CANBERRA, Australia, Nov. 13, 2025 /PRNewswire/ — Seeing Machines Limited, the advanced computer vision technology company that designs AI-powered operator monitoring systems to improve transport safety announces the launch of its enhanced distraction detection feature, known as “attention sharing”, in its leading Aftermarket safety solution, Guardian Generation 3. This feature is the first of its kind available to fleets worldwide, capturing subtle distraction events that other driver safety systems often miss.
Attention sharing marks a major leap forward in driver distraction detection, measuring not just where a driver looks, but precisely how much time they spend with their eyes off the road. By accurately tracking cumulative glance time away from the driving task, for example any number of quick glances to a phone or other in-cabin systems, attention sharing detection offers an unprecedented window into distraction risk, enabling intervention before safety is compromised.
“Distraction isn’t always obvious,” said Paul McGlone, CEO of Seeing Machines. “While many systems can detect a driver looking down for a few seconds, this new feature goes further – it identifies the repeated, split-second glances that, when added up, can be just as dangerous. This is a world-first for commercial fleets and a game-changer for road safety, exactly what we at Seeing Machines have set out to achieve.”
The launch of attention sharing detection comes at a critical time for the transport industry. Global studies show that short, frequent glances away from the road can significantly increase crash risk (Klauer et al., 2006; Olson et al., 2009) and occur more frequently than previously thought.[1], [2] The “attention sharing” feature is now available on all new Guardian Gen 3 systems and is being deployed over-the-air to Guardian Gen 3 systems already installed in trucks and buses.
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Unlike systems that rely solely on head position or single, prolonged glances, Guardian continuously tracks eye gaze direction in real time, delivering unmatched accuracy. This ensures drivers are alerted only when genuine risk is detected, reducing irritation and fatigue from unnecessary alarms.
Seeing Machines’ technology is already protecting millions of drivers across the globe, preventing fatigue- and distraction-related incidents daily. With attention sharing detection, commercial fleets using Guardian Generation 3 have an automotive-grade safety technology, scientifically designed and proven to significantly reduce risk and save lives.
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References |
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1. Klauer, S. G., Dingus, T. A., Neale, V. L., Sudweeks, J. D., & Ramsey, D. J. (2006). The impact of driver inattention on near-crash/crash risk: An analysis using the 100-car naturalistic driving study data (No. DOT HS 810 594). United States. Department of Transportation. National Highway Traffic Safety Administration. |
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2. Olson, R. L., Hanowski, R. J., Hickman, J. S., & Bocanegra, J. (2009). Driver distraction in commercial vehicle operations (No. FMCSA-RRT-09-042). United States. Department of Transportation. Federal Motor Carrier Safety Administration |
About Seeing Machines (AIM: SEE), a global company founded in 2000 and headquartered in Australia, is an industry leader in vision-based monitoring technology that enable machines to see, understand and assist people. Seeing Machines’ technology portfolio of AI algorithms, embedded processing and optics, power products that need to deliver reliable real-time understanding of vehicle operators. The technology spans the critical measurement of where a driver is looking, through to classification of their cognitive state as it applies to accident risk. Reliable “driver state” measurement is the end-goal of Driver Monitoring Systems (DMS) technology. Seeing Machines develops DMS technology to drive safety for Automotive, Commercial Fleet, Off-road and Aviation. The company has offices in Australia, USA, Europe and Asia, and supplies technology solutions and services to industry leaders in each market vertical. www.seeingmachines.com
SOURCE Seeing Machines Limited
