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.
RISE Webinar Highlights XAI for Systems with Functional Safety Requirements
Robert Lowe, Senior Researcher in AI and Driver Monitoring Systems from the Research Institutes of Sweden discussed the integration of explainable AI (XAI) algorithms into the machine learning (ML) lifecycles for safety-critical systems, i.e., systems with functional...
BSC receives visit from delegate from Taiwanese Institute for Information Industry
Figure 1: Photo by Francisco J. Cazorla, BSC representative also attending this meeting The SAFEXPLAIN project was thrilled to receive the visit of Stanley Wang, Director of the Digital Transformation Research Institute, part of the Institute for Information Industry...
Contributing to EU Sovereignty in AI, Data and Robotics at the ADRF24
SAFEXPLAIN participated as a Silver Sponsor of the 2024 AI, Data and Robotics Forum, which took place in Eindhoven, Netherlands from 4-5 November 2024. This two-day event gathered leading experts, innovators policymakers and enthusiasts from teh AI, Data and Robotics...
Webinar: XAI for systems with functional safety requirements
Robert Lowe, Senior Researcher in AI and Driver Monitoring Systems from partner RISE, will introduce new complexities to XAI in relation to functional safety, transparency and compliance with safety standards. In this 1.5 hour webinar, hosted by HiPEAC, Robert will...
SE keynote & WS as part of Gate4SPICE Event “Optimal Performance of Modern Development: Automotive SPICE® Fusion with Intelligent Systems and Agile Frameworks”
SAFEXPLAIN will present a keynote at the INTACS event "Optimal Performance of Modern Development: Automotive SPICE® Fusion with Intelligent Systems and Agile Frameworks" hosted by SEITech Solutions GmbH as part of the Gate4SPICE. The keynote, "A Tale of Machine...
Keynote at 36th Euromicro Conference on Real-Time Systems
SAFEXPLAIN research and results will have high visibility in the 2024 36th Euromicro Conference on Real-Time Systems. Francisco Cazorla, co-leader of the BSC’s Critical and AutOnomous Systems (CAOS) group delivered the keynote at this major international conference...