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.

翻译：准确检测和预测换道（LC）过程能够帮助自动驾驶车辆更好地理解周围环境，识别潜在安全隐患，并提升交通安全。本文聚焦于换道过程，比较不同机器学习方法从高维时间序列数据中识别换道意图的性能。为验证所提模型的表现，我们从CitySim数据集中提取了共计1023条车辆轨迹。对于换道意图识别问题，结果表明，在实现98%的分类准确率下，集成方法有效降低了第二类与第三类分类错误的影响。在不牺牲识别准确率的前提下，LightGBM模型的训练效率相比XGBoost算法提升了六倍。