Numerical Simulation and Analysis of Closed-Loop Driver/Articulated Vehicle Dynamic Systems
Why this work is in the frame
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Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">This paper presents a preliminary investigation of the closed-loop driver/articulated vehicle directional dynamics using numerical simulation. To date, a lot of attention has been focused on investigating the closed-loop directional dynamics of driver/single-unit vehicle systems. Little effort has been paid to examining the closed-loop directional dynamics of driver/articulated vehicle systems. Compared with single-unit passenger cars, multi-unit articulated vehicles have unique directional dynamic characteristics. Generally, a driver's behavior for an articulated vehicle is different from that for a passenger car. To investigate the impact of driver behavior on articulated vehicle directional dynamics, three driver models based on dynamic responses of tractor, trailer and combined tractor/trailer, respectively, have been developed. The three driver models are tested and compared through the numerical simulations of a low-speed path-following and a high-speed lateral stability test maneuvers for a driver/articulated vehicle system. The numerical studies are conducted in a Simulink-TruckSim simulation environment in such a way that the driver model is designed using Simulink from Matlab software, and the articulated vehicle model is constructed in TurckSim multibody dynamic package, then, the computer simulation can be implemented by combining the driver and vehicle models. With the benchmark comparisons, the distinguished features of different driver models are revealed and their applicability is demonstrated.</div></div>
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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 | 0.000 | 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 | 0.000 | 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