Trustworthy AI for CCAM

Informations projet

AI4CCAM

N° de convention de subvention: 101076911

DOI

10.3030/101076911

Projet clôturé

Date de signature de la CE 21 Octobre 2022

Date de début 1 Janvier 2023

Date de fin 31 Mars 2026

Financé au titre de

• Climate, Energy and Mobility

Coût total

€ 5 965 630,00

Contribution de l’UE

€ 5 965 630,00

Coordonné par

SIMULA RESEARCH LABORATORY AS
Norway

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)

AI4CCAM Validation Handbook

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)

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

Integration of Human Gaze Models in Hybrid AI Models

Integrated report on the AI based scenario generation and prediction models integrating human gaze estimation models. This deliverable is produced as part of T2.2 and T2.3.

Innovation Registry and Patent Filing

Elicitation of project innovations in AI4CCAM registry and two patents filed. This deliverable is produced as part of T5.3

Plan for dissemination and exploitation including communication activities (CDE) - Initial Report and Updated Plan

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

AI4CCAM Use Cases Implementation and Validation

Report including KPI measurements, validation criteria and results, impact assessment, best practises and lessons learnt. This deliverable is produced as part of T4.2, T4.3, T4.4 and T4.5

AI4CCAM Trustworty AI Documentation Framework - Final Version

Inclusion of AI risks and cyber-threats, stakeholders' perceptions and biases, end-users and VRUs perceptions with recommendations on user acceptance. This deliverable is produced as part of T3.2.

AI4CCAM Trustworthy AI Documentation Framework - Initial Version

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

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

Plan for dissemination and exploitation including communication activities (CDE) - Final Report

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

Data Management Plan (1)

Data Management Plan (DMP) - Final Report

Final report on data management in the project. This deliverable is produced as part of Task 6.3.

Other (3)

Data-driven simulation scenarios

Scenarios containing use cases simulation of VRUs interactions with CAVs. This deliverable is produced as part of T4.3

Participatory AI4CCAM Space - Final Version

Final version with AI risks and security threats discussion threads, data bias identification list, end-users and VRUs perception topics. This deliverable is produced as part of T3.3.

Participatory AI4CCAM Space - Initial Version

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

Peer reviewed articles (3)

Towards Trustworthy Automated Driving through Qualitative Scene Understanding and Explanations

Auteurs: Nassim Belmecheri, Arnaud Gotlieb, Nadjib Lazaar and Helge Spieker
Publié dans: SAE International Journal of Connected and Automated Vehicles, 2024, ISSN 2574-0741
Éditeur: SAE International
DOI: 10.48550/arXiv.2403.16908

Query-driven Qualitative Constraint Acquisition

Auteurs: Mohamed-Bachir Belaid, Nassim Belmecheri, Arnaud Gotlieb, Nadjib Lazaar, Helge Spieker
Publié dans: Journal of Artificial Intelligence Research, Numéro 79, 2024, ISSN 1076-9757
Éditeur: AAAI Press
DOI: 10.1613/jair.1.14752

Boosting the learning for ranking patterns

Auteurs: Nassim Belmecheri, Noureddine Aribi, Nadjib Lazaar, Yahia Lebbah, Samir Loudni
Publié dans: algorithms, 2023, ISSN 1999-4893
Éditeur: MDPI Open Access Publishing
DOI: 10.48550/arXiv.2203.02696

Other (3)

Acquiring Qualitative Explainable Graphs for Automated Driving Scene Interpretation

Auteurs: Belmecheri, Nassim; Gotlieb, Arnaud; Lazaar, Nadjib; Spieker, Helge
Publié dans: arXiv, 2023
Éditeur: Arxiv
DOI: 10.48550/arxiv.2308.12755

Trustworthy Automated Driving through Qualitative Scene Understanding and Explanations

Auteurs: Nassim Belmecheri, Arnaud Gotlieb, Nadjib Lazaar, Helge Spieker
Publié dans: arXiv, 2024
Éditeur: arXiv
DOI: 10.48550/arXiv.2403.09668

GSGFormer: Generative Social Graph Transformer for Multimodal Pedestrian Trajectory Prediction

Auteurs: Zhongchang Luo, Marion Robin and Pavan Vasishta
Publié dans: arXiv, 2023
Éditeur: arXiv
DOI: 10.48550/arXiv.2312.04479

Conference proceedings (1)

Evaluating Human Trajectory Prediction with Metamorphic Testing

Auteurs: Helge Spieker, Nassim Belmecheri, Arnaud Gotlieb, Nadjib Lazaar
Publié dans: MET'24: 9th ACM International Workshop on Metamorphic Testing, 2024
Éditeur: ISSTA/ECOOP 2024
DOI: 10.48550/arXiv.2407.18756