考虑多维驾驶特性的制动反应时间预测模型

doi:10.19562/j.chinasae.qcgc.2021.11.014

Abstract

Abstract:

In order to accurately predict the driver's braking reaction time （BRT）， a BRT prediction model based on the characteristics of differentiated drivers is built. The experiment is designed with multi task driving behavior as the inducing factors of differentiated driver characteristics， then the real vehicle experiment is carried out on closed urban road and the BRT data is collected. The drivers' multi-dimensional driving characteristics variable data is obtained by the self-reported information collection method. The structural equation model （SEM） is used to deconstruct the influencing factors of BRT and the path coefficients are used to optimize the weights of back propagation neural network （BPNN）. Finally， a prediction model of BRT based on SEM-BPNN is established. The verification and test results show that the overall regression R value of the proposed BRT prediction model is greater than 0.9 and the total error is 0.032 4. It has better prediction accuracy and fitting performance， moreover， it can reduce the problem of poor robustness caused by unstable network convergence while considering the multi-dimensional characteristics of drivers.

Key words: traffic engineering, automotive human factors engineering, breaking reaction time, driver characteristics, structural equation model, BP neural network

Baicang Guo,Xianyi Xie,Lisheng Jin,Hui Rong,Yang He,Bingdong Ji. Braking Response Time Prediction Model Based on Multi-dimensional Driving Characteristics[J].Automotive Engineering, 2021, 43(11): 1683-1692.

Figures/Tables 18

References 28

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项目	显变量	选项	描述	项目	显变量	选项	描述
生理属性	视力	差不良正常远视超远视	<0.6 0.8 1.0 1.2 >1.5	驾驶负荷	行为负荷量	低负荷较低负荷中负荷较高负荷高负荷	使用文献［19］的改进版NASA-TLX驾驶负荷评价方法计算得到。
生理属性	年龄/岁	18-25 26-35 36-45 46-55 >56	- - - - -		心理占用量	专注驾驶低风险心理类中风险心理类较高风险心理类高风险心理类	对应于1.3节分心驾驶行为类型。
驾驶经验	驾龄/年	<1 2-5 6-10 11-20 >21	- - - - -		动作占用量	专注驾驶低风险动作类中风险动作类较高风险动作类高风险动作类	使用文献［19］的风险驾驶行为类型设计方法与试验方案。
	驾驶里程/万km	低较低中较高高	<0.5 0.5-1 1-3 3-5 >5	期望	速度期望	低于周车速度略低于周车速度临近周车速度略高于周车速度高于周车速度	使用文献［21］中对驾驶人期望速度的类别划分方法。
	月驾驶天数	<5 6-10 11-15 16-20 >21	- - - - -		跟车距离期望	远距离跟车较远距离跟车中距离跟车较近距离跟车近距离跟车	设定方案参照文献［22］的不同车速下的期望间距值（表2）。
					舒适性期望	舒适型较舒适型普通型较不舒适不舒适	设定方案参照文献［23］的5.3章舒适性研究结果。

KMO取样适切性量数		0.919
Bartlett球形度检验	近似卡方	2 757.181
	自由度	91
	显著性	0

变量	Skewness	C.R.	Kurtosis	C.R.
制动反应时间（1）	-0.782	-6.505	-0.348	-1.447
制动反应时间（2）	-0.716	-5.957	-0.510	-2.122
制动反应时间（3）	-0.665	-5.531	-0.640	-2.662
驾龄	-0.796	-6.621	-0.346	-1.437
驾驶里程	-0.958	-7.968	-0.182	-0.755
驾驶频率	-0.880	-7.318	-0.273	-1.135
视力	-0.790	-6.569	-0.468	-1.947
年龄	-0.833	-6.926	-0.319	-1.327
速度期望	-0.638	-5.310	-0.821	-3.416
跟车距离期望	-0.635	-5.284	-0.691	-2.872
舒适性期望	-0.839	-6.981	-0.318	-1.322
行为负荷量	-0.790	-6.572	-0.333	-1.385
心理占用量	-0.946	-7.866	-0.121	-0.504
动作占用量	-0.794	-6.605	-0.321	-1.335
多元方差			30.306	14.584

拟合指数	合理标准	优秀标准	模型值	参数判断	是否达标
CMIN			98.249
CMIN/DF	<5	<3	1.445	优秀	是
GFI	>0.8	>0.9	0.967	优秀	是
AGFI	>0.8	>0.9	0.948	优秀	是
NFI	>0.8	>0.9	0.965	优秀	是
IFI	>0.8	>0.9	0.989	优秀	是
TLI	>0.8	>0.9	0.985	优秀	是
CFI	>0.8	<0.9	0.989	优秀	是
RMSEA	<0.08	<0.05	0.033	优秀	是

路径	非标准参数	S.E.	标准参数	C.R.	P
驾驶负荷→制动反应时间	0.432	0.082	0.403	5.287	***
驾驶经验→制动反应时间	0.221	0.057	0.218	3.879	***
期望→制动反应时间	0.174	0.051	0.177	3.39	***
生理属性→制动反应时间	0.408	0.073	0.380	5.552	***
驾驶负荷→行为负荷量	0.936	0.072	0.704	13.030	***
驾驶负荷→动作占用量	0.978	0.071	0.747	13.755	***
期望→舒适性期望	0.905	0.068	0.726	13.345	***
期望→速度期望	0.940	0.072	0.712	13.139	***
驾驶经验→驾龄	0.990	0.063	0.787	15.692	***
驾驶经验→驾驶里程	1.032	0.066	0.785	15.658	***
生理属性→视力	1.114	0.081	0.798	13.769	***
生理属性→年龄	1.000	-	0.751	-	-
驾驶负荷→心理占用量	1.000	-	0.733	-	-
期望→跟车距离期望	1.000	-	0.791	-	-
驾驶经验→驾驶频率	1.000	-	0.793	-	-
制动反应时间→ 制动反应时间（1）	1.020	0.058	0.807	17.575	***
制动反应时间→ 制动反应时间（2）	0.987	0.060	0.766	16.502	***
制动反应时间→ 制动反应时间（3）	1.000	-	0.778	-	-

Braking Response Time Prediction Model Based on Multi-dimensional Driving Characteristics

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预测模型及其训练算法	回归R值
预测模型及其训练算法	训练集	验证集	测试集	总体
BP神经网络预测模型（BR）	0.815 15		0.800 62	0.813 03
BP神经网络预测模型（LM）	0.812 18	0.802 88	0.810 31	0.810 50
BP神经网络预测模型（SCG）	0.785 32	0.780 63	0.779 80	0.783 75
SEM-BP神经网络预测模型	0.917 22	0.907 94	0.913 96	0.915 29

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