考虑车辆换道行为的驾驶风格识别OA

To address the limitations of traditional driving style identification methods in characterizing lateral interactive behaviors,this study optimizes the recognition model by integrating lane-changing behavior characteristics.By constructing a multidimensional feature system and applying unsupervised clustering,this paper achieves driving style identification that considers both longitudinal motion and lateral interactions.This study takes the SQM2 subset of the TrafficEye dataset as the research object.Based on vehicle trajectories and corresponding video data,a two-dimensional road coordinate system is constructed to extract six traditional longitudinal features:mean speed,speed standard deviation,mean acceleration,acceleration standard deviation,mean jerk,and jerk standard deviation;along with five lane-changing behavior features:lane-changing frequency,lane-changing duration,lane-changing initiation speed,lane-changing initiation acceleration,and lane-changing gap.The 11-dimensional features are standardized using Z-score normalization,and driving styles are identified through Principal Component Analysis(PCA)for dimensionality reduction,followed by K-means clustering.The results show that,considering sample coverage,the proportion of noise-free samples,and result interpretability,a stable clustering structure is formed when K=3.Compared with clustering using only traditional features,the incorporation of lane-changing features results in label changes for 149 out of 1 420 valid vehicle samples,accounting for 10.49%,with a consistency rate of 89.51%.The three clusters exhibit significant differences in terms of speed level,acceleration variability,lane-changing frequency,and lane-changing gap,among which mean speed,acceleration standard deviation,and lane-changing frequency contribute notably to cluster differentiation.The study demonstrates that lane-changing behavior features can complement traditional longitudinal motion information and provide an additional discrimination basis for boundary samples.The resulting aggressive-type,normal-type,and conservative-type labels represent relative classifications based on sample distribution,which can serve as a data reference for driving behavior modeling and personalized traffic management.