Factors Contributing to Guidance Performance when Using a CameraâBased Guidance Aid
Bibliographic record
Abstract
A guidance aid is a device that provides guidance information to the driver rather than replacing the driver. With a camera-based guidance aid, the view seen by a forward-looking video camera is displayed on a monitor situated within the operator station of the vehicle. As the vehicle moves forward, images of the ground scroll vertically across the monitor. The rate at which the image scrolls, the image velocity, is related to the forward velocity of the vehicle, the placement of the camera (height and tilt angle), and the optical characteristics of the guidance camera. When tested with a tractor at forward velocities between 1.6 and 12.8 km/h, lateral error increased linearly as image velocity increased. Driver self-confidence decreased linearly as image velocity increased. Based on subjective feedback, drivers preferred a camera tilt angle of 20 degrees (over either 30 degrees or 40 degrees) because it yielded the greatest look-ahead distance. Statistically, a tilt angle of 30 degrees was best for a camera with a narrow field of view (narrow FOV, 20 degrees in the lateral direction). For a camera with a wide field of view (wide FOV, 39 degrees in the lateral direction), there was no statistical difference. For the narrow FOV camera, a camera height of 1.1 m yielded statistically smaller lateral errors than a camera height of 1.5 m. There was no statistical difference for the wide FOV camera. Overall, the lateral error was statistically smaller for the narrow FOV camera than for the wide FOV camera due to the difference in the lateral ratio for each camera, where the lateral ratio is the ratio of the lateral field of view of the camera to the fixed monitor width.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".