Bibliographic record
Abstract
The following paper presents an outline of the current state of driver modeling along with the various methods that are employed in their development. In recent years, vehicle manufacturers have implemented various systems that, in some manner, improve the operation of their vehicles. Many of these systems include an electronically controlled device which is capable of making decisions based on the immediate conditions affecting the vehicle. Much of the influence to develop such systems stems from the issue of safety: in emergency situations the control device is capable of making a decision quicker than the driver and thus reduces the potential for some form of collision. Another motivating factor behind these systems is to improve fuel efficiency, specifically in regard to hybrid vehicles where more than one form of propulsion is used and such devices can aid the driver to operate in a more efficient manner. Although these systems have the potential to offer great benefits, in order to be able to implement them, they must function properly such that they provide assistance when necessary while at the same time not being overly intrusive. To achieve this goal, research into driver modeling is gaining more attention in the automotive industry so that driver-in-the-loop (DIL) control systems can be developed. DIL systems work to interpret the driver's intention in order to make a decision whether or not to intervene. The end result of these DIL systems is improved vehicle control without making the driver feel as if their control has been compromised.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".