  Accurately detecting and predicting lane change (LC)processes can help
autonomous vehicles better understand their surrounding environment, recognize
potential safety hazards, and improve traffic safety. This paper focuses on LC
processes and compares different machine learning methods' performance to
recognize LC intention from high-dimensionality time series data. To validate
the performance of the proposed models, a total number of 1023 vehicle
trajectories is extracted from the CitySim dataset. For LC intention
recognition issues, the results indicate that with ninety-eight percent of
classification accuracy, ensemble methods reduce the impact of Type II and Type
III classification errors. Without sacrificing recognition accuracy, the
LightGBM demonstrates a sixfold improvement in model training efficiency than
the XGBoost algorithm.
