Intervehicle-Communication-Assisted Localization
Why this work is in the frame
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Bibliographic record
Abstract
Vehicle localization is a key issue that has recently attracted attention in a wide range of applications. Navigation, vehicle tracking, emergency calling, and location-based services are examples of emerging applications with a great demand for location information. The Global Positioning System (GPS) has been the de facto standard solution for the vehicle-localization problem. Nevertheless, GPS-based localization is inaccurate and unreliable due to GPS' inherent poor performance in vertical positioning and the prevalent horizontal movement, in addition to anomalies caused by line-of-sight occlusions and multipath issues in urban canyons. Although augmenting GPS localization with inertial sensory data has demonstrated significant performance improvements, there remain situations that give rise to degraded localization accuracy-a deficiency that many applications cannot tolerate. In this paper, we propose intervehicle-communication-assisted localization, a localization technique that takes advantage of the emerging vehicle ad hoc networks environments. Communication among vehicles is utilized to compute a relative vehicle location, the integration of which with motion information and GPS location estimates leading to highly accurate vehicle localization. This proposed localization technique is tested in various simulated road-segment scenarios. It is evident from the simulation results that intervehicle communication has the potential to lead to the improvement of the robustness and accuracy of vehicle-location estimation.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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