Understanding Head Injury Risks during Car-to-Pedestrian Collisions Using Realistic Vehicle and Detailed Human Body Models
Bibliographic record
Abstract
Traumatic brain injury (TBI) is the leading cause of death and long-term disability in road traffic accidents (RTAs). Researchers have examined the effect of vehicle front shape and pedestrian body size on the risk of pedestrian head injury. On the other hand, the relationship between vehicle front shape parameters and pedestrian TBI risks involving a diverse population with varying body sizes has yet to be investigated. Thus, the purpose of this study was to comprehensively study the effect of vehicle front shape parameters and various pedestrian bodies ranging from 95th percentile male (AM95) to 6 years old (YO) child on the dynamic response of the head and the risk of TBIs during primary (vehicle) impact. At three different collision speeds (30, 40, and 50 km/h), a total of 36 car-to-pedestrian collisions (CPCs) were reconstructed using three different vehicle types (Subcompact passenger sedan, mid-sedan, and sports utility vehicle (SUV)) and four distinct THUMS pedestrian finite element (FE) models (AM50, AM95, AF05, and 6YO). We assessed skull stress and brain strains besides head linear and rotational kinematics. Our findings indicate that vehicle shape parameters especially bonnet leading edge height (BLEH), when being divided by the height of the Center of Gravity of the human body, correlated positively to head kinematics. The data from this study using realistic vehicle structures and detailed human body models showed that smaller BLEH/CG ratios reduced head injury criteria (HIC) and brain injury criteria (BrIC) values for the car center to mid-stance walking pedestrian impacts but with low-to-moderate R squared values between 0.2 to 0.5. Smaller BLEH/CG reduced head lateral bending velocities with R squared values of 0.57 to 0.63 for all impact velocities, and reduced HIC with R squared value of 0.62 for 50 km/h cases. In the future, simulations with realistic car structures and detailed human body models will be further used to simulate impacts at different locations and with various body shapes/postures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".