LPV Unknown Input Observer-Based Estimation of Driver Intervention Torque and Vehicle Dynamics for Human-Machine Shared Driving
Bibliographic record
Abstract
This paper presents an unknown input observer (UIO)-based method to jointly estimate the vehicle dynamics and the driver torque within the framework of human-machine shared driving. To deal with the time-varying vehicle longitudinal speed, the vehicle dynamics is represented as a linear parameter-varying (LPV) model. Based on an unknown input (UI) decoupling technique, an LPV observer is designed, which can guarantee an asymptotic estimation performance of both the vehicle dynamics and the driver torque. Via Lyapunov stability theory, we propose sufficient conditions, expressed in terms of linear matrix inequalities, for LPV unknown input observer design. High-fidelity Simulink-CarSim co-simulations are carried out to show the effectiveness of the proposed LPV UIO-based estimation method for driver-automation shared driving dynamics. Moreover, a comparative study is performed with a recent LPV method to highlight the practical interest of the new estimation solution.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".