Autonomous vehicles (AVs) are now operating on public roads, which makes their testing and validation more critical than ever. Simulation offers a safe and controlled environment for evaluating AV performance in varied conditions. However, existing simulation tools mainly focus on graphical realism and rely on simple rule-based models and therefore fail to accurately represent the complexity of driving behaviors and interactions. Artificial intelligence (AI) has shown strong potential to address these limitations; however, despite the rapid progress across AI methodologies, a comprehensive survey of their application to mixed autonomy traffic simulation remains lacking. Existing surveys either focus on simulation tools without examining the AI methods behind them, or cover ego-centric decision-making without addressing the broader challenge of modeling surrounding traffic. Moreover, they do not offer a unified taxonomy of AI methods covering individual behavior modeling to full scene simulation. To address these gaps, this survey provides a structured review and synthesis of AI methods for modeling AV and human driving behavior in mixed autonomy traffic simulation. We introduce a taxonomy that organizes methods into three families: agent-level behavior models, environment-level simulation methods, and cognitive and physics-informed methods. The survey analyzes how existing simulation platforms fall short of the needs of mixed autonomy research and outlines directions to narrow this gap. It also provides a chronological overview of AI methods and reviews evaluation protocols and metrics, simulation tools, and datasets. By covering both traffic engineering and computer science perspectives, we aim to bridge the gap between these two communities.

翻译：自动驾驶车辆（AV）现已驶入公共道路，这使得其测试与验证比以往任何时候都更为关键。仿真技术为评估AV在不同环境下的表现提供了安全可控的环境。然而，现有仿真工具主要侧重于图形逼真度，并依赖简单的基于规则模型，因此无法准确表征驾驶行为与交互的复杂性。人工智能（AI）在解决这些局限性方面展现出巨大潜力；然而，尽管AI方法论取得了快速进展，目前仍缺乏对其在混合自主交通仿真中应用的全面综述。现有综述要么聚焦于仿真工具而未深入探讨其背后的AI方法，要么覆盖以自我为中心的决策制定，而未解决对周围交通进行建模这一更广泛的挑战。此外，它们未能提供一个涵盖从个体行为建模到完整场景仿真的统一AI方法分类体系。为填补这些空白，本综述对混合自主交通仿真中AV与人类驾驶行为建模的AI方法进行了结构化回顾与综合。我们提出一种分类法，将方法归纳为三个系列：智能体级行为模型、环境级仿真方法，以及认知与物理信息方法。本综述分析了现有仿真平台如何未能满足混合自主研究的需求，并概述了缩小这一差距的方向。它还提供了AI方法的时间线回顾，并评述了评估协议与指标、仿真工具及数据集。通过覆盖交通工程与计算机科学两个视角，我们旨在弥合这两个领域之间的鸿沟。