Traffic Accident Prevention Through Acceptance of Advanced Driver Assistance System (ADAS) among Urban People
Bibliographic record
Abstract
Lead Indonesia's accident cases are caused mainly by human error, which can be reduced by implementing an Advanced Driver Assistance System (ADAS) technology.Protection motivation and acceptance of technology are generally a concern for drivers adopting ADAS.Therefore, this research aims to build a model of the Combined Protection Motivation Theory and Unified Theory of Acceptance and Use of Technology (C-PMT-UTAUT) to determine the factors that encourage drivers and owner-drivers' interest in using the ADAS.This research method applies quantitative research to a population of automotive users in Jakarta and Tangerang.A survey was conducted with a sample of 220 automotive users in Jakarta and Tangerang.Determination of the sample using the convenience sampling method and the samples collected and used were 220 samples.The data is then processed using Smart-PLS.The research results prove that perceived vulnerability affects the positive attitude of automotive users to use ADAS, likewise, with the performance expectancy and effort expectancy variables.Furthermore, the study's results also prove that attitude significantly affects the intention of automotive users to add ADAS features to their vehicles.The research results prove that the perceived vulnerability is proven to positively and significantly affect attitudes to ADAS.Likewise, the effect of performance expectancy on attitude toward ADAS is proven to be positive and significant.The effort expectancy also positively and significantly affects the attitude toward ADAS.One factor that has yet to be proven to be a concerned driver is severity because the perceived severity has not been proven to influence attitudes toward ADAS significantly.Then, the attitude positively and significantly affects the intention to use ADAS.The findings of this study have important practical implications for policymakers, car manufacturers, and technology developers working on ADAS.Policymakers can promote awareness, education and implement incentives.Car manufacturers can enhance perceived vulnerability, improve performance expectancy, and simplify the user experience.Technology developers can emphasize usability and address concerns about severity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".