  Humans race drones faster than algorithms, despite being limited to a fixed
camera angle, body rate control, and response latencies in the order of
hundreds of milliseconds. A better understanding of the ability of human pilots
of selecting appropriate motor commands from highly dynamic visual information
may provide key insights for solving current challenges in vision-based
autonomous navigation. This paper investigates the relationship between human
eye movements, control behavior, and flight performance in a drone racing task.
We collected a multimodal dataset from 21 experienced drone pilots using a
highly realistic drone racing simulator, also used to recruit professional
pilots. Our results show task-specific improvements in drone racing performance
over time. In particular, we found that eye gaze tracks future waypoints (i.e.,
gates), with first fixations occurring on average 1.5 seconds and 16 meters
before reaching the gate. Moreover, human pilots consistently looked at the
inside of the future flight path for lateral (i.e., left and right turns) and
vertical maneuvers (i.e., ascending and descending). Finally, we found a strong
correlation between pilots eye movements and the commanded direction of
quadrotor flight, with an average visual-motor response latency of 220 ms.
These results highlight the importance of coordinated eye movements in
human-piloted drone racing. We make our dataset publicly available.
