城市道路中间路段人车冲突中网联预警信息对驾驶行为的影响

王长帅; 邵永成; 朱彤; 焦彦利; 徐铖铖

doi:10.3963/j.jssn.1674-4861.2024.04.009

城市道路中间路段人车冲突中网联预警信息对驾驶行为的影响

doi: 10.3963/j.jssn.1674-4861.2024.04.009

王长帅^{1, 2,},
邵永成¹,
朱彤³,
焦彦利⁴,
徐铖铖^1, ,

1.
东南大学交通学院南京 201196
2.
蒙纳士大学土木工程学院墨尔本 3168
3.
长安大学运输工程学院西安 710064
4.
河北省交通规划设计研究院石家庄 050011

基金项目:

国家自然科学基金项目 52172343

江苏省杰出青年基金项目 BK20211515

国家重点研发计划项目 2019YFE0108000

详细信息

作者简介:
王长帅(1995—)，博士研究生. 研究方向：交通安全、驾驶模拟. E-mail: 230208831@seu.edu.cn

通讯作者:
徐铖铖(1987—)，博士，教授. 研究方向：智能网联与自动驾驶. E-mail：xuchengcheng@seu.edu.cn

中图分类号: U491.6
计量
- 文章访问数: 41
- HTML全文浏览量: 19
- PDF下载量: 6
- 被引次数: 0
出版历程
- 收稿日期: 2024-05-10
- 网络出版日期: 2024-11-25

Impacts of Connected Warning Information on Driver Behavior in Pedestrian-vehicle Conflict at the Mid-block of Urban Roads

1.
School of Transportation, Southeast University, Nanjing 211189, China
2.
Department of Civil Engineering, Monash University, Melbourne 3168, Australia
3.
College of Transportation Engineering, Chang'an University, Xi'an 710064, China
4.
Hebei Provincial Communications Planning, Design and Research Institute, Shijiazhuang, 050011, China

摘要

摘要: 本文旨在研究网联预警信息对城市道路人车冲突中驾驶人行为的影响。利用驾驶模拟器设计城市道路驾驶场景与网联预警信息系统，考虑不同类型的视觉盲区，构建了6个典型的行人与机动车冲突场景。将招募的70名驾驶人划分为实验组和对照组，进行驾驶模拟器实验，采集实验过程中的驾驶人行为和车辆轨迹数据。利用生存分析法研究了不同因素对冲突过程中驾驶人避让反应时间与制动持续时间的影响，并建立面向冲突过程的人车事故风险预测模型，评估网联预警信息对驾驶行为的影响。结果表明：由公交车、路侧停车及树木与车辆等导致的视觉盲区会降低驾驶绩效，增加冲突过程中驾驶人的避让反应时间，降低制动持续时间；人行横道的存在使平均避让反应时间减少0.90 s，使平均制动持续时间增加0.41 s，并降低人车事故风险；网联预警信息可使驾驶人的平均避让反应时间减少0.52 s，平均制动持续时间增加0.40 s，使制动过程更加平稳，显著降低冲突过程中的事故风险，保障行人安全。
- 交通安全 /
- 网联预警信息 /
- 人车冲突 /
- 生存分析 /
- 模拟器研究 /
- 驾驶行为
Abstract: This study investigates the impact of connected warning information on driver behavior during pedestri-an-vehicle conflicts on urban roads. Using a driving simulator, urban driving scenarios and connected warning infor-mation systems are designed, incorporating various types of visual blind areas to create six typical pedestrian-vehi-cle conflicts. Seventy participants are recruited and divided into experimental and control groups to complete the simulator tests, during which driver behavior and vehicle trajectory data are collected. Survival analysis is employed to examine the effects of different factors on drivers' reaction times and braking durations during conflicts. Addi-tionally, pedestrian-vehicle crash prediction models are developed to assess crash risk and evaluate the influence of connected warning information on driver behavior. Results indicated that blind spots caused by buses, trees and cars, and parked cars negatively impacted driver performance, resulting in longer reaction times and shorter braking durations. Contrarily, the presence of crosswalks reduced the mean avoidance reaction time by 0.90 s, increased the mean braking duration by 0.41 s, and lowered pedestrian-vehicle crash risk. Furthermore, connected warning infor-mation is found to positively affect driver behavior, reducing mean avoidance reaction times by 0.52 s and increas-ing braking duration by 0.40 s. This leads to smoother braking processes and significantly decreased crash risks, thereby enhancing pedestrian safety.
- traffic safety /
- connected warning information /
- pedestrian-vehicle conflict /
- survival analysis /
- simulator research /
- driving behavior

HTML全文

图 1 驾驶模拟试验平台

Figure 1. Driving simulation test platform

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图 2 典型的人车冲突场景

Figure 2. Typical pedestrian-vehicle conflict scenarios

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图 3 网联预警信息冲突场景

Figure 3. Conflict scenarios with connected warning information

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图 4 不同因素对避让反应时间的影响

Figure 4. Various factors' impacts on avoidance reaction time

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图 5 不同因素对制动持续时间的影响

Figure 5. Various factors' impacts on braking duration

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表 1 参与者信息

Table 1. Information of participants

属性	类别	频数	均值	标准差
性别	男	50
性别	女	20
年龄/岁		70	25.27	1.79
驾龄/年	新手驾驶人	36	2.47	0.50
驾龄/年	经验驾驶人	34	5.91	1.54

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表 2 冲突场景的描述

Table 2. Descriptions of the Conflict Scenarios

序号	视觉盲区类别	人行横道	场景描述
1	公交车	不存在	驾驶人的视野被右侧车道进站停靠的公交车遮挡，路侧的行人突然从公交车前方走出，引发交通冲突，见图 2(a)
2	路侧停车	不存在	路侧停车遮挡了驾驶人的视野，当车辆接近停车区域时，儿童从停放的车辆间冲出，形成交通冲突，见图 2(b)
3	树木	存在	车辆接近人行横道，行人从人行横道处过街，中央隔离带的树木遮挡了驾驶人的视野，形成交通冲突，见图 2(c)
4	树木和车辆	存在	左侧车道上行驶的车辆驾驶人发现了过街的行人，在人行横道前停车让行，让行的车辆与道路中央的树木遮挡了驾驶人的视野，产生交通冲突，见图 2(d)
5	树木和车辆	不存在	在左侧车道上行驶的车辆驾驶人发现了过街的行人，并及时停车让行，让行的车辆与道路中央的树木遮挡了驾驶人的视野，产生交通冲突，见图 2(e)
6	树木	不存在	行人从道路中间的树木中走出，在中间车道行驶的车辆驾驶员的视野被树木遮挡，产生交通冲突，见图 2(f)

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表 3 变量的定义及描述

Table 3. Definitions and descriptions of the variables

变量	定义及取值
性别	0：女性；1：男性
驾龄/年	0：≤3；1： > 3
驾驶风格	1：保守型；2：普通型；3：激进型
视觉障碍物类别	0：树木；1：公交车；2：路侧停车；3：树木与车辆
是否存在人行横道	0：不存在；1：存在
避让反应时间/s	行人触发时刻与驾驶人首次采取避让措施时刻的时间间隔
制动持续时间/s	制动过程的持续时间
最大制动减速度/(m/s²)	制动过程中的最大制动减速度
减速度标准差/(m/s²)	制动过程中减速度的标准差
被试组别	0：对照组；1：实验组
是否发生人车事故	0：无事故；1：发生事故

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表 4 平均制动持续时间与避让反应时间

Table 4. Mean braking duration and avoidance reaction time

变量名称	水平	制动持续时间/s	避让反应时间/s
被试组别	对照组	2.61	2.80
被试组别	实验组	3.01	2.28
是否存在人行横道	不存在	2.67	2.84
是否存在人行横道	存在	3.08	1.94
视觉障碍物类别	树木	3.14	1.86
	公交车	2.29	3.41
	路侧停车	2.56	3.10
	树木与车辆	2.86	2.50
驾驶风格	激进型	2.34	2.67
	普通型	2.85	2.51
	保守型	2.97	2.51

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表 5 避让反应时间的估计结果

Table 5. Estimated results of avoidance reaction time

变量类别	参数值	标准差	P值
是否存在人行横道	-0.151	0.028	< 0.001
被试组别	-0.256	0.032	< 0.001
公交车	0.517	0.038	< 0.001
路侧停车	0.392	0.037	< 0.001
树木与车辆	0.254	0.028	< 0.001
截距	0.932	0.043	< 0.001
ln(μ)	1.518	0.043
似然比检验	χ²(1) =12.91, p=0.000 2
样本量	420

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表 6 制动持续时间的估计结果

Table 6. Estimated results of brake duration

变量类别	参数值	标准差	P值
驾驶风格(激进型)	-0.214	0.056	< 0.001
被试组别	0.067	0.038	0.040
公交车	-0.162	0.043	< 0.001
路侧停车	-0.108	0.041	0.009
最大制动减速度	-0.040	0.013	0.002
减速度标准差	-0.041	0.016	0.010
截距	1.502	0.073	< 0.001
ln(μ)	1.429	0.044
似然比检验	χ²(1) =21.16, p= < 0.001
样本量	420

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表 7 Logit模型估计结果

Table 7. Estimated results of the Logit model

变量类别	参数值	标准差	P值
性别	-1.602	0.598	0.007
驾驶风格(激进型)	1.296	0.699	0.044
避让反应时间	1.674	0.436	< 0.001
制动持续时间	-0.959	0.348	0.006
是否存在人行横道	-2.737	1.218	0.025
公交车	2.273	1.249	0.049
被试组别	-1.664	0.599	0.005
截距	-6.062 5	2.011	0.001
LL(β)	-68.009	R_pse²	0.568
样本量	420

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