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.
Status at month 6: The SAFEXPLAIN consortium meets at IKERLAN
Figure 1: Members of the SAFEXPLAIN consortium meet at IKERLAN's headquarters SAFEXPLAIN partners spent two days in IKERLAN presenting the work carried out in the first six months of the project. Although project partners have been meeting frequently online to discuss...
Towards the Safety of Critical Embedded Systems Based on Artificial Intelligence: the Standardization Landscape
AI safety means ensuring that the operation of an AI system does not contain any unacceptable risks . It is essential to ensure that the AI system operates reliably, that unintended behavior is mitigated and that it is possible to explain how the AI system arrived at a particular decision
Press Release: SAFEXPLAIN facilitates the safety certification of critical autonomous AI-based systems for a more competitive EU industry
Barcelona, 13 February 2023. - The EU-funded SAFEXPLAIN (Safe and Explainable CriticalEmbedded Systems based on AI) project, launched on 1 October 2022, seeks to lay the foundationfor Critical Autonomous AI-based Systems (CAIS) applications that are smarter and safer...
No Results Found
The page you requested could not be found. Try refining your search, or use the navigation above to locate the post.