Anomaly Management: Reducing the Impact of Anomalous Drivers with Connected Vehicles.
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
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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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 | 0.000 |
| 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