Unnoticed eye movements can be key for better self driving cars, says study

Unnoticed eye movements serve a vital purpose in allowing us to stably recognize objects. These findings can be applied to machine vision, for example, making it easier for self driving cars to learn how to recognize important features on the road.

New Delhi: Artificial neural networks that learn to recognize objects more quickly and accurately have been developed by Andrea Benucci and colleagues at the RIKEN Center for Brain Science. The study, recently published in the scientific journal PLOS Computational Biology, focuses on all the unnoticed eye movements that we make.

It shows that they serve a vital purpose in allowing us to stably recognize objects. These findings can be applied to machine vision, for example, making it easier for self-driving cars to learn how to recognize important features on the road. What likely makes this perceptual stability possible are neural copies of the movement commands.

In addition to stable perception, evidence suggests that eye movements, and their motor copies, might also help us to stably recognize objects in the world, but how this happens remains a mystery. Benucci developed a Convolutional Neural Network (CNN) that offers a solution to this problem. The CNN was designed to optimize the classification of objects in a visual scene while the eyes are moving. (ANI)