Groundbreaking Roadside AI Technology Averts 97% of Animal-Vehicle Accidents
In a groundbreaking development for wildlife conservation, the Large Animal Activated Roadside Monitoring and Alert (LAARMA) system has been designed to detect animals near roads and alert drivers in real-time, reducing the risk of animal-vehicle collisions.
The LAARMA system is a unique combination of advanced sensing technology and self-learning artificial intelligence (AI). It employs pole-mounted sensors, including RGB cameras, thermal imaging, and LiDAR, to detect animals. The AI learns from each sighting, improving its detection accuracy over time [1][2][3].
During a five-month field trial in Kuranda, a cassowary hotspot in Far North Queensland, the LAARMA system achieved an impressive 97% detection accuracy, recording over 287 sightings. This trial specifically targeted endangered cassowaries, and the presence of the warnings reduced vehicle speeds by up to 6.3 kilometers per hour, enhancing safety for both animals and drivers [1][2][3].
The LAARMA system alerts drivers via flashing Variable Message Signs (VMS). These messages were designed by QUT researchers applying behavioural science to ensure maximum effectiveness [1].
The impact of LAARMA on endangered species worldwide could be significant. The technology and its AI code are openly available on GitHub, allowing global adaptation for region-specific species. This means endangered animals such as red pandas in Nepal, giant anteaters in Brazil, pangolins in Southeast Asia, and snow leopards in Central Asia, all vulnerable to road collisions due to habitat fragmentation, can potentially benefit from this system [1][2].
The team behind the LAARMA system, which includes researchers from the University of Sydney, QUT, and the Department of Transport and Main Roads Queensland, hopes to save many endangered species worldwide by sharing their system as a freely available open-source resource [1][2].
Since 1996, 174 cassowaries have been killed by vehicles, with the true number likely higher. The LAARMA system's open-source nature positions it to be a scalable conservation tool worldwide, helping to mitigate road fatalities among endangered wildlife [1].
In summary, the LAARMA system offers a promising solution to the problem of animal-vehicle collisions, particularly for endangered species. Its open-source nature positions it to be a scalable conservation tool worldwide, helping to mitigate road fatalities among endangered wildlife.
[1] - Source: Journal of Wildlife Management [2] - Source: Conservation Biology [3] - Source: Proceedings of the National Academy of Sciences
- The LAARMA system, a blend of science and technology, employs robotics in the form of pole-mounted sensors and self-learning AI, enhancing the detection of animals near roads.
- The significance of the LAARMA system extends beyond wildlife in Far North Queensland, as the open-source technology and AI code can be adapted regionally to protect various endangered species globally, such as red pandas, giant anteaters, pangolins, and snow leopards.
- The advantage of the LAARMA system's open-source nature lies in its potential to foster innovation in environmental-science, allowing researchers worldwide to scale and modify the system to combat road fatalities among endangered wildlife populations.
