Cooperative Multi-sensor Multi-vehicle Localization in Vehicular Adhoc Networks
Bibliographic record
Abstract
Intelligent Transportation System (ITS) is an important application domain for information reuse and integration. Efficient integration and deployment of information and communication technologies (ICT) can potentially reduce travel time and emission, improve usage of parking and public spaces, offer personalized travel related services, and more importantly, improve safety for drivers and pedestrians in large municipalities. Personalized travel related services and recommendation systems rely mainly on precise identification of position of the vehicles. In this paper we propose a cooperative multi-sensor multi-vehicle localization algorithm with high accuracy for terrestrial vehicles. Noisy observations in the form of GPS coordinates of nearby vehicles as well as inter-vehicle distance measurements are assumed to be available. These heterogeneous sources of information are fused together and used to estimate the number and motion model parameters of the vehicles in the field. The problem is formulated in the context of Bayesian framework and vehicle locations are estimated via a Sequential Monte-Carlo Probability Hypothesis Density (SMC-PHD) filter. Given that the GPS data and inter-vehicle distance measurements are available except for short periods of time, simulation results indicate that the proposed algorithm provides approximately threefold improvement in location accuracy compared to that achieved with GPS.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".