Abstract
Yaw stability is essential for vehicle lateral control and is strongly influenced by the nonlinear dynamics of tire-road interaction. Tire Lateral Stiffness (TLS), a key parameter in this process, varies with tire properties and road conditions. Accurate TLS estimation is crucial for autonomous driving safety, especially during aggressive maneuvers or on low-friction surfaces. This paper proposes a novel TLS identification framework using modified Gradient Descent Methods (GDM) inspired by deep learning. A theoretical link is established between Recursive Least Squares (RLS) and GDM, revealing that RLS is a special case of GDM with adaptive learning rates. Based on this, improved RLS variants are developed for real-time use. Simulations compare different GDM and RLS algorithms, showing effective TLS tracking under varying conditions. Adaptive methods like Adam, which adjust gradients and learning rates, achieve improved performance, with Relative Steady-State Error (RSSE) below 5% and t10 (response time to reach 10% error band) under 3 s. These results offer practical guidance for estimator selection in safety-critical autonomous vehicle systems.