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Artificial intelligence algorithms have been extensively applied in the fieldof intelligent transportation, especially for driving behavior analysis andprediction. This study proposes a novel framework by integrating fuzzytrajectory data, unsupervised learning and supervised learning methods topredict lane-changing behaviors taking multi driving styles into account. Themicroscopic trajectory data from the Highway Drone Dataset (HighD) are employedto construct two types of datasets, including precise trajectory datasets andfuzzy trajectory datasets for lane-changing prediction models. The fuzzytrajectory data are developed based on different driving styles, which areclustered by the K-means algorithm. Two typical supervised learning methods,including random forest and long-short-term memory combined with convolutionalneural network, are further applied for lane-changing behavior prediction.Results indicate that (1) the proposed integration approach performs betterthan the conventional lane-changing prediction; (2) the relative speed-relatedfeatures have a greater contribution to the lane-changing prediction afterbeing processed by fuzzy rules based on driving styles; and (3) the differenceamong driving styles is more reflected from the state of lateral movementrather than the lane-changing duration.

Integrating fuzzy trajectory data and artificial intelligence methods for multi-style lane-changing behavior prediction