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.
Exida in SAFEXPLAIN: Extending Functional Safety Compliance to Machine Learning Applications, NOW
Exida is part of two technical pillars that are associated with two major work-packages (WP) of the project: WP2- Safety Assessment and WP4 -Platforms and Toolset Support. The intermediate results of which are presented in this text.
Celebrating Women and Girls in Science Day with advice for young scientists
We´re celebraiting the 9th Anniversary of #FEBRUARY11 Global Movement with a look into the women in science and technology in the project.
The SAFEXPLAIN projects counts with the participation of many women in science who are driving the project´s success. See what advice they have for young scientists.
SAFEXPLAIN Partner to Give Keynote at CARS Workshop
SAFEXPLAIN will attend the 8th edition of the Critical Automotive Applications: Robustness & Safety Workshop on 8 April 2024. Partner Jon Perez Cerrolaza from Ikerlan will give the workshop keynote talk on “Artificial Intelligence, Safety and Explainability( SAFEXPLAIN) on day on of the workshop. SAFEXPLAIN will also participate in the workshop through its presentation on “AI-FSM: Towards Functional Safety Management for Artificial Intelligence-based Critical Systems”.
Mobile World Congress 2024
The 2024 Mobile World Congress (MWC) was held in Barcelona from 26-29 February. Partner Barcelona Supercomputing Center (BSC) attended and presented SAFEXPLAIN project technology and held meetings with several industry players. Hosted by the GSMA, the MWC Barcelona...