A Study of Adult Pedestrian Head Impact Conditions and Injury Risks in Passenger Car Collisions Based on Real-World Accident Data
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Bibliographic record
Abstract
OBJECTIVE: The aim of the current study was to study the kinematics of adult pedestrians and assess head injury risks based on real-world accidents. METHODS: A total of 43 passenger car versus pedestrian accidents, in which the pedestrian's head impacted the windscreen, were selected from accident databases for simulation study. According to real-world accident investigation, accident reconstructions were conducted using multibody system (MBS) pedestrian and car models under MADYMO environment (Strasbourg University) to calculate head impact conditions in terms of head impact velocity, head position, and head orientation. Pedestrian head impact conditions from MADYMO simulation results were then used to set the initial conditions in a simulation of a head striking a windscreen using finite element (FE) approach. RESULTS: The results showed strong correlations between vehicle impact velocity and head contact time, throw distance, and head impact velocity using a quadratic regression model. In the selected samples, the results indicated that Abbreviated Injury Scale (AIS) 2+ and AIS 3+ severe head injuries with probability of 50 percent were caused by head impact velocity at about 33 and 49 km/h, respectively. Further, the predicted head linear acceleration (head injury criterion, HIC) value, resultant angular velocity, and resultant angular acceleration for 50 percent probability of AIS 2+ and AIS 3+ head injury risk were 116 g, 825, 40 rad/s, 11,368 rad/s(2) and 162 g, 1442, 55 rad/s, 18,775 rad/s(2), respectively, and the predicted value of 50 percent probability of skull fracture was 135 g. CONCLUSIONS: The present study provides new insight into pedestrian head impact conditions in terms of velocity, angle, and impact location based on a number of real-world cases. Therefore, it may perform a critical analysis for current pedestrian head standard tests.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it