The paper addresses the issue of poor path-tracking control accuracy in autonomous vehicles under special driving conditions (such as icy and snowy roads, rainy surfaces, and high-speed lane changes). A Linear Quadratic Regulator (LQR) controller based on the Whale Optimization Algorithm (WOA), referred to as the WOA-LQR controller, is designed to tackle this problem. Initially, a tracking error model is established based on a two-degree-of-freedom vehicle dynamics model, and a discrete LQR controller is designed on this basis. Feedforward control is employed to eliminate errors caused by system simplification. To address the problem of low tracking accuracy and vehicle instability under special driving conditions due to the poor adaptability of the LQR controller with fixed weight coefficients, an adaptive weight adjustment strategy for the LQR controller is proposed based on WOA. This strategy takes into account the influence of lateral acceleration and front wheel steering angle on vehicle stability and sets corresponding weight coefficients for the evaluation indices, which include lateral error and yaw angle error. A fitness function with the minimum target value is designed to optimize the LQR weight coefficients using WOA. Finally, the WOA-LQR controller is validated through path-tracking simulations under various conditions using Carsim/Simulink co-simulation. The results demonstrate that the proposed control strategy provides excellent tracking performance under complex driving conditions, significantly improves the control accuracy during path tracking, and exhibits strong robustness.