  This paper introduces the GazeRace method for drone navigation, employing a
computer vision interface facilitated by eye-tracking technology. This
interface is designed to be compatible with a single camera and uses a
convolutional neural network to convert eye movements into control commands for
the drone. Experimental validation demonstrates that users equipped with the
eye-tracking interface achieve comparable performance to a traditional remote
control interface when completing a drone racing task.
  Ten participants completed flight tests in which they navigated a drone
through a racing track in a Gazebo simulation environment. Users reduced drone
trajectory length by 18% (73.44 m vs. 89.29 m) using the eye-tracking interface
to navigate racing gates effectively. The time taken to complete the route
using the eye-tracking method (average of 70.01 seconds) was only 3.5% slower
than using the remote control method (also average of 70.01 seconds),
indicating the good efficiency of the interface. It is also worth mentioning
that four of the participants completed the race with an average time that was
25.9% faster than the other participants. In addition, users evaluated highly
the performance (M = 34.0, SD = 14.2) and low frustration (M = 30.5, SD = 9.2)
with the eye-tracking interface compared to performance (M = 63.0, SD = 10.1)
and frustration (M = 49.0, SD = 11.7) with the baseline remote controller. The
hedonic quality (M = 1.65, SD = 0.45) was also evaluated high by the users in
the UEQ questionnaire.
