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.
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.
Safely docking a spacecraft to a target vehicle
The space scenario envisions a crewed spacecraft performing a docking manoeuvre to an uncooperative target (a space station or another spacecraft) on a specific docking site. The GNC system must be able to acquire the pose estimation of the docking target and of the spacecraft itself, to compute a trajectory towards the target and to send commands to the actuators to perform the docking manoeuvre. The safety goal is to dock with adequate precision and avoid crashing or damaging the assets.
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...
A Tale of Machine Learning Process Models at Automotive SPIN Italia
Announcement from the Automotive SPIN ITALIA website The SAFEXPLAIN project will mark its presence at the Automotive SPIN Italia 22º Workshop on Automotive Software & System. Carlo Donzella from partner exida development will share insights into "A Tale of Machine...