Stability analysis of dynamic decision-making for vehicle heading control
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, the problem of dynamic decision making for vehicle heading control to intercept moving targets is investigated. It is assumed that the targets arrive in the mission space sequentially. More precisely, there exist infinite number of targets that arrive the mission space one by one. The arrival times and positions of the targets are modeled using stochastic models. Furthermore, targets are assumed to move with unknown dynamics and unknown trajectories. Due to the probabilistic nature of the problem, it is desired to use a model predictive approach to control the heading of the vehicle. A reward allocation strategy is adopted for dynamic decision making and control design in order to move the vehicle toward the targets. Finite-time convergence analysis is presented for the case where the arrivals of targets occur sufficiently infrequently.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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