Convolutional and Recurrent Neural Networks for Driver Identification: An Empirical Study
Bibliographic record
Abstract
As a powerful non-intrusive method, driver identification based on driving data analysis has recently gained attention as it is beneficial for providing security, privacy, and personalization for driver assistance systems. Fortunately, the considerable variety of available in-vehicle sensors and net-working technologies has contributed to collecting high-quality data for driver identification purposes. Nevertheless, the main challenge in this task is extracting and capturing unique driving-related features and behavior of each individual. In this study, we analyze and compare the effectiveness of benchmark deep learning-based approaches in terms of driver identification accuracy. More specifically, we design an encoder-based framework to compare the performance of temporal convolutional and recur-rent neural networks in capturing the underlying features within the driving sequence data. We also provide insights on their strengths and limitations. Our qualitative and quantitative results demonstrate that a temporal convolution-based network can outperform recurrent architectures while reducing computational complexity by a factor of 5.6.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".