Lane Change Prediction in Autonomous Driving: A Statistical Survey on Intention and Trajectory Forecasting

ABSTRACT Lane‐change prediction has emerged as one of the core challenges in autonomous driving research over the past decade. Previous surveys have focused on either lane‐change intention or trajectory prediction, without offering a unified view of both tasks. They provide only limited statistical analysis and mostly qualitative insights, without capturing broader research trends. Additionally, they overlook interpretability and explainability, which are crucial for trustworthy prediction systems. In this survey, we conduct a comprehensive review of over 150 research articles to systematically analyze how the field has evolved across key dimensions: prediction tasks (intention, trajectory, or both), sensor types, input features, datasets employed, modeling approaches, and evaluation practices. Beyond summarizing existing efforts, this survey presents a statistical analysis that identifies critical gaps in the literature. The analysis reveals limited attention to model interpretability, transparency, and explainability, despite their importance for building reliable and trustworthy prediction systems. Additionally, only a small subset of studies incorporate decision‐making modules that use the predictions to guide vehicle actions, and even fewer provide simulation‐based or real‐world validation. Based on our findings, we outline recommendations for future research to advance toward more explainable, decision‐aware, and deployable lane‐change prediction systems.