Lateral Control for Autonomous Vehicles: A Robust Bounded Back-Stepping Technique
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Bibliographic record
Abstract
In this paper, we propose a conceptually different backstepping approach to solve the global asymptotic stabilization problem for a class of nonlinear input-coupled systems with parameter uncertainties and both state and input constraints. This approach avoids both input-decoupling transformations and the cancellation of time derivatives of virtual control functions— steps that are typically required in conventional backstepping-based control designs for input-coupled systems. As a by-product, it broadens the applicability of existing backstepping techniques and significantly reduces the computational burden— a major obstacle for real-time implementation of these methods. The proposed approach relies on an innovative combination of control tools, including non-quadratic Lyapunov-like analysis, the concept of Input-to-State Stability (ISS), and the Invariance Principle, enabling the construction of a control law without quadratic (smooth) control Lyapunov functions— an advantage over standard Lyapunov-based designs, where constructing such functions is challenging in the presence of input constraints. Applied to the nonlinear lateral dynamics of autonomous vehicles, particularly in lane-keeping scenarios, it solves the lateral control and trajectory tracking problem, effectively addresses key limitations of standard backstepping designs, and demonstrates clear advantages over a representative existing method— proving its potential practical applicability in real-world control applications within dynamic and complex driving environments, such as lane-changing scenarios.
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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 | 0.000 |
| 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