Investigating drivers' awareness of Advanced Driver Assistance Systems: An approach to enhancing road safety
Bibliographic record
Abstract
Background: Road traffic accidents remain a leading cause of injuries and fatalities worldwide, with human error as a major contributing factor. Advanced Driver Assistance Systems (ADAS) play a crucial role in reducing such accidents by supporting drivers in the decision-making process. However, drivers’ awareness, perception, and trust in ADAS are critical to ensuring their effective use.Objectives: The aim of this study was to assess the level of awareness, perception, and trust regarding ADAS among drivers in Tabriz, Iran, and to identify the demographic and vehicle-related factors influencing these variables.Methods: This study used a cross-sectional survey design with a quantitative approach. A self-administered questionnaire was distributed among 450 drivers. The data were analyzed using descriptive statistics, chi-square tests, Pearson correlations, and ordinal logistic regression models to determine significant relationships and predictive factors.Results: The results of the present study revealed that gender, age, driving experience, and vehicle type significantly influenced drivers’ awareness of ADAS. A strong positive correlation was found between ADAS awareness and the level of trust in the system (r = 0.62, p < 0.001). Ordinal logistic regression analysis showed that demographic factors significantly predicted ADAS awareness, explaining 37% of the variance (Pseudo R² = 0.37). Conclusion: Drivers’ awareness and perception of ADAS are influenced by several key demographic and vehicle-related factors. Promoting educational and advertising campaigns may enhance knowledge and trust in these technologies, ultimately improving road safety and reducing traffic accidents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".