基于组合赋权的网联HUD预警系统综合效用评价方法

王彦峰; 张钰; 赵晓华; 杨艳群

doi:10.3963/j.jssn.1674-4861.2025.03.008

基于组合赋权的网联HUD预警系统综合效用评价方法

doi: 10.3963/j.jssn.1674-4861.2025.03.008

1.
北京交通运输职业学院汽车经济管理系北京 102618
2.
北京工业大学交通工程北京市重点实验室北京 100124
3.
福州大学土木工程学院福州 350116

基金项目:

国家自然科学基金项目 52072012

详细信息

作者简介:
王彦峰（1975—），硕士，副教授. 研究方向：车辆工程. E-mail：jtxxwyf@163.com

通讯作者:
张钰（1998—），博士生. 研究方向：人机交互、驾驶行为与交通安全等. E-mail：2451098792@qq.com

中图分类号: U467.1⁺4
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- 被引次数: 0
出版历程
- 收稿日期: 2024-12-01
- 网络出版日期: 2025-10-11

Evaluation of Comprehensive Utility of Connected HUD Warning System Based on Combination Weighting Method

1.
Department of Automotive Economic Management, Beijing Vocational Transportation College, Beijing 102618, China
2.
Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China
3.
School of Civil Engineering, Fuzhou University, Fujian 350116, China

摘要

摘要: 为解决平视显示（head-up display, HUD）预警系统有效性评价维度单一、量化不足的问题，依托搭建的驾驶模拟平台，设计开发城市道路行人过街网联实验场景和三种预警系统（baseline/低头显示（head-down display，HDD）/HUD），实验测试获取驾驶人视觉、行为和主观数据；基于人因工程学、系统工程理论和交通工程学，结合驾驶人对于HUD预警系统的实际需求，从系统的安全、可靠、平稳及高效4个维度提取制动反应时间、后侵占时间、最大瞳孔面积、单位分心指数和恢复速度等10个典型关键指标建立综合评估指标体系；采用基于层次分析法（analytic hierarchy process，AHP）-熵权法（entropy weight method，EWM）的主客观赋权法，确定评估指标体系的组合权重；最后采用模糊综合评价方法量化3种预警系统在行人过街事件中的单一维度效用和综合效用。单一维度效用结果显示，HDD和HUD在4种维度上的效用表现存在差异，单一维度量化无法全面评估预警系统效用。具体而言，HDD和HUD系统在安全性得分上相较于Baseline条件分别提高了6.25%和18.98%；在可靠性方面，HDD略有下降0.61%，而HUD提高了1.97%；在稳定性上，HDD和HUD得分分别提高了5.97%和10.99%；在高效性方面，HDD和HUD得分分别下降了2.47%和3.5%，其中HUD表现相对较差。综合效用结果表明，在行人过街事件中，与基线条件相比，HDD和HUD系统的综合效用值分别提升了1.86%和6.36%。相比而言，HUD在兼顾安全、可靠、平稳和高效方面更具有优势，但未来HUD预警系统的设计仍需重点权衡安全与效率的关系。
- 网联环境 /
- 平视显示器(HUD) /
- 综合效用评价 /
- AHP-EWM组合赋权法 /
- 模糊综合评价 /
- 指标体系
Abstract: To solve the problem of single evaluation dimensions and insufficient quantification of the effectiveness of head-up display (HUD) warning systems, a driving simulation platform is developed to design and develop urban road pedestrian crossing connected experimental scenarios and three warning systems (baseline/head-down display (HDD)/HUD). The experimental tests obtained visual and behavioral data of drivers. Based on human factors engineering, systems engineering theory, and traffic engineering, combined with the actual needs of drivers for HUD warning systems, a comprehensive evaluation index system is established by extracting 10 typical key indicators from the four dimensions of system safety, reliability, stability, and efficiency, including braking response time, post encroachment time, maximum pupil area, average distraction index, and recovery speed. The subjective and objective weighting method based on analytic hierarchy process (AHP) - entropy weight method (EWM) is used to determine the combined weights of the evaluation index system. Finally, the fuzzy comprehensive evaluation method is used to quantify the single dimensional utility and comprehensive utility of the three warning systems in pedestrian crossing incidents. The results of single dimensional utility show that there are differences in the utility performance of HDD and HUD in four dimensions, and single dimensional quantification cannot comprehensively evaluate the utility of warning systems. Specifically, the HDD and HUD systems have improved their security scores by 6.25% and 18.98% respectively compared to the Baseline conditions; In terms of reliability, HDD slightly decreased by 0.61%, while HUD increased by 1.97%; In terms of stability, HDD and HUD scores increased by 5.97% and 10.99%, respectively; In terms of efficiency, HDD and HUD scores decreased by 2.47% and 3.5% respectively, with HUD performing relatively poorly. The comprehensive utility results indicate that in pedestrian crossing incidents, the comprehensive utility values of HDD and HUD systems increased by 1.86% and 6.36% respectively compared to Baseline conditions. In comparison, HUD has more advantages in balancing safety, reliability, stability, and efficiency, but the design of HUD warning systems in the future still needs to focus on balancing the relationship between safety and efficiency.
- connected environment /
- head-up display(HUD) /
- comprehensive utility evaluation /
- AHP-EWM combinative weighting method /
- fuzzy comprehensive evaluation /
- index system

HTML全文

图 1 驾驶模拟实验测试平台

Figure 1. Driving simulation experiment testing platform

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图 2 实验设备

Figure 2. Experimental equipment

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图 3 HDD/HUD界面设计

Figure 3. HDD/HUD interface design

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图 4 HDD/HUD设置位置

Figure 4. Location of HDD/HUD Settings

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图 5 行人过街场景设计

Figure 5. Pedestrian crossing scenario design

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图 6 实验流程

Figure 6. Experimental process

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图 7 指标体系

Figure 7. Index system

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图 8 人-车冲突时空轨迹图

Figure 8. Space-time trajectory of human-vehicle conflict

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图 9 AttenD算法规则

Figure 9. Algorithm rules of AttenD

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图 10 层次结构模型

Figure 10. Hierarchy model

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图 11 系统效用得分结果

Figure 11. Results of system utility score

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表 1 描述性统计及Friedman检验结果

Table 1. Descriptive statistics and Friedman test results

维度	变量	水平	均值	Friedman检验结果			成对比较结果
维度	变量	水平	均值	统计检验量	自由度	整体显著性	成对比较结果
安全水平	制动反应时间/s	Baseline	4.33±0.77	30.882	2	0.000
		HDD	3.66±1.15
		HUD	2.22±1
	最小碰撞时间/s	Baseline	1.8±1.2	21.412	2	0.000
		HDD	2.65±1.41
		HUD	4.23±1.98
	后侵占时间/s	Baseline	3.22±2.62	11.118	2	0.004
		HDD	3.76±1.43
		HUD	4.65±1.3
可靠水平	最大瞳孔面积/mm	Baseline	10.53±2.82	10	2	0.007
		HDD	10.73±3.14
		HUD	8.98±1.85
	单位分心指数	Baseline	0.07±0.09	8.194	2	0.017
		HDD	0.07±0.08
		HUD	0.13±0.12
平稳水平	加速度标准差/（m/s²）	Baseline	2.78±0.71	20.529	2	0.000
		HDD	2.39±0.79
		HUD	1.84±0.59
	车道偏移标准差/m	Baseline	0.14±0.1	5.471	2	0.065
		HDD	0.11±0.08
		HUD	0.14±0.09
高效水平	初始速度/（km/h）	Baseline	55.27±7.62	6.529	2	0.038
		HDD	52.4±7.18
		HUD	51.13±6.57
	平均速度/（km/h）	Baseline	33.66±9.42	10.059	2	0.007
		HDD	31.37±4.37
		HUD	28.35±3.84
	恢复速度/（km/h）	Baseline	25.16±13	2.882	2	0.237
		HDD	26.72±9.3
		HUD	28.46±7.59

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表 2 基于K-means聚类的等级取值范围

Table 2. Range of different grades based on K-means clustering

指标	1(优秀)	2(良好)	3(一般)	4(及格)	5(差)
制动反应时间/s	[0.010, 1.840]	[1.841, 2.883]	[2.884, 3.890]	[3.891, 4.822]	[4.823, 6.580]
最小碰撞时间/s	[7.488, 10.293]	[5.162, 7.487]	[3.369, 5.161]	[1.711, 3.368]	[0.000, 1.710]
后侵占时间/s	[6.967, 9.820]	[4.859, 6.966]	[3.344, 4.858]	[0.686, 3.343]	[-2.250, 0.685]
最大瞳孔面积/mm²	[4.524, 7.741]	[7.742, 10.066]	[10.067, 12.457]	[12.458, 15.507]	[15.508, 19.129]
单位分心指数	[0.000, 0.086]	[0.087, 0.262]	[0.263, 0.502]	[0.503, 0.707]	[0.708, 0.795]
加速度标准差/(m/s²)	[0.530, 1.471]	[1.472, 2.021]	[2.022, 2.591]	[2.592, 3.249]	[3.250, 4.130]
车道偏移标准差/m	[0.015, 0.092]	[0.093, 0.168]	[0.169, 0.328]	[0.329, 0.510]	[0.511, 0.606]
初始速度/(km/h)	[66.450, 75.802]	[55.145, 66.449]	[47.477, 55.144]	[38.558, 47.476]	[29.137, 38.557]
平均速度/(km/h)	[58.016, 65.303]	[45.196, 58.015]	[34.406, 45.195]	[23.896, 34.405]	[18.637, 23.895]
恢复速度/(km/h)	[57.984, 75.336]	[35.320, 57.983]	[26.456, 35.319]	[18.623, 26.455]	[3.552, 18.622]

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表 3 不同预警系统组指标隶属度矩阵

Table 3. Membership matrix of indicators of different warning system groups

指标	Baseline					HDD					HUD
指标	1	2	3	4	5	1	2	3	4	5	1	2	3	4	5
制动反应时间/s	0.00	0.07	0.20	0.54	0.19	0.00	0.15	0.51	0.26	0.08	0.23	0.53	0.18	0.06	0.00
最小碰撞时间/s	0.00	0.06	0.15	0.42	0.37	0.00	0.08	0.21	0.52	0.19	0.00	0.17	0.61	0.18	0.04
后侵占时间/s	0.00	0.16	0.35	0.41	0.08	0.00	0.17	0.56	0.26	0.01	0.09	0.30	0.43	0.18	0.00
最大瞳孔面积/mm²	0.10	0.28	0.47	0.15	0.00	0.08	0.25	0.52	0.16	0.00	0.19	0.53	0.20	0.08	0.00
单位分心指数	0.55	0.35	0.08	0.02	0.00	0.55	0.35	0.08	0.02	0.00	0.27	0.53	0.16	0.04	0.00
加速度标准差/(m/s²)	0.00	0.09	0.26	0.47	0.17	0.00	0.15	0.62	0.22	0.01	0.18	0.50	0.23	0.09	0.00
车道偏移标准差/m	0.20	0.47	0.26	0.07	0.00	0.30	0.46	0.19	0.05	0.00	0.21	0.50	0.23	0.06	0.00
初始速度/(km/h)	0.08	0.39	0.38	0.15	0.00	0.00	0.22	0.61	0.17	0.00	0.00	0.18	0.61	0.18	0.03
平均速度/(km/h)	0.00	0.11	0.34	0.41	0.13	0.00	0.08	0.25	0.50	0.16	0.00	0.06	0.18	0.56	0.21
恢复速度/(km/h)	0.00	0.14	0.28	0.39	0.19	0.00	0.17	0.34	0.32	0.18	0.00	0.18	0.42	0.26	0.14

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表 4 判断矩阵的标度和含义

Table 4. Scale and meaning of judgment matrix

标度	含义(a_ij)
1	因素i与因素j同等重要
3	因素i比因素j稍微重要
5	因素i比因素j明显重要
7	因素i比因素j强烈重要
9	因素i比因素j极端重要
2, 4, 6, 8	上述2个相邻判断的中间值
倒数	因j比因i更重要，a_ij = 1/a_ji

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表 5 判断矩阵结果

Table 5. Results of judgment matrix

因素	安全	可靠	平稳	高效
安全	1	5	3	5
可靠	1/5	1	1	3
平稳	1/3	1	1	3
高效	1/5	1/3	1/3	1

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表 6 权重向量与一致性结果

Table 6. Results of weight vector and consistency

因素	特征向量	权重值	λ_max	CI值	CR值
安全	2.944	0.565 0	4.116	0.039	0.044
可靠	0.88	0.169 0
平稳	1	0.191 9
高效	0.386	0.074 1

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表 7 EWM权重计算结果

Table 7. Weight result calculated by EWM

层面	指标	熵值e_i	差异系数g_i	权重w"_i
安全	制动反应时间/s	0.566	0.434	0.096
	最小碰撞时间/s	0.563	0.437	0.097
	后侵占时间/s	0.598	0.402	0.089
可靠	最大瞳孔面积/mm	0.430	0.570	0.126
可靠	单位分心指数	0.621	0.379	0.084
平稳	加速度标准差/(m/s)	0.554	0.447	0.099
平稳	车道偏移标准差/m	0.562	0.438	0.097
高效	初始速度/(km/h)	0.411	0.589	0.130
	平均速度/(km/h)	0.547	0.453	0.100
	恢复速度/(km/h)	0.619	0.381	0.084

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