A Survey on Data-Driven Modeling of Human Drivers' Lane-Changing Decisions

TL;DR

This survey reviews data-driven models of human drivers' lane-changing decisions, highlighting recent advances, challenges, and opportunities in using machine learning and empirical data for safer, adaptive driving systems.

Contribution

It provides a comprehensive overview of modeling frameworks, data sources, and validation methods for data-driven lane-changing decision models, emphasizing recent developments and future challenges.

Findings

Data-driven models improve understanding of human lane-changing behavior.

Machine learning enables adaptive and personalized decision modeling.

Challenges include data quality, model validation, and real-world deployment.

Abstract

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.