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SBB adopts AI to improve railway maintenance

Petar Tsankov
March 3, 2021

LatticeFlow collaborates with SBB, Siemens, and ETH to enable safe, efficient, and cost-effective AI-based railway maintenance

Swiss Federal Railways (SBB) has one of the world’s most dense railway networks spanning over 7,500 km of tracks with 300 tunnels and servicing over 1.25 million passengers every day. The intensive usage requires high investments into operational maintenance to ensure track security and safety.

SBB adopts AI to improve railway maintenance
Manual rail inspections require significant human labor and people deployed in dangerous environments that are sometimes difficult to access. Image source: SBB.

Track safety is traditionally done by manually inspecting the rails, a task requiring significant human labor and people deployed in dangerous environments that are sometimes difficult to access. SBB has set out to revolutionize railway maintenance by automating inspections using the latest generation of Artificial Intelligence (AI). A specially dedicated train instrumented with cameras will collect high-quality image data of the rails, which will then be processed by AI to identify any rail defects. This will make railway inspections efficient, safe, and cost-effective.

A specially dedicated train instrumented with cameras collects high-quality image data of the rails, which will then be processed by AI to identify any rail defects. Image source: Online.

Dr. Ilir Fetai and Dr. Andre Roger, who lead the Center of Competence in Machine Perception at SBB, are dedicated to making these plans a reality, reinforcing SBB’s position as the innovation leader in the railway domain. Dr. Fetai said:

Artificial Intelligence (AI) is one of the core topics at SBB, as we see a huge potential in its application for an improved, intelligent, and automated monitoring of our railway infrastructure.

Railway inspection is, however, a safety-critical task. How can we ensure that the new AI-based system is reliable? AI models often work well in lab environments, but perform poorly when deployed in the wild. This has thus far limited their use in such safety-critical tasks. Before SBB hands off the inspection task to the “eyes” of the new AI system, it needs to ensure that the trained AI models correctly identify rail defects under different environmental conditions. Lens scratches, raindrops or snow on the rails, and other variables that affect the image quality must not undermine the AI system’s reliability.

To this end, SBB has partnered with LatticeFlow, ETH, and Siemens.

Our collaboration on robust and reliable AI with LatticeFlow, ETH, and Siemens has a crucial role in enabling us to fully exploit the advantages of using AI” — Dr. Fetai added

As a first step, the teams will assess the reliability of SBB’s AI models using LatticeFlow’s platform for trustworthy AI. The models will then be improved if needed. In parallel to the technical work, the partners will analyze current safety standards and their application to the railway domain. The ultimate goal of the collaboration is to demonstrate that the AI models are reliable and can be safely deployed in production.

LatticeFlow is a company founded by leading researchers and professors from ETH Zurich. The company is building the world’s first product that enables AI companies to deliver trustworthy AI models, solving a fundamental roadblock to the wide adoption of AI. To learn more, visit https://latticeflow.ai.

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