基于支持向量机的制动意图识别方法

doi:10.13229/j.cnki.jdxbgxb20210187

Abstract

Abstract:

This paper focuses on a recognition method of braking intention based on the test data of real vehicle and the support vector machine（SVM）. Brake pedal displacement， brake pedal force and braking deceleration were selected as the identification parameters of brake intention， and the braking conditions were divided into emergency braking， continuous braking and conventional braking. The vehicle test system of braking intention recognition was built， and several groups of brake tests were carried out to obtain the test data of identification parameters. The SVM model of brake intention recognition was constructed， and the RBF kernel function was selected as the SVM kernel function. Based on k-fold cross validation method and grid search method， the penalty factor C and kernel function parameter σ were optimized. Based on the actual test data， three braking conditions， gentle braking， conventional braking and emergency braking， were selected to verify the SVM model. The results show that the SVM model has high recognition accuracy of braking intention， which provides a theoretical basis for further application.

Key words: vehicle engineering, braking intention, support vector machine, feature parameters, recognition model

CLC Number:

U463.5

Kui-yang WANG,Ren HE. Recognition method of braking intention based on support vector machine[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1770-1776.

Figures/Tables 3

Fig.1

Table 1

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References 17

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制动组别	车速/ （km·h^-1）	踏板力/N	减速度/g	踏板位移/mm
1	50.000	22.47	-0.24	22.81
1	45.376	22.75	-0.21	23.46
1	42.899	22.44	-0.22	23.59
1	39.238	22.45	-0.24	23.65
1	35.703	25.84	-0.26	24.12
1	25.981	27.49	-0.27	25.49
1	21.735	24.97	-0.30	25.39
1	16.591	25.11	-0.33	25.40
1	11.476	26.54	-0.33	25.46
1	6.105	25.87	-0.32	25.53
1	0.988	25.23	-0.24	25.51
2	47.609	23.21	-0.31	23.25
2	43.633	22.09	-0.28	24.07
2	38.398	36.05	-0.34	27.70
2	32.689	34.31	-0.38	28.32
2	27.014	40.93	-0.30	29.43
2	22.621	39.23	-0.40	29.83
2	18.046	41.01	-0.38	30.16
2	13.516	40.87	-0.42	30.50
2	9.383	43.53	-0.40	30.59
3	50.000	72.27	-0.67	39.16
3	44.767	108.47	-1.10	45.24
3	40.110	112.12	-0.84	46.43
3	33.980	121.11	-0.96	47.22
3	28.981	123.45	-1.02	47.81
3	22.579	126.13	-1.01	48.50
3	17.036	125.09	-1.05	48.51
3	11.288	118.09	-1.01	48.53
3	4.769	105.54	-0.95	48.36
3	0.000	98.67	-1.11	48.22

Recognition method of braking intention based on support vector machine

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