A Physics-Based Musculoskeletal Driver Model to Study Steering Tasks
Bibliographic record
Abstract
Realistic driver models can play an important role in developing new driver assistance technologies. A realistic driver model can reduce the time-consuming trial-and-error process of designing and testing products, and thereby reduce the vehicle's development time and cost. A realistic model should provide both driver path planning and arm motions that are physiologically possible. The interaction forces between a driver's hand and steering wheel can influence control performance and steering feel. The aim of this work is to develop a comprehensive yet practical model of the driver and vehicle. Consequently, a neuromuscular driver model in conjunction with a high-fidelity vehicle model is developed to learn and understand more about the driver's performance and preferences, and their effect on vehicle control and stability. This driver model can provide insights into task performance and energy consumption of the driver, including fatigue and cocontraction dynamics of a steering task. In addition, this driver model in conjunction with a high-fidelity steering model can be used to develop new steering technologies such as electric power steering.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".