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.
Halfway through the project, RISE hosts consortium in Lund
SAFEXPLAIN consortium meets halfway through the project at RISE venue in Lund With the first 18 months of the project behind it, the SAFEXPLAIN consortium met in Lund from 16-17 April to discuss project status and next steps for the next 18 months. Great strides have...
Exploring AI-specific redundancy patterns
AI-generated image of object detection for automotive Artificial intelligence, and more specifically, Deep Learning algorithms are used for visual perception classification tasks, like camera-based object detection. For these tasks to work, they need to identify the...
SAFEXPLAIN Opens CARS WS and Shares Work on AI-FSM
Jon Perez Cerrolaza presenting the CARS WS keynote The SAFEXPLAIN project opened the 8th edition of the Critical Automotive applications: Robustness & Safety (CARS) workshop on 8 April 2024, with a keynote talk, delivered by Ikerlan partner Jon Perez-Cerrolaza on...
Expert Panel on AI-Enabled Software Development Tools: Exploring Safety-Critical Applications
Location: Lisbon, Portugal Participants: Ikerlan's Jon Pérez and other industry experts Date: June 15, 2023 SAFEXPLAIN partner, Jon Pérez from Ikerlan was an invited speaker at the ADA-Europe International Conference on Reliable Software Technologies (AEiC) in...
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...