Propensity to trust technology and subjective, but not objective, knowledge predict trust in advanced driver assistance systems
Bibliographic record
Abstract
• With a survey, we investigated factors that predict trust in ADAS for current users. • Subjective knowledge, but not objective knowledge of limitations, predicted trust. • Objective and subjective knowledge were not correlated. • Propensity to trust technology in general also predicted trust in ADAS. Trust has been shown to influence whether drivers use advanced driver assistance systems (ADAS) appropriately, and thus understanding the factors influencing trust in ADAS may help inform interventions to support appropriate use. We surveyed 369 drivers to investigate the factors that predict trust in ADAS for current users. Participants were required to have experience using ADAS, specifically systems that simultaneously control longitudinal and lateral movement of the vehicle (participants reported using adaptive cruise control and lane keeping assist systems at the same time in their vehicle at least 1–4 times per month). In addition to assessing trust, the survey included questions to assess objective knowledge about ADAS limitations, self-reported understanding of ADAS (i.e., how correct and complete drivers thought their understanding of ADAS was), number of methods they had previously used to learn about ADAS, frequency of ADAS use, familiarity with technology, propensity to trust technology, and demographics. Regression results showed that self-reported understanding, but not objective knowledge, predicted trust in ADAS, with higher self-reported understanding being associated with higher trust. Self-reported understanding was not correlated with objective knowledge; participants rated their self-reported understanding highly, but only identified an average of 42% of the system limitations included in the survey. Propensity to trust technology was also a significant predictor of trust in ADAS, with higher propensity to trust technology in general associated with higher trust in ADAS. These findings suggest that interventions aimed at supporting appropriate trust in ADAS could be designed to increase drivers’ awareness of potential gaps in their understanding and adjust expectations of ADAS for those with a high propensity to trust technology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".