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基于SVM-LSTM的车辆跟驰行为识别与信息可信甄别

史宇辰 晏松 姚丹亚 张毅

史宇辰, 晏松, 姚丹亚, 张毅. 基于SVM-LSTM的车辆跟驰行为识别与信息可信甄别[J]. 交通运输工程学报, 2022, 22(3): 115-125. doi: 10.19818/j.cnki.1671-1637.2022.03.009
引用本文: 史宇辰, 晏松, 姚丹亚, 张毅. 基于SVM-LSTM的车辆跟驰行为识别与信息可信甄别[J]. 交通运输工程学报, 2022, 22(3): 115-125. doi: 10.19818/j.cnki.1671-1637.2022.03.009
SHI Yu-chen, YAN Song, YAO Dan-ya, ZHANG Yi. SVM-LSTM-based car-following behavior recognition and information credibility discirmination[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 115-125. doi: 10.19818/j.cnki.1671-1637.2022.03.009
Citation: SHI Yu-chen, YAN Song, YAO Dan-ya, ZHANG Yi. SVM-LSTM-based car-following behavior recognition and information credibility discirmination[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 115-125. doi: 10.19818/j.cnki.1671-1637.2022.03.009

基于SVM-LSTM的车辆跟驰行为识别与信息可信甄别

doi: 10.19818/j.cnki.1671-1637.2022.03.009
基金项目: 

国家重点研发计划 2018YFB1600600

详细信息
    作者简介:

    史宇辰(1999-),男,黑龙江哈尔滨人,清华大学工学博士研究生,从事智能交通研究

    张毅(1964-),男,北京人,清华大学教授,工学博士

  • 中图分类号: U491.2

SVM-LSTM-based car-following behavior recognition and information credibility discirmination

Funds: 

National Key Research and Development Program of China 2018YFB1600600

More Information
Article Text (Baidu Translation)
  • 摘要: 为利用智能车路协同系统内实时交互信息有效提升交通系统的安全性,提出了基于交通业务特征的交通信息可信甄别方法;重点构建了基于支持向量机(SVM)-长短时记忆(LSTM)神经网络的车辆跟驰行为识别与信息可信甄别模型,包括基于SVM的车辆跟驰行为识别模型和基于LSTM神经网络的车辆跟驰速度预测模型;设定了表征车辆行驶状态的特征向量,基于SVM的车辆跟驰行为识别模型将车辆行驶状态分为跟驰与非跟驰;对于跟驰车辆,基于LSTM神经网络的车辆跟驰速度预测模型根据其历史数据进行速度预测;SVM-LSTM信息可信甄别模型通过检验跟驰车辆的预测速度与其实际速度的差是否在合理范围来判断车辆数据的可信性,实现信息的可信甄别;采用公开数据集对提出的模型进行了训练与测试,并构建了不同异常类型和异常幅度的多个异常测试数据集,对基于SVM-LSTM神经网络的车辆跟驰行为识别与信息可信甄别模型进行了验证。研究结果表明:基于SVM的车辆跟驰行为识别模型对车辆行驶行为识别的准确率达到了99%,基于LSTM神经网络的车辆跟驰速度预测模型的跟驰速度预测精度达到了cm·s-1数量级;基于SVM-LSTM神经网络的车辆跟驰行为识别与信息可信甄别模型在正常数据测试集与多个异常数据测试集上的甄别正确率达到了97%。由此可见,提出的方法可用于路侧设备(RSUs)对车载单元(OBUs)实时信息和车载单元间实时信息的可信甄别。

     

  • 图  1  车辆驾驶场景与相关参数示意

    Figure  1.  Sketch of vehicle driving scenario and related parameters

    图  2  LSTM神经元结构

    Figure  2.  Structure of LSTM neuron

    图  3  跟驰场景下车辆速度预测的LSTM神经网络结构

    Figure  3.  LSTM neural network structure for speed prediction of vehicle under car-following scenario

    图  4  可信甄别计算流程架构

    Figure  4.  Framework of calculation process for credibility discrimination

    图  5  37号车辆行驶轨迹与行驶状态分类

    Figure  5.  Classifications of vehicle trajectory and driving states for vehicle No.37

    图  6  SVM二分类器超参数寻优

    Figure  6.  Hyper-parameter optimization of SVM binary classifier

    图  7  网络训练中损失变化

    Figure  7.  Loss changes in network training

    图  8  预测速度与实际速度对比

    Figure  8.  Comparison between predicted and real speeds

    图  9  100号车甄别结果

    Figure  9.  Discrimination results for vehicle No.100

    1.  Sketch of vehicle driving scenario and related parameters

    2.  Structure of LSTM neuron

    3.  LSTM neural network structure for speed prediction of vehicle under car-following scenario

    4.  Framework of calculation process for credibility discrimination

    5.  Classifications of vehicle trajectory and driving states for vehicle No. 37

    6.  Hyper-parameter optimization of SVM binary classifier

    7.  Loss changes in network training

    8.  Comparison between predicted and real speeds

    9.  Discrimination results for vehicle No. 100

    表  1  SVM输入数据参数

    Table  1.   Parameters of input data for SVM

    参数名称 符号
    t时刻纵向速度/(m·s-1) vx(t)
    t时刻横向速度/(m·s-1) vy(t)
    t时刻纵向加速度/(m·s-2) ax(t)
    t时刻车道编号变化 ΔL(t)
    t时刻与前车速度差/(m·s-1) Δv(t)
    t时刻与前车车头间距/m Δx(t)
    t时刻前车纵向加速度/(m·s-2) a'x(t)
    下载: 导出CSV

    表  2  SVM二分类器在训练集上的混淆矩阵

    Table  2.   Confusion matrix of SVM binary classifier on training set

    训练集 预测表现 总计
    1 0
    实际表现 1 24 853 114 24 967
    0 188 23 512 23 700
    总计 25 041 23 626 48 667
    下载: 导出CSV

    表  3  SVM二分类器在测试集上的混淆矩阵

    Table  3.   Confusion matrix of SVM binary classifier on testing set

    测试集 预测表现 总计
    1 0
    实际表现 1 10 616 84 10 700
    0 104 10 054 10 158
    总计 10 720 10 138 20 858
    下载: 导出CSV

    表  4  LSTM神经网络结构参数与训练参数

    Table  4.   Parameters of LSTM neural network structure and training

    参数 数值 参数 数值
    神经网络层数 2 批大小 32
    输入维度 3 损失函数 均方误差
    隐藏层维度 1 学习率 0.002
    序列长度 50 优化器 Adam
    训练轮次 60
    下载: 导出CSV

    表  5  模型评估指标

    Table  5.   Indices for model evaluation

    评估指标 La/(m·s-1) Ls/(m2·s-2)
    训练集 5.86×10-2 3.25×10-3
    验证集 5.91×10-2 2.96×10-3
    测试集 5.89×10-2 3.98×10-3
    下载: 导出CSV

    表  6  测试数据集划分

    Table  6.   Classification of testing dataset

    异常类型 低偏移幅度 高偏移幅度
    类型1 数据集1 数据集2
    类型2 数据集3 数据集4
    类型3 数据集5 数据集6
    类型4 数据集7 数据集8
    类型5 数据集9 数据集10
    正常数据 数据集11
    下载: 导出CSV

    表  7  启动模块对车辆行驶状态识别结果的混淆矩阵

    Table  7.   Confusion matrix of vehicle driving state recognition results in starting module

    识别结果 SVM识别 总计
    跟驰 非跟驰
    实际表现 跟驰 470 2 472
    非跟驰 2 226 228
    总计 472 228 700
    下载: 导出CSV

    表  8  可信甄别模块异常数据甄别结果

    Table  8.   Discrimination results of abnormal data in credibility discrimination module

    甄别结果 低偏移幅度 高偏移幅度
    异常数 准确率/% 异常数 准确率/%
    类型1 456 97.0 470 100.0
    类型2 470 100.0 470 100.0
    类型3 470 100.0 470 100.0
    类型4 459 97.7 470 100.0
    类型5 227 48.3 468 99.6
    正常数据 异常数 5 误报率/% 1.1
    下载: 导出CSV

    1.   Parameters of input data for SVM

    2.   Confusion matrix of SVM binary classifier on training set

    3.   Confusion matrix of SVM binary classifier on testing set

    4.   Parameters of LSTM neural network structure and training

    5.   Indices for model evaluation

    6.   Classification of testing dataset

    7.   Confusion matrix of vehicle driving state recognition results in starting module

    8.   Discrimination results of abnormal data in credibility discrimination module

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  • 收稿日期:  2021-12-31
  • 刊出日期:  2022-06-25

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