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Verifying an Aircraft Collision Avoidance Neural Network with Marabou

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NASA Formal Methods (NFM 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13903))

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Abstract

In this case study, we have explored the use of a neural network model checker to analyze the safety characteristics of a neural network trained using reinforcement learning to compute collision avoidance flight plans for aircraft. We analyzed specific aircraft encounter geometries (e.g., head-on, overtake) and also examined robustness of the neural network. We verified the minimum horizontal separation property by identifying conditions where the neural network can potentially cause a transition from a safe state to an unsafe state. We show how the property verification problem is mathematically transformed and encoded as linear-constraints that can be analyzed by the Marabou model checker.

The authors wish to thank Aleksandar Zeljic for his help using Marabou. This work was funded by DARPA contract FA8750-18-C-0099. The views, opinions and/or findings expressed are those of the author and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government. Approved for Public Release, Distribution Unlimited.

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References

  1. Cofer, D.: Unintended behavior in learning-enabled systems: detecting the unknown unknowns. In: 2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC), pp. 1–7 (2021)

    Google Scholar 

  2. Cofer, D., et al.: Flight test of a collision avoidance neural network with run-time assurance. In: 2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC), pp. 1–10 (2022)

    Google Scholar 

  3. Damour, M., et al.: Towards certification of a reduced footprint ACAS-Xu system: a hybrid ml-based solution. In: Habli, I., Sujan, M., Bitsch, F. (eds.) Computer Safety, Reliability, and Security, pp. 34–48. Springer International Publishing, Cham (2021)

    Chapter  Google Scholar 

  4. Huang, C., Fan, J., Chen, X., Li, W., Zhu, Q.: Polar: a polynomial arithmetic framework for verifying neural-network controlled systems. In: Bouajjani, A., Holík, L., Wu, Z. (eds.) Automated Technology for Verification and Analysis, pp. 414–430. Springer International Publishing, Cham (2022)

    Chapter  Google Scholar 

  5. Irfan, A., et al.: Towards verification of neural networks for small unmanned aircraft collision avoidance. In: 2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC), pp. 1–10. IEEE (2020)

    Google Scholar 

  6. Katz, G., Barrett, C., Dill, D.L., Julian, K., Kochenderfer, M.J.: Reluplex: an efficient SMT solver for verifying deep neural networks. In: Majumdar, R., Kunčak, V. (eds.) Computer Aided Verification, pp. 97–117. Springer International Publishing, Cham (2017)

    Chapter  Google Scholar 

  7. Katz, G., et al.: The marabou framework for verification and analysis of deep neural networks. In: International Conference on Computer Aided Verification, pp. 443–452 (2019)

    Google Scholar 

  8. Manzanas Lopez, D., Johnson, T.T., Bak, S., Tran, H.D., Hobbs, K.L.: Evaluation of neural network verification methods for air-to-air collision avoidance. J. Air Transp. 31(1), 1–17 (2023)

    Article  Google Scholar 

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Correspondence to Cong Liu .

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Liu, C., Cofer, D., Osipychev, D. (2023). Verifying an Aircraft Collision Avoidance Neural Network with Marabou. In: Rozier, K.Y., Chaudhuri, S. (eds) NASA Formal Methods. NFM 2023. Lecture Notes in Computer Science, vol 13903. Springer, Cham. https://doi.org/10.1007/978-3-031-33170-1_5

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  • DOI: https://doi.org/10.1007/978-3-031-33170-1_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-33169-5

  • Online ISBN: 978-3-031-33170-1

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