A team of researchers from the University of Sydney have developed and successfully tested a world-first roadside technology designed to prevent animal–vehicle collisions in regional Australia. The code powering the AI technology will be made available for free worldwide on GitHub, meaning researchers and conservationists will have the ability to develop animal-specific models globally, says TechXplore.
With the code now publicly available, many more endangered species could be saved—including red pandas in Nepal, giant anteaters in Brazil, pangolins in Southeast Asia, and snow leopards in Central Asia, all of which face dangers when crossing roads that fragment their natural habitats.
The study was a joint effort between the University of Sydney, QUT, and the Department of Transport and Main Roads Queensland.
Researchers from the Australian Center for Robotics (University of Sydney) and the Center for Accident Research and Road Safety Queensland led the project. Over 12 months, they developed and tested a system called LAARMA – the Large Animal Activated Roadside Monitoring and Alert system.
LAARMA is a low-cost, AI-powered roadside unit. It uses sensors to detect animals near roads. When it spots one, it triggers nearby flashing Variable Message Signs (VMS) to warn drivers.
The field trial took place in Far North Queensland, where cassowary collisions are common. The system detected cassowaries with 97% accuracy and recorded over 287 sightings. The warning signs worked. When activated, they led to noticeable reductions in vehicle speed, lowering the risk of crashes.
The LAARMA system includes a mix of pole-mounted sensors—RGB cameras, thermal imaging, and LiDAR. It also features self-training AI that learns and improves over time, even without labeled data. By the end of the trial, the system was correctly detecting animals 78.5% of the time within 100 meters.
Over five months, the team set up LAARMA poles in the wild cassowary hotspot of Kuranda.
The AI rapidly improved – going from catching just 4.2% of birds at first sight to 78.5% by the end of the trial. Meanwhile, driver speeds dropped by as much as 6.3 km/h when the signs flashed.
“The system teaches itself to get better,” Dr. Kunming Li from the University of Sydney’s Australian Center for Robotics said. “It’s self-supervised. Every time it spots a cassowary, it learns something new about it.”
Unlike older systems that need to be reprogrammed or trained by humans, LAARMA learns on its own. That means every time it sees a cassowary, it remembers how it looked—whether it was in the shadows, behind trees, or moving fast—and gets better at spotting them in the future.
“It doesn’t just look for a match,” Dr. Li explained. “It starts to understand what a cassowary usually looks like in different situations – like at dawn, in the rain, or half-hidden by bushes. That makes it smarter and more reliable the more it’s used.”
“This is a big step towards autonomous wildlife protection,” Dr. Li added. “LAARMA is far more adaptable and scalable than previous approaches. The more it’s used in the field, the more accurate it becomes. The technology doesn’t just function – it evolves.”
(Pic: Yay Images)
