A Longitudinal Speed Controller For Autonomous Multi-Trailer Articulated Heavy Vehicles
Why this work is in the frame
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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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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) | 0.001 | 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