Sim-ATAV
| Autor: | Georgios Fainekos, Hisahiro Ito, James Kapinski, Cumhur Erkan Tuncali |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Artificial neural network Computer science business.industry 020207 software engineering 02 engineering and technology Machine learning computer.software_genre Test (assessment) Adversarial system 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Artificial intelligence business computer Simulation based |
| Zdroj: | HSCC |
| DOI: | 10.1145/3178126.3187004 |
| Popis: | One of the main challenges in testing autonomous driving systems is the presence of machine learning components, such as neural networks, for which formal properties are difficult to establish. We present a simulation-based testing framework that supports methods used to evaluate cyber-physical systems, such as test case generation and automatic falsification. We demonstrate how the framework can be used to evaluate closed-loop properties of autonomous driving system models that include machine learning components. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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