人机共驾下的驾驶行为数据滤波方法

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

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。

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 manmachine 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.