## 1 Introduction  
Autonomous vehicles (AVs) have revolutionized the transportation sector, offering significant benefits in terms of road safety, reduced congestion, and environmental advantages [1]. However, realizing these advantages necessitates the development of sophisticated control algorithms to ensure consistent and reliable performance across diverse driving conditions. A critical challenge lies in designing a responsive lateral control algorithm that enables AVs to stay within lanes, avoid collisions, and navigate seamlessly, even on challenging roads [2, 3]. In recent times, substantial progress has been made in the development of lateral control algorithms for autonomous vehicles, with the ultimate goal of achieving safe road operation without human intervention [4]. Research has explored various aspects of lateral control, including trajectory planning, path following, and obstacle avoidance [5]. Model-based strategies, such as proportional-integral-derivative (PID) control, linear quadratic regulator (LQR) control, and model-predictive control (MPC), utilize feedback mechanisms to fine-tune the vehicle's steering angle and achieve precise trajectory tracking. Conversely, model-free approaches like reinforcement learning (RL), neural networks, and genetic algorithms leverage data-driven methods to learn optimal control strategies [6]. [PERSON] et al. [7] introduced a novel and robust distributed PID control method, specifically designed to ensure robust tracking performance for all vehicles within a fleet. However, it is important to note that the effectiveness of PID control hinges on precise parameter tuning. [PERSON] et al. [8] addressed the challenge of parameter tuning by employing a genetic algorithm (GA) for optimizing PID parameters during trajectory tracking. This approach offers an automated and data-driven method for achieving optimal control performance. In contrast, [PERSON] et al. [9] introduced real-time feedforward and feedback LQR control to ensure accurate tracking, vehicle stability, and reliability. This method also integrates real-time path planning and control input parameter optimization, contributing to precise and dynamic control. [PERSON] et al. [10] developed an online adaptive LQR control by optimizing parameters using a novel genetic algorithm fitness function. This approach adapts the control strategy in real time, enhancing its adaptability to changing conditions. [PERSON] et al. [11] proposed a robust H infinity state feedback control (RSC) method that demonstrated superior performance compared to traditional MPC controllers, particularly in lateral vehicle motion scenarios. For agile maneuvers, [PERSON] et al. [12] applied a Popov-H\(\infty\) controller using linear fractional transformation (LFT) to handle nonlinear constraints, enabling effective control during challenging maneuvers. In high-curvature situations, [PERSON] et al. [13] presented a suboptimal sliding mode control scheme that adjusted heading angle and velocity to enhance tracking accuracy, particularly in scenarios with sharp turns. [PERSON] et al. [14] effectively reduced chattering in second-order sliding mode (SOSM) control by integrating improved adding power integrator (API) technology, contributing to smoother control performance. [PERSON] et al. [15] utilized nonstationary fuzzy sets to estimate AV dynamic characteristics and derived adaptive laws for versatile path tracking, enabling robust control in varying conditions. [PERSON] et al. [16] proposed an adaptive mechanism that combines sliding mode control and fuzzy logic, offering efficient path following with adaptability to different dynamics and disturbances. Addressing complex dynamics, [PERSON] et al. [17] introduced a model-based deep reinforcement learning approach to motion control, leveraging deep reinforcement learning techniques to enhance control performance. Lastly, [PERSON] et al. [18] employed two deep reinforcement learning algorithms for controlling the speed, steering angle, and power distribution of the powertrain.