An Optimized GNSS RTK/INS/Vision Integration-Based Vehicle Positioning Model and Its Credibility Assessment
Bibliographic record
Abstract
Accurate positioning is critical to Intelligent Transportation Systems (ITSs). Current research primarily focuses on improving Global Navigation Satellite System (GNSS) positioning accuracy and continuity through multi-sensor integration. With the emergence of new industries such as assisted driving, the credibility of positioning results has gradually attracted attention. To explore the feasibility of achieving credible positioning, this paper presents an optimized Inertial Measurement Unit (IMU) and camera tightly augmented GNSS Real Time Kinematic (RTK) model, along with the positioning credibility assessment. In this model, multi-factors that affect the positioning errors are considered as the feature inputs of the Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) network, then a credible factor and its uncertainty are generated. Moreover, a positioning optimization algorithm is presented based on the credible factor. To evaluate the effectiveness of the presented model, several sets of vehicle-borne data in urban environments are processed and analyzed. Results illustrate that (1) the presented positioning model achieves comparable positioning and superior orientation determination accuracy compared to existing state-of-the-art methods; (2) the generated credible factor can envelop 94% horizontal positioning errors and 84% vertical positioning errors with envelope levels of 5 cm and 7cm; (3) the error coverage rate of confidence interval generated by the uncertainty of credible factor in horizontal and vertical directions can reach 94% and 87.57%, which is close to the theoretically set 95% confidence interval for horizontal direction; (4) the positioning results can be optimized while applying the position optimization algorithm based on credible factor.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".