AI-based CCAM: Trustworthy, Explainable, and Accountable

Informations projet

AIthena

N° de convention de subvention: 101076754

DOI

10.3030/101076754

Projet clôturé

Date de signature de la CE 27 Juillet 2022

Date de début 1 Novembre 2022

Date de fin 31 Octobre 2025

Financé au titre de

• Climate, Energy and Mobility

Coût total

€ 5 999 547,50

Contribution de l’UE

€ 5 999 547,50

Coordonné par

FUNDACION CENTRO DE TECNOLOGIAS DE INTERACCION VISUAL Y COMUNICACIONES VICOMTECH
Spain

CORDIS fournit des liens vers les livrables publics et les publications des projets HORIZON.

Les liens vers les livrables et les publications des projets du 7e PC, ainsi que les liens vers certains types de résultats spécifiques tels que les jeux de données et les logiciels, sont récupérés dynamiquement sur OpenAIRE .

Livrables

Documents, reports (10)

Methodology for explainable, trustworthy and human centric AI based system and function development

[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 plan

T613 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 plan

Update of the communication, dissemination and standardisation plan

ML DevOps-oriented data life-cycle governance and provenance framework

[T2.2] This deliverable will report RTD activities related to ML DevOps tools for data governance and provenance.

Publications

Conference proceedings (4)

Runtime Safety Assurance of Autonomous Vehicles

Auteurs: A. Forrai (Siemens Industry Software Netherlands B.V.), V. Neelgundmath, K.K. Unni, I. Barosan (Eindhoven University of Technology)
Publié dans: Proceedings: 2023 7th International Conference on System Reliability and Safety (ICSRS), 2023, ISSN 1272-4017
Éditeur: Zenodo
DOI: 10.5281/zenodo.12724017

Digital twin for synthetic data generation – application for automated driving systems

Auteurs: Hassan Hotait (HAN – University of Applied Sciences), Alexandru Forrai (Siemens Industry Software Netherlands B.V.)
Publié dans: Product solutions paper: 22nd Driving Simulation & Virtual Reality Conference, 2023, ISSN 1272-3883
Éditeur: Zenodo
DOI: 10.5281/zenodo.12723882

An Evaluation of Time-triggered Scheduling in the Linux Kernel

Auteurs: Paraskevas Karachatzis, Jan Ruh, Silviu S. Craciunas (TTTech Computertechnik AG, Vienna, Austria)
Publié dans: RTNS '23: Proceedings of the 31st International Conference on Real-Time Networks and Systems, 2023, ISBN 9781450399838
Éditeur: ACM
DOI: 10.1145/3575757.3593660

Explainable Multi-Camera 3D Object Detection with Transformer-Based Saliency Maps

Auteurs: Beemelmanns, Till; Zahr, Wassim; Eckstein, Lutz
Publié dans: Machine Learning for Autonomous Driving Workshop 2023 (NeurIPS), 2023, ISSN 2331-8422
Éditeur: ML4AD/arXiv
DOI: 10.48550/arxiv.2312.14606

Peer reviewed articles (2)

Trustworthiness Assurance Assessment for High-Risk AI-Based Systems

Auteurs: Georg Stettinger (Infineon Technologies AG); Patrick Weissensteiner (Virtual Vehicle Research GmbH); Siddartha Khastgir (International Manufacturing Centre, The University of Warwick)
Publié dans: IEEE Access, Numéro Volume: 12, 2024, ISSN 2169-3536
Éditeur: IEEE
DOI: 10.1109/ACCESS.2024.3364387

WebLabel: OpenLABEL-compliant multi-sensor labelling

Auteurs: Itziar Urbieta, Andoni Mujika, Gonzalo Piérola, Eider Irigoyen, Marcos Nieto, Estibaliz Loyo, Naiara Aginako
Publié dans: Multimedia Tools and Applications, Numéro Volume 83, 2023, ISSN 2213-7793
Éditeur: Springer
DOI: 10.1007/s11042-023-16664-4