EN4CER:MTU - On-Board Computer

EN4CER MTUEN4CER is an in-cab advanced driver advisory solution which references track data, signal data and GPS position to act as an in-cab signaling and alert system. It is an engineering solution to assist in mitigating the operational risks of train over-speed and signal passed at danger (SPAD) for better train driver and network performance.

The EN4CER solution combines a 4MTU (Machine Terminal Unit) Driver Machine Interface (DMI) with an on-board computer processor; brake enforcement technology; and cameras for vision processing and deep machine learning - depending on the level of enforcement required.

The 4Tel 4MTU™ is a mobile computing solution designed for use in the harsh locomotive operating environment. It is designed to support multiple software applications and share resources (e.g. location determination, data storage, communications) with other on-board systems and also back to the remote control centre.

This above rail solution is designed to empower the train driver with an intuitive technology solution to make safer and accurate decisions according to the trains location and capacity. The EN4CER system calculates the real-time information needed for speed, breaking distance and driver vigilance. The EN4CER solution is able to do this because the database includes the train configuration; below rail configuration; driver interface configuration; EN4CER over-speed and SPAD business rules; and EN4CER Data transmission rules.

The EN4CER™ Solution:

  • Monitors train performance against authorised track speeds
  • Warns the driver of an impending over-speed occurrence
  • Initiates a brake application to bring the train to a stop if the driver fails to bring the train under control (if necessary in accordance with business parameters)
  • Assists the driver to ‘call’ the aspect of the approaching signal and if necessary initiating a train brake application to stop the train (in accordance with business parameters)
  • EN4CER™ is a driver advisory and protection system, not a control system. It is designed to protect the train when the train driver fails to respond to a visual and audible prompt from the 4MTU™. Under normal operating conditions, EN4CER™ should never have to take control of the train by applying the brakes.
    EN4CER™ is designed to be used in locomotives operating on rail corridors that are controlled using either/or both, Train Order Working (TOW), or Rail Vehicle Detection Systems (RVDS).

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EN4CER:HORUS - On-Board Artificial Intelligence

HorusThe 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 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.

HORUS Processing:
  • Detection – Using sensors to understand the environment
  • Localisation - Using measurement to create a detailed map
  • Awareness - Interpreting real-time virtual 3D environment
  • Dynamics - Calculating dynamic movement
  • Monitoring – Continuous checking of operation


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