Rail is a system in which safety depends on thousands of elements operating simultaneously. Tracks, turnouts, the overhead line, level crossings, engineering structures and the surroundings of the line create an environment that must be continuously monitored.
The problem is no longer just collecting as much data as possible.
The problem is to see the threat early enough, interpret it correctly and pass the information to a person or system that can react.
This is exactly where artificial intelligence comes in.
AI in railways - from image capture to understanding reality
A classic monitoring system primarily records.
A camera can store hours of video material, but it does not answer the most important question by itself:
Has something happened in the observed image that requires a response?
A human can analyze the recording, but the scale of data quickly exceeds manual inspection capabilities. The more crossings, kilometers of line and cameras, the greater the problem of selecting information.
That is why the development of computer vision, machine learning and AI for railways changes the approach to infrastructure monitoring.
A system based on artificial intelligence can analyze an image for specific patterns and anomalies, classify events and forward to the operator only the information that requires attention.
This is a fundamental change.
Instead of: camera → recording → human → analysis
we get: camera → AI → anomaly detection → event context → reaction.
Edge Computing - why should AI operate close to the data source?
One of the key elements of modern AI systems for railways is Edge Computing.
In the traditional model, data may be sent to a central server or the cloud where it is analyzed. For monitoring railway infrastructure, however, this implies the need to transmit huge amounts of visual data.
VAR24 uses a different concept.
Analysis takes place locally, at the network edge, using a dedicated microcomputer and a camera system.
This is significant.
If a system is to operate in real time, you cannot base every decision on sending the entire data stream to an external processing center.
Edge AI allows part of the intelligence to be moved directly to a device located on the vehicle.
Data are analyzed where they are generated.
This makes it possible to reduce latency, decrease the volume of data transmitted further and create an architecture suitable for applications that require fast response.
That is why Edge Computing in railways is one of the interesting directions for the development of intelligent safety systems.
Current research on AI in infrastructure maintenance points to Edge AI as one of the development directions alongside Digital Twins and cyber-physical systems.
VAR24 - sees, analyzes, reacts
The VAR24 concept is based on three basic stages.
01. Sees
Front-facing cameras observe the track from the driver's perspective.
This is important because the system does not analyze an abstract model of the infrastructure. It observes the real environment in which the vehicle moves.
02. Analyzes
The image is processed locally by artificial intelligence.
The system's task is to recognize anomalies and potential hazards rather than being limited to passive recording of material.
03. Reacts
A detected event can be forwarded to the control center with context.
This stage is what allows the transition from monitoring to a decision support system.
And this is a fundamental difference.
Predictive Maintenance in railways - maintenance before a failure occurs
One of the most important applications of artificial intelligence in rail transport is Predictive Maintenance.
The traditional approach often relies on scheduled inspections, periodic procedures and reacting to detected damage.
Predictive Maintenance changes the logic of operation.
Instead of asking: “When should the next inspection be carried out?”
the system can move toward asking: “Based on available data, are there signs of a problem and how quickly can it become significant?”
This is a huge difference from the perspective of infrastructure management.
AI can use data from imaging, sensors, track geometry, vibrations and other sources to identify patterns indicating potential problems. A review of current scientific studies shows that imaging, vibration analysis and track geometry data are among the important data sources used in AI-based predictive maintenance for railways.
The International Union of Railways (UIC) ran a project Artificial Intelligence for Predictive Maintenance aimed at accelerating the use of AI in the railway sector and developing use cases from PoC and pilots to operational applications.
This shows that AI and Predictive Maintenance are no longer merely technological experiments. They are a real direction in the digitization of railways.
Why does railway infrastructure monitoring need AI?
Railway infrastructure is extensive. It comprises hundreds and thousands of kilometers of track, varying weather conditions, changing lighting, vehicle traffic, vegetation, animals, objects located near the track and the changing technical condition of the infrastructure.
A human cannot continuously observe the entire infrastructure. A computer system, however, can perform analysis continuously. This does not mean eliminating humans. On the contrary.
The most valuable model is one in which AI acts as an additional layer of perception and analysis, and humans receive information already appropriately filtered and classified.
This approach shifts humans from the role of observers of vast amounts of data to decision-makers who act on information prepared by the system.
From cameras to data fusion and LiDAR
Cameras are only the beginning.
The next stage in the development of perception systems can be fusion of data from various sensors.
VAR24 assumes integration with LiDAR technology, a system that uses laser pulses to create a spatial representation of the surroundings.
Why is this important?
A camera image provides visual information.
LiDAR can provide spatial information.
Combining different data sources allows building a more complete model of the observed environment.
This is particularly important in situations where image analysis alone may be hindered by lighting or atmospheric conditions.
Importantly, a similar direction is already present in Polish R&D work on automatic railway infrastructure monitoring. The UAVforRail project uses, among others, AI, RGB, LiDAR and thermography for automatic identification of infrastructure defects.
Extended perception horizon
The next step is not just more precise seeing. It is seeing further.
For a railway safety system, the condition of the track immediately in front of the vehicle is not the only important factor; the situation in its surroundings also matters.
Therefore, the VAR24 roadmap assumes extending the analysis to include, among others:
- the overhead line,
- the immediate surroundings of the track,
- animals on the track,
- vehicles at level crossings,
- landslides,
- other potential obstacles and anomalies.
In this way the system begins to move from infrastructure monitoring to transport environment perception.
And perception is one of the foundations of autonomy.
From observation to autonomous reaction
Autonomous rail transport does not begin with a train making all decisions on its own.
It starts much earlier.
First, the machine must: see.
Then: understand.
Next: assess risk.
And only later: react.
That is why computer vision, Edge AI, LiDAR and data fusion systems are so important for the future of autonomous railways.
Current studies indicate that full automation at GoA4 level is already used in some metro systems, while its application on mainline routes remains much more difficult due to safety requirements and the complexity of the open operational environment.
VAR24 therefore fits the logical development path: observation → analysis → prediction → environment perception → reaction → autonomization.
Railway safety requires real-time data
In critical systems, time matters.
If information about a potential threat appears only after the journey is completed or after manual analysis of the material, its operational value may be limited.
That is why real-time data analysis is one of the most important elements of the VAR24 concept.
The system should not only collect information. It should process it. It should recognize anomalies. It should deliver the appropriate message. And in the future—according to the project roadmap—it may become an element of active response to critical threats.
VAR24 - development roadmap
System development has been divided into successive phases.
Phase 1 - vision system / PoC
Advanced optics and artificial intelligence serve to detect infrastructure damage within the immediate range of the system.
Phase 2 - data fusion and LiDAR
Integration of additional spatial data sources and increased environment perception capabilities.
Phase 3 - extended perception horizon
Extending the analysis to the overhead line and the track surroundings.
Phase 4 - active response
The target concept assumes achieving 99.99% reliability and the ability to automatically initiate an emergency braking procedure if a critical threat is identified at a distance exceeding human reaction time.
This is the development goal of VAR24, not a declaration of the system's current capabilities.
And such a distinction is crucial in safety technologies.
Non-invasive technology - AI without infrastructure rebuild
One of the interesting elements of the VAR24 concept is its non-invasive installation.
Instead of rebuilding existing railway infrastructure, the system can be developed as an additional technological layer using cameras and a dedicated onboard computer.
This potentially opens a completely different path for implementing AI in railways.
You do not have to start by building the entire infrastructure from scratch.
You can begin with an intelligent perception layer that uses existing vehicle routes on the railway network to continuously collect and analyze information.
Polish technology for European railways
VAR24 is being developed as an R&D project originating from Web24.com.pl Sp. z o.o., a Polish software house operating since 2008.
According to the project's assumptions, VAR24 is self-financed and developed without dependence on external capital.
This is an important element of the project's story.
The aim is not merely to create another AI-based solution.
The goal is to build technology that can find application in one of the most demanding infrastructure sectors - rail transport.
The future of railways will be based on perception
For decades, railway development has focused on increasing speed, capacity, control automation and infrastructure modernization.
The next stage is system intelligence.
The train of the future should not only follow a planned route. It should increasingly understand the environment in which it operates. It should detect anomalies. It should predict potential problems. It should provide the operator with appropriate information. And in properly certified and safe applications—in the future—it may also take certain actions automatically.
Therefore, the development of AI for railways, Edge Computing, computer vision, LiDAR, Predictive Maintenance and autonomous systems are not five independent trends.
They are elements of one larger process. The process of moving from railways observed by humans to railways that can observe, analyze and react on their own.
VAR24 wants to be part of that process.
VAR24. Sees. Analyzes. Reacts.
