UNIVERSITY OF CALGARY Development of Map Aided GPS Algorithms for Vehicle Navigation in Urban Canyons
Bibliographic record
Abstract
A major portion of the Location-Based Services (LBS) market deals with applications involving in-car navigation systems. The Global Positioning System (GPS) is the most popular choice for positioning in such applications. Many LBS applications involve positioning in urban areas having high rise buildings. Although GPS has good positioning accuracy in open sky conditions, it suffers from line-of-sight issues in urban canyons. This thesis formulates some new methods for aiding GPS using maps for vehicle navigation in urban canyons. GPS satellite availability in urban canyons can be improved by using a High Sensitivity GPS (HS GPS) receiver which can track weak signals. However, this introduces large errors and noise in measurements. Thus, reliability monitoring becomes necessary with such receivers in signal degraded environments. Maps and Digital Elevation Models (DEM) provide effective constraints to compute an outlier-free solution. In this research, a robust fuzzy logic-based approach is developed for road segment identification. This identified road segment is then used in a GPS computation model and is referred to as Map Aided GPS (MAGPS). The performances of
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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 | 0.002 |
| 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 | 0.001 |
| 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".