An innovative method for the analysis of vehicle movements in roundabouts based on image processing
Bibliographic record
Abstract
SUMMARY The objective of this paper is to propose a method, based on the image processing of field survey data, to analyze vehicles movements into roundabouts. This research study consisted of three stages: a field survey to collect vehicular flow images captured by video cameras, the processing of these images using a proprietary software (VeTRA—Vehicle Tracking for Roundabout Analysis), and finally, the analysis of the collected data. The main feature of the software is that it allows the automatic computation of the main variables necessary to rank and evaluate a generic roundabout: the entry/exit (E/E) matrix with classification of vehicles (e.g., heavy, light, and motorbikes), vehicle trajectories, and vehicular speed diagrams along the paths inside the roundabout. The processing system is robust enough to withstand classic problems affecting image processing such as variable wind conditions, cloud cover, shadows, and obstructions. Calibration and error evaluation have been deduced from data collected by a high precision Real Time Kinematic GPS video recording system mounted on a probe vehicle. Data of E/E matrices generated by VeTRA are compared with those manually counted on the corresponding video images. A case study of an existing roundabout is featured in the paper. The results indicate that the software has a high capability of generating the E/E matrix. The analysis of vehicular trajectories with both the plot of curvature diagrams and the corresponding speed diagrams enable the evaluation of driver behavior relative to the geometric shape of the roundabout. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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".