Adaptive Trajectory Tracking Based on Backstepping Control of Integral Error for Autonomous Vehicles
Bibliographic record
Abstract
With the rapid development of transportation industry, autonomous driving technology has attracted a great deal of attention in modern society. The control method is of crucial significance for improving the overall performance and safety of autonomous driving. This paper mainly focuses on designing an effective vehicle control method that aims to enhance the real-time trajectory tracking capability of autonomous vehicles (AVs). Unlike the traditional backstepping control strategy, the proposed method reconstructs multiple virtual control quantities by integrating multiple error values from various aspects in vehicle operation. Moreover, by making use of rear-wheel feedback control, the AVs at different initial points can be tracked effectively along the desired trajectory. By conducting a series of purposely designed simulations and experiments under various conditions and scenarios, the feasibility of the proposed method is thoroughly proved. Both the simulation and experimental results have provided a solid theoretical and practical basis of the proposed method for its further application in the real AVs.
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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) | 0.000 | 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 | 0.000 |
| 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".