A Physically Intuitive Yaw-Plane Bond Graph Model for Vehicle Active Safety System Design
Bibliographic record
Abstract
As a platform for simulation-based design of active safety systems, a nonlinear yaw-plane vehicle model has been developed. The Newton-Euler formulation with an Euler Junction Structure is used for the car body, with coordinate transformations to express the front wheel longitudinal and lateral forces in a local wheel reference frame. Tire modeling is done via a continuous function which captures peak longitudinal and lateral forces, and saturation. A friction ellipse correction is made to reduce lateral force capacity in the presence of tractive/braking force. Vehicle forward speed is variable through powertrain torque inputs. Parameters are based on a mid-sized sedan, with the engine represented by a torque-speed curve, braking by a modulated R element, and transmission by a modulated transformer with shift logic. A speed controller tracks a desired forward speed, while a driver model generates steering inputs based on yaw angle error and on lateral errors in a sequence of look-ahead points. Straight-line acceleration/braking and double lane-change maneuvers are performed to demonstrate the model's ability to simulate coupled longitudinal-lateral dynamics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".