Data-driven Traffic Simulation: A Comprehensive Review

Autonomous vehicles (AVs) have the potential to significantly revolutionize society by providing a secure and efficient mode of transportation. Recent years have witnessed notable advance-ments in autonomous driving perception and prediction, but the challenge of validating the performance of AVs remains largely unresolved. Data-driven microscopic traffic simulation has be-come an important tool for autonomous driving testing due to 1) availability of high-fidelity traffic data; 2) its advantages of ena-bling large-scale testing and scenario reproducibility; and 3) its potential in reactive and realistic traffic simulation. However, a comprehensive review of this topic is currently lacking. This pa-per aims to fill this gap by summarizing relevant studies. The primary objective of this paper is to review current research ef-forts and provide a futuristic perspective that will benefit future developments in the field. It introduces the general issues of data-driven traffic simulation and outlines key concepts and terms. After overviewing traffic simulation, various datasets and evalua-tion metrics commonly used are reviewed. The paper then offers a comprehensive evaluation of imitation learning, reinforcement learning, generative and deep learning methods, summarizing each and analyzing their advantages and disadvantages in detail. Moreover, it evaluates the state-of-the-art, existing challenges, and future research directions.