用数据驱动方法建模人类变道决策，提升自动驾驶安全性和交通仿真精度。
A Survey on Data-Driven Modeling of Human Drivers' Lane-Changing Decisions
- 基于真实驾驶数据与机器学习，捕捉人类变道行为的复杂模式。
- 系统梳理了数据来源、模型结构与验证方法，覆盖完整建模流程。
- 适合自动驾驶、智能交通研究者，尤其关注行为建模与安全评估的团队。
变道行为是影响行车安全与交通流特性的关键驾驶动作，传统解析式变道决策（LCD）模型虽在特定场景有效，但常忽略驾驶行为异质性与复杂交互，难以真实反映实际变道决策。数据驱动方法通过利用丰富的实证数据和机器学习技术，挖掘隐藏的决策规律，实现动态环境下的自适应建模。随着人工智能快速发展及对联网汽车、自动驾驶需求上升，本文系统综述了面向人类驾驶员变道决策的数据驱动建模研究，涵盖建模框架中的数据来源与预处理、模型输入输出、目标设定、结构设计与验证方法。同时讨论了当前面临的挑战，如驾驶安全、不确定性建模，以及技术框架的集成与优化，为未来研究提供方向。
原文摘要 · Abstract (English)
Lane-changing (LC) behavior, a critical yet complex driving maneuver, significantly influences driving safety and traffic dynamics. Traditional analytical LC decision (LCD) models, while effective in specific environments, often oversimplify behavioral heterogeneity and complex interactions, limiting their capacity to capture real LCD. Data-driven approaches address these gaps by leveraging rich empirical data and machine learning to decode latent decision-making patterns, enabling adaptive LCD modeling in dynamic environments. In light of the rapid development of artificial intelligence and the demand for data-driven models oriented towards connected vehicles and autonomous vehicles, this paper presents a comprehensive survey of data-driven LCD models, with a particular focus on human drivers LC decision-making. It systematically reviews the modeling framework, covering data sources and preprocessing, model inputs and outputs, objectives, structures, and validation methods. This survey further discusses the opportunities and challenges faced by data-driven LCD models, including driving safety, uncertainty, as well as the integration and improvement of technical frameworks.
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