Pilot Workload Rating Predictions Using Image Data and Recurrent Neural Networks

Abstract

Verbatim from NASA NTRS. Not paraphrased, not summarized.

In this work, we augmented existing methods for estimating pilot workload ratings with deep neural networks trained using data from simulated flight tests in the Vertical Motion Simulator (VMS). We used an existing method, Spare Capacity Operations Estimator (SCOPE), along with a recurrent neural network and conducted comparison studies between the two methods individually, and when used together. We found that using both methods together can improve the result over using either approach alone. In our first test case, we achieved an improved linear correlation coefficient of 0.409 over that of SCOPE alone at 0.352 on the training dataset. Through cross validation, we also found that the results may be dependent on the split of training vs. validation data, and that further investigation should be conducted to understand what additional inputs to the neural network model should be made.

Authors

• Keiko Nagami Ames Research Center
• Carlos Malpica Ames Research Center
• Mac Schwager Stanford University

Keywords

• Image Data
• Recurrent Neural Networks
• Pilot Workload
• Predictions