Model-Based Control and Model-Free Control Techniques for Autonomous Vehicles: A Technical Survey

Abstract

Autonomous driving has the potential to revolutionize mobility and transportation by reducing road accidents, alleviating traffic congestion, and mitigating air pollution. This transformation can result in energy efficiency, enhanced convenience, and increased productivity, as valuable driving time can be repurposed for other activities. The main objective of this paper is to provide a comprehensive technical survey of the latest research in the field of lateral, longitudinal, and integrated control techniques for autonomous vehicles. The survey aims to explore a wide range of techniques and methodologies employed to achieve precise steering control while also considering longitudinal aspects. Model-based control techniques form the foundation for control, utilizing mathematical models of vehicle dynamics to design controllers that effectively track desired speeds and/or steering behavior. Unlike model-free control techniques such as reinforcement learning and deep learning algorithms facilitate the integration of longitudinal and lateral control by learning control policies directly from data and without explicit knowledge of the underlying dynamics. Through this survey, the paper delves into the strengths, limitations, and advancements in both model-based and model-free control approaches for autonomous vehicles. It investigates their performance in real-world scenarios and addresses the technical challenges associated with their implementation. These challenges may include uncertainties in the environment, adaptability to dynamic conditions, robustness, safety considerations, and computational complexity.