Privacy Concern and Acceptability of Driver Monitoring System
DOI: 10.54941/ahfe1004419
archive: archived pipeline: cataloged
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Summary
This study investigates the privacy concerns and acceptance of Driver Monitoring Systems (DMS) among Chinese drivers, motivated by the fact that over 90% of traffic accidents in China are attributable to human factors. While DMS technology is designed to monitor driver states such as fatigue and distraction to enhance safety, it is unclear how drivers perceive the privacy implications of different monitoring methods. The research aimed to determine how the specific type of DMS—facial image-based, electroencephalogram (EEG)-based, electrocardiogram (ECG)-based, or vehicle motion-based—influences drivers’ privacy concerns and overall acceptance of the technology. The researchers conducted an online survey with a one-way between-subjects design, recruiting 486 valid participants from a pool of 654 respondents. Participants were randomly assigned to one of the four DMS conditions and completed a questionnaire measuring seven dimensions: data sensitivity, collection concern, secondary use, perceived insecurity, perceived usefulness, trust, and behavioral intention. Exploratory factor analysis revealed that the items clustered into three primary factors: privacy concern (combining collection concern, secondary use, and perceived insecurity), general acceptance (combining perceived usefulness, trust, and behavioral intention), and data sensitivity. A confirmatory factor analysis validated a second-order model where privacy concern and general acceptance were treated as higher-order constructs. Results indicated that Chinese drivers expressed moderate privacy concern but maintained a positive attitude toward DMS, viewing the collected data as sensitive. While there were no significant main effects of DMS type on overall privacy concern or general acceptance, pairwise comparisons showed that vehicle motion-based DMS was significantly more accepted than facial image, EEG, and ECG-based systems. Specifically, vehicle motion-based DMS was perceived as more useful and trustworthy. Regression analysis further revealed that higher perceived data sensitivity was a positive predictor of general acceptance, suggesting that drivers who view data as sensitive also recognize its utility for accurate monitoring. Conversely, higher privacy concern was a significant negative predictor of acceptance. Demographic factors also played a role, with higher acceptance observed among individuals with lower education levels, higher monthly income, less driving experience, and higher annual mileage. The findings suggest that while drivers are generally open to adopting DMS, they prefer indirect monitoring methods like vehicle motion analysis over direct physiological or facial data collection. The counter-intuitive finding that higher data sensitivity correlates with higher acceptance implies that drivers value the precision offered by sensitive data, provided they trust the system. These insights have practical implications for DMS developers and policymakers, highlighting the need to balance safety benefits with robust data protection policies to mitigate privacy risks. The study concludes that while acceptance is promising, real-world adoption will depend on building trust and ensuring the effectiveness of the monitoring technology.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-08-09 |
| archive | success | canonical_url | — | — | 1 | 2026-08-09 |
| extract | success | cached | — | — | 4 | 2026-08-23 |
| clean | success | clean | — | — | 1 | 2026-08-09 |
| chunk | success | chunk | — | — | 1 | 2026-08-09 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-08-09 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.8-27b-gittensor | summ-v5 | 3 | 2026-08-23 |
| tag | success | vector_similarity | — | — | 11 | 2026-08-11 |
| verify | success | — | — | — | 2 | 2026-08-09 |
Summary generated by qwen3.8-27b-gittensor on 2026-08-23; verification: pending re-verification.
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- Empirical Findings: self report data
- Methodological Resource: validation psychometrics, tool software