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: Based on Automatic Train Operation (ATO), this case study seeks to check the viability of a safety architectural pattern composed of: DL artificial vision software elements that serve as “sensors” to provide information to safety-related software elements
Space: This case study envisions the use of state-of-the-art mission autonomy and artificial intelligence technologies to enable fully autonomous operations during space missions
Automotive: This case study will consider Apollo deployed on a variety of prototype vehicles. It supports state-of the-art hardware such as latest LIDARs and cameras as well as GPU acceleration
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
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...
Clustering Workshop: Establishing the next level of ‘intelligence’ and autonomy
On 2 March 2023, the SAFEXPLAIN project joined 8 other EU-funded projects under the HORIZON-CL4-2021-HUMAN-01-01 topic to share information on eachothers projects, get to know other fund recipients and explore synergies that could be pursued in common for trustworthy...
DATE 2023
Safexplain has a paper titled "SAFEXPLAIN: Safe and Explainable Critical Embedded Systems Based on AI" accepted in the DATE 2023 conference. Stay tuned for more information!
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