EN4CER:HORUS - On-board Artificial Inteligence

HorusThe 4Tel HORUS System (HORUS) is an Advanced Driver Advisory System (ADAS) using real-time sensors and software for assisting a driver in the safe operation of a locomotive. 4Tel has selectively applied modern autonomous car technology to deliver a very sophisticated artificial intelligence-based ADAS system.

For safe and efficient operations, a locomotive needs to know exactly where it is, recognise the objects around it, and continuously monitor the authorised route for normal operations. The HORUS system fulfils this role by integrating multiple sensor data in real-time to allow a comparison with the previous record of any given track section using neural network processing in an on-board processor. HORUS then provides the functionality to apply software processes to conduct the computationally intensive algorithms for object detection, localisation, awareness, dynamics and route monitoring.

The total system also requires a central data centre that collects as-run video that is then used to update the HORUS stylised track reference record called the "Master Sequence" for the route travelled. The overall process uses ‘deep learning’ techniques to ‘learn' any route changes and update the Master Sequence. On-board a locomotive, the differences allow a locomotive’s HORUS processor to recognise both normal and abnormal operations for any given track location.

Deep Learning plays a vital role through the entire HORUS system. In this way, computers are not simply programmed on what to expect along a route, but also with processes for learning route knowledge enabling the ‘artificial intelligence’ system to become increasingly ‘smarter’ based on experience.

The HORUS processing pipeline involves:
  • Detection – Using multiple sensors to understand the environment around the locomotive
  • Localisation - Using what’s measured to create a detailed localisation map
  • Awareness - Interpreting the real-time virtual 3D environment around the train as relevant to operations
  • Dynamics - Calculating how to drive smoothly and within movement authorities given the dynamic characteristics of the train
  • Monitoring – The continuous of checking that the train is operating within authorised parameters
The processing pipeline outcome is that the HORUS system has many operational ADAS modules, which are:
  • HORUS A – Location definition to a specific rail line or place along a rail line
  • HORUS B – Speed sign recognition and reading of speed
  • HORUS C – Signal Lamp Post recognition and displayed aspect
  • HORUS D – Unexpected rail geometry
  • HORUS E – Unexpected object on rail track
  • HORUS F – Monitoring of rail infrastructure
  • HORUS G –The automated reading of digital track signage

Notwithstanding the current capabilities offered or planned for the HORUS processor, it is not currently designed as an automation nor proxy for a control system of itself – it is in-effect a very comprehensive driver advisory system, with the purpose to allow a driver to operate a train more effectively by moving routine route monitoring tasks to a computerised assistant. Operational data will be collected and compared to computerised models over time to establish the maturity of the technology for more advanced automated roles in the future.

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