运行车速认知因子虚拟仿真试验

陈涛; 魏朗; 袁望方

运行车速认知因子虚拟仿真试验

长安大学交通部人-车-环境系统安全重点实验室, 陕西西安 710064

基金项目:

GM中国科学研究基金项目 50222206

详细信息

作者简介:
陈涛(1974-), 男, 陕西铜川人, 长安大学副教授, 博士, 从事交通安全研究

中图分类号: U491.25
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- 被引次数: 0
出版历程
- 收稿日期: 2007-10-21
- 刊出日期: 2008-04-25

Virtual simulation test of perception factors for operating speed

Key Laboratory for Driver-Vehicle-Environment System Safety of Ministry of Communications, Chang'an University, Xi'an 710064, Shaanxi, China

More Information

Author Bio:
Chen Tao(1974-), male, PhD, associate professor, +86-29-82334475, chentao@chd.edu.cn

摘要

摘要: 为准确预测道路交通安全评价中微观路段的运行车速, 确定了三级公路的驾驶员安全性认知模糊集, 建立了基于Multi Gen Creator软件的15个路段的三维仿真模型, 开发了基于Vega的虚拟仿真系统, 组织了53名驾驶员, 在大型三通道柱面投影仿真系统中, 进行了运行车速认知因子的评价试验。应用模糊统计方法对795组有效认知试验数据进行了分析, 建立了包含道路平曲线半径、纵坡度和行车视距认知因子的三级公路安全性认知评价模型。虚拟仿真试验与道路试验分析结果表明: 平曲线半径、纵坡度和行车视距认知因子评判等级的平均贴近度分别为0.63、0.74和0.70, 因此, 虚拟仿真试验方法可行。
- 道路交通安全 /
- 运行车速 /
- 虚拟仿真测试 /
- 三级公路 /
- 认知因子 /
- 贴近度
Abstract: In order to study the prediction of operating speed on microsection for road safety evaluation, the driver's safety perception factor sets for third-class highway were determined, 3-D simulation model including 15 sections was built by using MultiGen Creator software, and the virtual simulation system was developed by using Vega.The perception evaluation tests with 53 drivers were carried out in three-channel cylinder projection system.Based on the analysis of 795 groups test data, the evaluation model including horizontal curve radius, longitudinal gradient and sight distance for third-class highway was presented by means of fuzzy statistics method.Computation result shows that the mean nearnesses between simulation tests and road tests of the individual evaluation levels on safety perception factors are 0.63, 0.74 and 0.70 respectively.So virtual simulation test is an effective means for the prediction of operating speed.
- road traffic safety /
- operating speed /
- virtual simulation test /
- third-class highway /
- perception factor /
- nearness

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图 1 三维公路模型

Figure 1. 3-D highway model

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图 2 仿真系统运行流程

Figure 2. Working flow of simulation system

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图 3 认知评价虚拟试验现场

Figure 3. Virtual simulation field of perception evaluation

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图 4 平曲线半径安全性认知评价曲线

Figure 4. Evaluation curves of safety perception for horizontal curve radius

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图 5 纵坡度安全性认知评价曲线

Figure 5. Evaluation curves of safety perception for longitudinal gradient

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图 6 视距安全性认知评价曲线

Figure 6. Evaluation curves of safety perception for sight distance

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表 1 安全性认知评价模糊模型

Table 1. Fuzzy evaluation models of safety perception

平曲线半径模型	$μ_{11} (R) = {\begin{matrix} 1 & (R \geq 420) \\ \frac{1}{170} (R - 250) & (250 < R < 420) \\ 0 & (R \leq 250) \end{matrix}$	$μ_{14} (R) = {\begin{matrix} 0 & (R \geq 85) \\ \frac{1}{50} (85 - R) & (35 < R < 85) \\ 0 & (R \leq 35) \end{matrix}$
平曲线半径模型	$μ_{12} (R) = {\begin{matrix} 0 & (R \geq 500) \\ \frac{1}{200} (500 - R) & (300 < R < 500) \\ 1 & (220 \leq R \leq 300) \\ \frac{1}{120} (R - 100) & (100 < R < 220) \\ 0 & (R \leq 100) \end{matrix}$	$μ_{13} (R) = {\begin{matrix} 0 & (R \geq 250) \\ \frac{1}{150} (250 - R) & (100 < R < 250) \\ 1 & (90 \leq R \leq 100) \\ \frac{1}{60} (R - 30) & (30 < R < 90) \\ 0 & (R \leq 30) \end{matrix}$
纵坡度模型	$μ_{21} (G) = {\begin{matrix} 1 & (G \leq 0.8) \\ \frac{1}{0.5} (1.3 - G) & (0.8 < G < 1.3) \\ 0 & (G \geq 1.3) \end{matrix}$	$μ_{24} (G) = {\begin{matrix} 0 & (G \leq 0.38) \\ \frac{1}{0.8} (G - 3.8) & (3.8 < G < 4.6) \\ 1 & (G \geq 4.6) \end{matrix}$
纵坡度模型	$μ_{22} (G) = {\begin{matrix} 0 & (G \leq 0.5) \\ \frac{1}{0.8} (G - 0.5) & (0.5 < G < 1.3) \\ 1 & (1.3 \leq G < 1.4) \\ \frac{1}{1.8} (3.2 - G) & (1.4 \leq G \leq 3.2) \\ 0 & (G > 3.2) \end{matrix}$	$μ_{23} (G) = {\begin{matrix} 0 & (G \leq 1.4) \\ \frac{1}{1.8} (G - 1.4) & (1.4 < G < 3.2) \\ 1 & (3.2 \leq G \leq 4.0) \\ \frac{1}{0.8} (4.8 - G) & (4.0 < G < 4.8) \\ 0 & (G \geq 4.8) \end{matrix}$
视距模型	$μ_{31} (D) = {\begin{matrix} 1 & (D \geq 200) \\ \frac{1}{30} (D - 170) & (170 < D < 200) \\ 0 & (D \leq 170) \end{matrix}$	$μ_{34} (D) = {\begin{matrix} 1 & (D \leq 25) \\ \frac{1}{20} (45 - D) & (25 < D < 45) \\ 0 & (D \geq 45) \end{matrix}$
视距模型	$μ_{32} (D) = {\begin{matrix} 0 & (D \leq 65) \\ \frac{1}{35} (D - 65) & (65 < D < 100) \\ 1 & (100 \leq D \leq 170) \\ \frac{1}{50} (220 - D) & (170 < D < 220) \\ 0 & (D \geq 220) \end{matrix}$	$μ_{33} (D) = {\begin{matrix} 0 & (D \leq 20) \\ \frac{1}{40} (D - 20) & (20 < D < 60) \\ 1 & (60 \leq D \leq 70) \\ \frac{1}{30} (100 - D) & (70 < D < 100) \\ 0 & (D \geq 100) \end{matrix}$

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表 2 各评判等级的贴近度

Table 2. Nearnesses of evaluation levels

评价等级	1	2	3	4
平曲线半径	急弯	较急弯	缓弯	很缓弯
贴近度	N₁(μ_sA14, μ₁₄)	N₁(μ_sA13, μ₁₃)	N₁(μ_sA12, μ₁₂)	N₁(μ_sA11, μ₁₁)
贴近度值	0.53	0.53	0.52	0.92
纵坡度	陡坡	较陡坡	缓坡	很缓坡
贴近度	N₁(μ_sA24, μ₂₄)	N₁(μ_sA23, μ₂₃)	N₁(μ_sA22, μ₂₂)	N₁(μ_sA21, μ₂₁)
贴近度值	0.92	0.55	0.62	0.86
视距	很短	短	较长	很长
贴近度	N₁(μ_sA34, μ₃₄)	N₁(μ_sA33, μ₃₃)	N₁(μ_sA32, μ₃₂)	N₁(μ_sA31, μ₃₁)
贴近度值	0.71	0.56	0.67	0.85

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