吉林大学学报(工学版) ›› 2024, Vol. 54 ›› Issue (3): 589-599.doi: 10.13229/j.cnki.jdxbgxb.20230685

• 车辆工程·机械工程 •    

人机共驾下的驾驶行为数据滤波方法

高镇海1,2(),蔡荣贵1,2,孙天骏1,2(),于桐1,2,赵浩源1,班浩3   

  1. 1.吉林大学 汽车工程学院，长春 130022
    2.吉林大学 汽车底盘集成与仿生全国重点实验室，长春 130022
    3.吉林大学 长沙汽车创新研究院，长沙 410016
  • 收稿日期:2023-07-01 出版日期:2024-03-01 发布日期:2024-04-18
  • 通讯作者: 孙天骏 E-mail:gaozh@jlu.edu.cn;sun_tj@jlu.edu.cn
  • 作者简介:高镇海（1973-），男，教授，博士.研究方向：智能驾驶与智能座舱.E-mail：gaozh@jlu.edu.cn
  • 基金资助:
    大学生创新创业训练计划项目(202310183133);吉林大学长沙汽车创新研究院自由探索项目(CAIRIZT20220106);吉林大学研究生创新基金项目(451230411061);中央高校基本科研业务费专项资金项目(2022-JCXK-24);汽车动力传动与电子控制湖北省重点实验室开放基金项目(ZDK12023A05)

Data⁃filtering method for driving behavior based on vehicle shared autonomy

Zhen-hai GAO1,2(),Rong-gui CAI1,2,Tian-jun SUN1,2(),Tong YU1,2,Hao-yuan ZHAO1,Hao BAN3   

  1. 1.College of Automotive Engineering，Jilin University，Changchun 130022，China
    2.National Key Laboratory of Automotive Chassis Integration and Bionics，Jilin University，Changchun 130022，China
    3.Changsha Automobile Innovation Research Institute，Jilin University，Changsha 410016，China
  • Received:2023-07-01 Online:2024-03-01 Published:2024-04-18
  • Contact: Tian-jun SUN E-mail:gaozh@jlu.edu.cn;sun_tj@jlu.edu.cn

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摘要：

针对传统数据采集和分析过程中存在的低质量数据过冗余、特征数据难以挖掘以及人工记录耗时长的问题，提出了一种人机共驾环境下基于动态时间规整算法的驾驶行为数据滤波方法。首先，在Python环境下搭建了动态时间规整算法模型，实现以滚动时间窗的方式对两条序列进行实时偏差计算。然后，考虑不同距离计算方法的统计学特征设计了触发记录的偏差阈值，结合规整路径全局约束对模型进行优化。最后，进行仿真分析和实车测试，对比了不同约束条件下的数据滤波方法，可知基于Sakoe-Chiba约束条件的本文滤波方法在数据准备阶段就能平均自动滤除53.15%的无效数据，每小时可节省1.87 TB的数据存储空间，验证了本文方法的有效性和可行性。

关键词: 车辆工程, 智能驾驶, 人机共驾, 数据驱动, 动态时间规整

Abstract:

Aiming at the problems of over-redundancy of low-quality data， difficult mining of feature data and time-consuming manual recording in the traditional data acquisition and analysis process， a driving behavior data filtering method based on dynamic time regularization algorithm under man-machine co-driving was proposed. Firstly， a dynamic time warping algorithm model was built in Python environment to realize real-time deviation calculation of two sequences by means of rolling time window. Then， considering the statistical characteristics of different distance calculation methods， the deviation threshold of triggering records was designed， and the model was optimized with the global constraint of structured path. Finally， simulation analysis and real vehicle test were conducted to compare the data filtering methods under different constraints. It was found that the proposed filtering method based on Sakoe-Chiba constraints can automatically filter out an average of 53.15% of invalid data in the data preparation stage， saving 1.87 TB of data storage space per hour， the effectiveness and feasibility of the proposed method is verified.

Key words: vehicle engineering, autonomous vehicle, intelligent vehicle shared autonomy, data-driven, dynamic time warping

中图分类号: 

  • TP29

图1

驾驶行为大数据驱动的系统升级"

图2

驾驶行为数字化量化示意图"

图3

研究的主要技术流程"

图4

ED与DTW距离测量对比"

图5

DTW的路径全局约束图"

图6

“人-车”控制指令差异性分析模型的技术流程"

图7

实车数据采集平台"

图8

第一组离线测试中4种模型的差异性数据滤波效果图"

图9

第二组离线测试中4种模型的差异性数据滤波效果"

表1

差异性数据记录总时长汇总表"

组别测试时间/s模型1记录时间/s模型2记录时间/s模型3记录时间/s模型4记录时间/s
均值104.6058.8053.9858.7154.37
184.0031.3427.3430.4027.94
2117.0072.5061.8875.0062.30
3105.0076.9469.6473.6870.16
496.0034.7433.7435.2033.74
5121.0078.4877.3079.2677.72

表2

数据滤除率汇总"

组别数据滤除率/%
模型1模型2模型3模型4
均值45.2849.8445.4749.46
162.6967.4563.8166.74
238.0347.1135.9046.75
326.7233.6829.8333.18
463.8164.8563.3364.85
535.1436.1234.5035.77

图10

Sakoe-Chiba全局约束DTW模型效果图"

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