Connected vehicle enabled hierarchical anomaly behavior management system for city-level networks
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
Drivers who are distracted cannot operate their vehicles appropriately, which leads to error-prone behavior on the roads. This behavior increases the risk of collisions for both themselves and surrounding vehicles, making it urgent to manage anomalous vehicles with distracted drivers and mitigate their impact on driving safety. To address this problem, this paper presents an anomaly behavior management system that leverages connected vehicles to improve safety performance for both individual vehicles and the whole network. The proposed system integrates a hierarchical architecture that reduces the risk of collisions caused by anomalous vehicles in large-scale road networks. Connected vehicles monitor anomalous vehicles and estimate speed and lane-changing instructions to avoid dangerous behaviors. The benefits of the proposed system are evaluated using microscopic traffic simulation, which shows a reduction in the risk of collisions and improved mobility for both connected vehicles and the entire network. The paper also conducts a sensitivity analysis of the market penetration rates of connected vehicles and traffic demand levels to understand the system’s reliability at different development stages of connected vehicles and traffic congestion.
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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 | 0.000 | 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 | 0.000 |
| 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 it