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.
Gauging requirements and testing models for Space, Automotive and Railway Case Studies
Using three case studies from different industrial domains ensures that the project considers the needs of multiple fields whose common thread is the potential use of autonomous systems in complex environments, where AI can enable critical and powerful features.
COMPSAC 23: Presenting acceleration solutions based on Deep Neural Networks (DNNs) for use in safety-critical systems
BSC researcher Martí Caro presented “Efficient Diverse Redundant DNNs for Autonomous Driving” on 27 June 2023 at the Autonomous Systems Symposium (ASYS) within the 47th IEEE International Conference on Computers, Software & Applications (COMPSAC). The theme of...
EXIDA presents SAFEXPLAIN at Automotive Spin Italia
On 30 May 2023, G. Nicosia from the SAFEXPLAIN project presented information related to SAFEXPLAIN in the Functional Safety Session of the 21st Workshop on Automotive Software and Systems. He will present on "User Cases and Scenario Catalogue for ML/DL-based solutions...
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...