Use of Computer Vision Data for Detailed Driver Behavior Analysis and Trajectory Interpretation at Roundabouts
Bibliographic record
Abstract
With recent important upgrades to North American intersection design guides, roundabouts are gaining popularity as a method of reducing road conflicts, streamlining flow, and curbing excessive speeding at busy intersections. The current design approach, however, makes use of mean spot speed measures and design criteria that do not take into account yielding behavior and acceleration and deceleration, all of which may be affected by regional driving culture and local roundabout design. This research paper introduces the methodology being developed for the detailed analysis of driving behavior, trajectory interpretation, and conflict measures in modern North American roundabouts, based on video data extracted by means of computer vision. The analysis explores the methods used to prepare microscopic speed maps, compiled speed profiles, lane-change counts, and gap time measures. The paper introduces and discusses the interpretation of trajectories at the scale of roundabout merge sections instead of looking at safety from the point of view of a roundabout as a unified system. The research found significant variation in distributions of speed across five case study roundabouts in the province of Quebec, Canada; this result may be explained by regional differences in design and road use. The paper also found aggressive gap times and uneven traffic flow as contributing factors to speed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".