Path tracking control is a fundamental technology in intelligent vehicle systems, responsible for converting planned reference trajectories into executable steering and throttle commands that ensure the vehicle follows the desired path accurately and safely. This paper presents a comprehensive comparative review of four widely adopted path tracking control methods: Proportional-Integral-Derivative control, Linear Quadratic Regulator, Model Predictive Control, and Sliding Mode Control. A unified bicycle dynamics model is introduced as the common evaluation basis to ensure fair comparison. The working principles, design considerations, advantages, and practical limitations of each method are systematically analyzed. The four methods are compared across five key engineering dimensions: tracking accuracy, computational complexity, robustness to parameter uncertainties, constraint handling capability, and implementation difficulty. The analysis reveals that Proportional-IntegralDerivative control is simplest but lacks robustness; Linear Quadratic Regulator offers optimal performance under linear assumptions but cannot enforce constraints; Model Predictive Control excels in constraint handling and preview control but demands significant computation; Sliding Mode Control provides strong robustness but suffers from chattering. Hybrid control architectures and learning-enhanced strategies are discussed as promising future directions.