Through the literature review of PID control, robust control, sliding mode control and model predictive control, the characteristics of each method’s application in autonomous driving are analyzed. PID control is simple to implement but limited in complex environments. Robust control can deal with uncertainty and interference, but the design tends to be conservative. Sliding mode control offers rapid response and strong resistance to disturbances, yet it may cause chattering issues. Model predictive control provides precise trajectory optimization which requires high computational resources. The study shows that PID control is suitable for simple environments, robust control is suitable for situations requiring high stability, sliding mode control is applied to tasks that require for rapid adjustments, and model predictive control is suitable for scenarios that demand high precision. Future research will focus on integratiing multi-strategy to improve performance, adapt to various working conditions, and ensure stability and accuracy. Moreover, it is also necessary to develop efficient real-time algorithms, combine machine learning to enhance adaptability, improve control efficiency and reliability, and achieve accurate path tracking.