Combining Unsupervised Anomaly Detection and Neural Networks for Driver Identification
Bibliographic record
Abstract
This paper proposes an algorithm for real-time driver identification using the combination of unsupervised anomaly detection and neural networks. The proposed algorithm uses nonphysiological signals as input, namely, driving behavior signals from inertial sensors (e.g., accelerometers) and geolocation signals from GPS sensors. First anomaly detection is performed to assess if the current driver is whom he/she claims to be. If an anomaly is detected, the algorithm proceeds to find relevant features in the input signals and use neural networks to identify drivers. To assess the proposed algorithm, real-world data are collected from ten drivers who drive different vehicles on several routes in real-world traffic conditions. Driver identification is performed on each of the seven-second-long driving behavior signals and geolocation signals in a streaming manner. It is shown that the proposed algorithm can achieve relatively high accuracy and identify drivers within 13 seconds. The proposed algorithm also outperforms the previously proposed driver identification algorithms. Furthermore, to demonstrate how the proposed algorithm can be deployed in real-world applications, results from real-world data associated with each operation of the proposed algorithm are shown step-by-step.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".