Assessment of operator trust in and utilization of automated decision-aids under different framing conditions

Bisantz, Ann M.; Seong, Younho

doi:10.1016/s0169-8141(01)00015-4

Cited by 79 publications

(67 citation statements)

References 14 publications

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“…This conclusion is consistent with the finding that when operators more accurately understand automation behavior they adopt a more effective attention allocation strategy (Wickens et al, 2000). Similarly, when informed of the capabilities of the automation operators rely more appropriately (Bisantz and Seong, 2001;Dzindolet et al, 2003).…”

Section: Driver Interventionsupporting

confidence: 87%

Making adaptive cruise control (ACC) limits visible

Seppelt

Lee

2007

International Journal of Human-Computer Studies

198

104

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Previous studies have shown adaptive cruise control (ACC) can compromise driving safety when drivers do not understand how the ACC functions, suggesting that drivers need to be informed about the capabilities of this technology. This study applies ecological interface design (EID) to create a visual representation of ACC behavior, which is intended to promote appropriate reliance and support effective transitions between manual and ACC control. The EID display reveals the behavior of ACC in terms of time headway (THW), time to collision (TTC), and range rate. This graphical representation uses emergent features that signal the state of the ACC. Two failure modes-exceedance of braking algorithm limits and sensor failures-were introduced in the driving contexts of traffic and rain, respectively. A medium-fidelity driving simulator was used to evaluate the effect of automation (manual, ACC control), and display (EID, no display) on ACC reliance, brake response, and driver intervention strategies. Drivers in traffic conditions relied more appropriately on ACC when the EID display was present than when it was not, proactively disengaging the ACC. The EID display promoted faster and more consistent braking responses when braking algorithm limits were exceeded, resulting in safe following distances and no collisions. In manual control, the EID display aided THW maintenance in both rain and traffic conditions, reducing the demands of driving and promoting more consistent and less variable car-following performance. These results suggest that providing drivers with continuous information about the state of the automation is a promising alternative to the more common approach of providing imminent crash warnings when it fails. Informing drivers may be more effective than warning drivers. r

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Section: Driver Interventionsupporting

confidence: 87%

Making adaptive cruise control (ACC) limits visible

Seppelt

Lee

2007

International Journal of Human-Computer Studies

198

104

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“…As with the distinctions made by Bisantz and Seong (2001) and Riley (1994), this framework shows that trust and its effect on behavior depend on a dynamic interaction among the operator, context, automation, and interface. Trust combines with other attitudes (e.g., subjective workload, effort to engage, perceived risk, and self-confidence) to form the intention to rely on the automation.…”

Section: A Dynamic Model Of Trust and Reliance On Automationsupporting

confidence: 52%

Trust in Automation: Designing for Appropriate Reliance

Lee¹,

See²

2004

hfes

2,709

1,709

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“…Several measurement scales of a human's degree of trust in computer automation have been previously proposed and evaluated [13,16]. Other groups have quantified how trust varies with respect to the performance and error rate of the humanrobot team [6,9,18], the nature of these failures [2], and the user's mental load [6].…”

Section: Related Workmentioning

confidence: 99%

“…We iterated over 11 window duration values of W = [0. 5,1,2,3,4,6,8,10,12,15,20] seconds, and . Each stepwise regression model was built using the same procedure as described in Sect.…”

Section: Parameter Learningmentioning

confidence: 99%

Towards Modeling Real-Time Trust in Asymmetric Human–Robot Collaborations

Dudek

2016

Springer Tracts in Advanced Robotics

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We are interested in enhancing the efficiency of human-robot collaborations, especially in "supervisor-worker" settings where autonomous robots work under the supervision of a human operator. We believe that trust serves a critical role in modeling the interactions within these teams, and also in streamlining their efficiency. We propose an operational formulation of human-robot trust on a short interaction time scale, which is tailored to a practical tele-robotics setting. We also report on a controlled user study that collected interaction data from participants collaborating with an autonomous robot to perform visual navigation tasks. Our analyses quantify key correlations between real-time human-robot trust assessments and diverse factors, including properties of failure events reflecting causal trust attribution, as well as strong influences from each user's personality. We further construct and optimize a predictive model of users' trust responses to discrete events, which provides both insights on this fundamental aspect of real-time human-machine interaction, and also has pragmatic significance for designing trust-aware robot agents.

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Assessment of operator trust in and utilization of automated decision-aids under different framing conditions

Cited by 79 publications

References 14 publications

Making adaptive cruise control (ACC) limits visible

Making adaptive cruise control (ACC) limits visible

Trust in Automation: Designing for Appropriate Reliance

Towards Modeling Real-Time Trust in Asymmetric Human–Robot Collaborations

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