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1 Visualization of Uncertainty in the Common Operating Picture WorkGroup 4: Justin Hollands David Baar Thomas Porathe Liesa Lapinski Harvey Smallman Szymon.

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Presentation on theme: "1 Visualization of Uncertainty in the Common Operating Picture WorkGroup 4: Justin Hollands David Baar Thomas Porathe Liesa Lapinski Harvey Smallman Szymon."— Presentation transcript:

1 1 Visualization of Uncertainty in the Common Operating Picture WorkGroup 4: Justin Hollands David Baar Thomas Porathe Liesa Lapinski Harvey Smallman Szymon Stachniak Nada Ivanovic Carolyn MacGregor

2 2 Overview: Uncertainty Visualization Problem  Decision-makers need to know what is not known as much as what is known  Existing displays imply a ground truth that can mislead Objective  Produce a COP that allows users to tailor their information needs on uncertainty to rapidly changing ROEs Approach - Develop a taxonomy of task-relevant uncertainties - Design and evaluate display concepts to query and visualize them Benefits  Better informed, flexible decision making

3 3 Problem: Displays and “Ground Truth” Displays implicitly adopt uncertainty criteria Identity attributes: all certain Location & temporal: certain Identity attributes: category certain, platform/affiliation not Location & temporal: certain Risky More Conservative Criteria

4 4 Our Approach Develop and evaluate explicit visualisation concepts to  Make uncertainty, underlying assumptions and display criteria explicit  Query uncertainty levels of tracks  Directly manipulate the consequences of choosing different uncertainty levels  Support decision-making needs by tailoring uncertainty levels to extant ROEs.

5 5 Creating a Taxonomy of Uncertainty The different types of uncertainty inherent in visualizing the COP  Uncertainty inherent in display technology »Display / data sampling (spatial, temporal)  Uncertainty in the data displayed »Conflicting / missing attributes for track ID »Spatial and temporal sensor uncertainties  Uncertainty in the perceptual response »Imprecision (e.g., with viewing angle). Weber fractions.

6 6 Existing representations of Uncertainty Limited array of existing methods for displaying uncertainty  Track ID »Icon in icon (e.g. AADC threat coding) »Disintegrated attribute representations (CROs)  Spatial uncertainty »Spatial envelopes (e.g. sonar, meterology displays) »Blobology (dispersion/distribution of troops across space – USMC)  Temporal »???

7 7 Design & Control Issues What are appropriate designs for customizable displays for levels of uncertainty?  How might querying of information be integrated with displays of target uncertainty?  Should the customizable displays differ depending on the target?  Global or track-specific queries?  Integration of temporal information?  Making the uncertainty information decision-relevant (response plan management)?  What are the trade-offs between displaying uncertainty and display clutter?

8 8 Direct manipulation interface (70%) 100% 0% (10%) 0% 100% Certainty of ID Spatial Certainty

9 9 Direct manipulation interface (70%) 100% 0% (50%) 0% 100% Certainty of ID Spatial Certainty Time???

10 10 Direct manipulation interface (70%) 100% 0% (95%) M29 Certainty of ID 100% 0% 100% Spatial Certainty

11 11 Direct manipulation interface (95%) 100% 0% 100% Certainty of ID Spatial Certainty (95%)

12 12 Drill-down for assumptions etc (95%) 100% 0% 100% Certainty of ID Spatial Certainty (95%)

13 13 Integration with associated displays 0% 100% Assumptions 1 … 2… 3… 4... (updated) (95%) 100% 0% Certainty of ID Spatial Certainty (95%) Priorities 1… 2… 3… 4… (updated) Sensor Management/ Intel Management (updated) Control of Decision Rules of Engagement (updated)

14 14 Integration with response management (70%) 100% 0% (95%) M29 Certainty of ID 100% 0% 100% Spatial Certainty Shoot Query

15 15 Interpretation of Uncertainty How does viewpoint contribute to the operator’s assessment of uncertainty?  Line of sight (occluded regions, exact location along the line of sight, cartesian location in 3D space)  Distance estimation (relative vs actual  Spatial resolution of display (aka blobology) What is the relationship between available time to decide and assessment of uncertainty as a useful decision- making variable in order to act?  How should temporal representation be conveyed? What are the parameters of uncertainty that might be considered by an intelligent agent?  Should uncertainty be displayed by an intelligent/automated agent?


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