Making critical autonomous AI-based systems safe

Making critical autonomous AI-based systems safe

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 @ AI, Data, Robotics Forum

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

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...