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 joins EU AI Community with Digital Booth @ ADR Exhibition
The 2024 European Convergence Summit, hosted by the AI, Data and Robotics Association ecosystem, was held online on 19 June 2024 and brought together influential players from AI, Data and Robotics to discuss the impact of these technologies on society. The summit...
SAFEXPLAIN invited talk, workshop and panel participations at 28th Ada-Europe conference
Coordinator Jaume Abella introduces Irune Yarza as part of SAFEAI workshop co-located within 28th Ada-Europe conference The 28th Ada-Europe International Conference on Reliable Software Technologies (AEiC 2024) was held in Barcelona, Spain from 11-14 June 2024. This...
Developing safe and explainable AI for autonomous driving: Automotive case study
NAVINFO has been working to validate the real-world applicability of their work by deploying an autonomous driving system on an embedded compute platform. Two videos showcase the performance of their driving agent in relevant safety scenarios.
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...
HIPEAC Workshop: Mixed Critical Systems – Safe and Secure Intelligent CPS and the development cycle
This year the SAFEXPLAIN project will partake in this workshop lead by IKERLAN on mixed critical systems. The workshop will take place on 19 January from 10h-17:30h. The HiPEAC conference is the premier European forum for experts in computer architecture, programming...