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.
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.
SAFEAI Workshop at 2024 Ada-Europe Conference
The SAFEXPLAIN project will participate in the 28th Ada-Europe International Conference on Reliable Software Technologies. This conference represents a leading international forum for providers, practitioners, and researchers in reliable software technologies....
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”.