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SAFEXPLAIN

Financiado

SAFE AND EXPLAINABLE CRITICAL EMBEDDED SYSTEMS BASED ON AI

Deep Learning (DL) techniques are key for most future advanced software functions in Critical Autonomous AI-based Systems (CAIS) in cars, trains and satellites. Hence, those CAIS industries depend on their ability to design, imple... ver más

30/09/2025

BARCELONA SUPERCOM...

4M€

Presupuesto del proyecto: 4M€

Líder del proyecto

BARCELONA SUPERCOMPUTING CENTERCENTRO NACIONA... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5 | 118M€

Fecha límite participación Sin fecha límite de participación.

Ver 6 Participantes

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-06-13

HORIZON-CL4-2021-HUMAN-01-01: Verifiable robustness, energy effic... ExpectedOutcome:Proposal results are expected to contribute to at least one of the following expecte...

I+D

Cerrada hace 3 años

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

6 Participantes

BARCELONA SUPERCOMPUTING CEN...

809.38K€ | Lider

AIKO

551.25K€ | Participante

NAVINFO

600.63K€ | Participante

RISE

611.25K€ | Participante

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Duración del proyecto: 39 meses Fecha Inicio: 2022-06-13
Fecha Fin: 2025-09-30

Líder del proyecto

BARCELONA SUPERCOMPUTING CENTERCENTRO NACIONA... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5 | 118M€

Presupuesto del proyecto 4M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Deep Learning (DL) techniques are key for most future advanced software functions in Critical Autonomous AI-based Systems (CAIS) in cars, trains and satellites. Hence, those CAIS industries depend on their ability to design, implement, qualify, and certify DL-based software products under bounded effort/cost. There is a fundamental gap between Functional Safety (FUSA) requirements of CAIS and the nature of DL solutions needed to satisfy those requirements. The lack of transparency (mainly explainability and traceability), and the data-dependent and stochastic nature of DL software clash against the need for deterministic, verifiable and pass/fail test-based software solutions for CAIS. SAFEXPLAIN tackles this challenge by providing a novel and flexible approach to allow the certification – hence adoption – of DL-based solutions in CAIS by (1) architecting transparent DL solutions that allow explaining why they satisfy FUSA requirements, with end-to-end traceability, with specific approaches to explain whether predictions can be trusted, and with strategies to reach (and prove) correct operation, in accordance with certification standards. SAFEXPLAIN will also (2) devise alternative and increasingly complex FUSA design safety patterns for different DL usage levels (i.e. with varying safety requirements) that will allow using DL in any CAIS functionality, for varying levels of criticality and fault tolerance. SAFEXPLAIN brings together a highly skilled and complementary consortium to successfully tackle this endeavor including 3 research centers, RISE (AI expertise), IKR (FUSA expertise), and BSC (platform expertise); and 3 CAIS case studies, automotive (NAV), space (AIKO), and railway (IKR). SAFEXPLAIN DL-based solutions are assessed in an industrial toolset (EXI). Finally, to prove that transparency levels are fully compliant with FUSA, solutions are reviewed by internal certification experts (EXI), and external ones subcontracted for an independent assessment.

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