A Longitudinal Speed Controller For Autonomous Multi-Trailer Articulated Heavy Vehicles
Bibliographic record
Abstract
This paper presents an automated longitudinal speed controller for multi-trailer articulated heavy vehicles (MTAHVs).A 6 degrees of freedom (DOF) yaw-plane vehicle model is generated to represent a MTAHV with the configuration of A-train double.A vehicle states prediction approach and a Mamdani fuzzy interface system are utilized to devise the automated driving controller for forward speed control of the MTAHV.Due to multiple articulation joints and heavy and long architectures, MTAHVs exhibit low high-speed lateral stability.They often experience amplified lateral motion of trailing units in transient curved path negotiations.Most of the speed planning schemes and control strategies introduced in the literature have been proposed for single unit vehicles.To enhance the automated speed control performance of the MTAHV, an anticipatory/compensatory lateral acceleration controller strategy considering the states of all the vehicle units and the MTAHV performance envelope is proposed.This speed controller distinguishes itself from others with several features.To evaluate the effectiveness of the innovative speed control strategy, co-simulations are carried out by combining the nonlinear A-train double model generated in TruckSim with an integrated controller designed in MATLAB/ SIMULINK.
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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 | 0.000 | 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".