基于车辆位置与速度特征的驾驶行为模式分类方法

精细车辆轨迹中包含连续的时间戳、位置，以及速度等信息。通过对车辆轨迹数据进行量化表达与挖掘分析，可以实现对车辆行为模式的分类。现有研究大多关注对位置的聚类，很少对车速、加速度等特征进行研究分析，而车速等是反映驾驶行为模式的重要特征。为了将轨迹多维信息纳入分析框架，研究了基于位置与速度特征的车辆轨迹行为模式分类方法。为克服现有行为模式分类方法的维度单一性，运用豪斯多夫轨迹距离算法计算出位置和速度特征的综合距离矩阵，针对豪斯多夫距离算法鲁棒性差的缺点，采用单向豪斯多夫距离90%分位值对算法进行了改进，降低噪声影响。同时，引入了车辆位置和速度来进一步提高分类的准确性，运用多次分层聚类算法依次对位置与速度轨迹图进行分类，得到车辆位置和速度上的行为模式。以HighD数据集为样本，提取了三车道上的行车轨迹，验证了基于速度与位置特征的车辆行为模式分类方法。结果表明:①本方法可以得到位置和速度的综合行为模式，聚类平均准确率达到94.8%，优于DBTCAN准确率89.3%和t-Cluster准确率86.4%；②基于换道模式轨迹偏移率曲线的分析，得到了4种互异的典型车辆换道模式。该方法可利用多维轨迹数据对行车模式进行分类及行为辨识，在车辆轨迹分类与不良行为辨识方面具有应用潜力。 

A Method for Classifying Driving Behavior Based on Vehicle Position and Speed

Vehicle trajectory data contains vehicle movement information, including time stamp, vehicle position, speed, etc. By analyzing vehicle trajectory data, driving patterns can be classified. As important features from such data reflect driving behavior, vehicle positioning characteristics have been widely studied, but the others such as speed and acceleration are rarely analyzed. In order to incorporate the multi-dimensional information from vehicle trajectory data into the analysis framework, a method for classifying driving patterns based on the characteristics of vehicle position and speed is studied. To overcome the issue of a single dimension of the existing classification methods, the algorithm for Hausdorff trajectory distance is applied to calculate a comprehensive distance matrix of vehicle position and speed. Given the fact that the robustness of the Hausdorff distance algorithm is low, the algorithm is improved by using 90% percentile value of the one-way Hausdorff distance to reduce the influence of noise. At the same time, vehicle position and speed are introduced to further improve the accuracy of classification, and a multiple hierarchical clustering algorithm is used to classify the trajectory diagrams of position and trajectory diagrams of speed in sequence. At the end, the driving patterns based on vehicle position and speed are obtained. The HighD dataset is used as a sample, the vehicle trajectories on three lanes are extracted to verify the proposed classification method. Study results show that ①the proposed method can provide the comprehensive driving patterns of vehicle position and speed, and the average accuracy of clustering is 94.8%, which is higher than the accuracy of DBTCAN (89.3%) and t-Cluster (86.4%). ②Based on the analysis of trajectory deviation curve of lane changing, four typical driving patterns are obtained. The proposed method can use multidimensional trajectory data to classify driving patterns, which has potentials in trajectory classification and identifying abnormal behavior.