AI-Powered In-Vehicle Passenger Monitoring Using Low-Cost mm-Wave Radar
Bibliographic record
Abstract
We propose a novel algorithm to identify occupied seats in a motor vehicle, i.e., the number of occupants and their positions, using a frequency modulated continuous wave radar. Instead of using a high-resolution radar, which increases the cost and device size, and performing complex signal processing with several variables to be tuned for each scenario, we integrate machine learning algorithms with a low-cost radar system. Based on heat maps obtained from the Capon beamformer, we train a machine classifier to predict the number of occupants and their positions in a vehicle. We follow two different classification methods: multiclass classification and binary classification. We compare three classifiers: support vector machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF), in terms of accuracy and computational complexity for both testing and training sets. Our proposed system using an SVM classifier achieved an overall accuracy of 97% in classifying the defined scenarios in both multiclass classification and binary classification methods. In addition, to show the effectiveness of our proposed in-vehicle occupancy detection method, we provide the results of a commonly available people counting and tracking method for occupancy detection. Compared to common methods, the effectiveness, robustness, and accuracy of our proposed in-vehicle occupancy detection method are demonstrated.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".