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 Opens CARS WS and Shares Work on AI-FSM
Jon Perez Cerrolaza presenting the CARS WS keynote The SAFEXPLAIN project opened the 8th edition of the Critical Automotive applications: Robustness & Safety (CARS) workshop on 8 April 2024, with a keynote talk, delivered by Ikerlan partner Jon Perez-Cerrolaza on...
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.
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...
MCS: International Workshop on Mixed Critical Systems – Safe and Secure Intelligent CPS and the development cycle
Nowadays society is surrounded by modern embedded systems that typically integrate multitude of functionalities with potentially different criticality (safety-security) levels into a single system. This is what we call Mixed‐Criticality Cyber‐Physical Systems (MCCPS)....
Safexplain Kick-off-Meeting
The eFlows4HPC Kick-off-Meeting takes place on 15-16 March 2021 in a digital format.