基于支持向量机的制动意图识别方法

doi:10.13229/j.cnki.jdxbgxb20210187

摘要/Abstract

摘要：

探索了一种采用实车试验数据，基于支持向量机（SVM）的制动意图识别方法。选取制动踏板位移、制动踏板力及制动减速度为制动意图识别参数，制动工况分为紧急制动、持续制动和常规制动；搭建制动意图识别整车测试系统，进行了多组制动工况试验，获取识别参数试验数据；构建了制动意图识别SVM模型，选取RBF核函数为SVM核函数，基于k折交叉验证法和网格搜索法对惩罚因子C与核函数参数σ进行了寻优；基于实际试验数据，选取持续制动、常规制动及紧急制动3种制动工况，对所构建的SVM模型进行了离线验证。结果表明：所构建的SVM模型具有较高的制动意图识别准确率，为后续的进一步应用提供了理论基础。

关键词: 车辆工程, 制动意图, 支持向量机, 特征参数, 识别模型

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

中图分类号:

U463.5

王奎洋,何仁. 基于支持向量机的制动意图识别方法[J]. 吉林大学学报(工学版), 2022, 52(8): 1770-1776.

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.

图/表 3

图1

表1

图2

参考文献 17

1	尘帅,王吉忠,张西龙,等. 面向车辆纵向动力学控制的制动意图识别综述[J]. 河北科技大学学报, 2019, 40(2): 105-111.
	Chen Shuai, Wang Ji-zhong, Zhang Xi-long, et al. Overview of braking intention recognition for longitudinal dynamic control of vehicles[J]. Journal of Hebei University of Science and Technology, 2019, 40(2): 105-111.
2	Kim I H, Kim J W, Haute S, et al. Detection of braking intention in diverse situations during simulated driving based on EEG feature combination[J]. Journal of Neural Engineering, 2015, 12(1): 016001.
3	Haufe S, Kim J W, Kim I H, et al. Electrophysiology-based detection of emergency braking intention in real-world driving[J]. Journal of Neural Engineering, 2014, 11(5): 056011.
4	Hernández L G, Mozos O M, Ferrández J M, et al. EEG-based detection of braking intention under different car driving conditions[J]. Frontiers in Neuroinformatics, 2018, 12(29): 1-14.
5	Teng T, Bi L Z, Liu Y L. EEG-based detection of driver emergency braking intention for brain-controlled vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(6): 1766-1773.
6	乔丙辰. 基于驾驶员下肢表面肌电信号的紧急制动意图识别研究[D]. 武汉: 武汉理工大学自动化学院, 2019.
	Qiao Bing-chen. Research on emergency braking intention recognition based on driver's lower limb EMG signal[D]. Wuhan: School of Automation, Wuhan University of Technology, 2019.
7	刘晏宇,喻凡,宋娟娟,等. 采用神经网络与模糊控制的制动需求识别[J]. 中国机械工程, 2020, 31(23): 2847-2855.
	Liu Yan-yu, Yu Fan, Song Juan-juan, et al. Detection of brake requests using neural network and fuzzy control[J]. China Mechanical Engineering, 2020, 31(23): 2847-2855.
8	杨为,刘杰,周仕仕,等. 电动液压助力制动系统制动意图识别方法[J]. 重庆大学学报, 2021, 44(10): 1-12.
	Yang Wei, Liu Jie, Zhou Shi-shi,et al. Braking intention identification for electric power hydraulic booster braking system[J]. Journal of Chongqing University, 2021, 44(10): 1-12.
9	Zhao X, Wang S, Ma J, et al. Identification of driver's braking intention based on a hybrid model of GHMM and GGAP-RBFNN[J]. Neural Computing and Applications, 2018, 31: 161-174.
10	王波,唐先智,王连东, 等. 基于EEMD和熵理论的电动汽车制动意图识别方法[J]. 汽车工程, 2018, 40(8): 936-941.
	Wang Bo, Tang Xian-zhi, Wang Lian-dong, et al. Braking intention identification method for electric vehicles based on EEMD and entropy theory[J]. Automotive Engineering, 2018, 40(8): 936-941.
11	潘宁, 于良耀,宋健. 考虑舒适性的电动汽车制动意图分类与识别方法[J]. 清华大学学报: 自然科学版, 2016, 56(10): 1097-1103.
	Pan Ning, Yu Liang-yao, Song Jian. Braking intention classification and identification considering braking comfort for electric vehicle[J]. Journal of Tsinghua University(Science and Technology), 2016, 56(10): 1097-1103.
12	曲代丽. 基于模糊神经网络的驾驶员制动意图辨识技术研究[D]. 长春: 长春工业大学机电工程学院, 2016.
	Qu Dai-li. Research on identification technology of driver's braking intention based on fuzzy neural network[D]. Changchun: School of Mechanical and Electrical Engineering, Changchun University of Technology, 2016.
13	王骏骋,何仁. 面向全制动工况的液压制动双环预测控制策略[J]. 吉林大学学报: 工学版, 2020, 50(3): 820-833.
	Wang Jun-cheng, He Ren. Double-loop predictive control scheme of hydraulic braking system for all braking conditions[J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(3): 820-833.
14	王庆年,王俊,陈慧勇,等. 混合动力车辆中的加速与制动意图识别[J]. 吉林大学学报: 工学版, 2014, 44(2): 281-286.
	Wang Qing-nian, Wang Jun, Chen Hui-yong, et al. Accelerating and braking intention identification in hybrid vehicle[J]. Journal of Jilin University(Engineering and Technology Edition), 2014, 44(2): 281-286.
15	谷新平,韩云鹏,于俊甫. 基于决策机理与支持向量机的车辆换道决策模型[J]. 哈尔滨工业大学学报, 2020, 52(7): 111-121.
	Gu Xin-ping, Han Yun-peng, Yu Jun-fu, et al. Vehicle lane-changing decision model based on decision mechanism and support vector machine[J]. Journal of Harbin Institute of Technology, 2020, 52(7): 111-121.
16	张元侠. 基于SVM学习模型的换挡决策研究[D]. 长春: 吉林大学汽车工程学院, 2019.
	Zhang Yuan-xia. Research on gear shift decision based on SVM learning model[D]. Changchun: College of Automotive Engineering, Jilin University, 2019.
17	李航. 统计学习方法[M]. 北京: 清华大学出版社, 2012.
	Li Hang. Statistical Learning Methods[M]. Beijing: Tsinghua University Press, 2012.

相关文章 15

[1]	刘汉武,雷雨龙,阴晓峰,付尧,李兴忠. 增程式电动汽车增程器多点控制策略优化[J]. 吉林大学学报(工学版), 2022, 52(8): 1741-1750.
[2]	王骏骋,吕林峰,李剑敏,任洁雨. 分布驱动电动汽车电液复合制动最优滑模ABS控制[J]. 吉林大学学报(工学版), 2022, 52(8): 1751-1758.
[3]	高青,王浩东,刘玉彬,金石,陈宇. 动力电池应急冷却喷射模式实验分析[J]. 吉林大学学报(工学版), 2022, 52(8): 1733-1740.
[4]	郝帅,程川泰,王军年,张君媛,俞有. 运动型SUV驾驶室布置人机优化设计与测试评价[J]. 吉林大学学报(工学版), 2022, 52(7): 1477-1488.
[5]	张家旭,郭崇,王晨,赵健,王欣志. 基于半实物仿真平台的自动泊车系统性能评价[J]. 吉林大学学报(工学版), 2022, 52(7): 1552-1560.
[6]	杨红波,史文库,陈志勇,郭年程,赵燕燕. 基于某二级减速齿轮系统的齿面修形优化[J]. 吉林大学学报(工学版), 2022, 52(7): 1541-1551.
[7]	聂光明,谢波,田彦涛. 基于Frenet框架的协同自适应巡航控制算法设计[J]. 吉林大学学报(工学版), 2022, 52(7): 1687-1695.
[8]	华琛,牛润新,余彪. 地面车辆机动性评估方法与应用[J]. 吉林大学学报(工学版), 2022, 52(6): 1229-1244.
[9]	李雄,兰凤崇,陈吉清,童芳. Hybird III假人模型与CHUBM人体生物力学模型的正碰损伤对比[J]. 吉林大学学报(工学版), 2022, 52(6): 1264-1272.
[10]	刘兴涛,刘晓剑,武骥,何耀,刘新天. 基于曲线压缩和极限梯度提升算法的锂离子电池健康状态估计[J]. 吉林大学学报(工学版), 2022, 52(6): 1273-1280.
[11]	张英朝,李昀航,郭子瑜,王国华,张喆,苏畅. 长头重型卡车气动减阻优化[J]. 吉林大学学报(工学版), 2022, 52(4): 745-753.
[12]	纪少波,李洋,李萌,苏士斌,马晓龙,何绍清,贾国瑞,程勇. 纯电动共享汽车驾驶行为对能耗的影响[J]. 吉林大学学报(工学版), 2022, 52(4): 754-763.
[13]	史文库,张曙光,张友坤,陈志勇,江逸飞,林彬斌. 基于改进海鸥算法的磁流变减振器模型辨识[J]. 吉林大学学报(工学版), 2022, 52(4): 764-772.
[14]	李杰,陈涛,郭文翠,赵旗. 汽车非平稳随机振动空间域虚拟激励法及应用[J]. 吉林大学学报(工学版), 2022, 52(4): 738-744.
[15]	李伟,宋海生,陆浩宇,史文库,王强,王晓俊. 复合材料板簧迟滞特性线性辨识方法[J]. 吉林大学学报(工学版), 2022, 52(4): 829-836.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

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