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.
Four Ways of Testing how to Minimize Risk of Injury in Automatic Train Operation
Partner Ikerlan is pursuing the development of two safety functions that will minimize the risk associated with Automatic Train Operation. Two scenarios offer approaches for minimizing the risk of a train running over or injuring people on the track as well as avoiding damages during opening/closing operations on the platform. Four activities are underway to support these scenarios.
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...
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