# Enhancing Autonomous Vehicle Navigation with a Clothoid-Based Lateral Controller
## 1 Introduction
However, despite its advantages, the pure pursuit method faced challenges in accurately tracking the vehicle, particularly around tight corners, leading to comparatively high tracking errors when compared to other control strategies in this field. A key drawback was the tuning of the lookahead distance, as an improperly set lookahead distance could result in steering oscillations for the vehicle. The following paragraphs focus on the different types of lateral control methods in ground-based autonomous vehicles, including PID-based control, model predictive control (MPC), sliding mode control (SMC), nonlinear controls [16, 17], and AI/learning-based controls [18]. [PERSON] et al. [19], and [PERSON] et al. [20] provide a comprehensive review of recent advancements in the lateral control of autonomous vehicles, summarizing various approaches and techniques that have been proposed in the literature. One of the key challenges in the development of CAVs (Connected Autonomous vehicles) is improving the existing lateral control algorithms to decrease cross-track error, both at low and high speeds [19]. Additionally, for articulated vehicles such as semi-tractor trailers, reducing off tracking while tracking a given trajectory is also a crucial aspect to consider. PID-based control is a widely used method for lateral control in autonomous vehicles. [PERSON] et al. [21] propose an adaptive PID neural network for lateral tracking control for intelligent vehicles. They established a vehicle dynamics model based on transfer functions and employed a PID controller to regulate the steering angle. [PERSON] [22] introduced a fractional-order PID control algorithm for lateral control, demonstrating its effectiveness in overcoming tracking errors and maintaining comfort, stability, and safety. In another study [23], a deep learning (DL) model was designed to replicate the behavior of an adaptive proportional-integral-derivative (PID) controller. The DL model incorporated various factors, including communication latency, packet loss, communication range, as well as considerations pertaining to reliability, robustness, and security. In the context of vision-based autonomous vehicles, a nested PID steering control system was devised to facilitate lane keeping on roads with uncertain curvature [11]. It was designed using PID control on the lateral offset to counteract disturbances caused by curvature variations over time. Similarly, a separate investigation [24] on lateral control in articulated mining vehicles implemented a PID-based steering controller. The framework's performance was assessed for its ability to track predefined paths, even in the presence of disruptions such as uneven terrain and external forces. Model predictive control (MPC) is another control architecture which has gained significant attention in recent years due to its ability to handle complex control problems. [PERSON] et al. [12] provides a comprehensive review of MPC and its applications in both a single and multiple autonomous ground vehicles (AGVs). They discuss the advantages of MPC in terms of trajectory planning, collision avoidance, and control management. [PERSON] [25] presented an MPC approach for longitudinal and lateral control with real-time local path planning for obstacle avoidance. A recent study [26] introduced a hybrid control system combining (ACC) adaptive cruise control with (NMPC) nonlinear model predictive control to enhance energy efficiency for autonomous electric vehicles on curvy roads while ensuring safe vehicle spacing. Another work [13] is centered on enhancing vehicle stability during high-speed scenarios by amalgamating steady-state response and MPC techniques. Furthermore, the research of [27] delves into the comparison between decoupled and coupled adaptive MPC-based control for lateral guidance. In the coupled controller, adaptive model predictive control (MPC) is employed to manage both lateral and longitudinal control aspects.