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 Reaches out to industry at MWC24

SAFEXPLAIN Reaches out to industry at MWC24

Figure 1: Project coordinator, Jaume Abella, at the BSC booth at MWC24 The 2024 Mobile World Congress in Barcelona offered the SAFEXPLAIN project the opportunity to meet key industry players from the global mobile ecosystem. Moreover, it granted the project partner...

SAFEAI Workshop at 2024 Ada-Europe Conference

The SAFEXPLAIN project 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....

SAFEXPLAIN Partner to Give Keynote at CARS Workshop

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”.

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