Quantitative operator strategy comparisons across human supervisory control scenarios

Conference Paper

Human-automation collaborations, like automated driving assistance and piloting drones, have become prevalent as these technologies become more commonplace. Designers need tools that help them understand how and why design interventions may change the strategies of operators in such complex human supervisory control systems. To this end, we demonstrate that when the divergence metric is applied to Hidden Markov Model (HMM) comparisons, it can accurately capture statistical differences between operator strategies for interfaces that embody different tasks. However, the use of such an approach is problematic when used to compare HMM strategy models with non-equivalent observations. To address this limitation, we developed an observation reduction approach and conducted a sensitivity analysis to assess the impact of this approach. Our results show that when comparing two non-equivalent interfaces, our observation reduction approach does not fundamentally change the divergence metric, thus allowing for direct model comparison. The results further show that HMMs from different interfaces produce a much higher divergence metric than model comparison from the same people who repeatedly use the same interface. Future work will examine if this method can detect differences in models with different tasks or modified interfaces.

Full Text

Duke Authors

Cited Authors

  • Zhu, H; Xu, R; Cummings, ML

Published Date

  • October 24, 2020

Published In

Start / End Page

  • 10968 - 10974

Electronic International Standard Serial Number (EISSN)

  • 2153-0866

International Standard Serial Number (ISSN)

  • 2153-0858

International Standard Book Number 13 (ISBN-13)

  • 9781728162126

Digital Object Identifier (DOI)

  • 10.1109/IROS45743.2020.9341135

Citation Source

  • Scopus