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.
Coming Soon! SAFEXPLAIN technologies converge in an open demo
As the project enters into its 27th month, releases for most technologies are already available and undergoing the last steps towards completing their integration.To demonstrate the SAFEXPLAIN approach, project partners are working towards creating an open source demo...
Developing Scenario Catalogues for SAFEXPLAIN Case Studies: the Railway Case
Part of Exida developnet SRL’s work in the SAFEXPLAIN project includes developing a Catalogue of Scenarios and Test Cases for each case study. These scenarios are performed in either a real or simulated testing environment.
RISE Webinar Highlights XAI for Systems with Functional Safety Requirements
Robert Lowe, Senior Researcher in AI and Driver Monitoring Systems from the Research Institutes of Sweden discussed the integration of explainable AI (XAI) algorithms into the machine learning (ML) lifecycles for safety-critical systems, i.e., systems with functional...
SAFEXPLAIN @ AI, Data, Robotics Forum
SAFEXPLAIN is happy to support the 2024 edition of the AI, Data and Robotics Forum. This two-day event is helping to unite the AI, Data and Robotics (ADR) community to support responsible innovation. The theme of this year's forum is "European Sovereignty in AI, Data...
Webinar: XAI for systems with functional safety requirements
Robert Lowe, Senior Researcher in AI and Driver Monitoring Systems from partner RISE, will introduce new complexities to XAI in relation to functional safety, transparency and compliance with safety standards. In this 1.5 hour webinar, hosted by HiPEAC, Robert will...
SE keynote & WS as part of Gate4SPICE Event “Optimal Performance of Modern Development: Automotive SPICE® Fusion with Intelligent Systems and Agile Frameworks”
SAFEXPLAIN will present a keynote at the INTACS event "Optimal Performance of Modern Development: Automotive SPICE® Fusion with Intelligent Systems and Agile Frameworks" hosted by SEITech Solutions GmbH as part of the Gate4SPICE. The keynote, "A Tale of Machine...