A Comparative Analysis of Machine Learning Methods for Lane Change Intention Recognition Using Vehicle Trajectory Data
This work addresses lane change prediction for autonomous vehicles to enhance traffic safety, but it is incremental as it compares existing methods on a specific dataset.
The paper tackled lane change intention recognition from vehicle trajectory data by comparing machine learning methods, achieving 98% classification accuracy with ensemble methods and a sixfold improvement in training efficiency using LightGBM over XGBoost.
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.