ENHANCING URBAN VEHICULAR NAVIGATION: IMPROVING CLASSICAL TOPOLOGICAL MAP MATCHING THROUGH RAY-CASTING
Bibliographic record
Abstract
Abstract. Navigation is of paramount importance for land vehicles as it enables efficient and accurate movement from one location to another. Whether it is for personal navigation, commercial transportation, or emergency services, reliable navigation systems play a crucial role in ensuring safety, optimizing routes, and enhancing overall operational efficiency. This paper presents the integration of classical Topological Map Matching (TMM) with the Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS), addressing the limitations of relying solely on road centerlines. A novel solution is proposed, leveraging the ray-casting algorithm to determine the predicted position’s area and employing a two-stage kinematic update process for enhanced positioning accuracy. The solution’s efficacy is evaluated through tests conducted on simulated GNSS outages within a road experiment conducted in the City of Toronto, demonstrating substantial improvements compared to the classical TMM approach. Notably, the proposed method achieved a considerable 82.33% reduction in RMS positioning error and a 33.71% improvement in maximum positioning error during the longest GNSS outage. By overcoming the limitations of classical TMM algorithms, this research contributes to the advancement of navigation and tracking systems, with future work focusing on practical implementations and optimization for diverse navigation scenarios.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".