abstract

Towards Enhancement of Unmanned Aerial Vehicle (UAV) Operators' Situation Awareness: How Often Should their Command and Control Map be Updated

Authors:

Yisrael Parmet,

Tal Oron-GiladAuthors Info & Claims

HRI '20: Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction

Pages 538 - 540

https://doi.org/10.1145/3371382.3378237

Published: 01 April 2020 Publication History

Abstract

Operators of military Unmanned Aerial Vehicles (UAVs) work in dynamic environments, where they must use shared command and control (C2) maps to orient, plan and perform their work. The map is overloaded with information that is irrelevant to their immediate operational mission. This clutter may harm their situation awareness (SA) and increase workload. An intelligent and dynamic filter algorithm has been developed to reduce the clutter by filtering information items on the map based on the environmental context. Implementing it raises questions regarding the update rate of the map filter. Two update rates were tested and their effect on UAV operators' workload and SA was examined empirically. Operators benefited from higher update rates in terms of SA and workload. This is an important step towards the development of the algorithm, which conceptualizes how intelligent algorithms can be used to improve human operators' interaction with autonomous systems.

References

[1]

Yonatan Back, Yuval Zak, Yisrael Parmet, and Tal Oron-Gilad. 2019. Using Cognitive Work Analysis to Understand UAS Operators' Map Display Needs . Submitted to Requirements Engineering, April 2019 (2019).

[2]

Gloria L. Calhoun, Mark H. Draper, Michael F. Abernathy, Michael Patzek, and Francisco Delgado. 2005. Synthetic vision system for improving unmanned aerial vehicle operator. In Proc. SPIE 5802, Enhanced and Synthetic Vision 2005, Vol. 5802. 219--230.

[3]

Mica R Endsley. 2000. Theoretical Underpinnings of Situation Awareness: A Critical Review . In Situation Awareness Analysis and Measurement. Lawrence Erlbaum Associates, 3--32.

[4]

J. Everaerts. 2008. The Use of Unmanned Aerial Vehicles (UAVs) for Remote Sensing and Mapping . The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVII, Part B1 (2008), 1187--1192.

[5]

Dexter Filkins. 2010. Operators of Drones Are Faulted in Afghan Deaths . https://www.nytimes.com/2010/05/30/world/asia/30drone.html

[6]

James J Gibson and Laurence E Crooks. 1938. A Theoretical Field-Analysis of Automobile-Driving . The American journal of psychology, Vol. 51, 3 (1938), 453--471.

[7]

Kurtulus Izzetoglu, Hasan Ayaz, James T. Hing, Patricia A. Shewokis, Scott C. Bunce, Paul Y. Oh, and Banu Onaral. 2015. Uav Operators Workload Assessment by Optical Brain Imaging Technology (fNIR) . In Handbook of Unmanned Aerial Vehicles . Springer Netherlands, Dordrecht, 2475--2500.

[8]

Lanny Lin and Michael A. Goodrich. 2014. Hierarchical heuristic search using a gaussian mixture model for UAV coverage planning . IEEE Transactions on Cybernetics, Vol. 44, 12 (12 2014), 2532--2544.

[9]

Laura R. Marusich, Jonathan Z. Bakdash, Emrah Onal, Michael S. Yu, James Schaffer, J. ODonovan, T. Ho llerer, Norbou Buchler, and Cleotilde Gonzalez. 2016. Effects of Information Availability on Command-and-Control Decision Making: Performance, Trust, and Situation Awareness . Human Factors: The Journal of the Human Factors and Ergonomics Society, Vol. 58, 2 (3 2016), 301--321.

[10]

Gary B. Reid and Thomas E. Nygren. 1988. The Subjective Workload Assessment Technique: A Scaling Procedure for Measuring Mental Workload . Advances in Psychology, Vol. 52, C (1 1988), 185--218.

[11]

Gary B Reid, Scott S Potter, and Jeine R Bressler. 1988. Subjective Workload Assessment Technique (SWAT): A User's Guide. Technical Report. Armstrong Aerospace Medical Research Laboratory.

[12]

C. Sandom. 2000. Operator Situational Awareness and System Safety. In IEE One Day Seminar on Systems Dependency on Humans, Vol. 2000. IEE, 5--5.

[13]

Thom Shanker and Matt Richtel. 2011. In New Military, Data Overload Can Be Deadly . http://www.nytimes.com/2011/01/17/technology/17brain.html

[14]

Yuval Zak, Tal Oron-Gilad, and Yisrael Parmet. 2018. Operator Workload Reduced in Unmanned Aerial Vehicles : Making Command and Control ( C2 ) Maps More Useful. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting. Philadelphia, PA, USA.

[15]

Yuval Zak, Yisrael Parmet, and Tal Oron-Gilad. 2019 a. Making Command and Control Maps More Useful for Operators of Unmanned Aerial Systems : A Preliminary Model and Empirical Approach. In HSI2019 . Biarritz, France.

[16]

Yuval Zak, Yisrael Parmet, and Tal Oron-Gilad. 2019 b. Towards the Development of a Display Filter Algorithm for Command and Control (C2) Maps for Operators of Unmanned Aerial Systems. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (CHI EA '19). ACM, Glasgow, UK.

Digital Library

Cited By

Huang HZhang QGeng KYin G(2023)‘’Impact of Visual Encoding on Automation Transparency in Drone Interface2023 7th CAA International Conference on Vehicular Control and Intelligence (CVCI)10.1109/CVCI59596.2023.10397402(1-6)Online publication date: 27-Oct-2023
https://doi.org/10.1109/CVCI59596.2023.10397402
Zak YParmet YOron-Gilad T(2022)Facilitating the Work of Unmanned Aerial Vehicle Operators Using Artificial Intelligence: An Intelligent Filter for Command-and-Control Maps to Reduce Cognitive WorkloadHuman Factors: The Journal of the Human Factors and Ergonomics Society10.1177/0018720822108196865:7(1345-1360)Online publication date: 7-Apr-2022
https://doi.org/10.1177/00187208221081968

Recommendations

Towards the Development of a Display Filter Algorithm for Command and Control (C2) Maps for Operators of Unmanned Aerial Systems
CHI EA '19: Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems

Operators of military unmanned aerial vehicles (UAVs) work in highly dynamic environments. They have to complete numerous tasks, sometimes simultaneously, while maintaining high situational awareness (SA) and making rapid decisions. Their main focus is ...
A decomposition of UAV-related situation awareness
HRI '06: Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction

This paper presents a fine-grained decomposition of situation awareness (SA) as it pertains to the use of unmanned aerial vehicles (UAVs), and uses this decomposition to understand the types of SA attained by operators of the Desert Hawk UAV. Since UAVs ...
Unmanned Aerial Vehicle in the Machine Learning Environment
Abstract
Unmanned Aerial Vehicles and machine learning have started gaining attentions of academic and industrial research. The Unmanned Aerial Vehicles have extended the freedom to operate and monitor the activities from remote locations. This study ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

HRI '20: Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction

March 2020

702 pages

ISBN:9781450370578

DOI:10.1145/3371382

General Chairs:
Tony Belpaeme
Ghent University, Belgium
,
James Young
University of Manitoba, Canada
,
Program Chairs:
Hatice Gunes
University of Cambridge, UK
,
Laurel Riek
UC San Diego, USA

Copyright © 2020 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 April 2020

Check for updates

Author Tags

Qualifiers

Abstract

Conference

HRI '20

Sponsor:

HRI '20: ACM/IEEE International Conference on Human-Robot Interaction

March 23 - 26, 2020

Cambridge, United Kingdom

Acceptance Rates

Overall Acceptance Rate 192 of 519 submissions, 37%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
183
Total Downloads

Downloads (Last 12 months)10
Downloads (Last 6 weeks)1

Reflects downloads up to 27 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Huang HZhang QGeng KYin G(2023)‘’Impact of Visual Encoding on Automation Transparency in Drone Interface2023 7th CAA International Conference on Vehicular Control and Intelligence (CVCI)10.1109/CVCI59596.2023.10397402(1-6)Online publication date: 27-Oct-2023
https://doi.org/10.1109/CVCI59596.2023.10397402
Zak YParmet YOron-Gilad T(2022)Facilitating the Work of Unmanned Aerial Vehicle Operators Using Artificial Intelligence: An Intelligent Filter for Command-and-Control Maps to Reduce Cognitive WorkloadHuman Factors: The Journal of the Human Factors and Ergonomics Society10.1177/0018720822108196865:7(1345-1360)Online publication date: 7-Apr-2022
https://doi.org/10.1177/00187208221081968

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten