Utilizing a Hybrid Model for Human Injury Severity Analysis in Traffic Accidents
Bibliographic record
Abstract
Road safety has been prioritized by governments globally, resulting in the implementation of numerous initiatives aimed at curtailing traffic accidents.Despite these efforts, the complete eradication of accidents remains unattainable.Therefore, swift and accurate responses to accident sites, accompanied by appropriate medical aid, are paramount in saving lives.Existing systems, primarily designed to alert medical personnel in the aftermath of an accident, rely solely on Vehicle Damage (Vd) to assess accident severity, neglecting Human Injury (Hi) considerations.This study proposes a hybrid model equipped with an attention mechanism, designed to classify accident severity based on both Vd and Hi.The proposed model accepts video or image inputs and classifies accident severity levels accordingly.Moreover, an extension of the model has been developed to obfuscate sensitive areas in accident imagery based on severity, particularly when such images are disseminated on public platforms without obtaining necessary consent.The proposed hybrid model, therefore, not only facilitates a more comprehensive severity assessment of traffic accidents but also ensures the protection of privacy and promotes ethical image sharing practices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".