Lateral Control for Autonomous Vehicles: A Robust Bounded Back-Stepping Technique
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".