Redesign the Front Shape of the Sedan Car for Pedestrian Safety and Mitigating Leg Injuries at Accidents
Bibliographic record
Abstract
The number of fatal and non-fatal pedestrian injuries, due to car accidents, has increased. For this reason, design of least possible harm cars (i.e. pedestrian friendly cars) has attracted the interest of many researchers and companies in order to reduce mortality and mitigate injuries. The use of friendly cars can also lead to the reduction of the expensive medical treatment costs for pedestrian injuries and consequently alleviates the financial burdens of the governments and insurance companies. In the sedan cars accidents, the common first pedestrian body part which experiences impact is the legs. In this research, a simulation study is conducted for investigating the effects of car bumper profile and plastic grille tilt angle on the severity of collision. LS-Dyna software is used to simulate, numerically investigate and analyze the mechanism of collision between a simulated leg and a vehicle bumper. In this simulation, three parameters are considered, impact line between pedestrian leg and car bumper, the height of the bumper relative to the knee, and the plastic grille tilt angle. The results of this study showed that the increase of impact line, increase of bumper height and decrease the plastic grille tilt angle alleviate the severity of pedestrians leg injuries.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".