Adaptable and Adaptive Automation for Supervisory Control of Multiple Autonomous Vehicles

Kidwell, Brian; Calhoun, Gloria L.; Ruff, Heath A.; Parasuraman, Raja

doi:10.1037/e572172013-089

Cited by 8 publications

(8 citation statements)

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“…The performance benefits observed in the adaptable and hybrid schemes can be explained, in part, by participants' rather excessive use of high-level trajectory control automation, rather than by the automation scheme per se. This finding is consistent with findings from a previous study on adaptable and adaptive automation for supervisory control of autonomous vehicles (Kidwell et al, 2012). This tendency is worrisome given the findings from our earlier study (Li et al, under revision) which showed the significant performance cost associated with failures of high levels of automation.…”

Section: Discussionsupporting

confidence: 92%

Supporting Human-Automation Collaboration Through Dynamic Function Allocation

Sarter

Wickens

et al. 2013

Proceedings of the Human Factors and Ergonomics Society Annual

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Context-sensitive function allocation can take two forms: (a) 'adaptable automation', where changes in the allocation of functions are initiated by the user, or (b) 'adaptive automation', where the automation triggers changes. However, operational experience suggests that neither approach by itself is sufficient and appropriate. This paper describes a hybrid dynamic function allocation scheme, in which the system and the operator collaborate on selecting and activating appropriate automation levels. The default mode of the system is adaptive automation, but operators can override system selections when they feel the need and have the time to do so. The effectiveness of the hybrid system was compared to fixed function allocations, to an adaptive scheme and to adaptable function allocation in the context of simulated robotic arm control tasks. The hybrid system improved performance and reduced workload in routine scenarios. The system was also preferred over the fixed and adaptive approach by the operators. However, these benefits can be explained mainly by the excessive use of high levels of automation in this condition. The findings have implications on future designs of dynamic function allocation schemes in complex systems.

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Section: Discussionsupporting

confidence: 92%

Supporting Human-Automation Collaboration Through Dynamic Function Allocation

Sarter

Wickens

et al. 2013

Proceedings of the Human Factors and Ergonomics Society Annual

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“…Both past studies demonstrated the utility of a LOA adaptation scheme based on each participant's individual performance on multiple task types. However, in one study, changes across trials to increase the LOA outnumbered changes to decrease LOA by, approximately, a 3:1 ratio (Kidwell, et al, 2012). Calhoun, et al (2011b) showed a similar trend.…”

Section: Introductionmentioning

confidence: 88%

“…One participant commented that it would be better for the operator to be able to control LOA. For a description of an experiment specifically comparing adaptive and adaptable control schemes, see Kidwell, et al (2012).…”

Section: Performance-based Adaptive Automation Implementationmentioning

confidence: 99%

“…Research is underway examining an entire adaptive cycle in multi-autonomous vehicle simulations in which task LOA both increases and decreases in response to operator performance. The adaptive automation algorithm being employed is based on real-time performance from multiple tasks in an attempt to capture a more accurate measure of participants' current workload (Calhoun, Ward, & Ruff, 2011b;Kidwell, Calhoun, Ruff, & Parasuraman, 2012). Specifically, task completion time is compared to an "expected time window" established for each task type and a count is kept of the frequency in which the time is not within the established window.…”

Section: Introductionmentioning

confidence: 99%

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Tailored Performance-based Adaptive Levels of Automation

Calhoun

Ruff

Spriggs

et al. 2012

Proceedings of the Human Factors and Ergonomics Society Annual

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Adaptive automation may help balance system autonomy with human interaction in supervisory control environments. The present experiment evaluated application of performance-based adaptive automation for an image analysis/decision task in a multiple autonomous vehicle simulation. An asymmetrical algorithm was employed in which the performance threshold differed for the two steps of the adaptive cycle: 1) increasing level of automation (to relieve participant when overloaded) and 2) decreasing level of automation (to re-engage participant when less loaded). Results showed that performance-based adaptation of the autonomy level improved both the speed and accuracy of performance on the image analysis task. Most participants indicated that adaptive automation reduced their cognitive workload and aided situation awareness. Additionally, the results suggest that the asymmetrical algorithm used to implement performance-based adaptation helped keep participants at a lower autonomy level where automation-induced problems are less likely.

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“…Automation was applied to an image analysis task in a multi-UAS simulation. Mean task completion time did not significantly differ between the two control schemes indicating there was not a performance cost associated with the additional automation management task in the adaptable condition (Kidwell, Calhoun, Ruff, & Parasuraman, 2012). Moreover, the results suggested that the act of setting task autonomy level may serve to better keep the operator in-the-loop and alert to unexpected stimuli, as mean performance on a secondary change detection task was slightly better for trials with adaptable automation.…”

Section: Introductionmentioning

confidence: 87%

Adaptable Automation Interface for Multi-Unmanned Aerial Systems Control

Calhoun

Draper

Miller

et al. 2013

Proceedings of the Human Factors and Ergonomics Society Annual

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With advances in automation technologies, systems are now being considered wherein a single operator supervises multiple unmanned aerial vehicles. Supervisory control of highly autonomous systems will require a new interface design. The present effort extends a delegation control concept to enable a pilot to flexibly change the role of automation during the course of a mission, seamlessly transitioning between four different control modes ranging from manual (pilot controls the vehicle's flight with stick and throttle control) to high level "plays" (pilot's command initiates a series of automated tasks). This novel concept was instantiated into a dynamic laptop simulation to support a usability evaluation in which participants employed the multi-level control architecture during ninety minute sessions. Data include comments recorded with a think-aloud paradigm and questionnaire responses. Results indicated that this adaptable pilot-automation interface for multi-unmanned systems control is promising. The findings include perspectives from both pilot and gamer participants that will help advance design of multi-level control for future aviation systems.

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Adaptable and Adaptive Automation for Supervisory Control of Multiple Autonomous Vehicles

Cited by 8 publications

References 0 publications

Supporting Human-Automation Collaboration Through Dynamic Function Allocation

Supporting Human-Automation Collaboration Through Dynamic Function Allocation

Tailored Performance-based Adaptive Levels of Automation

Adaptable Automation Interface for Multi-Unmanned Aerial Systems Control

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