1. ZHAW digitalcollection
  2. School of Engineering
  3. Publikationen
  4. Publikationen School of Engineering
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dc.contributor.authorFrischknecht-Gruber, Carmen-
dc.contributor.authorHeuberger, Benjamin-
dc.contributor.authorReif, Monika Ulrike-
dc.contributor.authorWeng, Joanna-
dc.contributor.authorSenn, Christoph-
dc.date.accessioned2024-01-04T12:44:08Z-
dc.date.available2024-01-04T12:44:08Z-
dc.date.issued2023-
dc.identifier.isbn978-981-18-8071-1de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/29451-
dc.description.abstractThe future of mobility is set to be reformed as the rapidly increasing use of driver assistance systems and highly automated vehicles (HAVs) show their great potential. The use of deep neural networks in autonomous driving systems has led to significant progress in this area. However, the increase in accidents involving HAVs highlights the need for effective testing and validation methods to increase the overall safety of these vehicles. With many technology companies and manufacturers aiming to put Level 4 and 5 vehicles into operation soon, the safety of HAVs remains a major concern. Rigorous testing and validation against potential failures and misbehaviour are required to ensure the reliability and robustness of these systems. This paper provides an overview of state of the art in testing and evaluation methods for machine learning-based HAVs. A literature review on these topics is provided to give valuable insights to researchers, practitioners and policymakers. As such, the review describes different types of validation, verification and testing methods, including real-world testing, simulation testing, hardware-in-the loop testing, adversarial robustness, and methods used for explainability and interpretability in AI. The advantages and limitations are discussed and current challenges are highlighted. Finally, open research questions and future directions in the field are identified.de_CH
dc.language.isoende_CH
dc.publisherResearch Publishingde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectSafe AIde_CH
dc.subjectTrustworthy AIde_CH
dc.subjectSafety critical systemsde_CH
dc.subjectAutonomous drivingde_CH
dc.subject.ddc629: Luftfahrt- und Fahrzeugtechnikde_CH
dc.titleEnsuring safety in highly automated vehicles : a review of testing and validation methods for robustness and reliabilityde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Angewandte Mathematik und Physik (IAMP)de_CH
zhaw.publisher.placeSingaporede_CH
dc.identifier.doi10.3850/978-981-18-8071-1_P414-cdde_CH
zhaw.conference.details33rd European Safety and Reliability Conference (ESREL), Southampton, United Kingdom, 3-7 September 2023de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end264de_CH
zhaw.pages.start257de_CH
zhaw.parentwork.editorBrito, Mário P.-
zhaw.parentwork.editorAven, Terje-
zhaw.parentwork.editorBaraldi, Piero-
zhaw.parentwork.editorČepin, Marko-
zhaw.parentwork.editorZio, Enrico-
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsProceeding of the 33rd European Safety and Reliability Conferencede_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Frischknecht-Gruber, C., Heuberger, B., Reif, M. U., Weng, J., & Senn, C. (2023). Ensuring safety in highly automated vehicles : a review of testing and validation methods for robustness and reliability [Conference paper]. In M. P. Brito, T. Aven, P. Baraldi, M. Čepin, & E. Zio (Eds.), Proceeding of the 33rd European Safety and Reliability Conference (pp. 257–264). Research Publishing. https://doi.org/10.3850/978-981-18-8071-1_P414-cd
Frischknecht-Gruber, C. et al. (2023) ‘Ensuring safety in highly automated vehicles : a review of testing and validation methods for robustness and reliability’, in M.P. Brito et al. (eds) Proceeding of the 33rd European Safety and Reliability Conference. Singapore: Research Publishing, pp. 257–264. Available at: https://doi.org/10.3850/978-981-18-8071-1_P414-cd.
C. Frischknecht-Gruber, B. Heuberger, M. U. Reif, J. Weng, and C. Senn, “Ensuring safety in highly automated vehicles : a review of testing and validation methods for robustness and reliability,” in Proceeding of the 33rd European Safety and Reliability Conference, 2023, pp. 257–264. doi: 10.3850/978-981-18-8071-1_P414-cd.
FRISCHKNECHT-GRUBER, Carmen, Benjamin HEUBERGER, Monika Ulrike REIF, Joanna WENG und Christoph SENN, 2023. Ensuring safety in highly automated vehicles : a review of testing and validation methods for robustness and reliability. In: Mário P. BRITO, Terje AVEN, Piero BARALDI, Marko ČEPIN und Enrico ZIO (Hrsg.), Proceeding of the 33rd European Safety and Reliability Conference. Conference paper. Singapore: Research Publishing. 2023. S. 257–264. ISBN 978-981-18-8071-1
Frischknecht-Gruber, Carmen, Benjamin Heuberger, Monika Ulrike Reif, Joanna Weng, and Christoph Senn. 2023. “Ensuring Safety in Highly Automated Vehicles : A Review of Testing and Validation Methods for Robustness and Reliability.” Conference paper. In Proceeding of the 33rd European Safety and Reliability Conference, edited by Mário P. Brito, Terje Aven, Piero Baraldi, Marko Čepin, and Enrico Zio, 257–64. Singapore: Research Publishing. https://doi.org/10.3850/978-981-18-8071-1_P414-cd.
Frischknecht-Gruber, Carmen, et al. “Ensuring Safety in Highly Automated Vehicles : A Review of Testing and Validation Methods for Robustness and Reliability.” Proceeding of the 33rd European Safety and Reliability Conference, edited by Mário P. Brito et al., Research Publishing, 2023, pp. 257–64, https://doi.org/10.3850/978-981-18-8071-1_P414-cd.


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