Visual Attention Prediction Improves Performance of Autonomous Drone Racing Agents. (arXiv:2201.02569v1 [cs.RO])

Humans race drones faster than neural networks trained for end-to-end
autonomous flight. This may be related to the ability of human pilots to select
task-relevant visual information effectively. This work investigates whether
neural networks capable of imitating human eye gaze behavior and attention can
improve neural network performance for the challenging task of vision-based
autonomous drone racing. We hypothesize that gaze-based attention prediction
can be an efficient mechanism for visual information selection and decision
making in a simulator-based drone racing …

agents arxiv attention autonomous performance prediction racing