# Enhancing Autonomous Vehicle Navigation with a Clothoid-Based Lateral Controller
## 1 Introduction
In contrast, the decoupled strategy separates the longitudinal dynamics, managed with a PID controller, from lateral dynamics, initially overseen by MPC and subsequently by a lateral controller. This innovative approach harnesses adaptive MPC to guide lateral movements and regulate speed by adjusting the front-wheel steering angle and acceleration/deceleration. Sliding mode control (SMC) is another popular approach for lateral control in autonomous vehicles. [PERSON] et al. [28]. introduced a model reference control scheme for a four-wheel steering system, incorporating sliding mode control theory. The Sliding Mode Four-Wheel Steering (SM4 WS) method not only enhances directional stability and responsiveness but also exhibits robust disturbance rejection capabilities, particularly concerning unexpected side winds. [PERSON] et al. [29] investigated nonlinear path-tracking systems for autonomous vehicles equipped with four-wheel steering (4 WS). Their research centers on the development of a robust adaptive sliding mode controller. To account for control input nonlinearity and the parameter-varying nature of tire cornering stiffness during extreme handling scenarios, a Takagi-Sugeno (T-S) fuzzy model is employed. [PERSON] et al. [30] delves into research concerning path planning and path-tracking control for autonomous vehicles, utilizing an improved Artificial Potential Field (APF) approach and SMC. This controller integrates lateral and heading errors, enhancing the sliding mode function, and elevating path-tracking precision. Recently, several research studies have investigated AI-based [18; 31; 32; 33] lateral control methods for autonomous cars and trucks. [PERSON] et al. [31] addresses challenges in lateral motion control for Automated Highway Systems, focusing on lane keeping and changing. It introduces a multi-model fuzzy controller, with four local controllers for varying speeds, ensuring smooth transitions. [PERSON] et al. [34] enhances lateral control for Automated Vehicles (AVs) by combining sliding mode control and radial basis function neural networks, and it effectively mitigates chattering and reduces the impact of external disturbances in diverse conditions. Another work [35] explores AI-based lateral control for automated vehicles, developing two controllers--one using fuzzy logic and another using ANFIS with expert driver data. These controllers outperform a PID controller in simulations, showing promise for lateral control applications. [PERSON] [36] presents a model-free control strategy for lateral vehicle control across various speeds, achieving accurate trajectory tracking, system stability, and passenger comfort, as demonstrated in simulation and real vehicle tests. Lastly, a survey paper [37] delves into deep learning technologies in autonomous driving, offering insights into their applications, strengths, and limitations. This paper's structure is as follows: Section 2 explains clothoids' role in smooth path generation and the implementation of a clothoid-based tracking controller. Section 3 details the importance and the mathematical approach for determining adaptive lookahead distance for navigation. Section 4 addresses the constraints on steering rate and their implications on vehicle dynamics, while Section 5 details the design and validation of the lead filter, a key contribution for mitigating inherent lateral dynamics lag. The results of the implemented controller, assessing its tracking performance and behavior in various driving scenarios are thoroughly examined in Section 6. Finally, Section 7 concludes the study, reflecting on its contributions and future research directions.