Multi-Adversarial In-Car Activity Recognition Using RFIDs
Why this work is in the frame
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Bibliographic record
Abstract
In-car human activity recognition opens a new opportunity toward intelligent driving behavior detection and touchless human-car interaction. Among the many sensing technologies (e.g., using cameras and wearable sensors), radio frequency identification (RFID) exhibits unique advantages given its low cost, easy deployment, and less privacy concerns. Existing RFID-based solutions for activity recognition are mostly confined to working in stable indoor spaces. The inside space of a car however is much more compact and complex, not to mention the fast-changing driving conditions. All these introduce non-negligible noises that pollute the activity-related information, and the existence of various car models in the market further complicates the problem. In this article, we for the first time closely examine the distinct factors that affect the RFID-based in-car activity recognition. We present RF-CAR, a novel RFID-based tag-free solution that well adapts to different in-car environments. RF-CAR smartly filters the domain-specific features in RF signals and retains activity-related features to the maximum extent. It then integrates a deep learning architecture and an advanced multi-adversarial domain adaptation network for training and prediction. With only one-time pre-training, RF-CAR can adapt to new data domains such as new driving conditions, car models, and human subjects for robust activity recognition. We also demonstrate that it is readily deployable in cars with commercial off-the-shelf (COTS) RFID devices. Our extensive experiments suggest that RF-CAR achieves an overall recognition accuracy of around 95 percent, which significantly outperforms the state-of-the-art solutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| 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