Integrated Positioning for Connected Vehicles
Bibliographic record
Abstract
In the era of autonomous cars, accurate vehicular positioning becomes very essential. The global navigation satellite systems (GNSS) suffer from signal blockage and severe multipath in urban canyons, which degrades the positioning accuracy and availability. Therefore, vehicles solely relying on positioning from GNSS receivers have limited performance. In this research, we present a novel unified cooperative positioning solution which enhances positioning accuracy and availability in urban canyons. The proposed system exploits the fact that vehicles have different positioning resources and is based on angle approximation, which artificially generates the hindered pseudorange by sharing angle information between vehicles using dedicated short-range communication. In addition, we propose a system that employs the proposed cooperative technique to assist the loose integration between the inertial navigation system (INS) and the GPS system (using extended Kalman filter) during partial GPS outages. Using raw data from inertial sensors and GPS receivers in the real road trajectories, we implement the cooperative INS/GPS loose integration and show that our cooperative integrated system outperforms the non-cooperative integrated system. The performance metrics used are the 2-D positioning root-mean-square error, the maximum 2-D positioning error, and the positioning accuracy gain (PAG). Specifically, the PAG gain is around 88%, 80%, and 60% when the number of blocked satellites is one, two, and three, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".