Research on the Effect of AEB Braking on the Protection of Occupants in Collisions
Bibliographic record
Abstract
The application of automatic emergency braking (AEB) can effectively reduce accident injuries and improve vehicle safety, but it also brings new technical problems that need to be solved. In this paper, the possible effects of AEB on the injury of Occupants in the vehicle during the collision process were studied. By establishing a driver side dummy injury evaluation model before and after the AEB effect, the potential impact of AEB equipment on the driver's movement trajectory and various body injury indicators during the collision process is deeply studied. The results indicate that the early action of AEB during the collision process will change the motion trajectory of the passenger dummy before and after the collision, potentially increasing the injury indicators of the dummy's head, neck, and chest before the collision, but to some extent, it will reduce the damage values of various parts of the dummy's body during the collision stage. At the same time, due to the emergency braking of AEB, the driver's position relative to the interior changes, and auxiliary restraint system devices such as airbags and seat belts usually operate according to the normal position of the Occupants, which to some extent exacerbates the damage to the Occupants. Therefore, it is urgent to conduct research on the protection of Occupants in the out of position state under the action of AEB.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".