  Vertical take-off and landing (VTOL) aircraft do not require a prolonged
runway, thus allowing them to land almost anywhere. In recent years, their
flexibility has made them popular in development, research, and operation. When
compared to traditional fixed-wing aircraft and rotorcraft, VTOLs bring unique
challenges as they combine many maneuvers from both types of aircraft. Pilot
workload is a critical factor for safe and efficient operation of VTOLs. In
this work, we conduct a user study to collect multimodal data from 28 pilots
while they perform a variety of VTOL flight tasks. We analyze and interpolate
behavioral patterns related to their performance and perceived workload.
Finally, we build machine learning models to estimate their workload from the
collected data. Our results are promising, suggesting that quantitative and
accurate VTOL pilot workload monitoring is viable. Such assistive tools would
help the research field understand VTOL operations and serve as a stepping
stone for the industry to ensure VTOL safe operations and further remote
operations.
