How is the Pilot Doing: VTOL Pilot Workload Estimation by Multimodal Machine Learning on Psycho-physiological Signals

TL;DR

This paper presents a multimodal machine learning approach to estimate VTOL pilots' workload using physiological signals, aiming to enhance safety and operational efficiency in VTOL aircraft.

Contribution

It introduces a novel method combining multimodal data and machine learning to accurately estimate pilot workload during VTOL operations.

Findings

Workload estimation models show promising accuracy.

Physiological signals correlate with perceived workload.

Potential for real-time workload monitoring in VTOLs.

Abstract

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.