Development of a Dynamic Simulation of an Automobile that Incorporates Four Wheel Steering and a Driver Model
Bibliographic record
Abstract
In this thesis, the development of a four-wheel steering vehicle dynamics model and the implementation of a driver controller are detailed. The model is intended to be used for supporting research in the cybersecurity of conventional and autonomous road vehicles. It incorporates a 10 degree-of-freedom vehicle model and a driver controller capable of steering all four wheels for improved vehicle lateral dynamics performance. The vehicle model is developed as a clean-sheet design using the fundamental principles of vehicle dynamics engineering and implemented in the Matlab/Simulink computing environment. A suitable driver-controller model that meets the industrystandard requirements was adopted from the literature review and implemented in the model. The functionality of the model is demonstrated with established standard vehicle manoeuvres that were used as part of the validation process. It is also shown that the model compares favorably with an "equivalent" ADAMS/Car model. Experience with the model confirms that it is suitable for its intended purpose. This was evaluated by considering the effect of corrupt four wheel steering feedback signals and investigating the relative dynamic response of conventional and four wheel steering vehicles. The model shows moderate improvement in the lateral performance and stability of four-wheel steering vehicles compared to equivalent front-wheel only steering vehicles. The developed model is also shown to be easily adaptable to performing sensor integrity and cybersecurity research of four-wheel steering vehicles.
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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".