Making critical autonomous AI-based systems safe

Making critical autonomous AI-based systems safe

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.

Recognizing the Contributions of Women in Science

Recognizing the Contributions of Women in Science

Women have made huge contributions to the scientific community. Raising their visibility and representation of women in science is key for ensuring that the next generation of scientists have positive role models and learn to value diversity and equity in...

Safexplain at HiPEAC conference 2023

Safexplain at HiPEAC conference 2023

Safexplain gives a talk and presents a poster during the HiPEAC conference 2023, that took place from 16 – 18 January 2023 in Toulouse, France.

SAFEXPLAIN, part of the Adra-e and AI4Europe launch event

SAFEXPLAIN, part of the Adra-e and AI4Europe launch event

The SAFEXPLAIN project was presented during the Adra-e and AI4Europe Launch Event: Paving the way towards the next generation of R&I excellence in AI, Data and Robotics on 17 October 2022. See the full video of all projects being presented. The second video...

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