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Management of Multiple Dynamic Human Supervisory Control Tasks

MIT-Boeing Research Project Review Meeting – Feb. 2, 2005. Management of Multiple Dynamic Human Supervisory Control Tasks. Outline. Motivation Experiment Objective Experiment Scenario Experimental Design Human-Automation Interaction Modeling Wait Time Modeling

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Management of Multiple Dynamic Human Supervisory Control Tasks

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  1. MIT-Boeing Research Project Review Meeting – Feb. 2, 2005 Management of Multiple Dynamic Human Supervisory Control Tasks

  2. Outline • Motivation • Experiment Objective • Experiment Scenario • Experimental Design • Human-Automation Interaction Modeling • Wait Time Modeling • Key Experiment Display Elements • Expected Research Results / Benefits • Multi Aerial Unmanned Vehicle Experiment (MAUVE) Program Demo • HAL Lab Tour

  3. Motivation • Consequences of NCW Volume of information Number of information sources Operational tempo • Greater attentional demands on operators • Efficient attention allocation becomes critical to human & system performance!

  4. Experiment Objectives • To investigate: • How operators cope with managing multiple HSC processes simultaneously • What kinds of decision support can aid operators in these situations • What effects human performance limitations have on the overall system

  5. Predator and Global Hawk, both UCAVs currently in use. Experiment Scenario • UAV domain has immediate military applications • NCW concept of swarms • Subject is an operator supervising four separate, independent unmanned aerial vehicles (UAVs) • Objective: • To destroy a set of targets (which may change) within a certain time period, while taking minimum damage from enemy air defenses

  6. The Patriot missile battery, a prominent example of a high level of automation in use today. Experimental Design – Independent Variables • Level of Decision Support (Scheduling Assistance) • Between subjects • 4 levels • Manual = LOA 1 • Passive = LOA • Active = LOA 4 • Super Active = LOA 6 • Amount of Schedule Re-Planning • Within subjects • 3 levels • None • Infrequent • Frequent

  7. Experimental Design – Dependent Variables • Primary task performance – Number/priority of deadlines missed • Performance score • Combines target and threat events • To provide insight into overall test session performance • Secondary task performance – Chat Box • Percentage correct answers Situation awareness metric • Average time to respond Workload metric • Wait Times • Result from deviations from “ideal” mission plan

  8. Human-Automation Interaction Modeling • First proposed by Olsen and Wood (2004) with regard to traditional human-robot interactions • Interaction Time (IT) • The human operator is actively engaged in improving the performance of the vehicle, allowing overall mission accomplishment to occur • Neglect Time (NT) • The vehicle is operating autonomously, needing no input from an operator to continue its mission • Wait Time (WT) • The vehicle needs input from the human to execute its mission

  9. Human-Automation Interaction Modeling

  10. Wait Time Modeling • Wait times dramatically impact system performance and risk of failure in time-critical applications (eg. C2) • Two main categories • Workload Wait Times (W-WT) • Result from operator overload • Situation Awareness Wait Times (SA-WT) • Result from loss of situational awareness • Work-in-progress • Need to accurately model then measure individual WT components • WT can be further broken down

  11. Key Experiment Display Elements – Mission Plan • Current mission plan for each UAV is shown • UAV that operator is currently interacting with highlighted in green • Active targets = red diamonds • Threat areas = yellow circles • Way Points = black triangles • Loiter Points = directed circles

  12. 3 1 2 4 Key Experiment Display Elements - Decision Support Separate marching timeline for each UAV Represents mission plan as laid out on map display Current/future tasks color coded by action

  13. Expected Research Results / Benefits • Validation of wait time models • Conclusions on how different types of wait times influence the overall cost function and fan out • Workload predictive model based on wait times • Further results on the validity of an imbedded chat box as a measure of secondary workload • An evaluation of the timeline decision support tool, with comparisons across its various levels and effectiveness under different re-planning conditions

  14. MAUVE Demo & HAL Lab Tour • Questions?

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