Pa-Count: Passenger Counting in Vehicles Using Wi-Fi Signals
Why this work is in the frame
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Bibliographic record
Abstract
Passenger counting is crucial for many applications such as vehicle scheduling and traffic capacity assessment. However, most of the existing solutions are either high-cost, privacy invasive or not suitable for passengers the vehicle scenarios. In this work, we propose the <u>Pa</u> - <u>Count</u> , an effective real-time <u>Pa</u> ssenger <u>Count</u> ing system deployed inside the vehicle via using Wi-Fi <i>CSI</i> (Channel State Information). Specifically, in Pa-Count, we design a set of combined filters to eliminate environmental interference and enhance CSI quality. In so doing, we can identify the fluctuation of weak CSI caused by passengers’ subtle movement, i.e., the fidgeting, and then obtain the distribution of fidgeting period and silent period. Following that, we describe the subtle movements of passengers via power law with exponential cutoff distribution and establish a counting model based on the queuing theory. A mathematical inference method with a priori probability is devised to calculate the number of real-time passengers through CSI. We evaluate the performance of the Pa-Count by conducting a set of experiments in real-world vehicle scenarios (including private car and subway). Experimental results show that Pa-Count can achieve robust performance with an average accuracy of over 92 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> .
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it