A Cloud-Based Environment-Aware Driver Profiling Framework using Ensemble Supervised Learning
Bibliographic record
Abstract
Driver profiling is an emerging scheme that has a wide range of applications in the field of Intelligent Transportation Systems (ITS). Driver profiling is the real-time process of detecting driving behaviors and computing a driver's competence level based on detected behaviors. In this paper, a novel driver profiling framework is presented. A risk prediction model is hosted in the cloud to determine the risk associated with detected behaviors in specific driving environments. Risk values along with a driver's compliance to warnings are both utilized to compute a driver's risk profile. Using SHRP2 large-scale Naturalistic Driving (ND) dataset, the development of the risk prediction model is presented herein with the underlying sub-processes of data preprocessing, error analysis, and model selection. Validation results show that a developed randomized trees supervised learning model is proven to have a good tradeoff between bias and variance with evidently high performance results.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".