This paper presents a comparative study of two path-tracking controllers, Stanley and Linear Quadratic Regulator (LQR) that are evaluated across four trajectories with different speeds and levels of curve complexity in the Carla Simulator to access accuracy, comfort, and computational efficiency. The study uses the kinematic bicycle model and sensor data from the Global Navigation Satellite System (GNSS), Inertial Measurement Unit (IMU), and wheel odometry sensor to continuously get the state of the vehicle. The results of the simulations showed that the Stanley controller offers superior real-time performance, smoother control, and lower steering effort, which makes it suitable for applications focused on comfort. While LQR achieves higher tracking accuracy and adaptive flexibility through tunable weights but at the cost of increased computational load and jerk. The research emphasizes the advantages and compromises of both controllers, suggesting Stanley for stability-focused tasks and LQR for precision-demanding environments.