CPBW: A Change-Point-Detection and Bag-of-Words-Based Mechanism Utilizing Smartphone Triaxial Accelerometer Data for Driver Identification
Bibliographic record
Abstract
Effective driver identification is one of critical aspects of Internet of Vehicles (IoV) applications, playing a pivotal role in various contexts, such as vehicle anti-theft, fleet management, personalized insurance, vehicle settings automation, digital forensics, and so on. In this article, we propose CPBW, a novel mechanism that combines change point detection and Bag-of-Words (BoW). The CPBW utilizes the smartphone triaxial accelerometer data to accurately identify drivers. The key innovation of CPBW lies in its exceptional efficiency within short time windows, significantly enhancing the real-time performance. The study adopts naturalistic driving studies, collecting the unrestricted real-world data to increase applicability. However, challenges arise from dynamic urban environments influencing driving behavior and the need to balance hardware costs, privacy concerns, and data reliability. In comparison to the previous methodologies, CPBW demonstrates a reduced time requirement for driver identification. Particularly, our proposed CPBW mechanism showcases impressive performance, achieving accuracy, precision, recall, and F1-score up to 98.1%, 98.1%, 98.1%, and 98.0%, respectively. As a result, CPBW markedly enhances the practicality of driver identification in real-world scenarios.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".