STUDY OF CONTROL CHARACTERISTICS OF AN ARTICULATED VEHICLE DRIVER.
Bibliographic record
Abstract
A closed–loop articulated vehicle–driver model, incorporating the lateral position and orientation errors, lateral accelerations of the two units and the rate of steering, is proposed to study the control characteristics of the driver. The driver model is formulated to minimize the lateral acceleration of vehicle, and the lateral position and orientation errors between the previewed and the actual path of the tractor. The driver's delays and gains associated with the limb movement and muscle activities are represented by the proprioceptive information. Various driver models reported in the literature are reviewed to identify a range of model parameters and their sensitivity to variations in directional manoeuvres and speed. Driver model parameters are identified through minimizing a weighted performance index subject to an array of limit constraints established from the reported data. The proposed model and the identification methodology are validated using the field measured directional response of a seven–axle articulated vehicle under an evasive manoeuvre. The simulation of three double lane change manoeuvres is performed and the influence of vehicle speed on various driver model parameters are discussed. The results of the study may serve as an effective guide to enhance the driver's actions to improve the safety of the driver/vehicle system through improved directional control strategies.
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 | 0.000 | 0.002 |
| 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) | 0.001 | 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 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".