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.
Taking Automatic Train Operation further: implementing safety functions to minimize risk
The challenge faced by the railway case study is closing the gap between Functional Safety Requirements and the nature of Deep Learning (DL) solutions. Functional Safety systems need deterministic, verifiable and pass/fail test-based software solutions.
SAFEXPLAIN talks Safety and AI at the 2023 VDA Conference on Quality, Safety and Security for automotive Software-based Systems
VDA pocket programme cover SAFEXPLAIN partner EXIDA development presented the SAFEXPLAIN project and the platform framework overview during the first day of the VDA Automotive SYS conference focusing on Quality, Safety and Security for Automotive Software-based...
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.
No Results Found
The page you requested could not be found. Try refining your search, or use the navigation above to locate the post.