Adapting an Articulated Vehicle to its Drivers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A yaw plane model with limited roll-DOF of a five-axle tractor semitrailer is developed to study the open-loop directional dynamics of the vehicle. A driver model incorporating path preview, low and high frequency compensatory gains and time delays, and prediction of the vehicle state, is developed and integrated with the vehicle model. The coupled model is analyzed to investigate the vehicle design, which could be best adapted in view of the control limits of different driver, which are identified in terms of preview distance, reaction time and compensatory gain. A performance index based upon the vehicle path tracking, directional response characteristics and the driver’s steering effort is formulated and minimized using Gauss-Newton method to derive the desirable ranges of vehicle parameters, that could be adapted for drivers with varying skills. It is concluded that the adaptability and thus the directional performance of the vehicle can be enhanced through variations in the weights and dimensions, and compliant properties of the suspension, tire and the fifth wheel. The results of the study suggest that a driver with superior driving skill can easily adapt a vehicle with relatively large size, soft suspension and higher degree of oversteer. The results further show that the driver-adapted vehicle yields up to 33 percent reduction in the steering effort demand posed on the driver, while the roll angle and yaw rate response decrease by up to 40 percent.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it