Vehicular Collaborative Technique for Location Estimate Correction
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
Awareness of a vehicle's precise location in VANET is vital so that any vehicle can provide accurate data to its peers. Currently, typical localization techniques integrate the GPS receiver data and the measurements of the vehicle's motion. However, when the vehicle passes through an environment that creates multipath signals, these techniques fail to produce the high localization accuracy that they attain in open environments. The goal of this research is to minimize the multipath effect with respect to the localization accuracy of the vehicles in VANET. The proposed technique, IVCAL, takes advantage of the communications among the VANET vehicles in order to obtain more information from the vehicle's neighbours. The proposed technique integrates all these pieces of information with the vehicle's own data and applies optimization techniques to minimize the error in the location estimate. The simulation results in this paper show a decrease of up to 53% in the location estimate error compared to the error in the traditional techniques.
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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