A dragonfly's ability to predict the movement of its prey is being harnessed to improve the way driverless cars manoeuvre in traffic.
Researchers from the University of Adelaide in South Australia and Lund University in Sweden have found a neuron in dragonfly brains that anticipates movement.
The properties of the target-detecting neurons are being replicated in a small robot in Adelaide to test its potential for artificial vision systems used in driverless cars.
The study of the neuron, known as CSTMD1, is published today in the journal eLife.
Research supervisor and lecturer in the University of Adelaide’s Medical School Steven Wiederman said the new discovery would have an impact on driverless cars and other robotic vision systems.
“It is one thing for artificial systems to be able to see moving targets but tracing movement so it can move out of the way of those things is a really important aspect to self-steering vehicles,” Dr Wiederman said.
“What we found was the neuron in dragonflies not only predicted where a target would reappear, it also traced movement from one eye to the other – even across the brain hemispheres.
“This is also evident in cluttered environments where an object might be difficult to distinguish from the background.”
The research team, led by University of Adelaide PhD student Joseph Fabian, found that target-detecting neurons increased dragonfly responses in a small “focus” area just in front of the location of a moving object being tracked.
If the object then disappeared from the field of vision, the focus spread forward over time, allowing the brain to predict where the target was most likely to reappear based on the previous path along which the object was moving.
Dr Wiederman said this phenomenon was not only evident when dragonflies hunted small prey but when they chased after a mate as well.
This is similar to when a human judges the trajectory of a ball as it is thrown to them, even when it is moving against the backdrop of a cheering crowd.
Techniques based on this phenomena are being actively tested for autonomous vehicles, allowing them to make efficient use of robotic vision systems in busy, fast-paced situations.