AUTOC-AR: A Car Design and Specification as a Work Safety Guide Based on Augmented Reality Technology
Bibliographic record
Abstract
The development of Augmented Reality (AR) technology until now continues to increase. Utilization of AR has been used in various aspects of life, including aspects of education, is no exception for automotive engineering education. In recent years, a variety of ideas and the latest innovations about automotive by utilizing AR technology began to boom, especially in the area of car design aimed at car production companies. At the car production stage, human resources skilled in understanding the design and specifications of car features are required. The seeds of educated human resources start from vocational students in automotive engineering expertise programs. This study aims to develop and implement an application called AUTOC-AR that functions to help and facilitate students in learning automotive engineering skills in vocational schools and supporting safety in the workplace. The research methodology consisted of a literature review and excavation of problems and needs, solution recommendations, application development, testing, results and discussion, conclusions and future work. The Extreme Programming (XP) model was used as a development method. Marker-based tracking was used as a detection approach. As many as 25 students as end-users were involved to use AUTOC-AR. The result is that all features in the AUTOC-AR application function properly based on the expected specifications. Non-functional testing has been carried out by adopting a user experience approach with a final average value of 4.83 with a percentage of 96.6%.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".