Volume 41 Issue 1
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ZHANG Weichong, YANG Tao, LYU Nengchao. A Method for Classifying Driving Behavior Based on Vehicle Position and Speed[J]. Journal of Transport Information and Safety, 2023, 41(1): 85-94. doi: 10.3963/j.jssn.1674-4861.2023.01.009
Citation: ZHANG Weichong, YANG Tao, LYU Nengchao. A Method for Classifying Driving Behavior Based on Vehicle Position and Speed[J]. Journal of Transport Information and Safety, 2023, 41(1): 85-94. doi: 10.3963/j.jssn.1674-4861.2023.01.009
shu

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

doi: 10.3963/j.jssn.1674-4861.2023.01.009
  • 1.

    Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China

  • 2.

    Wuhan China Communication Transportation Engineering Company, Wuhan 430000, China

  • Received Date: 2022-09-28
    Available Online: 2023-05-13
    Abstract
  • 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.

     

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