Cooperative localization via DSRC and multi-sensor multi-target track association
Bibliographic record
Abstract
Vehicles in the near future will be equipped with dedicated short-range communications (DSRC) transceiver which holds great promise of significantly reducing vehicle collisions by enabling vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. In addition, modern vehicles will be equipped with different on-board sensors such as GPS receivers and ranging sensors (e.g., cameras, radars, and lidars). Using these technologies, this paper proposes a comprehensive system design to improve the positioning of a host vehicle based on Kalman filters. In this approach, the host vehicle fuses its own position information obtained by the on-board GPS receiver with position information of nearby vehicles collected by the on-board ranging sensor(s) and the messages received via the DSRC transceiver from other equipped vehicles. This process also involves performing track matching using a multi-sensor multi-target track association algorithm. We provide insights on the system design and present simulation results that show significant performance gains of the proposed method in terms of localization accuracy and matching accuracy.
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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".