Mass Flow Meter and Vehicle Information DR Land Vehicles Navigation System in Indoor Environment
Bibliographic record
Abstract
Land vehicle navigation has many challenges in some operating environment such as urban canyons and indoor environments (underground parking, and tunnels) where GNSS signals are suffering from multipath or blockage. A Dead Reckoning (DR) land vehicle navigation system is proposed for indoor environments based on a non-conventional navigation sensor which is multiple mass flow meters and the vehicle forward velocity obtained from On-Board Diagnostics II (OBD-II). Two mass flow sensors are placed on the front bumper in a lateral direction of the land vehicle motion direction to estimate the vehicle's heading change in indoor scenarios. A relation between a reference change of heading along with the mass flow sensors data and the land vehicle odometer is estimated through a regression model. Experimental tests have been implemented in underground parking using two mass flow meters of model (SFM3000) and a commercial OBD-II. The results show that the proposed DR system provides a promising navigation solution with an accuracy of 1.58% of the traveled distance for 130 seconds of motion of 443 meters. The proposed DR system opens a new research opportunity using a nontypical sensor that is used for medical applications to be implemented in low-cost land vehicle navigation systems especially with the mass production for such sensors.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".