Active Machine Learning to Test Autonomous Driving 2021 (Engelska)Ingår i: 2021 IEEE International Conference On Software Testing, Verification And Validation Workshops (Icstw 2021), Institute of Electrical and Electronics Engineers (IEEE) , 2021, s. 286-286Konferensbidrag, Publicerat paper (Refereegranskat)

Abstract [en]

Autonomous driving represents a significant challenge to all software quality assurance techniques, including testing. Generative machine learning (ML) techniques including active ML have considerable potential to generate high quality synthetic test data that can complement and improve on existing techniques such as hardware-in-the-loop and road testing.

Ort, förlag, år, upplaga, sidor

Institute of Electrical and Electronics Engineers (IEEE) , 2021. s. 286-286

Serie

IEEE International Conference on Software Testing Verification and Validation Workshops, ISSN 2159-4848

Nyckelord [en]

autonomous driving, machine learning, synthetic data, system testing

Nationell ämneskategori

Datavetenskap (datalogi)

Identifikatorer

URN: urn:nbn:se:kth:diva-299977DOI: 10.1109/ICSTW52544.2021.00055ISI: 000680833800042Scopus ID: 2-s2.0-85108028705OAI: oai:DiVA.org:kth-299977DiVA, id: diva2:1586639

Konferens

14th IEEE Conference on Software Testing, Verification and Validation (ICST), APR 12-16, 2021, ELECTR NETWORK

Anmärkning

Part of proceedings: ISBN 978-1-6654-4456-9, QC 20230117

Tillgänglig från: 2021-08-20 Skapad: 2021-08-20 Senast uppdaterad: 2023-01-17Bibliografiskt granskad

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Förlagets fulltextScopus

Person

Meinke, Karl

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Av författaren/redaktören

Meinke, Karl

Av organisationen

Teoretisk datalogi, TCS

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Datavetenskap (datalogi)

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