A Survey on Data-Driven Modeling of Human Drivers' Lane-Changing Decisions
It provides a comprehensive overview for researchers and practitioners in transportation and autonomous vehicles, but is incremental as it synthesizes existing work without new empirical results.
This survey reviews data-driven models for human drivers' lane-changing decisions, addressing limitations of traditional models by leveraging machine learning to capture behavioral heterogeneity and complex interactions in dynamic environments.
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.