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CORDIS

Trustworthy AI for CCAM

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Deliverables

AI4CCAM Validation Handbook (opens in new window)

Use case implementation guidelines, KPI definition and validation methodologies. This deliverable is produced as part of T4.1.

Plan for dissemination and exploitation including communication activities (CDE) (opens in new window)

Initial plan describing communication and dissemination activities. This deliverable is produced as part of T5.1

Plan for dissemination and exploitation including communication activities (CDE) - Initial Report and Updated Plan (opens in new window)

Initial report on communication and dissemination activities and updated plan. This deliverable is produced as part of T5.1

AI4CCAM Trustworthy AI Documentation Framework - Initial Version (opens in new window)

Guidelines for fairness and diversity, for improved transparency-led user agency, communication, and acceptance; Governance techniques for accountability in AI-based CCAM operations with ethical dilemma identification. This deliverable is produced as part of T3.2.

Methodology for Trustworthy AI in CCAM (opens in new window)

Methodology including scenario description (9) with functional and Trustworthy AI requirements considering AI standards. This deliverable is produced as part of T1.1, T1.2 and T1.3

Participatory AI4CCAM Space - Initial Version (opens in new window)

Initial version of the participatory space with Trustworthy AI in CCAM, CAV User Acceptance topics and Q/A. This deliverable is produced as part of T3.1

Publications

Towards Trustworthy Automated Driving through Qualitative Scene Understanding and Explanations (opens in new window)

Author(s): Nassim Belmecheri, Arnaud Gotlieb, Nadjib Lazaar and Helge Spieker
Published in: SAE International Journal of Connected and Automated Vehicles, 2024, ISSN 2574-0741
Publisher: SAE International
DOI: 10.48550/arXiv.2403.16908

Query-driven Qualitative Constraint Acquisition (opens in new window)

Author(s): Mohamed-Bachir Belaid, Nassim Belmecheri, Arnaud Gotlieb, Nadjib Lazaar, Helge Spieker
Published in: Journal of Artificial Intelligence Research, Issue 79, 2024, ISSN 1076-9757
Publisher: AAAI Press
DOI: 10.1613/jair.1.14752

Boosting the learning for ranking patterns (opens in new window)

Author(s): Nassim Belmecheri, Noureddine Aribi, Nadjib Lazaar, Yahia Lebbah, Samir Loudni
Published in: algorithms, 2023, ISSN 1999-4893
Publisher: MDPI Open Access Publishing
DOI: 10.48550/arXiv.2203.02696

Acquiring Qualitative Explainable Graphs for Automated Driving Scene Interpretation (opens in new window)

Author(s): Belmecheri, Nassim; Gotlieb, Arnaud; Lazaar, Nadjib; Spieker, Helge
Published in: arXiv, 2023
Publisher: Arxiv
DOI: 10.48550/arxiv.2308.12755

Trustworthy Automated Driving through Qualitative Scene Understanding and Explanations (opens in new window)

Author(s): Nassim Belmecheri, Arnaud Gotlieb, Nadjib Lazaar, Helge Spieker
Published in: arXiv, 2024
Publisher: arXiv
DOI: 10.48550/arXiv.2403.09668

GSGFormer: Generative Social Graph Transformer for Multimodal Pedestrian Trajectory Prediction (opens in new window)

Author(s): Zhongchang Luo, Marion Robin and Pavan Vasishta
Published in: arXiv, 2023
Publisher: arXiv
DOI: 10.48550/arXiv.2312.04479

Evaluating Human Trajectory Prediction with Metamorphic Testing (opens in new window)

Author(s): Helge Spieker, Nassim Belmecheri, Arnaud Gotlieb, Nadjib Lazaar
Published in: MET'24: 9th ACM International Workshop on Metamorphic Testing, 2024
Publisher: ISSTA/ECOOP 2024
DOI: 10.48550/arXiv.2407.18756

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