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.
SAFEXPLAIN Reaches out to industry at MWC24
Figure 1: Project coordinator, Jaume Abella, at the BSC booth at MWC24 The 2024 Mobile World Congress in Barcelona offered the SAFEXPLAIN project the opportunity to meet key industry players from the global mobile ecosystem. Moreover, it granted the project partner...
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.
2023 VDA Automotive SYS Conference in Berlin
The Quality Management Centre of the German Association of the Automotive Industry (VDA) hosted the VDA Automotive SYS Conference in Berlin, Germany from 10-13 July 2023. This conference served as a platform for industry leaders and experts to discuss and showcase the...
Clustering Workshop: Establishing the next level of ‘intelligence’ and autonomy
On 2 March 2023, the SAFEXPLAIN project joined 8 other EU-funded projects under the HORIZON-CL4-2021-HUMAN-01-01 topic to share information on eachothers projects, get to know other fund recipients and explore synergies that could be pursued in common for trustworthy...
DATE 2023
The 2023 Design, Automation and Test in Europe Conference, The European Event for Electronic System Design and Test was held 17 April 2023. The DATE conference is the main European event bringing together designers and design automation users, researchers and vendors...