Reliable Traffic Density Estimation in Vehicular Network
Bibliographic record
Abstract
Traffic density estimation relying on vehicular ad hoc networks can facilitate many applications, including traffic management, infrastructure planning, pollutant measurement, etc. Traditional density estimation is achieved by counting the number of vehicles located in a certain area with inductive loop detectors and cameras, which suffers from limited coverage and high cost. In this paper, we propose to fuse vehicle spacing information collected through the vehicular network and compute average spacing in a specific place within a short period. With received information on spacing, a Data Center (DC) can estimate average spacing with a maximum likelihood estimator. More importantly, modern vehicles can be attacked through their open interfaces, sending modified information to the DC. We analyze the estimations under different kinds of Byzantine attacks and propose corresponding estimation methods. The estimation mechanism proposed in this paper can exploit the historical prior probability to obtain unbiased spacing estimation without the necessity of identifying whether a specific vehicle is attacked or not. The theoretical analyses in normal states and under Byzantine attacks are presented, respectively. Finally, we carry out experiments with the US Highway 101 data and show that the average spacing estimation is consistent with the real mean value.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".