Supporting operator reliance on automation through continuous feedback
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Summary
This dissertation addresses the problem of inappropriate operator reliance on imperfect automation, specifically within the context of driving with Adaptive Cruise Control (ACC). The complexity of dynamic interactions between automated systems and their environments often prevents operators from accurately tracking system behavior, leading to misuse (relying on failing automation) or disuse (ignoring functional automation). The research is motivated by the need to calibrate operator trust and explicit understanding (mental models) to ensure safety in time-critical situations. The central hypothesis posits that providing continuous feedback on the state and behavior of automation informs operators of the evolving relationship between system performance and operating limits, thereby promoting accurate mental models and calibrated trust. The study pursued three specific aims to test this hypothesis. First, it applied a quantitative model to define the effects of understanding on driver-ACC interaction failures and to predict driver responses to feedback. This involved modeling ACC states, transitions, and driver mental models to identify conditions leading to collisions or interaction failures. Second, the research developed a systematic approach to define the necessary feedback for supporting appropriate reliance in a demanding multi-task domain. This led to the design of continuous visual and auditory (sonification) displays that provide peripheral, non-obtrusive information about ACC constraints, such as sensor degradation and braking limits. Third, the study empirically evaluated the costs and benefits of these continuous feedback interfaces. The experimental design involved a driving simulator study where participants interacted with ACC under various event types, including braking exceedences, sensor degradation, and lateral detection limits. The study compared driver performance, trust calibration, and mental model accuracy across different interface conditions, including standard warnings versus continuous visual and auditory feedback. The results indicated that continuous feedback on automation’s behavior is a viable means to promote calibrated trust and reliance. Drivers exposed to continuous visual and auditory displays demonstrated improved mental model accuracy regarding ACC process knowledge and subjective performance compared to those receiving only standard warnings. These drivers were more likely to initiate proactive responses—disengaging automation prior to precipitating failure events—rather than reactive responses after failures occurred. The continuous displays helped operators anticipate system limitations, reducing the likelihood of catastrophic events caused by delayed interventions. Furthermore, the study found that auditory and visual continuous interfaces could be designed to minimize distraction, allowing drivers to maintain focus on the primary driving task while monitoring automation status. Trust ratings aligned more closely with actual system capability when continuous feedback was provided, indicating better-calibrated trust. The significance of this work lies in its contribution to human-automation interaction theory and practice. It demonstrates that providing purpose, process, and performance information through continuous, concurrent displays can effectively bridge the gap between operator understanding and automation behavior. By supporting explicit understanding through continuous feedback, operators can maintain appropriate reliance on imperfect automation, enhancing safety and performance in complex, dynamic domains like driving. The findings suggest that future automation interfaces should move beyond discrete warnings to include continuous, peripheral feedback mechanisms that inform operators of system states and constraints in real-time. This approach helps mitigate the risks associated with automation-induced complacency and misunderstanding, offering a robust solution for managing human-automation breakdowns.
Key finding
Continuous visual and auditory feedback on the state and behavior of adaptive cruise control promotes more accurate mental models and proactive driver responses compared to discrete warnings.
Methodology
mixed_methods
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via openalex_abstract on 2026-05-08.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | — | — | — | 1 | 2026-05-07 |
| archive | success | canonical_url | — | — | 13 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | openalex | — | — | 2 | 2026-05-08 |
| promote | success | — | — | — | 1 | 2026-05-07 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | partial | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified_with_issues.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- trust calibration
- automation
- automation surprise
- automation complacency bias
- trust in automation foundations
- mode awareness
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Theoretical Contribution: computational model