Technology

The European Train Control System ETCS is part of the ERTMS - European Rail Traffic Management System -, an EU project for the standardisation of European rail traffic in the area of automatic train control, radio and traffic control. The main task was and is the standardisation of the technical interfaces between infrastructure and vehicles with the aim of free and above all non-discriminatory technical access for railway operators (RUs) to the European infrastructures.

The Digital interlocking (DSTW) is a modification of the electronic interlockings (ESTW). It is controlled by a new operating system as part of the digital control-command and signalling. Digital interlockings replace many different types of interlockings of various generations. In comparison to the ESTW, a DSTW transmits the control commands digitally via fibre optic cable to

The integrated control and operating system combines all operationally required applications of control points and standardises the operation of interlockings. Standardisation is guaranteed by using a newly developed interface of digital interlocking and ETCS. All interlockings can be operated, regardless of the manufacturer. This harmonisation significantly simplifies operation, training and maintenance.

Digital railway technologies demand a new and safe data processing infrastructure for use on trains and track. Oriented on the latest trends in cloud computing, the technology still meets stringent railroad standards. Digitale Schiene Deutschland relies on technologies such as artificial intelligence, high-precision positioning systems and environmental perception using sensor technology to control train traffic even more efficiently in future and guarantee journeys at optimal intervals. This requires large amounts of data to be processed in real time.

Due to the complexity of the digital railroad system architecture, the desired high degree of automation and the multitude of newly introduced technologies such as artificial intelligence and sensor technology, the railroad system of the future offers many weak spots for possible cyber attacks. Even if they generally do not lead to personal injury, they can still cause considerable commercial and financial damage. For this reason, the topic of IT/OT security is a high priority for Digitale Schiene Deutschland and in the development of the digital railroad system.

Due to the digitalization of the railway system, real-time data transmission between train and track through a high-performance mobile radio network is becoming increasingly important. In addition to rapidly clocked control information through the use of the European Train Control System (ETCS) in conjunction with e.g., Automatic Train Operation technologies, the data collected by sensors and cameras must also be processed and transmitted. This leads to increasing data rates in railway operations.

Numerous pilot tests conducted by Digitale Schiene Deutschland over the past four years underline the need for highly accurate, up-to-date and digitally available infrastructure data. Therefore Digitale Schiene Deutschland is working intensively on a so-called “Digital Register (DR)”, which is intended to provide a uniform data basis for digital railway operating systems.

To digitize the railroad system, technology is required that can detect the train’s surroundings. For this purpose, high-performance sensor technology (camera, LiDAR & radar) is used in and on the train. Technical realization is achieved by sensors mounted on the front of the train.

In future, to facilitate the precise control of train traffic at all times and achieve optimal utilization of the existing network, the AI-based capacity and traffic management system must know the exact position of all moving trains. Innovative digital technologies are becoming established in this area too.  In order to be able to reliably establish the precise location of a train, various independent location measurements usually need to be made and consolidated.

Automated vehicle systems are an essential prerequisite for highly and fully automated driving on the railways. They can be regarded as an autopilot for train traffic. The technology implements predefined instructions for accelerating and braking the train. The reaction times between transmission and implementation of the driving commands are significantly shorter and less subject to fluctuations than with manual control.

To guarantee safe train traffic, trains must maintain a safe distance from each other due to their long braking distance. For this purpose, a line is nowadays divided into stationary, physical block sections, which are closed to the following train as long as the preceding train has not yet left them. With the Advanced Protection System, Digitale Schiene Deutschland is therefore working with European partners to develop a train-centric safety technology that breaks away from securing train traffic via fixed blocks and introduces a new type of safety logic. This results in more capacity, more flexible operations and simplified trackside equipment. 

To be able to drive in fully automated mode (Grade of Automation 4, GoA4), an environmental perception sensor system coupled with artificial intelligence must be capable of recognizing and interpreting track environments and obstacles. The technical systems must assume control over complex tasks such as route monitoring during train journeys and make smart decisions in the event of irregular occurrences. Due to the high number of possible situations encountered during railway operations, developing, and training the AI required for this represents a major challenge.

When thousands of trains are on the move in dense traffic, rapid decisions must be made to adjust the operationsin the event of disruptions., These can trigger a cascade of further necessary changes. At this level of complexity, today’s largely manual planning and dispatching processes quickly reach their limits. To date, it has only been possible to partly support these processes with IT solutions. The main obstacle has been the lack of scalability of existing methods. It is now possible to overcome this limitation with AI systems that are capable of learning.