Numerical Simulation and Analysis of Closed-Loop Driver/Articulated Vehicle Dynamic Systems
Bibliographic record
Abstract
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.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".