Measuring the Distance between the Two Vehicles Using Stereo Vision with Optical Axes Cross
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We will see smart cars on the street in future that has got ability to recognize the car, and has the ability to estimate the direction and distance from other vehicles or pedestrians and they can implement operations corresponding to the track for the navigation. In this paper by using stereo vision with the optical axis intersecting, by using two cameras, a camera has got rotation around on the Y-axis and using modern methods of image processing and focusing on the area of 1/5 m and using MATLAB software to estimate the distance between the vehicles. In this paper the whole process of stereo vision from image acquisition distortion compensation, image smoothing, stereo correspondence, and finally measure the distance is handled. The method used in this paper for calibration has got flexible more than other methods. The main advantage of the used method for calibration is its easy setup so that just by creating a calibration page anyone can use it. The results shows at day and in the laboratory conditions that has got acceptable accuracy %89/9.
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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 | 0.002 | 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 | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.007 | 0.002 |
| 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 it