Vehicle navigator using a mixture particle filter for inertial sensors/odometer/map data/GPS integration
Bibliographic record
Abstract
The market for vehicular navigators boomed over the last few years. These navigators rely mainly on satellite based navigation systems such as the Global Positioning System (GPS) to assist drivers. Due to interruption or degradation in such systems in dense urban scenarios, they have to be augmented with other systems to achieve continuous and accurate vehicular navigation. GPS is integrated with low-cost micro-electro mechanical system (MEMS)-based inertial sensors. However, these sensors provide inadequate performance in degraded GPS environments because of their complex error characteristics that often lead to large position drift errors. This paper proposes a continuous and accurate solution integrating low-cost MEMS-based inertial sensors, the vehicle odometer, GPS, and map data from road networks. Despite the traditional inadequate performance of MEMS-based sensors in this problem, the performance is enhanced through: (i) a special combination of inertial sensors and odometer that has better performance for land vehicles than traditional solutions; (ii) The use of map information from road networks to constrain the positioning solution; (iii) The use of an advanced particle filtering (PF) technique to perform the integration, which work with nonlinear models and better modeling of inertial sensor errors, in addition to better integration with the map data. The performance of the proposed positioning system has been verified extensively on real road tests in downtown trajectories with degraded or totally denied GPS for long durations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 0.001 |
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".