Anomaly Management: Reducing the Impact of Anomalous Drivers with Connected Vehicles.
Bibliographic record
Abstract
Anomalous drivers with errorable behaviors result in dangerous driving environments on roads, and they significantly increase risk of vehicle collisions for themselves and their surrounding vehicles. Eliminating the impact of anomalous drivers to the surrounding vehicles is very critical to improve driving safety. In this paper, an anomaly management system is developed with the help of connected vehicles to solve the problem. An errorable car-following model is introduced to model the dynamics of anomalous vehicles and to analyze their impacts to other vehicles. The system utilizes connected vehicles to monitor the errorable behaviors of the anomaly drivers and estimates acceleration and lane changing advice for connected vehicles to avoid dangerous behaviors. The anomaly management system is evaluated with both synthetic experiments and microscopic traffic simulations to understand its benefits on mitigating the risk of vehicle collisions. In the synthetic experiments, the proposed system shows its capability of removing collision and near-collision events completely. The microscopic simulation indicates that the system can reduce the probability of collisions by up to 10% and the ratio of time to collision by 22%.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".