SAFEXPLAIN: From Vision to Reality

AI Robustness & Safety

Explainable AI

Compliance & Standards

Safety Critical Applications
THE CHALLENGE: SAFE AI-BASED CRITICAL SYSTEMS
- Today’s AI allows advanced functions to run on high performance machines, but its “black‑box” decision‑making is still a challenge for automotive, rail, space and other safety‑critical applications where failure or malfunction may result in severe harm.
- Machine- and deep‑learning solutions running on high‑performance hardware enable true autonomy, but until they become explainable, traceable and verifiable, they can’t be trusted in safety-critical systems.
- Each sector enforces its own rigorous safety standards to ensure the technology used is safe (Space- ECSS, Automotive- ISO26262/ ISO21448/ ISO8800, Rail-EN 50126/8), and AI must also meet these functional safety requirements.
MAKING CERTIFIABLE AI A REALITY
Our next-generation open software platform is designed to make AI explainable, and to make systems where AI is integrated compliant with safety standards. This technology bridges the gap between cutting-edge AI capabilities and the rigorous demands for safety-crtical environments. By joining experts on AI robustness, explainable AI, functional safety and system design, and testing their solutions in safety critical applications in space, automotive and rail domains, we’re making sure we’re contribuiting to trustworthy and reliable AI.
Key activities:
SAFEXPLAIN is enabling the use of AI in safety-critical system by closing the gap between AI capabilities and functional safety requirements.
See SAFEXPLAIN technology in action
CORE DEMO
The Core Demo is built on a flexible skeleton of replaceable building blocks for Interference, Supervision or Diagnoistic components that allow it to be adapted to different secnarios. Full domain-specific demos are available in the technologies page.
SPACE
Mission autonomy and AI to enable fully autonomous operations during space missions
Specific activities: Identify the target, estimate its pose, and monitor the agent position, to signal potential drifts, sensor faults, etc
Use of AI: Decision ensemble
AUTOMOTIVE
Advanced methods and procedures to enable self-driving carrs to accurately detect road users and predict their trajectory
Specific activities: Validate the system’s capacity to detect pedestrians, issue warnings, and perform emergency braking
Use of AI: Decision Function (mainly visualization oriented)
A Tale of Machine Learning Process Models at Automotive SPIN Italia
Carlo Donzella from exida development presents at the Automotive SPIN Italia 22º Workshop on Automotive Software & System SAFEXPLAIN partner Carlo Donzella, from exida development, presented at the Automotive SPIN Italia 22º Workshop on Automotive Software &...
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.
Paving the way towards the next generation of R&I excellence in AI, Data and Robotics
Safexplain coordinator, Jaume Abella from BSC, participated at the "Paving the way towards the next generation of R&I excellence in AI, Data and Robotics" event that took place online on the 17th October online. Artificial intelligence, data and robotics are at...





