Accessible Time Interval Based Local Positioning System: Applications for Self-Driving Cars in Smart Cities
Bibliographic record
Abstract
The LRIMa City testbed is a comprehensive expandable, and versatile smart city framework designed to advance research and learning in the fields of Internet of Things. In this paper, we propose an accessible yet accurate Local Positioning System (LPS) leveraging ultrasonic signals to determine vehicle locations by using a network of towers equipped with ultrasonic receivers placed at the corners of the city. Each vehicle is equipped with an antenna that emits ultrasound waves in all directions, which are then detected by the towers. Then, we use the time differences between signal receptions, also known as difference time of arrival (DTOA), to triangulate the vehicle's position. Through preliminary simulations and testing, we estimate that our LPS system is a promising solution with a location accuracy of 2.6 cm. Moreover, we developed a City Centralized Web Interface, named CCWI, to enable seamless control and monitoring of fog computing on-device solutions. Finally, our proposed LPS system is to be directly connected to the CCWI to allow vehicle localization at a small scale within the LRIMa Smart City testbed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".