基于改进DTW算法的车辆操纵一致性评价方法

doi:10.12382/bgxb.2023.1104

摘要/Abstract

摘要：

时间序列匹配技术广泛应用于车辆操纵一致性评价。针对某型教练车在协同驾驶过程中的动作匹配度评判问题,提出一种基于分段式动态时间弯曲距离的车辆油门操纵动作一致性评估方法。在有效解决教练车协同操控试验过程中样本数据点数量不一致问题的基础上,根据动态弯曲路径的斜率约束条件,将传统的动态时间弯曲距离(Dynamic Time Warping,DTW)矩形搜索区域通过改变路径搜索斜率方式转变为平行四边形搜索区域,以缩小搜索区域面积,从而极大程度地减小计算量。选取4组典型的油门动作曲线组进行50轮迭代实验进行验证,通过分段式DTW方法计算得到实车A、教练车B动作曲线之间的DTW距离矩阵,利用最小离差平方法对A、B两车动作进行聚类对象合并,从而完成油门动作数据的一致性评判。实验结果表明,改进后的动态时间弯曲算法在各油门动作的平均匹配精度可达89.2%,相较于单一的DTW算法提升约3.2%,平均匹配时间约为92.45s,降低约12.6%,从而验证了分段式DTW算法在油门动作一致性评判的可行性和优越性。

关键词: 车辆, 协同驾驶, 油门动作曲线, 一致性评价, 分段式DTW

Abstract:

Time series matching technology is widely used in vehicle handling consistency evaluation.An evaluation method of vehicle throttle control action consistency based on segmented dynamic time warping (DTW) is proposed for the motion consistency evaluation of a coach vehicle in the process of cooperative driving.On the basis of the inconsistent number of sample data points in the cooperative control test of coach vehicle and the slope constraint of dynamic bending path, the traditional DTW rectangular search area is transformed into a parallelogram search area to reduce the area of the search area by changing the slope of search path, thus greatly reduce the calculation amount.Four groups of typical throttle action curves are selected to carry out 50 rounds of iterative experiments for verification, and the DTW distance matrix between the action curves of real vehicles A and B is calculated by segmented DTW method.A minimum deviation leveling method is used to combine the cluster objects for the actions of vehicles A and B, so as to complete the consistency evaluation of throttle action data.The experimental results show that the average matching accuracy of the improved DTW algorithm in each throttle action can reach 89.2%, which is about 3.2% higher than that of the single DTW algorithm, and the average matching time is about 92.45s, which is about 12.6% lower, thus verifying the feasibility and superiority of the segmented DTW algorithm in the consistency evaluation of throttle action.

Key words: vehicle, cooperative driving, throttle action curve, consistency evaluation, segmented DTW

中图分类号:

钟岳, 徐峰, 闫猛飞, 刘义乐. 基于改进DTW算法的车辆操纵一致性评价方法[J]. 兵工学报, 2025, 46(1): 231104-.

ZHONG Yue, XU Feng, YAN Mengfei, LIU Yile. An Evaluation Method of Vehicle Handling Consistency Based on Improved DTW Algorithm[J]. Acta Armamentarii, 2025, 46(1): 231104-.

图/表 14

图1 A车油门动作曲线

Fig.1 Throttle action curve of vehicle A

图2 B车油门动作曲线

Fig.2 Throttle action curve of vehicle B

图3 动态弯曲距离路径示意图

Fig.3 Dynamic bending distance path diagram

图4 DTW平行四边形搜索区域

Fig.4 DTW parallelogram search area

图5 A1、A2组原始动作曲线和改进动态弯曲后的动作曲线

Fig.5 Original action curves of Groups A1 and A2 and their action curves after improved dynamic bending

图6 B1、B3组曲线的原始数据和DTW距离

Fig.6 Raw data and DTW distances of Groups B1 and B3 action curves

图7 A3、B1油门动作曲线原始数据和DTW距离

Fig.7 Raw data and DTW distances of Groups A3 and B1 action curves

图8 A2、B2油门动作曲线原始数据和DTW距离

Fig.8 Raw data and DTW distances of Groups A2 and B2 action curves

图9 DTW改进前后的匹配精度对比

Fig.9 Comparison of matching accuracies before and after DTW improvement

图10 DTW改进前后的匹配时间对比

Fig.10 Comparison of matching times before and after DTW improvement

表1 DTW算法改进前后的匹配精度

Table 1 The matching accuracies of DTW algorithm before and after improvement

匹配算法	平均匹配精度
匹配算法	A1A2	B1B3	A3B1	A2B2
DTW	0.866	0.876	0.844	0.85
分段式DTW	0.894	0.912	0.88	0.882

表2 DTW算法改进前后的匹配时间

Table 2 The matching times of DTW algorithm before and after improvement

匹配算法	平均匹配时间/s
匹配算法	A1A2	B1B3	A3B1	A2B2
DTW	96.48	79.88	126.86	119.9
分段式DTW	89.5	76.46	105.36	98.46

表3 两车油门动作间的DTW距离

Table 3 DTW distances between the throttle actions of two vehicles

曲线编号	A1	A2	A3	A4	B1	B2	B3	B4
A1	0				A组曲线的平均DTW距离:21.761 B组曲线的平均DTW距离:2.547 AB曲线的平均DTW距离:26.692
A2	9.382	0
A3	9.544	12.734	0
A4	38.835	36.698	23.374	0
B1	9.76	27.777	15.559	52.053	0
B2	6.6019	20.42	15.297	52.633	2.104	0
B3	8.2896	24.6453	16.960	53.585	1.757	1.375	0
B4	12.439	29.7243	20.105	61.220	2.116	4.669	3.260	0

图11 油门动作曲线DTW距离聚类谱系图

Fig.11 DTW distance clustering spectrum of throttle action curve

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