Introducing SAFEXPLAIN:
Safe and Explainable Critical Embedded Systems based on AI
Objectives
To improve the explainability and traceability of DL components
To provide clear safety patterns for the incremental adoption of DL software in Critical Autonomous AI-based Systems (CAIS)
To integrate the SAFEXPLAIN libraries with an industrial system-testing toolset
To create architectures of DL components with quantifiable and controllable confidence, and that have the ability to identify when predictions should not be released based on applicability’s scope or security concerns
To design, implement, or update selected representative DL software libraries according to safety patterns and safety lifecycle considerations, meeting specific performance requirements on relevant platforms
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
Case studies
Railway: This case studies the viability of a safety architectural pattern for the completely autonomous operation of trains (Automatic Train Operation, ATO) using intelligent Deep Learning (DL)-based solutions.
Space: This case employs state-of-the-art mission autonomy and artificial intelligence technologies to enable fully autonomous operations during space missions. These technologies are developed through high safety-critical scenarios.
Automotive: This case develops advanced methods and procedures that enable self-driving cars to accurately detect road users, estimate their distance from the vehicle, and predict their trajectories while adhering to both safety and explainability requirements.
Mixed Critical Systems Workshop at HiPEAC 2024
Irune Agirre, from partner IKERLAN, discusses functional safety approaches for AI-based critical systems at HiPEAC2024 On 19 January 2024, members of the SAFEXPLAIN consortium participated in the 12th Workshop on "MCS: Mixed Critical Systems – Safe and Secure...
Sustained performance and segregation through hardware-level support
Exploiting the computational power of complex hardware platforms is opening the door to more extensive and accurate Artificial Intellgence (AI) and Deep Learning (DL) solutions. Performance-eager AI-based solutions are a common enabler of increasingly complex and...
SAFEXPLAIN consortium meets with industrial advisory board to ensure alignment with industry needs
In an important checkpoint for the SAFEXPLAIN project, the project consortium met with an Industrial Advisory Board comprised of eight influential industry actors on 24 November 2023. At just over the one year point, this meeting sought to present the project´s...
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