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Risultati finali
[T1.1-3] This deliverable will materialize the AITHENA methodology into a reference report to include definition of KPI related to trusted AI features (see GO-8).
Report on initial AI algorithm development[T3.2-5] This report covers the initial AI algorithm developments including their initial feature validation according to the specified requirements and specifications.
Report on initial use case evaluation[T5.2-4] This report covers the initial demonstrator validation and evaluation of the demonstrators against the specified requirements and specifications.
Privacy-preserving methods[T2.3] This deliverable will report the designed privacy-preserving methods for application of GDPR-compliant ML.
Life-cycle management framework for ML models[T3.1] This report covers the designed AI-framework used for the development and life-cycle assessment of individual ML algorithms.
User group needs report and technical use case definition[T1.4] This deliverable reports the action taken to identify user groups and gather from them requirements to detail the AITHENA use cases
Initial communication, dissemination and standardisation planT613 Initial strategy for AITHENA based on an initial market and stakeholder analysis
Testing and evaluation methodology for AI-driven CCAM systems[T5.1] This report outlines a joint testing and evaluation methodology for AI-driven CCAM systems to be applied in the corresponding use cases of task 5.2 to task 5.4.
Updated communication, dissemination and standardisation planUpdate of the communication, dissemination and standardisation plan
Pubblicazioni
Autori:
A. Forrai (Siemens Industry Software Netherlands B.V.), V. Neelgundmath, K.K. Unni, I. Barosan (Eindhoven University of Technology)
Pubblicato in:
Proceedings: 2023 7th International Conference on System Reliability and Safety (ICSRS), 2023, ISSN 1272-4017
Editore:
Zenodo
DOI:
10.5281/zenodo.12724017
Autori:
Hassan Hotait (HAN – University of Applied Sciences), Alexandru Forrai (Siemens Industry Software Netherlands B.V.)
Pubblicato in:
Product solutions paper: 22nd Driving Simulation & Virtual Reality Conference, 2023, ISSN 1272-3883
Editore:
Zenodo
DOI:
10.5281/zenodo.12723882
Autori:
Paraskevas Karachatzis, Jan Ruh, Silviu S. Craciunas (TTTech Computertechnik AG, Vienna, Austria)
Pubblicato in:
RTNS '23: Proceedings of the 31st International Conference on Real-Time Networks and Systems, 2023, ISBN 9781450399838
Editore:
ACM
DOI:
10.1145/3575757.3593660
Autori:
Beemelmanns, Till; Zahr, Wassim; Eckstein, Lutz
Pubblicato in:
Machine Learning for Autonomous Driving Workshop 2023 (NeurIPS), 2023, ISSN 2331-8422
Editore:
ML4AD/arXiv
DOI:
10.48550/arxiv.2312.14606
Autori:
Georg Stettinger (Infineon Technologies AG); Patrick Weissensteiner (Virtual Vehicle Research GmbH); Siddartha Khastgir (International Manufacturing Centre, The University of Warwick)
Pubblicato in:
IEEE Access, Numero Volume: 12, 2024, ISSN 2169-3536
Editore:
IEEE
DOI:
10.1109/ACCESS.2024.3364387
Autori:
Itziar Urbieta, Andoni Mujika, Gonzalo Piérola, Eider Irigoyen, Marcos Nieto, Estibaliz Loyo, Naiara Aginako
Pubblicato in:
Multimedia Tools and Applications, Numero Volume 83, 2023, ISSN 2213-7793
Editore:
Springer
DOI:
10.1007/s11042-023-16664-4
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