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Visual Attention Prediction Improves Performance of Autonomous Drone Racing Agents by Pfeiffer, Christian; Wengeler, Simon; Loquercio, Antonio; Scaramuzza, Davide is a scholarly article available to read on EtoBox.

What is Visual Attention Prediction Improves Performance of Autonomous Drone Racing Agents about?

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 task. We test this hypothesis using eye gaze and flight trajectory data from 18 human drone pilots to train a visual attention prediction model. We then use this visual attention prediction model to train an end-to-end controller for vision-based autonomous drone racing using imitation learning. We compare the drone racing performance of the attention-prediction controller to those using raw image inputs and image-based abstractions (i.e., feature tracks). Comparing success rates for completing a challenging race track by autonomous flight, our results show that the attention-prediction based controller (88% success

Author

Pfeiffer, Christian; Wengeler, Simon; Loquercio, Antonio; Scaramuzza, Davide

Published

2022

Language

EN