Quantifying Occupant Head to Head Restraint Relative Position for use in Injury Mitigation in Rear End Impacts
Bibliographic record
Abstract
Improving the safety of vehicle occupants has gained increasing attention among automotive manufacturers and researchers over the past three decades. Generally, more recent vehicle safety improvement and injury prevention techniques could benefit from accurate knowledge of the occupant presence, characteristics, and/or position within the interior space of the vehicle. There is increased potential for injury mitigation systems to be applied more effectively if the proximity of the occupant to restraint devices is obtained in real-time during vehicle operation. A particular application is the position of the head relative to the head restraint for mitigating neck injuries from rear end impacts, which has led to the development of “active” head restraint systems. The current paper focuses on the head restraint application and initially presents a critical review of the available sensor and measurement technologies for real-time monitoring of the occupant's head position sensing system, including optical and capacitive sensors. To obtain accurate and reliable data, various scenarios related to the driver and passengers are considered in the process of sensor and technology selection, including confounding elements such as their clothing and a changing environment. Based on the results of this critical review, this paper outlines the subsequent research performed to identify the proximity of the vehicle occupant's head relative to the head restraint using a capacitive sensor array. As a prelude to extensive human and sensor system evaluation, the experimental set-up and data collection process for using a real human head are presented and explained, and the results containing the proximity data of the occupant's head relative to the head restraint are reported and discussed. Based on the experiments provided within this paper, the accuracy of head position quantification is less than 0.5 cm.
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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) | 0.001 | 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 | 0.000 |
| 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".