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Advanced Video Activity Analytics (AVAA): Human Performance Model Report
"This report describes the initial human performance modeling effort of the Advanced Video Activity Analytics (AVAA) system. AVAA was designed to help US Army Intelligence Analysts exploit full-motion video more efficiently and effectively. The goal of the modeling effort is to provide an understanding of the current state of the system with respect to the impact on human performance and workload and the predicted impact of enhanced computer vision algorithms on overall system performance (e.g., throughput, operator workload) in the context of realistic missions. Modelers used Command, Control, and Communications--Techniques for Reliable Assessment of Concept Execution (C3TRACE) to develop task network models for forensic and real-time intelligence gathering. Data from future field experiments can be incorporated into the basic modeling environment to validate and extend the preliminary results. This human performance modeling effort was conducted as part of a larger human systems integration effort to evaluate the usability and effectiveness of AVAA."
U.S. Army Research Laboratory
Plott, Beth M.; McDermott, Patricia L.; Barnes, Michael
2017-12
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Decision Support for Network-Centric Command and Control: Adaptive Automation for Human-Robot Teaming in Future Command and Control Systems
"Advanced command and control (C2) systems such as the U.S. Army's Future Combat Systems (FCS) will increasingly use more flexible, reconfigurable components including numerous robotic (uninhabited) air and ground vehicles. Human operators will be involved in supervisory control of uninhabited vehicles (UVs) with the need for occasional manual intervention. This paper discusses the design of automation support in C2 systems with multiple UVs. Following a model of effective human-automation interaction design (Parasuraman et al. 2000), we propose that operators can best be supported by high-level automation of information acquisition and analysis functions. Automation of decision-making functions, on the other hand, should be set at a moderate level, unless 100 percent reliability can be assured. The use of adaptive automation support technologies is also discussed. We present a framework for adaptive and adaptable processes as methods that can enhance human-system performance while avoiding some of the common pitfalls of 'static' automation such as over-reliance, skill degradation, and reduced situation awareness. Adaptive automation invocation processes are based on critical mission events, operator modeling, and real-time operator performance and physiological assessment, or hybrid combinations of these methods. We describe the results of human-in-the-loop experiments involving human operator supervision of multiple UVs under multi-task conditions in simulations of reconnaissance missions. The results support the use of adaptive automation to enhance human-system performance in supervision of multiple UVs, balance operator workload, and enhance situation awareness. Implications for the design and fielding of adaptive automation architectures for C2 systems involving UVs are discussed."
Command and Control Research Program (U.S.)
Cosenzo, Keryl A.; Parasuraman, R.; Barnes, Michael
2007
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RoboLeader: A Surrogate for Enhancing the Human Control of a Team of Robots
"RoboLeader, an intelligent agent, was developed to assist human operators in controlling a team of unmanned vehicles through route planning tasks. Though there were no significant differences between the RoboLeader and baseline conditions in target detection, the RoboLeader group reduced their mission completion times by approximately 13% compared to the baseline group. Operators' target detection performance in the four- and eight-vehicle conditions were analyzed, with results showing significantly fewer targets identified in the eight-vehicle condition compared to the four-vehicle condition. Participants with higher spatial ability detected more targets than those with lower spatial ability. Participants experienced significantly higher workload in the eight-vehicle condition compared to the four-vehicle condition. Participants with better attentional control reported lower workload than those with poorer attentional control, and females reported significantly higher workload than males."
U.S. Army Research Laboratory
Barnes, Michael J.; Qu, Zhihua; Chen, Jessie Y. C.
2010-02
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Designing for Humans in Autonomous Systems: Military Applications
"The purpose of this report is to review U.S. Army Research Laboratory (ARL)-sponsored research on the human's role in future autonomous systems and to derive design guidelines to foster human/autonomy collaboration. The research was conducted as part of a larger Army program, Safe Operations for Unmanned systems for Reconnaissance in Complex Environments (SOURCE), that focused on developing safe autonomy for urban applications. The human-autonomy design research encompasses agent reliability, span of control, safety issues, individual differences, training, function allocation, and results from field experiments evaluating advanced interface solutions. The main sections of this report cover (1) autonomy and intelligent agents, (2) RoboLeader, (3) safety for autonomous systems, (4) naturalistic interfaces, and (5) situation understanding using unmanned vehicle imagery. After each section, implications of the results are summarized to develop design guidelines for incorporating humans into autonomous military systems."
U.S. Army Research Laboratory. Human Research and Engineering Directorate
Barnes, Michael J.; Chen, Jessie Y.; Jentsch, Florian . . .
2014-01
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