A Safe and Computationally Efficient Tracking Control Algorithm for Autonomous Vehicles
Bibliographic record
Abstract
This paper investigates the use of control barrier functions in conjunction with a recently developed output-tracking technique, with the aim of guaranteeing safe and smooth driving conditions for autonomous vehicles. The tracking technique in question has been developed by the authors of this paper for effectiveness as well as efficiency of computation, but its implementation may be fraught with large early input transients and state oscillations. The main objective of this paper is to investigate the use of control barrier functions for the dual purpose of safety guarantees and drastic reductions in the input oscillations and state overshoots. Furthermore, if the plant subsystem is differentially flat then we exploit this fact to further reduce the complexity of the control algorithm. As this is an initial study we focus the discussion on the particular example of a kinematic bicycle model, but argue for its potential generality to more complicated models of self-driving cars such as the dynamic bicycle. We test the aforementioned ideas by simulation and on a hardware platform, and the results suggest that the developed controller may have merits in autonomous vehicle applications.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".