To Trust or Not to Trust? A Simulation-Based Experimental Paradigm [supporting datasets]
archive: archived pipeline: cataloged verified
Get this paper ↗ (full text — opens at the source; we link to it, we don't host it)
Summary
This document serves as a metadata record and description for a dataset supporting the research report "To Trust or Not to Trust? A Simulation-based Experimental Paradigm" by Knodler et al. (2019). The underlying study addresses the critical challenge of human-automation interaction in the context of automated driving systems. While these systems are expected to improve traffic safety and flow, their effectiveness is contingent upon user acceptance and appropriate utilization. The research is motivated by the understanding that inappropriate trust levels—specifically over-trust or under-trust—can negate the benefits of the technology. The study posits that trust is a dynamic construct comprising initial (dispositional) trust, formed before system experience, and history-based trust, which evolves through user interaction. The primary objective was to identify factors influencing these trust components to enhance human-automation interaction models. The methodology involved a simulator-based experiment designed to investigate the determinants of both initial and history-based trust in automated vehicles. The study included a literature review of trust in automation and previous trust models to identify research gaps. The experimental design utilized questionnaires administered at various stages: pre-study, mid-study (two instances), and post-study, alongside a final trust survey from a pilot round. The dataset preserves the raw data from this experiment, allowing for replication and further analysis of the factors correlated with driver trust. The provided text does not contain the specific empirical results or statistical findings of the experiment, as it is a data repository record rather than the full research report. However, it details the structure of the supporting data, which includes two main collections. The first, "ToTrust_CSV.zip," contains seven comma-separated value files capturing demographic information and perception questionnaire responses from pre-, mid-, and post-study phases, as well as pilot round trust surveys. The second, "ToTrust_Data.zip," contains 300 .plt files representing individual subject data, named according to subject ID and drive conditions. These files constitute the empirical evidence base for the study’s conclusions regarding trust dynamics. The significance of this work lies in its contribution to the understanding of driver trust in automated vehicles, a key factor in the safe deployment of autonomous technology. By providing open access to the replication data via the Harvard Dataverse Repository and the National Transportation Library, the authors facilitate transparency and further research into human-automation interaction. The study aims to help developers and policymakers better understand how drivers form and adjust their trust in automated systems, ultimately supporting the design of systems that foster appropriate trust levels and maximize safety benefits. The dataset is preserved under the U.S. Department of Transportation’s Public Access Plan, ensuring long-term availability for the research community.
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 bulk_ingest_rosap on 2026-05-23 (8 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 94 | 2026-07-02 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-nvidia | summ-v5 | 96 | 2026-07-02 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | success | — | — | — | 1 | 2026-07-03 |
Summary generated by qwen3.6-27b-nvidia on 2026-07-02; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
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).
- Empirical Findings: self report data
- Methodological Resource: tool software, dataset resource