Dynamic performance of multi-trailer articulated heavy vehicles with advanced control systems
Bibliographic record
Abstract
With the current situation of exponential growth in freightage and traffic congestion, Multi-Trailer Articulated Heavy Vehicles (MTAHVs) have become an economical and pragmatic solution to transportation. However, problems like high-speed lateral stability and low-speed maneuverability are still challenges with which MTAHVs are still facing due to high centers of gravity, multi-unit structures and large sizes. Jackknifing, rollover, and fish-tailing are three typical unstable motions that cause severe road accidents at high speeds. Steerable axles including passive and active steering systems have been recognized as a promising method to improve the dynamic performance of MTAHVs. Dynamic performance tests were conducted to compare the MTAHVs equipped with active and passive trailers steering systems against a baseline MTAHV without any steerable trailer axles installed. To examine and evaluate the capabilities of each steering system, an A-train double MTAHV with high degrees of fidelity was developed in TruckSim. Advanced driver models and passive and active steering systems were designed and implemented using MATLAB/Simulink. The baseline set was implemented with trailer and dolly axles fixed and without any steering input. Two road conditions were considered based on different tire-road friction coefficients to simulate dry and wet road surface conditions. Two typical vehicle dynamics performance test scenarios, High Speed Lane Change (HSLC) and Low Speed Turn (LST), were conducted. The simulation results showed that on good road condition, both passively and actively steered trailer axles considerably improved low speed maneuverability in comparison to fixed-axle baseline trailer axles. It was observed that the passive steering mechanism reduced the MTAHVs' high speed lateral stability while the active steering mechanism was able to significantly improve driving performance at both high and low speeds at both high and low friction road conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 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".