CAF SIGNALLING IS INVOLVED IN DIFFERENT RESEARCH PROJECTS RELATED TO COMPUTER VISION (CV) AND ARTIFICIAL INTELLIGENCE (AI) ENHANCED SYSTEMS DEVELOP IN ORDER TO REACH A HIGHER AUTONOMY IN URBAN VEHICLES AND ALIGN THEM WITH RAILWAY EUROPEAN NORMATIVE.
However, as many companies across the sector, CAF Signalling is facing up different computational capabilities challenges for CV&AI-enhanced autonomous train operation which needs real-time & safety-critical computing platforms for correct performance.
The future of CV&AI breakthroughs in railway sector will require large arrays of memory devices at the same accuracy as a Graphical Processing Unit (GPU)-based system, hardware accelerators and new platforms. These achievements will expand the scale of CV&AI processing-calculations making them larger and faster (this means energy-efficiency must improve dramatically). In other to conduct a research in this field, CAF Signalling joined SELENE project consortium.
SELENE follows a radically new approach and proposes an open-source Safety-critical Cognitive Computing Platform (CCP) with self-awareness, self-adapting, and autonomous capabilities. SELENE’s CCP uses artificial intelligence (AI) techniques fed with on-line monitors and external sensors to adapt the system to the particular internal and external (environmental) conditions with the aim of maximizing the efficiency of the system being able at the same time of meeting application requirements. AI techniques are applied in a transparent way preserving the safety of the system. To ensure safety requirements are preserved, SELENE’s CCP relies on the strong isolation capabilities provided at hardware and software levels.
CAF Signalling will use the SELENE approach on AI-enabled computing platforms to execute some functionalities developed in CV&AI field for autonomous train operation.
More precisely:
- a) Automatic platform detection,
- b) Automatic accurate stop at platforms and
- c) Safe passenger transfer.
SELENE project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 871467.