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Moving from automated trains to autonomous trains
The SAFEXPLAIN railway case study will check the viability of a safety architectural pattern for the completely autonomous operation of trains (Automatic Train Operation, ATO). The project will employ intelligent Deep Learning (DL)-based solutions, including artificial vision elements, to detect and locate people and obstacles on the track and in the way of the train doors and to estimate their position to ensure the train does not collide with obstacles or injure passengers. Safety-related software elements and DL software elements implement the safety function that allow trains to make safe and optimal decisions in real-time.
AI explainability may help during the process of safety system certification by proposing evidence that helps show certification authorities that AI-based sensors are properly trained and work as expected. AI explainability may also help by adding diagnosis capabilities to the safety system to implement redundant systems with diagnosis capabilities during safety system operation.
IKERLAN, the SAFEXPLAIN partner responsible for the railway use case, is working on the algorithms and the environmental tools needed to run and simulate their operational scenarios. Stubbing (simulating) the inputs and outputs of the algorithms is essential to avoid buying costly devices and instruments and recreating complex subsystems and interfaces, which in any case fall out of the scope of the project. With proper software stubbing, the system can tightly mimic the operations in the real scenario.
For more information, visit https://safexplain.eu/taking-automatic-train-operation-further-implemen…
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