Financed by EU (H2020 cascade funding SecurIT)

Duration: 1.9.2022 to 30.8.2023

Partners :

• FOKUS TECH d.o.o., Celje, Slovenia (with UM FERI as a subcontractor),
• ALTPRO d.o.o., Zagreb, Croatia.

Additional information: https://securit-project.eu/

In the SecuRAIL project which is a part of SecurIT, an intelligent powerful LiDAR sensor is being developed. The necessary intelligence will be provided by the supporting software that we are developing. This will enable the sensor to autonomously detect passengers and objects that fall from railway platforms or are deliberately pushed onto railway tracks. In case of such a dangerous incident, an alarm will be immediately sent from the sensor to stop the train and alert the security staff.

Both project consortium partners, FOKUS TECH and ALTPRO, are focused on providing LiDAR sensors with adequate technical performances, their synchronization (the passenger platform is usually too long to be monitored by a single sensor), determination of crucial traffic events (e.g. arrival of a train, the train stopping, start of the train departure…), regimes of monitoring and event-based transitions between them, design and selection of reference test scenarios, and providing real test environments by obtaining consent of railway infrastructure managers, while the Laboratory of Geospatial Modelling and Artificial Intelligence (GeMMA) at the subcontractor UM FERI is responsible for software implementation, which will be jointly validated by all three partners before the SecuRAIL completion.

After the project completion, the open platform of the developed LiDAR sensors system will allow the addition of further safety applications. An example of such an application is “trap and drag” accident detection, which prevents a train from dragging behind a passenger or other object grabbed by a train door.

Placement of LiDAR sensors along a railway passenger platform (source: archive of FOKUS TECH d.o.o.).

Co-funded by the Horizon 2020 programme of the European Union under grant agreement 101005292