Game-Theoretic Frameworks for Autonomous Driving Decision Making: A Literature Review

Abstract

The field of autonomous driving has witnessed significant advancements in recent years, reshaping the landscape of transportation and mobility. Autonomous vehicles, equipped with an array of sensors, processors, and sophisticated algorithms, have shown the potential to revolutionize road safety, traffic efficiency, and accessibility. In addition to deep learning techniques, game theory approaches have found valuable applications in the development of autonomous driving algorithms, addressing various challenges related to coordination, interaction, and decision-making in intricate traffic scenarios. By conceptualizing driving scenarios as strategic interactions among vehicles, game theory offers a framework to optimize actions, improve efficiency, and ensure safety. This paper offers an overview of the background, techniques, and challenges associated with integrating game theory into autonomous driving decision-making. By delving into perception, decision-making, control strategies, and human-machine interaction, this review synthesizes the interdisciplinary nature of research in autonomous driving.