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.

准确检测和预测车道变换过程可以帮助自动驾驶汽车更好地理解周围环境，识别潜在的安全危险并提高交通安全。本文侧重于车道变换过程，并比较了不同的机器学习方法在从高维时序数据中识别车道变换意图方面的性能。验证提出模型的性能，从 CitySim 数据集中提取了 1023 个车辆轨迹。对于车道变换意图识别问题，结果表明，集成方法减少了二型和三型分类错误的影响，具有 98% 的分类准确度。在不牺牲识别准确度的情况下，LightGBM 模型训练效率比 XGBoost 算法提高了 6 倍。