A rollover prediction method for multi-trailer articulated heavyvehicles
Bibliographic record
Abstract
A new method is proposed for rollover prediction for multi-trailer articulated heavy vehicles (MTAHVs).Due to multi-unit configurations, large sizes, and high center of gravities, MTAHVs exhibit poor high-speed lateral stability.The high-speed instable motion modes, e.g., jackknifing, trailer sway and rollover, frequently lead to fatal traffic accident in highway operations.To increase the safety of MTAHV operations on highways, we explore the rollover prediction method for MTAHVs.To this end, numerical simulation is conducted to validate this method for a B-Train MTAHV roll-stability estimation.A linear 4 degrees-offreedom (DOF) yaw-plane B-Train model and a linear 2 DOF roll-plane single vehicle unit model is generated.The roll dynamics of the second trailer of the B-train is predicted by the integral model combining the yaw-plane and roll-plane models.Since the linear integral yaw-roll model may not accurately predict the performance of the B-train in nonlinear dynamic region, a neural network trained with TruckSim data is used to refine the estimated roll dynamics of the second trailer of the B-train.The Time-To-Rollover (TTR) of the trailer is calculated.The simulation results show that with the recurrent neural network (RNN), the roll performance measure of the trailer can be more accurately estimated than only with the linear integral yaw-roll model.Thus, more accurate TTR of the trailer can be achieved, and a reliable signal can be used for rollover warning.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".