kth.sePublications KTH
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Active Machine Learning to Test Autonomous Driving
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Theoretical Computer Science, TCS.ORCID iD: 0000-0002-9706-5008
2021 (English)In: 2021 IEEE International Conference On Software Testing, Verification And Validation Workshops (Icstw 2021), Institute of Electrical and Electronics Engineers (IEEE) , 2021, p. 286-286Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2021. p. 286-286
Series
IEEE International Conference on Software Testing Verification and Validation Workshops, ISSN 2159-4848
Keywords [en]
autonomous driving, machine learning, synthetic data, system testing
National Category
Computer Sciences
Identifiers
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
Conference
14th IEEE Conference on Software Testing, Verification and Validation (ICST), APR 12-16, 2021, ELECTR NETWORK
Note

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

Available from: 2021-08-20 Created: 2021-08-20 Last updated: 2023-01-17Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Meinke, Karl

Search in DiVA

By author/editor
Meinke, Karl
By organisation
Theoretical Computer Science, TCS
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 185 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf