Toward increasing the localization accuracy of vehicles in VANET
Bibliographic record
Abstract
In this paper, we propose a constrained weighting scheme of inter-vehicle communication assisted localization (CWS-IVCAL). CWS-IVCAL takes into consideration the location estimate uncertainty maintained by vehicles that use IVCAL as a localization technique. In IVCAL, communication among vehicles is utilized to compute inter-vehicle distances, which are integrated with motion information and GPS measurements in order to improve vehicle location estimates in multipath environments. The proposed scheme was tested and compared with a regular IVCAL scheme in a variety of simulated road segment scenarios. CWS-IVCAL has shown resilience not just against GPS unreliability in urban environments but also against erroneous inter-vehicle distance estimates. It is evident from the simulation that utilizing more location information has the potential to lead to improvement in the robustness and accuracy of vehicle location estimation.
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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 | 0.000 | 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 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".