A Comparative Study of Modern Control Techniques for Autonomous Vehicles

Abstract

The advent of autonomous vehicles (AVs) marks a transformative era in transportation, promising to revolutionize mobility across various sectors. This comparative study explores modern control techniques integral to AVs, analyzing their theoretical foundations, practical implementations, and real-world effectiveness. AVs rely on sophisticated control systems to interpret sensory data, compute trajectories, and execute maneuvers autonomously. Traditional techniques like PID controllers provide robustness, while advanced methods such as Model Predictive Control (MPC), reinforcement learning (RL), and neural networks offer enhanced adaptability to dynamic environments. This article reviews key control methodologies, assessing their performance metrics including accuracy, computational complexity, safety, and scalability. Case studies across diverse domains illustrate their applications, from urban driving challenges to off-road navigation and high-speed maneuvering. Challenges such as safety certification, sensor integration, and ethical considerations are discussed, highlighting avenues for future research and development. By comprehensively evaluating these control techniques, this study aims to inform stakeholders in advancing AV technology towards safer, more efficient autonomous transportation systems.