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Cited by 11Human-Automation Interaction and SafetySocial Robot Interaction and HRIRisk and Safety Analysis

Supporting operator reliance on automation through continuous feedback

Open access
Seppelt, Bobbie Danielle·2018
DOI 10.17077/etd.90t9uqnh
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Abstract

In driving, multiple variables in automated systems such as adaptive cruise control (ACC) and active steering, and in the environment dynamically change and interact. This complexity makes it difficult for operators to track the activities and responses of automation. The inability of operators to monitor and understand automation's behavior contributes to inappropriate reliance, i.e. when an operator uses automation that performs poorly or fails to use automation that is superior to manual control. The decision to use or not use automation is one of the most important an operator can make, particularly in time-critical or emergency situations, therefore it is essential that an operator is calibrated in their automation use. An operator's decision to rely on automation depends on trust. System feedback provided to the operator is one means to calibrate trust in automation in that the type of feedback may differentially affect trust. The goal of this research is to help operators manage imperfect automation in real-time and to promote calibrated trust and reliance. A continuous information display that provides information on system behavior relative to its operating context is one means to promote such calibration. Three specific aims are pursued to test the central hypothesis of this dissertation that continuous feedback on the state and behavior of the automation informs operators of the evolving relationship between system performance and operating limits, therefore promoting accurate mental models and calibrated trust. The first aim applies a quantitative model to define the effect of understanding on driver-ACC interaction failures and to predict driver response to feedback. The second aim presents a systematic approach to define the feedback needed to support appropriate reliance in a demanding multi-task domain such as driving. The third aim assesses the costs and benefits of presenting drivers with continuous visual and auditory feedback. Together these aims indicate that continuous feedback on automation's behavior is a viable means to promote calibrated trust and reliance. The contribution of this dissertation is in providing purpose, process, and performance information to operators through a continuous, concurrent information display that indicates how the given situation interacts with the characteristics of the automation to affect its capability.

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  • Most existing studies on trust calibration have investigated how to maintain appropriate trust calibration by continuously presenting system information to prevent over-trust or under-trust.

    “Most of the studies investigated how to maintain appropriate trust calibration by continuously presenting system information to prevent over-trust or under-trust.”

    From Adaptive trust calibration for human-AI collaboration (2020) · cites this paper as [7]

  • This result is not in line with previous studies [6, 7, 15] that emphasized the effectiveness of continuous trust calibration with system transparency.

    “This result is not in line with the previous studies [6, 7, 15] that emphasized the effectiveness of the continuous trust calibration with system transparency.”

    From Adaptive trust calibration for human-AI collaboration (2020) · cites this paper as [7]

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Authors
Seppelt, B. D.
Corresponding author
Seppelt, Bobbie Danielle
Publisher
The University of Iowa
Published
2009-12-01
DOI
10.17077/etd.90t9uqnh
OpenAlex
W101536327
Cited by
11
References
117
Citations by year
2025: 1 · 2022: 1 · 2021: 2 · 2020: 3 · 2019: 2 · 2018: 2
Type
dissertation
Language
en
Open access
Licence
Locations
  • Iowa Research Online (The University of Iowa) (submittedVersion, cc-by)
  • Iowa Research Online (University of Iowa) (submittedVersion)
Topics
  • Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
  • Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Risk and Safety Analysis
Keywords
automation reliance, trust in automation, continuous feedback, adaptive cruise control, driver-automation interaction, mental models, visual and auditory feedback, information visualization, active steering

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