Automated Analysis of Road Safety with Video Data
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
Traffic safety analysis has often been undertaken with historical collision data. However, well-recognized availability and quality problems are associated with collision data. In addition, the use of collision records for safety analysis is reactive: a significant number of collisions has to be recorded before action is taken. Therefore, the observation of traffic conflicts has been advocated as a complementary approach in the analysis of traffic safety. However, incomplete conceptualization and the cost of training observers and collecting conflict data have been factors inhibiting extensive application of the traffic conflict technique. The goal of this research is to develop a method for automated analysis of road safety with video sensors to address the problem of dependency on the deteriorating collision data. The method automates the extraction of traffic conflicts from video sensor data. This method should address the main shortcomings of the traffic conflict technique. A comprehensive system is described for traffic conflict detection in video data. The system is composed of a feature-based vehicle tracking algorithm adapted for intersections and a traffic conflict detection method based on the clustering of vehicle trajectories. The clustering uses a K-means approach with hidden Markov models and a simple heuristic to find the number of clusters automatically. Traffic conflicts can then be detected by identifying and adapting pairs of models of conflicting trajectories. The technique is demonstrated on real-world video sequences of traffic conflicts.
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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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 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 it