Journal of Jilin University(Engineering and Technology Edition) ›› 2019, Vol. 49 ›› Issue (6): 1810-1817.doi: 10.13229/j.cnki.jdxbgxb20181122

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Road roughness identification based on vehicle responses

Jie LI(),Wen-cui GUO,Qi ZHAO,Sheng-feng GU   

  1. State Key Laboratory of Automotive Simulation and Control，Jilin University，Changchun 130022，China
  • Received:2018-11-12 Online:2019-11-01 Published:2019-11-08

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Abstract:

To solve the problem of road roughness identification, a NARX neural network identification method and its applicability are studied based on vehicle responses. A four degree of freedom plane model of vehicle vibration system is established, thus, the vehicle responses and road roughness of wheel can be obtained by simulation. The application selection, input scheme optimization and evaluation index of NARX neural network are studied, and the solutions of vehicle response selection and its combination optimization are put forward. The NARX neural network is used to identify the road roughness at front wheel of a car under the common road grade B and 60 km/h driving speed, for which the correlation coefficient and root mean square error are 96.75% and 0.003 3, respectively. The influences of training sampling points, vehicle response random noise, vehicle speed, and road grade on the NARX neural network are considered, and the adaptability of NARX neural network method for road roughness identification based on vehicle responses is illustrated. The results show that the use of orthogonal test design to determine the optimal input scheme of the NARX neural network and the identification of road roughness based on vehicle responses can achieve satisfactory performance and good applicability.

Key words: vehicle engineering, road roughness identification, vehicle response, NARX neural network

CLC Number: 

  • U461.4

Fig.1

Four degree of freedom plane model of vehicle vibration system"

Fig.2

Typical configuration of NARX neural network"

Table 1

Evaluation indexes of NARX neural network based on orthogonal experimental design"

方案 R/% RMSE 方案 R/% RMSE
1 0.00 0.015 00 17 96.29 0.003 48
2 26.47 0.013 40 18 95.05 0.004 38
3 16.72 0.013 52 19 93.06 0.004 66
4 36.87 0.012 22 20 96.21 0.003 54
5 29.07 0.013 50 21 92.85 0.004 84
6 43.41 0.014 30 22 91.18 0.006 00
7 43.36 0.012 38 23 94.93 0.003 76
8 32.84 0.014 28 24 84.89 0.005 98
9 75.44 0.008 96 25 42.26 0.013 62
10 93.43 0.004 50 26 32.13 0.015 26
11 72.52 0.008 94 27 48.30 0.014 24
12 93.59 0.004 44 28 47.91 0.012 02
13 94.39 0.004 26 29 19.28 0.015 32
14 94.51 0.004 38 30 21.52 0.015 42
15 95.44 0.003 70 31 36.46 0.013 44
16 96.35 0.003 30 32 28.76 0.014 38

Fig.3

Identification results of optimized input scheme for NARX neural network"

Table 2

Evaluation indexes of different trainingsample points"

采样点数 R/% RMSE
216 38.75 0.012 0
240 58.69 0.012 3
270 65.46 0.012 1
310 83.93 0.007 0
360 83.39 0.006 8
432 91.33 0.006 6
540 93.17 0.005 9
720 93.29 0.006 0
1080 93.56 0.005 0

Table 3

Evaluation indexes of different SNRs"

信噪比/dB R/% RMSE
1 68.10 0.013 2
3 77.97 0.008 0
5 84.47 0.007 5
10 87.82 0.006 7
20 92.71 0.004 6

Table 4

Evaluation indexes of different speeds"

车速/(km·h-1) R/% RMSE
40 89.66 0.005 6
50 96.94 0.002 9
60 95.25 0.003 7
70 96.18 0.003 1
80 89.56 0.004 4

Table 5

Evaluation indexes of different road grades"

路面等级 R/% RMSE
A级 92.26 0.005 6
B级 94.70 0.005 0
C级 85.02 0.014 6
D级 62.06 0.036 8
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