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DryVR 2.0: A tool for verification and controller synthesis of black-box cyber-physical systems

Published: 11 April 2018 Publication History

Abstract

We present a demo of DryVR 2.0, a framework for verification and controller synthesis of cyber-physical systems composed of black-box simulators and white-box automata. For verification, DryVR 2.0 takes as input a black-box simulator, a white-box transition graph, a time bound and a safety specification. As output it generates over-approximations of the reachable states and returns "Safe" if the system meets the given bounded safety specification, or it returns "Unsafe" with a counter-example. For controller synthesis, DryVR 2.0 takes as input black-box simulator(s) and a reach-avoid specification, and uses RRTs to find a transition graph such that the combined system satisfies the given specification.

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Parasara Sridhar Duggirala, Sayan Mitra, Mahesh Viswanathan, and Matthew Potok. 2015. C2E2: A Verification Tool for Stateflow Models. In tacas. 68--82.
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Chuchu Fan, Bolun Qi, Sayan Mitra, and Mahesh Viswanathan. 2017. DryVR: Data-Driven Verification and Compositional Reasoning for Automotive Systems. In CAV, Part I. 441--461.
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Cited By

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  • (2024)BEACON: A Bayesian Evolutionary Approach for Counterexample Generation of Control SystemsIEEE Access10.1109/ACCESS.2024.343651512(106455-106465)Online publication date: 2024
  • (2024)Formal Methods for Safe AutonomyundefinedOnline publication date: 11-Oct-2024
  • (2023)Data-Driven Reachability and Support Estimation With Christoffel FunctionsIEEE Transactions on Automatic Control10.1109/TAC.2023.328174968:9(5216-5229)Online publication date: Sep-2023
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cover image ACM Conferences
HSCC '18: Proceedings of the 21st International Conference on Hybrid Systems: Computation and Control (part of CPS Week)
April 2018
296 pages
ISBN:9781450356428
DOI:10.1145/3178126
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 11 April 2018

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View all
  • (2024)BEACON: A Bayesian Evolutionary Approach for Counterexample Generation of Control SystemsIEEE Access10.1109/ACCESS.2024.343651512(106455-106465)Online publication date: 2024
  • (2024)Formal Methods for Safe AutonomyundefinedOnline publication date: 11-Oct-2024
  • (2023)Data-Driven Reachability and Support Estimation With Christoffel FunctionsIEEE Transactions on Automatic Control10.1109/TAC.2023.328174968:9(5216-5229)Online publication date: Sep-2023
  • (2023)Data-Driven Estimation of Forward Reachable SetsComputation-Aware Algorithmic Design for Cyber-Physical Systems10.1007/978-3-031-43448-8_8(165-185)Online publication date: 25-Aug-2023
  • (2023)Exploring the role of simulator fidelity in the safety validation of learning‐enabled autonomous systemsAI Magazine10.1002/aaai.12141Online publication date: 27-Nov-2023
  • (2021)Data-Driven Reachability Analysis with Christoffel Functions2021 60th IEEE Conference on Decision and Control (CDC)10.1109/CDC45484.2021.9682860(5067-5072)Online publication date: 14-Dec-2021
  • (2021)Robust hybrid supervisory control for spacecraft close proximity missionsAnnual Reviews in Control10.1016/j.arcontrol.2021.11.00152(316-329)Online publication date: 2021

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