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.
Integrating Explainable AI techniques into Machine Learning Life Cycles
Written by Robert Lowe & Thanh Bui, Humanized Autonomy Unit, RISE, Sweden. Machine Learning life cycles for data science projects that deal with safety critical outcomes require assurances of expected outputs at each stage of the life cycle for them to be...
SAFEXPLAIN to present in COMPSAC Autonomous Systems Symposium
The paper "Efficient Diverse Redundant DNNs for Autonomous Driving", coauthored by BSC authors Martí Caro, Jordi Fornt and Jaume Abella, has been accepted for publication in the 47th IEEE International Conference on Computers, Software & Applications (COMPSAC)....
SAFEXPLAIN Presentation on Safe and Explainable Critical Embedded Systems Based on AI at DATE Conference in Antwerp
SAFEXPLAIN presented its latest research on Safe and Explainable Critical Embedded Systems Based on AI at the Design, Automation and Test in Europe Conference (DATE) in Antwerp, Belgium. The DATE conference is a leading event for electronic system design and test,...
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