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Approaching the Localisation Challenge for Rail Operators

How radar contributes in solving the challenge of perception and localisation for rail operators.

The future railway network requires a higher capacity to accommodate the growing need for public transportation. To achieve this goal, Deutsche Bahn, within the scope of the sector initiative “Digital Rail for Germany”, aims to use cutting-edge technology in order to increase capacity and reliability of the existing railway network, reduce operating costs and CO2 emissions and allow a higher level of flexibility in railway scheduling. Two key elements of overcoming the challenge are precise localisation and train-front object detection.

To assess emerging technologies in these and other fields Deutsche Bahn has created the ‘advanced TrainLab‘. This allows new technology companies to be brought in and enables continuous studies in real case scenarios for railway applications.

Perception and localisation in all weather and light conditions

Deutsche Bahn has used the Navtech Radar sensor for their feasibility studies as the radar’s range fits their application needs. Our world-leading sensors work in all environments, all the time, providing reliable performance in adverse conditions when other sensors fail. With unrivalled availability, our high-resolution, 360°, long-range radar builds a data-rich picture of its surroundings, whatever the conditions, so you can operate your fleet 24/7, safely and efficiently.

It means detection reliability when you need it the most. Working beyond the limits of a person’s capability, the sensor can see when humans can’t, adding an extra level of safety to challenging environments.

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With global coverage, we will get you up and running swiftly and seamlessly. Our sensors are fully ROS compatible and easy to integrate with low power requirements. Radar has long been recognised as the best sensor technology to deliver reliable performance in adverse environmental conditions. You can rely on our sensors, when you need them the most.

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