车辆碰撞预警系统对行车风险的干预效果

许甜; 高健强; 刘建蓓; 赵超杰; 刘国图

doi:10.3963/j.jssn.1674-4861.2022.01.018

车辆碰撞预警系统对行车风险的干预效果

doi: 10.3963/j.jssn.1674-4861.2022.01.018

1.
中交第一公路勘察设计研究院有限公司西安 710065
2.
同济大学交通运输工程学院上海 200092

基金项目:

国家重点研发计划项目 2017YFC0803900

陕西省青年科技新星项目 2020KJXX-046

详细信息

通讯作者:
许甜（1987—），硕士，高级工程师. 研究方向：交通安全、智能交通. E-mail: 77459997@qq.com

中图分类号: U491.6U676.1
计量
- 文章访问数: 944
- HTML全文浏览量: 409
- PDF下载量: 42
- 被引次数: 0
出版历程
- 收稿日期: 2021-09-30
- 网络出版日期: 2022-03-31

A Field Study for Evaluating the Effectiveness of Vehicle Collision Warning Systems

1.
CCCC First Highway Consultants Co., Ltd, Xi'an 710065, China
2.
College of Transportation Engineering, Tongji University, Shanghai 200092, China

摘要

摘要:
各类碰撞预警系统已广泛应用于具有驾驶辅助功能的车辆上，为研究预警系统对风险状态下车辆交互行为特征的影响机理，并评估其对行车风险的干预效果，采用15辆搭载预警系统的试验车辆在真实高速公路场景下进行实车群组试验，通过有-无预警情形对比试验及分析，从车辆交互行为特征指标、道路总体运行风险、驾驶员对预警系统认可度3个维度，对行车风险干预效果进行定量化综合评估。试验结果表明：微观层面，有预警情形下，跟驰、超车换道2类行车安全事件下的车头时距均值分别增加了0.37 s和0.34 s，方差分析结果显示预警系统开闭状态对车头时距有显著影响（p＜0.05）；中观层面，试验路段2类行车安全事件频数分别下降了16.0%和23.7%，试验车辆群组在路段上的运行速度分布离散性显著降低；调查问卷显示试验人员中，86.7%在接收到预警信息后会采取趋于安全的措施，73.3%非常认同预警系统对道路交通安全提升的积极作用。
- 交通安全 /
- 车辆碰撞预警 /
- 风险干预效果评估 /
- 实车试验 /
- 高精度定位
Abstract:
Advanced driver assistance systems have been widely installed on various types of vehicles. To study the effectiveness of the collision warning systems, a field test was conducted on real-world expressways. A group of 15 vehicles underwent a comparative test equipped with or without the system. During the testing, a method for evaluating the effectiveness of the early warning system has been proposed from the following three aspects: interactions between vehicles, safety risk, and acceptance of the driver. Study results show that, at the microscopic level, under the scenarios that the vehicles are equipped with the system, when accidents take place during car-following and lane-changing for overtaking action, the average time headway (THW) observed increases by 0.37 s and 0.34 s, respectively. It is also found that using the system has a significant impact on the THW (p < 0.05). In contrast, at the mesoscopic level, the frequency of the above two types of accidents drops by 16.0% and 23.7%, respectively, as a result of early warning provided by the system. The dispersion degree of the vehicle speed in the group is also found to decrease significantly. The questionnaire survey shows that 86.7% of drivers will take safety measures after receiving the warningfrom the system, and 73.3% of drivers agree that the early warning system improves road safety.
- road safety /
- vehicle collision warning /
- effective evaluation /
- field test /
- high precision positioning

HTML全文

图 1 预警系统工作流程示意图

Figure 1. Diagram of early warning system workflow

下载: 全尺寸图片幻灯片

图 2 车载预警终端及平台

Figure 2. On board early warning terminal and platform

下载: 全尺寸图片幻灯片

图 3 试验路段及行驶路径

Figure 3. Test section and driving paths

下载: 全尺寸图片幻灯片

图 4 搜索算法几何模型

Figure 4. Geometric model of search algorithm

下载: 全尺寸图片幻灯片

图 5 后车的超车换道过程示意

Figure 5. Schematic diagram of overtaking and lane changing process of rear vehicles

下载: 全尺寸图片幻灯片

图 6 CIEs事件提取流程图

Figure 6. Flow Chart of Extracting CIEs

下载: 全尺寸图片幻灯片

图 7 有、无预警情形下减速跟驰行为车头时距分布

Figure 7. Headway distribution of deceleration following behavior warning and no-warning

下载: 全尺寸图片幻灯片

图 8 有、无预警情形下超车换道行为车头时距分布

Figure 8. Headway distribution of overtaking behavior warning and no-warning

下载: 全尺寸图片幻灯片

图 9 有、无预警情形下CIEs车头时距箱型对比图

Figure 9. Comparison of cies headway box type of warning and no-warning

下载: 全尺寸图片幻灯片

图 10 有、无预警情形下试验路段车辆断面运行速度分布图（500 m间隔断面）

Figure 10. Comparison of speed distribution in test section of warning and no-warning(500 m interval section)

下载: 全尺寸图片幻灯片

图 11 问卷调查统计结果

Figure 11. Statistical results of questionnaire survey

下载: 全尺寸图片幻灯片

表 1 试验车辆驾驶任务分配

Table 1. Driving tasks assignment of test vehicles

车辆编号	任务描述	起终点	试验里程/km
1~4	①运行速度由100 km/h降至70 km/h，单程执行10次 ②在龙门服务区驶出再汇入 ③在龙门互通驶出主线，在收费站掉头后等待群组返程时从匝道汇入	蓝田互通~龙门互通	49
5~15	①单程执行6次超车任务 ②在各互通出入口2 km预告标志处需执行1次换道任务	蓝田互通~龙江互通	60

下载: 导出CSV

表 2 采集原始数据与预处理数据一览表

Table 2. List of original data and preprocessed data

原始数据		预处理数据
终端数据	道路数据	车路映射关系	车车交互关系
车辆ID时间戳		与道路中心线距离/m	前后车相对速度/（km/h）后车加速度/（m/s²）前后车车头时距/s
坐标（x y）	道路中心线坐标（x y）	与起始桩号距离/m
速度/（km/h）	里程桩号	与道路中心线夹角/（°）
方向角/（°）		断面速度/（km/h）

下载: 导出CSV

表 3 2类典型CIEs定义及提取准则

Table 3. Definition and judgment of two types of cies

CIEs	定义	提取准则
减速跟驰	与前车车头时距小于5 s，后车减速跟随行驶，或换道失败跟随行驶	车头时距小于5 s行驶车道保持不变前后车辆满足式（2）前后车车头时距持续减小直到最小的瞬间
超车换道	与前车车头时距小于5 s，后车换道并超过前车继续行驶	换道前车头时距小于5 s前后车辆满足式（3）后车向相邻车道换道离开原车道的瞬间

下载: 导出CSV

表 4 CIEs提取结果汇总情况

Table 4. Summary of CIEs Extractions

CIE类型	CIEs/组
CIE类型	无预警状态	有预警状态
减速跟驰	382	321
超车换道	219	167
总计	601	488

下载: 导出CSV

表 5 预警状态对2类行车安全事件THW影响分析

Table 5. Analysis results of the influence of FCW working state on THW of the two typical CIEs

CIEs 类型	因子	均值/s （标准差）	d.f.	F	p
减速跟驰	预警状态		2	3.983	0.046^*
	无	1.562（1.139）
	有	1.931（1.199）
换道超车	预警状态		2	3.882	0.049^*
	无	2.181（1.063）
	有	2.522（1.095）
注：^*表示p < 005

下载: 导出CSV

参考文献(21)

[1]	张旭欣, 王雪松, 马勇, 等. 驾驶行为与驾驶风险国际研究进展[J]. 中国公路学报, 2020, 33(6): 1-17. doi: 10.3969/j.issn.1001-7372.2020.06.001 ZHANG X X, WANG X S, MA Y, et al. International research progress on driving behavior and driving risks[J]. China Journal of Highway and Transport, 2020, 33(6): 1-17. (in Chinese) doi: 10.3969/j.issn.1001-7372.2020.06.001
[2]	HANEEN F, KOUTSOPOULOS H N. Do cooperative systems make drivers' car-following behavior safer?[J]. Transportation Research Part C: Emerging Technologies, 2014, 41 (1): 61-72. http://www.sciencedirect.com/science/article/pii/s0968090x14000291
[3]	MOHEBBI R, GRAY R, TAN H Z. Driver reaction time to tactile and auditory rear-end collision warnings while talking on a cell phone[J]. Human Factors, 2009, 51(2): 102-110. http://hfs.sagepub.com/content/51/1/102.full.pdf?cited-by=yes&legid=sphfs;51/1/102
[4]	REINMUELLER K, STEINHAUSER M. Adaptive forward collision warning: the impact of imperfect technology on behavioral adaptation, warning effectiveness and acceptance[J]. Accident Analysis & Prevention, 2019, 128(7): 217-229. http://www.sciencedirect.com/science/article/pii/S0001457518311655
[5]	ADELL E, VARHEL A, FONTANA M D. The effects of a driver assistance system for safe speed and safe distance: A real-life field study[J]. Transportation Research Part C: Emerging Technologies, 2011, 19(1): 145-155. doi: 10.1016/j.trc.2010.04.006
[6]	SPICER R, VAHABAGHAIE A. Field effectiveness evaluation of advanced driver assistance systems[J/OL]. (2018-12-3) https://doi.org/10.1080/15389588.2018.1527030
[7]	CICCHINO J B. Effects of lane departure warning on police-reported crash rates[J]. Journal of Safety Research, 2018, 66(9): 61-70. http://www.onacademic.com/detail/journal_1000040387311210_766c.html
[8]	LI G F, LI S B. Field operational test of advanced driver assistance systems in typical Chinese road conditions: the influence of driver gender, age and aggression[J]. International Journal of Automotive Technology, 2015, 16(5): 739-750. doi: 10.1007/s12239-015-0075-5
[9]	杨曼, 吴超仲, 张晖, 等. 行车安全事件的驾驶风险影响因素研究[J]. 交通信息与安全, 2018, 5(36): 34-39. doi: 10.3963/j.issn.1674-4861.2018.05.005 YNAG M, WU C Z, ZHANG H, et al. Influencing factors of driving risk based on critical incident events[J]. Journal of Transport Information and Safety, 2018, 36(56): 34-39. (in Chinese) doi: 10.3963/j.issn.1674-4861.2018.05.005
[10]	WANG J Q, ZHANG L, LI K Q. An adaptive longitudinal driving assistance system based on driver characteristics[J]. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(1): 1-12. doi: 10.1109/TITS.2012.2205143
[11]	王雪松, 朱美新, 陈铭. 工况紧急程度对驾驶员避撞行为的影响[J]. 同济大学学报(自然科学版), 2016, 44(6): 876-883. https://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ201606009.htm WANG X S, ZHU M X, CHEN M. Impacts of situational urgency on drivers' collision avoidance behaviors[J]. Journal of Tongji University(Natural Science), 2016, 44(6): 876-883. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ201606009.htm
[12]	吕能超, 郑梦凡, 郝威, 等. 基于客观风险感知特性的前向碰撞预警算法优化与标定[J]. 交通运输工程学报, 2020, 20 (2): 105-113. https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC202002014.htm LYU N C, ZHENG M F, HAO W, et al. Forward collision warning algorithm optimization and calibration based on objective risk perception characteristic[J]. Journal of Traffic and Transportation Engineering, 2020, 20(2): 105-113. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC202002014.htm
[13]	吴明先, 许甜, 刘建蓓, 等. 基于高频高精度定位信息的车辆轮廓冲突瞬时预测方法[J]. 中国公路学报, 2019, 32(6): 105-113. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201906012.htm WU M X, XU T, LIU J B, et al. Instantaneous prediction of vehicle outlines conflict using the high-frequency and high-precision position information[J]. China Journal of Highway and Transport, 2019, 32(6): 105-113. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201906012.htm
[14]	潘兵宏, 王晓东, 孙元琪. 路线外一点对应路线中桩算法研究[J]. 交通信息与安全, 2014, 32(1): 80-84. doi: 10.3963/j.issn.1674-4861.2014.01.016 PAN B H, WANG X D, SUN Y Q. Research on algorithm of route center stake corresponding to a point outside the route[J]. Journal of Transport Information and Safety, 2014, 32(1): 80-84. (in Chinese) doi: 10.3963/j.issn.1674-4861.2014.01.016
[15]	吴斌, 朱西产, 沈剑平. 基于自然驾驶数据的驾驶员紧急制动行为特征[J]. 同济大学学报(自然科学版), 2018, 46 (11): 1514-1519. doi: 10.11908/j.issn.0253-374x.2018.11.007 WU B, ZHU X C, SHEN J P. Driver emergency braking behavior based on naturalistic driving data[J]. Journal of Tongji University(Natural Science), 2018, 46(11): 1514-1519. (in Chinese) doi: 10.11908/j.issn.0253-374x.2018.11.007
[16]	吴斌, 朱西产, 沈剑平, 等. 自然驾驶工况的驾驶员紧急转向变道行为[J]. 同济大学学报(自然科学版), 2017, 45(4): 554-561. https://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ201704014.htm WU B, ZHU X C, SHEN J P, et al. Analysis of driver emergency steering lane changing behavior based on naturalistic driving data[J]. Journal of Tongji University(Natural Science), 2017, 45(4): 554-561. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ201704014.htm
[17]	刘有军, 李可, 余俊等. 四车道高速公路跟驰换道模型与参数机理[J]. 中国公路学报, 2014, 27(12): 96-105. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201412016.htm LIU Y J, LI K, YU J, et al. Model and parameter incentive of four lane expressway following and changing lanes[J]. China Journal of Highway and Transport, 2014, 27(12): 96-105. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201412016.htm
[18]	贺玉龙, 刘磊, 迟佳欣. 高速公路车辆换道行为风险研究[J]. 重庆交通大学学报(自然科学版), 2021, 40(4): 26-33. https://www.cnki.com.cn/Article/CJFDTOTAL-CQJT202104005.htm HE Y L, LIU L, CHI J X. Risk of vehicle lane change behavior on highway[J]. Journal of Chongqing Jiaotong University (Natural Science), 2021, 40(4): 26-33. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CQJT202104005.htm
[19]	WU K F, AGUERO-VALVERDE J, JOVANIS P P. Using naturalistic driving data to explore the association between traffic safety-related events and crash risk at driver level[J]. Accident Analysis & Prevention, 2014, 72(11): 218. http://www.researchgate.net/profile/Paul_Jovanis/publication/264430361_Using_naturalistic_driving_data_to_explore_the_association_between_traffic_safety-related_events_and_crash_risk_at_driver_level/links/553a7bc30cf29b5ee4b64c80.pdf
[20]	FENG G, FANG Y J. Individual driver risk assessment using naturalistic driving data[J]. Accident Analysis & Prevention, 2013, 61(12): 3-9. http://www.onacademic.com/detail/journal_1000036031112110_c716.html
[21]	何民, 荣建, 任福田. 判定跟驰状态的研究[J]. 公路交通科技, 2001, 18(4): 74-78. https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK200104020.htm HE M, RONG J, REN F T. Research on following state determination[J]. Science and Technology of Highway Traffic, 2001, 18(4): 74-78. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK200104020.htm