Vehicle Occupant Head Position Quantification Using an Array of Capacitive Proximity Sensors
Bibliographic record
Abstract
Currently, proper vehicle head restraint (HR) positioning requires manual adjustment and knowledge on the part of the occupant. By removing the requirement for manual adjustment, an HR positioning system that autonomously moves and adapts the HR position properly to any seated occupant could serve to reduce the social and personal costs of whiplash injuries, as compared with existing manual systems. To achieve this, quantification of the occupant's head position relative to the HR is required. In a previous paper, a rectangular capacitive proximity-sensing array for the purpose of occupant head position quantification was studied. Here, a range-maximized sensor was first designed to reduce electrostatic and environmental disturbances. To achieve this, numerical modeling and laboratory experiments were conducted, and then, a design process for an interdigitated comb-shaped sensor geometry was proposed to maximize the sensing range of the sensor. The effects of temperature and humidity on the sensor were also evaluated and compensated. A novel capacitive proximity-sensing array was then designed, which had the minimum number of the aforementioned comb-shaped sensors necessary for accurately estimating the 3-D position of an occupant's head in near real time. This estimation was carried out using a nonparametric neural network. The above capacitive array was then integrated and fully contained in a customized HR as part of a vehicle seat safety system. The system was tested and demonstrated that such a head position sensing methodology would be capable of autonomously positioning an HR to meet the guidelines of the Insurance Institute for Highway Safety (IIHS).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".