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 deliverables now available!
Twelve deliverables reporting on the work undertaken by the project have been published in the results section of the website. The SAFEXPLAIN deliverables provide key details about the project and how it is progressing. The following deliverables have been created for...
SAFEXPLAIN takes part in 1st intacs® certified ML for Automotive SPICE® (pilot) training
SAFEXPLAIN partner, exida development provided invaluable contributions to the two days of pilot training for the intacs® certified machine learning (ML) automotive SPICE® training.
Integrating the Railway Case Study into the Reference Safety Architecture Pattern
Within the SAFEXPLAIN (SE) project, project partner, Ikerlan, leads the railway case study (CS), which is specifically centred on Automatic Train Operation (ATO). This article highlights how this CS is integrated into the reference safety architecture, building on the...
Keynote at 36th Euromicro Conference on Real-Time Systems
SAFEXPLAIN research and results will have high visibility in the 2024 36th Euromicro Conference on Real-Time Systems. Francisco Cazorla, co-leader of the BSC’s Critical and AutOnomous Systems (CAOS) group delivered the keynote at this major international conference...
Webinar: AI-FSM- Towards Functional Safety Management for Artificial Intelligence-based Critical Systems
Javier Fernandez from partner IKERLAN will share the SAFEXPLAIN project approach to integrating AI into Functional Safety Management in a safe, trustworthy and transparent way. In this 1.5 hour webinar hosted by HiPEAC, Javier will introduce a AI-FSM lifecycle that...
European Convergence Summit-Digital Booth ADR Exhibition
SAFEXPLAIN will have a digital booth as part of the ADR Digital Exhibition, co-located within the European Convergence Summit 2024. This digital booth will showcase the work conducted as part of the SAFEXPLAIN project, including videos, publications, and presentations...