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Digital
Library of the European Council for Modelling and Simulation |
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Title: |
Formal Verification of Neural Networks: a Case Study about Adaptive Cruise Control |
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Authors: |
Stefano Demarchi, Dario Guidotti, Andrea Pitto, Armando Tacchella |
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Published in:
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(2022). ECMS 2022,
36th Proceedings DOI: http://doi.org/10.7148/2022 ISSN:
2522-2422 (ONLINE) ISSN:
2522-2414 (PRINT) ISSN:
2522-2430 (CD-ROM) ISBN: 978-3-937436-77-7 Communications of the ECMS , Volume 36, Issue 1, June 2022, Ålesund, Norway May 30th - June 3rd, 2022 |
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Citation
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Stefano Demarchi, Dario Guidotti, Andrea Pitto, Armando Tacchella (2022). Formal Verification of Neural Networks: a Case Study about Adaptive Cruise Control, ECMS 2022 Proceedings Edited By: Ibrahim A. Hameed, Agus Hasan, Saleh Abdel-Afou Alaliyat, European Council for Modeling and Simulation. doi:10.7148/2022-0310 |
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DOI: |
https://doi.org/10.7148/2022-0310 |
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Abstract: |
Formal verification of neural networks is a promising technique to improve their dependability for safety critical applications. Autonomous driving is one such application where the controllers supervising different functions in a car should undergo a rigorous certification process. In this paper we present an example about learning and verification of an adaptive cruise control function on an autonomous car. We detail the learning process as well as the attempts to verify various safety properties using the tool NeVer2 a new framework that integrates learning and verification in a single easy-to-use package intended for practictioners rather than experts in formal methods and/or machine learning. |
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