Rail Vision Ltd. (NASDAQ: RVSN, FSE: C80) announced that its AI-powered ShuntingYard system has been integrated into Railserve’s newly introduced YardGUARD industrial railyard safety system. The integration positions the ShuntingYard system as a core technology within Railserve’s WatchGUARD situational awareness and obstacle detection solution. This development follows a recently signed memorandum of understanding between the two companies.
The combined platform merges Rail Vision’s perception technology with Railserve’s communications and cloud-based monitoring capabilities, offering real-time visibility, obstacle detection, synchronized alerts, and automatic braking support. The ShuntingYard system is currently being showcased at Railway Interchange 2026 in Omaha, Nebraska, as part of Railserve’s broader yard safety platform.
Rail Vision is an early commercialization stage technology company that develops advanced AI-integrated sensing systems for railway safety. Its proprietary multi-spectral electro-optic platforms provide extended-range situational awareness and real-time hazard detection using machine learning algorithms to identify and classify obstacles. The company’s cloud-based platform transforms railway operational data into actionable insights, helping to optimize performance, reduce downtime, and improve safety.
As Rail Vision expands its global footprint, its AI-driven perception technology supports safer operations, reduces operational risk, and enables the transition to fully autonomous operations. The company also holds a 51% stake in Quantum Transportation, which has an exclusive sub-license for rail technologies under an innovative pending patent in quantum error correction owned by Ramot, the technology transfer company of Tel Aviv University.
For more information, visit the company’s newsroom at http://ibn.fm/RVSN. The full press release is available at https://ibn.fm/7wqO8.
This integration underscores the growing importance of AI and advanced sensing in railway safety, offering tangible improvements in obstacle detection and operational efficiency for industrial railyards.