On message filtering for cooperative localisation of vehicles in an urban environment
Why this work is in the frame
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Bibliographic record
Abstract
In this paper we present cooperative localisation of vehicles within an urban environment using short-range radio communication technologies such as Dedicated Short Range Communication (DSRC) and IEEE 802.11 (WiFi) etc. In our application environment vehicles are moving along streets and the geometric combination that they form is mostly degenerate for the purpose of position estimation. We explore different scenarios where incorporation of information received from all other vehicles might be suboptimal for the purpose of position estimation. We calculate a confidence measure based on the geometric configuration of different sub-groups of vehicles and propose that the position estimates from each sub-group be weighted accordingly to arrive at the final estimate. Simulation results illustrate that the proposed method can effectively filter out degenerate configurations and achieve considerable performance gain over standard averaging of position estimates.
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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