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Taking Automatic Train Operation further: implementing safety functions to minimize risk

Taking Automatic Train Operation further: implementing safety functions to minimize risk

by Jaume Lozano | Sep 18, 2023 | Uncategorized

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, an instance on Automotive Functional Safety at the Automotive SPIN Italia

Safexplain, an instance on Automotive Functional Safety at the Automotive SPIN Italia

by Jaume Lozano | Jun 6, 2023 | Uncategorized

Safexplain project partner EXIDA-dev presented at the 21st Workshop on Automotive Software and Systems hosted by Automotive SPIN Italia

SAFEXPLAIN, part of the Adra-e and AI4Europe launch event

SAFEXPLAIN, part of the Adra-e and AI4Europe launch event

by Jaume Lozano | Nov 17, 2022 | Uncategorized

The SAFEXPLAIN project was presented during the Adra-e and AI4Europe Launch Event: Paving the way towards the next generation of R&I excellence in AI, Data and Robotics. From minute 2:05:20, Jaume Abella, principal investigator and coordinator of SAFEXPLAIN...

Recent Posts

  • Taking Automatic Train Operation further: implementing safety functions to minimize risk
  • SAFEXPLAIN talks Safety and AI at the 2023 VDA Conference on Quality, Safety and Security for automotive Software-based Systems
  • Gauging requirements and testing models for Space, Automotive and Railway Case Studies
  • COMPSAC 23: Presenting acceleration solutions based on Deep Neural Networks (DNNs) for use in safety-critical systems
  • Safexplain, an instance on Automotive Functional Safety at the Automotive SPIN Italia

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    This project has received funding from the European Union’s Horizon Europe programme under grant agreement number 101069595.

    This project has received funding from the European Union’s Horizon Europe programme under grant agreement number 101069595.

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