Trustworthy AI for CCAM

Informazioni relative al progetto

AI4CCAM

ID dell’accordo di sovvenzione: 101076911

DOI

10.3030/101076911

Progetto chiuso

Data della firma CE 21 Ottobre 2022

Data di avvio 1 Gennaio 2023

Data di completamento 31 Marzo 2026

Finanziato da

• Climate, Energy and Mobility

Costo totale

€ 5 965 630,00

Contributo UE

€ 5 965 630,00

Coordinato da

SIMULA RESEARCH LABORATORY AS
Norway

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

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

Pubblicazioni

Peer reviewed articles (3)

Towards Trustworthy Automated Driving through Qualitative Scene Understanding and Explanations

Autori: Nassim Belmecheri, Arnaud Gotlieb, Nadjib Lazaar and Helge Spieker
Pubblicato in: SAE International Journal of Connected and Automated Vehicles, 2024, ISSN 2574-0741
Editore: SAE International
DOI: 10.48550/arXiv.2403.16908

Query-driven Qualitative Constraint Acquisition

Autori: Mohamed-Bachir Belaid, Nassim Belmecheri, Arnaud Gotlieb, Nadjib Lazaar, Helge Spieker
Pubblicato in: Journal of Artificial Intelligence Research, Numero 79, 2024, ISSN 1076-9757
Editore: AAAI Press
DOI: 10.1613/jair.1.14752

Boosting the learning for ranking patterns

Autori: Nassim Belmecheri, Noureddine Aribi, Nadjib Lazaar, Yahia Lebbah, Samir Loudni
Pubblicato in: algorithms, 2023, ISSN 1999-4893
Editore: MDPI Open Access Publishing
DOI: 10.48550/arXiv.2203.02696

Other (3)

Acquiring Qualitative Explainable Graphs for Automated Driving Scene Interpretation

Autori: Belmecheri, Nassim; Gotlieb, Arnaud; Lazaar, Nadjib; Spieker, Helge
Pubblicato in: arXiv, 2023
Editore: Arxiv
DOI: 10.48550/arxiv.2308.12755

Trustworthy Automated Driving through Qualitative Scene Understanding and Explanations

Autori: Nassim Belmecheri, Arnaud Gotlieb, Nadjib Lazaar, Helge Spieker
Pubblicato in: arXiv, 2024
Editore: arXiv
DOI: 10.48550/arXiv.2403.09668

GSGFormer: Generative Social Graph Transformer for Multimodal Pedestrian Trajectory Prediction

Autori: Zhongchang Luo, Marion Robin and Pavan Vasishta
Pubblicato in: arXiv, 2023
Editore: arXiv
DOI: 10.48550/arXiv.2312.04479

Conference proceedings (1)

Evaluating Human Trajectory Prediction with Metamorphic Testing

Autori: Helge Spieker, Nassim Belmecheri, Arnaud Gotlieb, Nadjib Lazaar
Pubblicato in: MET'24: 9th ACM International Workshop on Metamorphic Testing, 2024
Editore: ISSTA/ECOOP 2024
DOI: 10.48550/arXiv.2407.18756