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.

翻译：自动驾驶汽车（AV）有望通过提供安全高效的交通方式，对社会产生重大变革。近年来，自动驾驶感知与预测领域取得了显著进展，但验证自动驾驶汽车性能的挑战仍未完全解决。数据驱动的微观交通仿真已成为自动驾驶测试的重要工具，其原因在于：1）高保真交通数据的可用性；2）其支持大规模测试和场景可重复性的优势；3）其在反应式、逼真的交通仿真中的潜力。然而，目前尚缺乏对该主题的全面综述。本文旨在通过总结相关研究来填补这一空白。本文的主要目标是回顾当前的研究工作，并提供一个前瞻性的视角，以促进该领域的未来发展。文章介绍了数据驱动交通仿真的通用问题，概述了关键概念和术语。在概述交通仿真后，综述了常用的各种数据集和评估指标。随后，本文对模仿学习、强化学习、生成式与深度学习方法进行了全面评估，逐一总结并详细分析了它们的优缺点。此外，本文还评估了当前最新技术、现有挑战及未来研究方向。