K-12 STEM camps and collaboration with government and industry partners are also designed to inspire students early and promote public understanding of CPS, which aims to build a healthy and sustainable CPS workforce supply. The designed activities are uniquely positioned to attract members of underrepresented groups with a focus to enhance the diversity of the federal, state, and local CPS workforce.This proposal was funded under the NSF CPS CAREER program.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.