Methodology to Analyze Adaptation in Driving Simulators
Bibliographic record
Abstract
Adaptation to a driving simulator is one of the necessary conditions for the validity of almost all driving simulator studies. Learning how to control a simulated vehicle requires practice, which will put some mental load on drivers and can distract them from their main task (i.e., driving). Such distraction during the learning phase affects drivers’ reactions to different situations compared with what they would have done in the real world; therefore, having a tool to confirm that adaptation occurred is necessary to have accurate data. This methodology needs to be sensitive to the diversity of driving styles and applicable to a variety of driving tasks and performance measures. A comprehensive review of the literature indicated a general deficiency in the methodologies being used. Common approaches include having participants drive for a predefined time or driving distance or drive until the participants report that they feel comfortable. Such approaches do not ensure that adaptation has indeed occurred. This paper proposes a methodology to evaluate adaptation by analyzing driver performance measures. The methodology is based on the concept of experience curve effect and is intended to complete and further develop the idea of the learning curve effect that was used in the authors’ earlier research. The methodology was tested for adaptation to acceleration and braking tasks using UBCDrive driving simulator. The results indicated that experience and learning curve effects can be used to identify adapted, adapting, and nonadapting participants.
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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 | 0.020 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".