Integrated Design and Validation of a Robust Lane Centering Controller for Automated Driving.

Lane centering is a critical active safety feature whose effectiveness depends on robust design and validation across diverse driving conditions. This paper presents the development of a Lane Centering Controller (LCC) using a structured model-based design workflow in MATLAB and Simulink. A kinematic bicycle model was employed to simulate vehicle dynamics and evaluate an angle based steering controller integrating both feedforward and feedback control paths. The controller was tested across multiple road geometries and speeds up to 65 mph to ensure tracking consistency and stability under nominal and perturbed conditions. Perception noise models for lane curvature and curvature rate were extracted from onboard camera data under controlled conditions, revealing Gaussian characteristics. No filtering was applied, allowing direct evaluation of the controller’s inherent robustness to raw signal variability. The LCC maintained a peak lateral offset within ±0.35 m and lateral jerk within ±9 m/s³, while respecting a steering comfort limit of ±3 Nm, thereby satisfying both functional and driver comfort requirements. The MATLAB based workflow also facilitated requirement traceability and automated test case validation, enabling quantitative comparisons of control response across different speeds and curvature transitions. These results establish a clear link between simulation fidelity and control performance, providing a reference for calibration transfer in higher fidelity environments. The paper concludes with discussion on extending the algorithm to real time Hardware-in-the-Loop (HIL) and Vehicle-in-the-Loop (VIL) platforms, demonstrating scalability toward full vehicle implementation and providing a validated framework for future high speed lane centering development.