Skip to main content

Seoul Robotics on track with Herzog

Companies link up to create automated obstacle detection system for railway/road safety
By Adam Hill April 5, 2022 Read time: 2 mins
SENSR-I enables the detection of more than 500 objects up to 200m ahead (© Val Armstrong | Dreamstime.com)

Seoul Robotics has integrated its 3D perception software with Herzog Technologies' occupancy detection suite to create an automated obstacle detection and warning system.

Herzog builds, operates, and maintains rail systems in North America and Critical Asset Monitoring (CAM) uses Seoul Robotics’ SENSR-I to track and classify objects such as humans, vehicles and bicycles.

The company says that SENSR-I enables the detection of more than 500 objects up to 200m ahead, predicts motion up to three seconds in advance, and provides real-time object perception.

It can be used in areas - such as roads crossing railway tracks - that see trains in close proximity to other modes, including pedestrians.

Since 2011, over 23,000 incidents have occurred where trains have struck trains or people, resulting in 2,700 fatalities and 9,500 injuries, according to the U.S. Department of Transportation Federal Railroad Administration.

CAM's edge detection is combined with an ability to analyse information in real time and make timely decisions on notifying decision makers.

"CAM provides customers with the ability to monitor aspects of their critical infrastructure in ways previously unavailable," says Glen Dargy, VP of technology at Herzog.

“By integrating our products and services with Seoul Robotics’ software platform, we are providing an industry-leading solution.”

The solution is being implemented by Trinity Railway Express, a commuter rail operator between Fort Worth and Dallas, Texas.

"For a rail detection system, every second is critical," says Jerone Floor, VP of product at Seoul Robotics. 

"Trains need adequate time to fully stop and require advanced warning to reduce the chance of a collision."

 

For more information on companies in this article

Related Content

  • PTV & Econolite highlight integration in Umovity mobility update
    October 25, 2023
    Developments include new tool to merge data from different networks in PTV Visum
  • Citilog’s AID ramps up traffic safety with deep learning
    September 17, 2024
    Deep learning is revolutionising traffic safety and reducing congestion by empowering Artificial Intelligence (AI) to more accurately detect incidents, dramatically improving response times. Traditional AI systems often struggle with accuracy, generating false positives that distract from real incidents and require more resources to analyse manually.
  • New York's award-winning traffic control system
    February 28, 2013
    A comprehensive ITS strategy in New York built on a system of key building blocks has been crowned with an IRF award for the city’s Midtown in Motion adaptive control system. Jon Masters reviews New York’s ITS modernisation plan as the city looks to the next phase of expansion. In January this year the International Road Federation (IRF) presented TransCore and the New York City Department of Transportation (NYCDOT) with the IRF Global Road Achievement Award. This was for deployment of New York’s Midtown in
  • Keeping a watching brief over traffic flows
    March 11, 2015
    Monitoring traffic flows is set to become an even bigger challengebut a revolution in camera technology can help, as Patrik Anderson explains. By 2025 almost 60% of the world’s population will live in urban areas and in those cities there will be an estimated 6.2 billion private motorised trips every day. In order to manage this level of traffic growth, traffic management centres (TMCs) will need to both increase their monitoring capabilities and be able to detect traffic problems quickly, efficiently and r