基于模糊聚类的车辆运动轨迹建模

Modeling of Vehicle Motion Trajectories Based on Fuzzy Clustering

摘要:

为提高车辆运动行为模式的学习与分析水平，综合考虑车辆运动轨迹特点及其建模的相关要求，提出了一种车辆运动轨迹建模方法。该方法主要由轨迹模糊聚类和路径建模两部分组成，首先拓展采用改进Hausdorff距离衡量轨迹之间的几何相似性，并基于此构建了改进模糊C均值轨迹聚类算法，用于实现车辆运动轨迹的聚类；在轨迹聚类的基础上，建立了基于离散状态的路径模型，并进而提出了相应的轨迹异常检测算法。最后，在真实场景下的试验结果验证了采用本文方法的适用性和有效性。

Abstract:

In order to improve the learning and analysis level of vehicle’s motion patterns, considering the characteristics of trajectories and the requirements of trajectory modeling, a modeling method for trajectories was proposed. This method consisted of two parts, which were trajectory fuzzy clustering and path modeling. Firstly, the improved Hausdorff distance was extended and used to measure the geometrical similarity between trajectories, and the improved fuzzy C-means trajectory clustering algorithm was further established to realize the clustering of vehicle trajectories. Based on the results of trajectory clustering, the path models based on discrete state were established and the Corresponding trajectory anomaly detection algorithm was proposed. Finally, the experimental results in the real scene verified the applicability and validity of the proposed method .