Torque-Vectoring-Based Vehicle Control Robust to Driver Uncertainties
Bibliographic record
Abstract
Driver-in-the-loop stability is a central issue in vehicle control systems. However, since a general human behavior model to explore it in a quantitative fashion has been lacking, little is known about how the vehicle can be controlled while considering the driver effects. Indeed, applying a control method without considering the driver effects, and instead separating human level and machine dynamic layers, guaranteeing stability of a vehicle, is impossible. Here, a new formulation of the problem that involves a driver model and a linear vehicle model is proposed. Given that practical controllers usually do not have access to the future road preview data, this information is also modeled in terms of bounded uncertainties. The design allows the tools of robust control to stabilize the system, offering an implementable approach to overcome ranges of delay and uncertainties of closed-loop modeling due to the human presence. The formulation can further deepen the understanding of the effects of a driver during vehicle steering. To evaluate the proposed controller, a nonlinear full vehicle model along with a driver model in CarSim are used. The simulations performed for a standard harsh double-lane-change scenario under different driver and vehicle conditions demonstrate that vehicle stability is enhanced using the proposed controller.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".