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, an instance on Automotive Functional Safety at the Automotive SPIN Italia
Safexplain project partner EXIDA-dev presented at the 21st Workshop on Automotive Software and Systems hosted by Automotive SPIN Italia
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’s Presentation “Efficient Diverse Redundant DNNs for Autonomous Driving” Accepted for COMPSAC 2023
SAFEXPLAIN presented at the Conference on Computers, Software, and Applications (COMPSAC) 2023. The paper, 'Efficient Diverse Redundant DNNs for Autonomous Driving' was accepted for publication at the IEEE Computer Society Signature Conference on Computers, Software,...
Standardizing the Probabilistic Sources of Uncertainty for the sake of Safety Deep Learning
The Safexplain team from the CAOS research group of the Barcelona Supercomputing Center (BSC-CNS) presented their latest research at the AAAI's Workshop on Artificial Intelligence Safety of the AAAI 2023, held on February 14, 2023, in Washington D.C. The team's...