AI-based CCAM: Trustworthy, Explainable, and Accountable

Projektinformationen

AIthena

ID Finanzhilfevereinbarung: 101076754

DOI

10.3030/101076754

Projekt abgeschlossen

EK-Unterschriftsdatum 27 Juli 2022

Startdatum 1 November 2022

Enddatum 31 Oktober 2025

Finanziert unter

• Climate, Energy and Mobility

Gesamtkosten

€ 5 999 547,50

EU-Beitrag

€ 5 999 547,50

Koordiniert durch

FUNDACION CENTRO DE TECNOLOGIAS DE INTERACCION VISUAL Y COMUNICACIONES VICOMTECH
Spain

CORDIS bietet Links zu öffentlichen Ergebnissen und Veröffentlichungen von HORIZONT-Projekten.

Links zu Ergebnissen und Veröffentlichungen von RP7-Projekten sowie Links zu einigen Typen spezifischer Ergebnisse wie Datensätzen und Software werden dynamisch von OpenAIRE abgerufen.

Leistungen

Documents, reports (16)

Report on dissemination and standardisation activities

[T6.2-3] Report on the dissemination and standardisation activities carried out in tasks T6.2 and T6.3.

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 data sharing and integration with European data lakes, OpenData and OpenTool

[T6.4] Report on the plan and actions taken to integrate created data into data sharing initiatives at European level, including novel OpenData and OpenTool concepts.

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.

Lessons learned, policy recommendations

[T6.5] Lessons learned, and derived policy recommendations for the exploitation of AI solutions in CCAM.

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

Report on final use case evaluation

[T5.2-4] This report covers the final demonstrator validation and evaluation of the demonstrators against the specified requirements and specifications. In addition, for each specific AI-driven approach in the use cases a dedicated AI lifecycle assessment is outlined.

Report on physical set-up, digital twin and hybrid testing approaches

[T4.2-4] This is the deliverable about activities T4.2-4, including report on physical set-up, digital twin and hybrid testing approaches.

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.

Report on final AI algorithm development

[T3.2-5] This report covers the final AI algorithm developments including their final feature validation according to the specified requirements and specifications. In addition, all strength and weaknesses of the specific AI-driven approaches are outlined.

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.

Veröffentlichungen

Conference proceedings (15)

A Multimodal Sensor Setup for In Situ Comparison of Driving Dynamics, Physiological Responses and Passenger Comfort in Autonomous Vehicles

Autoren: Harald Devriendt, Mathieu Sarrazin, Thomas D'hondt, Konstantinos Gkentsidis, Karl Janssens
Veröffentlicht in: AHFE International, Intelligent Human Systems Integration (IHSI 2025): Integrating People and Intelligent Systems, Ausgabe 160, 2025
Herausgeber: AHFE International
DOI: 10.54941/AHFE1005852

Identifying the influence of different environmental conditions on driving behavior using behavioral data

Autoren: Guido Linden (geb. Küppers), Lutz Eckstein
Herausgeber: Veröffentlicht auf dem Publikationsserver der RWTH Aachen University
DOI: 10.18154/RWTH-2025-06321

What Did I Learn? Operational Competence Assessment for AI-Based Trajectory Planners

Autoren: Michiel Braat, Maren Buermann, Marijke van Weperen, Jan-Pieter Paardekooper
Herausgeber: arXiv
DOI: 10.48550/ARXIV.2510.00619

Runtime Safety Assurance of Autonomous Vehicles

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

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

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

V2AIX: A Multi-Modal Real-World Dataset of ETSI ITS V2X Messages in Public Road Traffic

Autoren: Guido Kueppers, Jean-Pierre Busch, and Lennart Reiher, Lutz Eckstein
Herausgeber: arXiv
DOI: 10.48550/ARXIV.2403.10221

OCCUQ: Exploring Efficient Uncertainty Quantification for 3D Occupancy Prediction

Autoren: Severin Heidrich, Till Beemelmanns, Alexey Nekrasov, Bastian Leibe, Lutz Eckstein
Herausgeber: arXiv
DOI: 10.48550/ARXIV.2503.10605

AITHENA: towards a trustworthy AI for CCAM development

Autoren: Oihana Otaegui, Marcos Nieto, Sinziana Ioana Rasca, Jos den Ouden, Carles Ubach, Michael Stolz, Justyna Beckmann
Herausgeber: Zenodo
DOI: 10.5281/ZENODO.16085152

Rethinking Backbone Design for Lightweight 3D Object Detection in LiDAR

Autoren: Adwait Chandorkar, Hasan Tercan, Tobias Meisen
Herausgeber: arXiv
DOI: 10.48550/ARXIV.2508.00744

Generative AI for Privacy Protection of Images in Autonomous Vehicles

Autoren: Ruben Naranjo, Nerea Aranjuelo, Marcos Nieto, Oihana Otaegui, and Itsaso Rodriguez-Moreno
Herausgeber: Zenodo
DOI: 10.5281/ZENODO.16087344

Simplifying Traffic Anomaly Detection with Video Foundation Models

Autoren: Svetlana Orlova, Tommie Kerssies, Brunó B. Englert, Gijs Dubbelman
Herausgeber: arXiv
DOI: 10.48550/ARXIV.2507.09338

Bridging trust, safety, efficiency and innovation: AI and explainable AI in road transport

Autoren: Silvia Barbaro, Ted Zotos
Herausgeber: Zenodo
DOI: 10.5281/ZENODO.17723684

An Evaluation of Time-triggered Scheduling in the Linux Kernel

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

MultiCorrupt: A Multi-Modal Robustness Dataset and Benchmark of LiDAR-Camera Fusion for 3D Object Detection

Autoren: Till Beemelmanns, Quan Zhang, Christian Geller, Lutz Eckstein
Herausgeber: arXiv
DOI: 10.48550/ARXIV.2402.11677

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

Autoren: Beemelmanns, Till; Zahr, Wassim; Eckstein, Lutz
Veröffentlicht in: Machine Learning for Autonomous Driving Workshop 2023 (NeurIPS), 2023, ISSN 2331-8422
Herausgeber: ML4AD/arXiv
DOI: 10.48550/arxiv.2312.14606

Peer reviewed articles (8)

Trustworthiness Assurance Assessment for High-Risk AI-Based Systems

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

Toward Explainability in Urban Motion Prediction—Survey and Outlook

Autoren: Ilma Okanovic, Michael Stolz, Bernhard Hillbrand
Veröffentlicht in: SAE International Journal of Connected and Automated Vehicles, Ausgabe 08, 2025, ISSN 2574-0741
Herausgeber: SAE International
DOI: 10.4271/12-08-01-0009

Meta-YOLOv8: Meta-Learning-Enhanced YOLOv8 for Precise Traffic Light Color Detection in ADAS

Autoren: Vasu Tammisetti, Georg Stettinger, Manuel Pegalajar Cuellar, Miguel Molina-Solana
Veröffentlicht in: Electronics, Ausgabe 14, 2025, ISSN 2079-9292
Herausgeber: MDPI AG
DOI: 10.3390/ELECTRONICS14030468

LaANIL: ANIL with Look-Ahead Meta-Optimization and Data Parallelism

Autoren: Vasu Tammisetti, Kay Bierzynski, Georg Stettinger, Diego P. Morales-Santos, Manuel Pegalajar Cuellar, Miguel Molina-Solana
Veröffentlicht in: Electronics, Ausgabe 13, 2025, ISSN 2079-9292
Herausgeber: MDPI AG
DOI: 10.3390/ELECTRONICS13081585

Exploring the potential of standardized behaviour competencies in automated driving systems

Autoren: Georg Stettinger, Patrick Weissensteiner, Nayel Fabian Salem, Marcus Nolte, Siddartha Khastgir
Veröffentlicht in: IFAC Journal of Systems and Control, Ausgabe 33, 2025, ISSN 2468-6018
Herausgeber: Elsevier BV
DOI: 10.1016/J.IFACSC.2025.100320

A Methodology to Enhance Transparency for Trustworthy Artificial Intelligence for Cooperative, Connected, and Automated Mobility

Autoren: Paola Natalia Cañas, Marcos Nieto, Oihana Otaegui, Igor Rodriguez
Veröffentlicht in: SAE International Journal of Connected and Automated Vehicles, Ausgabe 08, 2025, ISSN 2574-0741
Herausgeber: SAE International
DOI: 10.4271/12-08-01-0010

WebLabel: OpenLABEL-compliant multi-sensor labelling

Autoren: Itziar Urbieta, Andoni Mujika, Gonzalo Piérola, Eider Irigoyen, Marcos Nieto, Estibaliz Loyo, Naiara Aginako
Veröffentlicht in: Multimedia Tools and Applications, Ausgabe Volume 83, 2023, ISSN 2213-7793
Herausgeber: Springer
DOI: 10.1007/s11042-023-16664-4

Explainable Safety Argumentation for the Deployment of Automated Vehicles

Autoren: Patrick Weissensteiner, Georg Stettinger
Veröffentlicht in: Electronics, Ausgabe 13, 2025, ISSN 2079-9292
Herausgeber: MDPI AG
DOI: 10.3390/ELECTRONICS13234606

Other (1)

Supporting automated driving systems development with synthetic data

Autoren: Alexandru Forrai and Hamid Abdolhay
Herausgeber: Zenodo
DOI: 10.5281/ZENODO.16993081