| Autor: |
Arthur Clavière, Laura Altieri Sambartolomé, Eric Asselin, Christophe Garion, Claire Pagetti |
| Přispěvatelé: |
Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE), Office National d'Etudes et Recherches Aérospatiales - ONERA (FRANCE), Collins Aerospace (FRANCE), Département d'Ingénierie des Systèmes Complexes - DISC (Toulouse, France) |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Popis: |
In this paper, we conduct a comparison of the existing formal methods for verifying the safety of cyber-physical systems with machine learning based controllers. We focus on a particular form of machine learning based controller, namely a classifier based on multiple neural networks, the architecture of which is particularly interesting for embedded applications. We compare both exact and approximate verification techniques, based on several real-world benchmarks such as a collision avoidance system for unmanned aerial vehicles. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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