Adaptive comfort-oriented vehicle lateral control with online controller adjustments according to driver behaviour and look-ahead dynamics
Bibliographic record
Abstract
This paper presents a comfort-oriented adaptive fuzzy-model predictive control strategy to control the lateral motion of a vehicle with the steering angle as the input while preventing sudden changes and unwanted motions. This is reached by utilizing three main contributions: an adaptive fuzzy model based on look-ahead dynamics, limiting the controller to the acceptable range of states to the driver and introducing an adjustment factor to the controller's cost function. Using adaptive-fuzzy models to describe the vehicle lateral dynamics and driver behaviour for the purpose of the control-oriented model enables this method of control to advantage from the low computational effort of the models while maintaining the accuracy and adaptive properties which are crucial to the performance and robustness of the system during the manoeuvres. Also, the characteristics of each driver's behaviour modify the controller towards a more comfort-oriented drive with means of the cost function and constraints. The comparative simulation indicates that the developed method leads to a comfort-oriented drive, in that the vehicle's states stay within the limits of the driver behaviour range, the fluctuations are insignificant and the control strategy is more accurate than previous methods.
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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) | 0.001 | 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".