Intermittent Predictive Steering Control as an Automobile Driver Model
Why this work is in the frame
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Bibliographic record
Abstract
The originality of this paper is the evaluation of intermittent control as a viable candidate to represent an automobile driver in a path tracking scenario. The control algorithm is based on general predictive control where the road curvature is considered known for a horizon in front of the automobile. The computed steering wheel command is used in an intermittent fashion, the intermittence period being one of the system parameter to study. Simulations are carried out and parameters of the driver, the automobile, and the road are varied. An intermittence period range giving satisfactory performances is observed. A comparison is made with actual car/driver behavior measurements for a lane change maneuver. It is concluded that, according to this driver model, there is a wide range of intermittence period that the automobile driver may be operating. Moreover, it is suggested to consider the intermittency of information as an important parameter for vehicle safety systems.
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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 | 0.001 | 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 it