基于卷积神经网络的不良驾驶行为辨识
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U491.6

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交通运输工程校级重点学科开放课题(XJAUTE2022K02)中国学位与研究生教育学会项目(2020MSA274)


Identification of Bad Driving Behavior Based on Convolutional Neural Network
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    摘要:

    为量化描述驾驶员驾驶行为的动态变化过程与不良程度,研究了不良驾驶行为谱的构建与分析方法,用于不良驾驶行为的实时辨识。首先,基于拉格朗日插值法对轨迹数据清洗处理后提取特征指标参数构建驾驶行为谱,采用风险度量法对急转向、急加速、急减速、超速4种不良驾驶行为进行量化表达。其次,使用大样本统计分布的IQR与客观赋权的CRITIC方法确定不良驾驶行为特征指标参数阈值与权重,结合隶属度函数构造模糊综合评价模型对不良驾驶行为谱特征值进行确定以标定不良行驶车辆。最后,将不良驾驶行为谱特征值作为输入,基于人工智能卷积神经网络(CNN)算法对不良驾驶行为进行辨识,并与SVM、RF、BP等传统机器学习算法在辨识误差上进行比较。结果表明:CNN算法对不良驾驶行为辨识的理论误差值MAE为0.059、RMSE为0.084、R2高达0.911。可见,不良驾驶行为谱作为一种客观量化不良驾驶行为的方法与CNN算法相结合,能依据车辆运行轨迹对不良驾驶行为进行自动辨识,具有客观性、可靠性与适应性。

    Abstract:

    In order to quantitatively depict the dynamic fluctuations and gravity of driver behavior,a method for constructing and analyzing a bad driving behavior spectrum was studied for real-time identification of bad driving behavior. Firstly, based on the Lagrange interpolation method, trajectory data was cleaned and feature indicators were extracted to construct a driving behavior spectrum. The risk measurement method was used to quantify four types of bad driving behaviors, including abrupt turning, rapid acceleration, sudden deceleration, and overspeeding. Secondly,The IQR of a large sample statistical distribution and the CRITIC method with objective weighting were used to determine the threshold values and weights of the feature indicators for bad driving behavior. A fuzzy comprehensive evaluation model was constructed using a membership degree function to determine the characteristic values of bad driving behavior spectrum and calibrate bad driving vehicles. Then, based on the bad driving behavior spectrum characteristic values as input, an artificial intelligence convolutional neural network (CNN) algorithm was used to identify bad driving behavior, and the identification error was compared with traditional machine learning algorithms[ ] such as SVM, RF, and BP. The results showed that the theoretical error value MAE of the CNN algorithm for identifying bad driving behavior was 0.059, RMSE was 0.084, and R2 was as high as 0.911. Therefore, combining the bad driving behavior spectrum as an objective quantitative method for bad driving behavior with the CNN algorithm can automatically identify bad driving behavior based on vehicle operating trajectory, and has objectivity, reliability, and adaptability.

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朱兴林,丁双伟,姚亮,等. 基于卷积神经网络的不良驾驶行为辨识[J]. 科学技术与工程, 2024, 24(15): 6493-6501.
dingshuangwei, Dingshuangwei, Yaoliang, et al. Identification of Bad Driving Behavior Based on Convolutional Neural Network[J]. Science Technology and Engineering,2024,24(15):6493-6501.

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  • 收稿日期:2023-06-29
  • 最后修改日期:2024-05-08
  • 录用日期:2023-10-24
  • 在线发布日期: 2024-06-04
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