自动驾驶汽车乘员个性化乘坐舒适性辨识方法

doi:10.19562/j.chinasae.qcgc.2021.08.007

Abstract

Abstract:

For the problem that autonomous vehicle trajectory planning control algorithm cannot meet the personalized comfort of occupants， combining natural driving data and occupant comfort requirements， a method for identifying occupants’ personalized comfort is established. Firstly， the subjective comfort evaluation method is determined. Based on the standard ISO2631， frequency domain and time domain weighted filter functions are built. Subjective and objective characteristic parameters of occupant comfort of autonomous vehicles are extracted and the relationship between occupant’s personalized comfort and autonomous vehicle driving planning parameters is identified. Then， a natural driving data acquisition platform is established to collect the driving parameters and subjective and objective parameters that affect comfort. Factor analysis is used to reduce the dimensions of driving parameters to obtain three?way motion （lateral impact， longitudinal acceleration， and vertical vibration）， driving risk and efficiency influencing factors. Finally， the weighted analysis method is used to identify the model， and the Kalman filter algorithm is applied to quickly and accurately identify the individual needs of the occupant， and the weighted root?mean?square threshold of comfort is obtained. The identification results show that the correlation between the subjective and objective comfort of the occupant reaches 85.81%； the three?way motion factor has a greater impact on the occupant comfort than the driving risk and efficiency factors； the identification rate of occupant personalized comfort is as high as 93%. The study can provide theoretical support for constructing personalized trajectory planning control algorithm considering occupant comfort.

Key words: autonomous driving, occupant comfort, identification, evaluation, personalization

Fengchong Lan,Shicheng Li,Jiqing Chen,Zongmao Shen. Identification Method for Occupant Personalized Ride Comfort of Autonomous Vehicles[J].Automotive Engineering, 2021, 43(8): 1168-1176.

Figures/Tables 22

References 18

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可接受度	不可接受			临界		可接受
主观感受	极差	剧烈	差	较差	临界	一般	好	较好	极好	完美
分数	1	2	3	4	5	6	7	8	9	10

编号	时域加权	频域加权	编号	时域加权	频域加权
1	0.268	0.269	6	0.269	0.260
2	0.577	0.615	7	0.303	0.323
3	1.309	1.103	8	0.278	0.261
4	0.508	0.546	9	0.391	0.425
5	0.853	0.779	10	0.422	0.403

编号	参数名称	编号	参数名称
1	换道时间	10	横向加速度绝对值平均值
2	换道速度	11	横向加速度标准差
3	换道纵向行驶距离	12	横向冲击绝对值最大值
4	换道横向距离	13	横向冲击绝对值平均值
5	纵向加速度绝对值最大值	14	垂向加速度最大值
6	纵向加速度平均值	15	垂向加速度最小值绝对值
7	纵向加速度标准差	16	垂向加速度绝对值平均值
8	横向加速度最大值	17	垂向加速度标准差
9	横向加速度最小值的绝对值	18	主车与前车距离

KMO取样适切性量数		0.756 1
巴特利特球形度检验	近似卡方	6 259.130
	自由度	153
	显著性	0.00

参数编号	初始特征值			参数编号	初始特征值
参数编号	特征根	方差贡献/%	累积贡献/%	参数编号	特征根	方差贡献/%	累积贡献/%
1	5.588	31.047	31.047	10	0.272	1.511	96.109
2	3.791	21.064	52.111	11	0.249	1.385	97.495
3	2.467	13.707	65.818	12	0.175	0.970	98.464
4	1.659	9.218	75.036	13	0.118	0.656	99.121
5	1.006	5.590	80.626	14	0.061	0.340	99.460
6	0.895	4.971	85.597	15	0.041	0.226	99.686
7	0.799	4.440	90.037	16	0.026	0.144	99.830
8	0.475	2.640	92.677	17	0.016	0.090	99.921
9	0.346	1.921	94.599	18	0.014	0.079	100.000

Identification Method for Occupant Personalized Ride Comfort of Autonomous Vehicles

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Figures/Tables 22

References 18

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参数编号	行驶车辆状态参数公共因子
参数编号	1	2	3	4	5
1	-0.579	0.045	-0.016	-0.147	0.488
2	0.084	0.298	-0.162	0.750	0.293
3	-0.310	0.280	-0.152	0.572	0.554
4	0.088	-0.013	-0.033	0.070	0.807
5	0.072	0.077	0.975	-0.025	-0.015
6	0.113	-0.093	0.875	0.161	-0.125
7	0.103	0.039	0.858	-0.212	0.031
8	0.766	0.097	0.147	0.189	0.035
9	0.834	0.153	0.042	-0.175	0.047
10	0.928	-0.036	0.031	0.181	-0.012
11	0.971	-0.007	0.063	0.096	-0.036
12	0.874	0.034	0.089	-0.079	-0.012
13	0.967	-0.016	0.058	0.057	-0.048
14	-0.026	0.926	-0.048	-0.089	0.026
15	0.026	0.874	0.017	-0.011	0.052
16	0.132	0.877	0.094	0.308	0.011
17	0.046	0.973	-0.016	0.079	0.013
18	0.191	-0.082	0.072	0.729	-0.127

因子编号	1	2	3	4	5
旋转因子贡献率	0.295 4	0.198 1	0.142 1	0.097 0	0.073 6
加权系数	0.366 4	0.245 7	0.176 2	0.120 3	0.091 3

系数	k₁	k₂	k₃	k₄	k₅	k₆
数值	0.080 7	0.098 4	0.034 5	-0.028 3	-0.001 3	0.422 9
加权	0.331 8	0.404 6	0.141 9	0.116 4	0.005 3

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