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Abstract-Discrete event simulations for futuristic unmanned vehicle (UV) systems enable a cost and time effective methodology for evaluating various autonomy and humanautomation design parameters. Operator mental workload is an important factor to consider in such models. We present that the effects of operator workload on system performance can be modeled in such a simulation environment through a quantitative relation between operator attention and utilization, i.e., operator busy time used as a surrogate real-time workload measure. In order to validate our model, a heterogeneous UV simulation experiment was conducted with 74 participants. Performancebased measures of attention switching delays were incorporated in the discrete event simulation model via UV wait times due to operator attention inefficiencies (WTAI). Experimental results showed that WTAI is significantly associated with operator utilization (UT), such that high UT levels correspond to higher wait times. The inclusion of this empirical UT-WTAI relation in the discrete event simulation model of multiple UV supervisory control resulted in more accurate replications of data, as well as more accurate predictions for alternative UV team structures. These results have implications for the design of future human-UV systems, as well as more general multiple task supervisory control models.

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A cognitive task analysis (CTA) is an effective analysis technique for deriving design requirements for many task domains. However, traditional CTA approaches have limited applicability to futuristic systems because CTA approaches generally require access to subject matter experts, documentation, and previous implementations from which to draw assumptions and expertise. In this paper, we introduce a hybrid CTA framework that allows the generation of information and display requirements for futuristic systems for which no current implementations exist. This analysis technique involves a four-step process including: 1) generating a scenario task overview, 2) generating an event flow diagram, 3) generating situation awareness requirements, and 4) creating decision ladders for critical decisions. We demonstrate the effectiveness of this process through a case study in which functional and interface requirements are generated for the supervisory control of multiple, heterogeneous unmanned vehicles.

Advances in automation are making it possible for a single operator to control multiple unmanned vehicles (UVs). However, the complex nature of these teams presents a difficult and exciting challenge for designers of human-UV systems. To build such systems effectively, models must be developed that describe the behavior of the human-UV team and that predict how alterations in team composition and system design will affect the system's overall performance. In this paper, we present a method for modeling human-UV systems consisting of a single operator and multiple independent UVs. Via a case study, we demonstrate that the resulting models provide an accurate description of observed human-UV systems. Additionally, we demonstrate that the models can be used to predict how changes in the human-UV interface and the UVs' autonomy alter the system's performance.