THE INFLUENCE OF RISK ON DRIVER TRUST IN AUTONOMOUS DRIVING SYSTEMS
DOI: 10.4271/2024-01-3748
archive: archived pipeline: cataloged verified
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Summary
This study investigates how internal and external risk factors influence driver trust in autonomous driving systems (ADS). The research is motivated by the observation that drivers often underutilize or refuse to rely on ADS due to a lack of trust, which undermines the safety and productivity benefits of automation. The authors aim to determine whether internal risk (uncertainty regarding the ADS’s reliability) and external risk (uncertainty regarding the driving environment) moderate the relationship between trust and reliance on ADS. To address this, the researchers conducted a human-subject experiment using a static driving simulator with 36 licensed drivers. Participants operated a simulated semi-autonomous vehicle while performing a secondary visual search task. The study employed a within-subjects design with two levels of internal risk (100% reliable vs. 70% reliable forward collision warnings) and two levels of external risk (high visibility vs. low visibility due to fog). Internal risk was manipulated by introducing false positive alarms in the high-risk condition, while external risk was manipulated by reducing visible distance from 1000 feet to 500 feet. Data collection included pre- and post-experiment surveys measuring demographics, risk tolerance, perceived trust, and workload, as well continuous physiological measures such as eye-tracking, heart rate, and galvanic skin response. Preliminary results based on self-reported trust surveys from 27 participants indicate that internal risk has a significant negative influence on trust in ADS. When the warning system was unreliable, drivers reported lower trust levels. In contrast, external risk had a minor impact on self-reported trust. The findings suggest that the reliability of the automation itself is a stronger determinant of driver trust than the conditions of the driving environment. The authors note that self-reported measures may not fully capture actual trusting behaviors, indicating a need for further analysis using continuous physiological and behavioral data. The significance of this work lies in its contribution to understanding the dynamics of human-automation interaction. By demonstrating that internal system reliability is a critical factor in establishing trust, the study provides insights for designing more effective ADS interfaces and control models. The authors plan to use these findings to develop robust models that predict real-time trust intentions, aiming to optimize the balance between human oversight and automated control in both military and civilian applications.
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 author_sweep_intake on 2026-05-27.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 2 | 2026-05-27 |
| archive | success | canonical_url | — | — | 7 | 2026-06-09 |
| extract | success | cached | — | — | 2 | 2026-06-09 |
| 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 | — | — | — | 1 | 2026-05-27 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-09 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 1 | 2026-06-09 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-09; verification: verified.
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- Empirical Findings: self report data
- Theoretical Contribution: computational model, theory or model