Testing Vehicle-to-Vehicle Relative Position and Attitude Estimation using Multiple UWB Ranging
Bibliographic record
Abstract
Ultra-wideband (UWB) is an emerging technology that has the ability to accurately measure ranges over short to medium distances (10 cm to 300 meters) in outdoor and indoor situations[1]. The technology has been investigated for many applications that require both positioning and communication. Among these applications are robot automation and vehicle positioning. Many companies are trying to benefit from UWB signals and many IC manufacturers are already offering chipsets that can calculate the Time of Flight (TOF) for Two-Way-Ranging (TWR) with UWB signals. Range measurements acquired through UWB have an accuracy on the order of 10 cm, which is more accurate than conventional GPS for estimating a vehicle position in outdoor environments. This paper proposes and tests a method of using UWB ranges from two radios on one vehicle to two radios on another vehicle to estimate the 2-D relative position and heading of the second vehicle in the body frame of the first. The method is described, a positioning algorithm based on an extended Kalman filter is presented and an initial fields test using two moving vehicles on a suburban street is presented.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".