Abstract: In recent years, with the rapid development of big data and artificial intelligence technology, data-driven automatic driving vehicle lane change intention recognition has become an active research area in the transportation field. Numerous studies have reported innovative and practical research results. However, this field still presents common technical challenges, such as accurately identifying the lane change process, handling missing lane change labels, and addressing imbalanced data categories. These issues remain the focal points of current research. This paper aims to classify and organize various data-driven methods, mainly focusing on lane change intention recognition methods based on traditional machine learning, deep learning, and ensemble learning. In the academic community, two primary approaches exist for identifying lane change behavior. The first approach mainly focuses on the vehicle not crossing the lane line, which is suitable for early recognition of the driver’s intention to change lanes. The second approach focuses on the actual crossing of lane markings by vehicles, which is often considered the complete lane change process. In academic research on lane change intention annotation, the selection of fixed time windows and heading angle thresholds plays a crucial role in the accuracy of annotation. These parameters affect the accurate recognition of lane change behavior and are directly related to the stability and reliability of autonomous driving and intelligent transportation system performance. Therefore, researchers have conducted in-depth investigations on the impact of these two parameters on annotation accuracy. To identify the optimal fixed time window and heading angle threshold, researchers have used the grid search optimization algorithm. This method performs well in fixed driving scenarios by traversing all possible parameter combinations and selecting the optimal parameters according to preset evaluation criteria. However, in practical applications, driving scenarios often exhibit diversity and complexity. Different driving environments, road conditions, and driving styles can impact the recognition of lane change intentions. Therefore, achieving adaptive parameter adjustment so that the annotation algorithm maintains high accuracy across various driving scenarios remains a challenging problem. To address the issue of imbalanced data categories in lane changing, researchers adopt two strategies. The first strategy involves adjusting the data sampling method, and under-sampling and oversampling techniques are used to balance the number of samples in each category. The second strategy involves the use of classification models with strong adaptability to imbalanced data, such as ensemble learning algorithms or cost-sensitive learning models, to maintain good classification performance.