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.
SAFEXPLAIN invited talk, workshop and panel participations at 28th Ada-Europe conference
Coordinator Jaume Abella introduces Irune Yarza as part of SAFEAI workshop co-located within 28th Ada-Europe conference The 28th Ada-Europe International Conference on Reliable Software Technologies (AEiC 2024) was held in Barcelona, Spain from 11-14 June 2024. This...
Developing safe and explainable AI for autonomous driving: Automotive case study
NAVINFO has been working to validate the real-world applicability of their work by deploying an autonomous driving system on an embedded compute platform. Two videos showcase the performance of their driving agent in relevant safety scenarios.
SAFEXPLAIN shares strategies for diverse redundancy in ML/AI Critical Systems session at ERTS ’24
Martí Caro from the Barcelona Supercomputing Center presents at the 2024 Embedded Real Time System Congress Barcelona Supercomputing Center researcher Martí Caro presented "Software-Only Semantic Diverse Redundancy for High-Integrity AI-Based Functionalities" at the...
HIPEAC Workshop: Mixed Critical Systems – Safe and Secure Intelligent CPS and the development cycle
This year the SAFEXPLAIN project will partake in this workshop lead by IKERLAN on mixed critical systems. The workshop will take place on 19 January from 10h-17:30h. The HiPEAC conference is the premier European forum for experts in computer architecture, programming...
Presentation at Smart City Expo World Congress: Safe and Trustworthy AI in critical systems (automotive and rail)
On 9 November 2023, SAFEXPLAIN coordinator, Jaume Abella from the Barcelona Supercomputing Center will be presenting at the 2023 Smart City Expo World Congress in Barcelona, Spain. The presentation, Safe and Trustworthy AI in critical systems (automotive and rail)...
SAFEXPLAIN SILVER SPONSOR of the AI, Data and Robotics Forum
On 8-9 November 2023, the SAFEXPLAIN project participated in the 2023 AI, Data, Robotics Forum as a silver sponsor. BSC partners Francisco J. Cazorla and Axel Brando presented the SAFEXPLAIN project during the poster session and gave a brief talk to AI experts. ...