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...
TÜV Rheinland International Symposium 2023
Image from TÜV Rheinland International Symposium website The TÜV Rheinland International Symposium is a specialist event intended as a platform for intensive experience exchange for those involved in Functional Safety and Cybersecurity of different industrial...
EXIDA Automotive Symposium 2023
The 2023 Exida-hosted Automotive Symposium will be held from 18-20 October 2023 in the alpine town of Spitzingsee, Germany. This two-day event will encourage the exchange of information and contacts in the automotive industry.