Reducing multipath effects in vehicle localization by fusing GPS with machine vision
Why this work is in the frame
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Bibliographic record
Abstract
Vehicle localization is an important component of Intelligent Transportation Systems and telematics applications. Localization systems typically rely on Global Positioning System (GPS) technology; however, the accuracy and reliability of GPS are degraded in urban environments due to satellite visibility and multipath effects. We propose to use a Kalman filter to fuse data from a GPS receiver and a machine vision system to position the vehicle with respect to objects in its environment. Data association is needed to identify the detected objects, and to identify the road driven by the vehicle. For this purpose we employMultiple Hypothesis Tracking to consider multiple data association hypotheses simultaneously. Experimental results show that using machine vision reduces the effect that GPS measurement errors have on localization accuracy. Vision also improves the identification of the road being driven by the vehicle, which is important for the problem of map matching in vehicle localization.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it