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 joins EU AI Community with Digital Booth @ ADR Exhibition

SAFEXPLAIN joins EU AI Community with Digital Booth @ ADR Exhibition

The 2024 European Converge 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 Rovotics to discuss the impact of these technologies on society. The summit focused...

European Convergence Summit-Digital Booth ADR Exhibition

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...

TrustworthyAI Cluster Webinar hosted by ADRA-e

TrustworthyAI Cluster Webinar hosted by ADRA-e

SAFEXPLAIN partner Enric Mezzetti from Barcelona Supercomputing Center will join the ADRA-e hosted webinar on "Trustworthy AI: Landscaping veriable robustness and transparency" on 29 May 2024 from 10-12h. The TrustworthyAI Cluster, nine EU-projects under call Horizon...