Data-driven Robust Scoring Approach for Driver Profiling Applications
Bibliographic record
Abstract
Driving behavior profiling has important relevance in many driving applications. For instance, car insurance companies have been recently applying a new insurance paradigm in which a driver's insurance premium is adapted based on realtime driving behavior. Driver profiling process is composed of two sub processes. The first is the detection of certain driving behaviors by acquiring data from onboard devices such as smartphones and OBDII units, whereas the second is the scoring process in which the detected behaviors are used to measure the actual driving risk. The scoring process has been viewed as an intricate problem due to the lack of reliable and large-scale datasets that can provide statistically trustworthy insights. This paper presents a data-driven approach for calculating a driver's risk score by utilizing the SHRP2 naturalistic driving dataset, which is the largest dataset of its kind to date. Two machine learning algorithms, which are support vector regression (SVR) and decision tree regression (DTR) are trained to reflect a driver's score. Driver's score is quantified in terms of the additive inverse of the predicted risk probability. After data filtering and preprocessing, models are trained using thirteen predictors, which represent twelve unique driving behaviors and the total driving time per driver. Validation results show that risk probability can be accurately predicted using the proposed models.
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.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".