论文标题
集成模糊轨迹数据和人工智能方法，用于多式式车道行为预测
Integrating fuzzy trajectory data and artificial intelligence methods for multi-style lane-changing behavior prediction
论文作者
论文摘要
人工智能算法已广泛应用于智能运输领域，特别是用于驱动行为分析和预测。这项研究通过整合模糊轨迹数据，无监督的学习和监督学习方法，提出了一个新颖的框架，以预测将多个驾驶方式考虑在内的行为。来自高速公路无人机数据集（HighD）的显微轨迹数据用于构建两种类型的数据集，包括用于换车的预测模型的精确轨迹数据集和模糊轨迹数据集。模糊轨迹数据是基于不同的驾驶方式开发的，这些驱动方式由K-均值算法聚集。两种典型的监督学习方法，包括随机森林和长短记忆与卷积神经网络相结合，进一步应用于改变车道的行为预测。结果表明（1）所提出的集成方法的性能优于传统的车道改变的预测； （2）相对速度相关的功能在基于驾驶样式的模糊规则处理后，对改变车道的预测有更大的贡献； （3）驾驶方式之间的差异更多地反映在横向运动状态，而不是改变车道的持续时间。
Artificial intelligence algorithms have been extensively applied in the field of intelligent transportation, especially for driving behavior analysis and prediction. This study proposes a novel framework by integrating fuzzy trajectory data, unsupervised learning and supervised learning methods to predict lane-changing behaviors taking multi driving styles into account. The microscopic trajectory data from the Highway Drone Dataset (HighD) are employed to construct two types of datasets, including precise trajectory datasets and fuzzy trajectory datasets for lane-changing prediction models. The fuzzy trajectory data are developed based on different driving styles, which are clustered by the K-means algorithm. Two typical supervised learning methods, including random forest and long-short-term memory combined with convolutional neural network, are further applied for lane-changing behavior prediction. Results indicate that (1) the proposed integration approach performs better than the conventional lane-changing prediction; (2) the relative speed-related features have a greater contribution to the lane-changing prediction after being processed by fuzzy rules based on driving styles; and (3) the difference among driving styles is more reflected from the state of lateral movement rather than the lane-changing duration.
