Anti-trap protection for an intelligent smart car door system
Bibliographic record
Abstract
A majority of driver assistance systems focuses on assisting the driver when the car is in motion. There is relatively less, to almost no work on assistance systems when the car is stationary. The focus of this paper is on such a system and describes the design and realization of a novel, camera-based anti-trap protection system for smart car doors. Our system uses a single omnidirectional camera that is integrated in each inner door of a vehicle, and detects obstacles such as fingers, hands or legs at the A and B-pillar, door sill and car door without any contact. This is an enhancement to currently available systems that require an active contact between obstacles and sensors in the door area for the detection of trapping. The paper also proposes a scheme for re-calibrating the camera position that may change due to vibrations during the life-time of the car door. Experiments on a fully realized car door prototype show that the proposed schemes prevent trapping by robustly detecting obstacles such as small fingers or hands in critical door regions, and output results in real-time. The latter is important when door users slam the car door. A first study with 20 test users showed that our system strongly supports the door users to prevent trapping.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".