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 seeks synergies within TrustworthyAI Cluster

SAFEXPLAIN seeks synergies within TrustworthyAI Cluster

Representatives of the coordinating teams of SAFEXPLAIN and ULTIMATE met to share progress, lessons learnt, and look for potential opportunities for synergies. They delved deeper into the issues that concern both projects: TrustworthyAI.

Exida Development SRL Invited to Speak at InnoVEX 2024

Exida Development SRL Invited to Speak at InnoVEX 2024

SAFEXPLAIN partner, Carlo Donzella, from Exida Development SRL, has been invited to deliver the first keynote speech of the EV Era Forum session, as part of the 2024 InnoVEX Startup Exhibition, the Innovation Hub of Asia. The event will take place from 4-7 June 2024....

SAFEAI Workshop at 2024 Ada-Europe Conference

SAFEAI Workshop at 2024 Ada-Europe Conference

SAFEXPLAIN project partners will participate in the 28th Ada-Europe International Conference on Reliable Software Technologies. This conference represents a leading international forum for providers, practitioners, and researchers in reliable software technologies....