Adaptive Velocity and Acceleration Control of Autonomous Vehicle Systems
Why this work is in the frame
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Bibliographic record
Abstract
This article proposes a combined adaptive velocity and acceleration control (CAVAC) law for autonomous vehicles in the presence of uncertainties and nonlinearities. In particular, the global asymptotic stability of the nonlinear vehicle system under the proposed adaptive velocity control is shown to hold without real-time estimation of the vehicle parameters. Moreover, the CAVAC law is shown to achieve the string stability against energy bounded disturbances, and ensures the required intervehicle spacing, crucial to avoiding collisions in vehicle platoons. The simulation studies illustrate the advantages of the CAVAC law for the autonomous vehicle platoon in dealing with the speed limit changes, in mitigating issues induced by merging-exiting, in suppressing energy bounded disturbances, and in improving collision avoidance. The experiments on an electric vehicle (EV) validate the effectiveness of the proposed control laws.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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