视线遮挡条件下面向弱势道路使用者的避撞策略研究

吴子祥; 黄合来; 陈吉光; 郑德红; 查武平

doi:10.3963/j.jssn.1674-4861.2021.04.002

视线遮挡条件下面向弱势道路使用者的避撞策略研究

doi: 10.3963/j.jssn.1674-4861.2021.04.002

吴子祥^{1, 2,},
黄合来^{1, 2, ,},
陈吉光^{1, 2},
郑德红^{1, 2},
查武平³

1.
中南大学交通运输工程学院长沙 410075
2.
中南大学智慧交通湖南省重点实验室长沙 410075
3.
湖南省公安厅交警总队长沙 410100

基金项目:

国家自然科学基金面上项目 71971222

详细信息

作者简介:
吴子祥(1996—), 硕士研究生.研究方向: 交通安全.E-mail: 2445198602@qq.com

通讯作者:
黄合来(1979—), 博士, 教授.研究方向: 智能网联交通安全.E-mail: huanghelai@csu.edu.cn

中图分类号: U461.91
计量
- 文章访问数: 552
- HTML全文浏览量: 278
- PDF下载量: 45
- 被引次数: 0
出版历程
- 收稿日期: 2021-03-04

A Study on Collision Avoidance Strategy for Vulnerable Road Users Under Visual Obstruction

WU Zixiang^{1, 2
,},
HUANG Helai^{1, 2
, ,},
CHEN Jiguang^{1, 2},
ZHENG Dehong^{1, 2},
ZHA Wuping³

1.
School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
2.
Smart Transport Key Laboratory of Hunan Province, Central South University, Changsha 410075, China
3.
Traffic Police Corps of Public Security Department of Hunan Province, Changsha 410100, China

摘要

摘要: 由于视线障碍物造成的“鬼探头”事故已经成为当前城市道路交通事故的主要类型之一。针对汽车碰撞视线遮挡条件下横穿的弱势道路使用者(VRU)的场景, 设计了1种基于碰撞时间比和安全制动距离的避撞策略, 建立车辆与VRU的交通状态数学模型, 分析“鬼探头”场景下的制动避撞临界距离。结合临界距离和车辆与VRU的碰撞时间比, 将可以避免碰撞的场景分为3种工况, 分别采用不同的制动减速度, 建立自动紧急制动避撞策略。通过Euro NCAP CPNC测试场景对该策略与传统TTC制动算法进行比较分析。结果表明, 在Euro NCAP CPNC测试场景中, 自车利用该避撞策略在理想情况下能够在更高的车速情况下完成避撞; 在不能避免碰撞的高速行驶工况中较传统TTC算法能够更加有效降低碰撞速度, 同时降低事故重伤风险和死亡风险, 提高车辆的安全性。
- 横穿 /
- VRU /
- 视线遮挡 /
- AEB /
- 避撞策略
Abstract: The traffic accident caused by visual obstruction is one of the main types of current urban-road traffic accidents. The work proposes an autonomous emergency-brake avoidance control strategy based on the collision time ratio and the safe braking distance, aiming at the crossroad traffic accident caused by a visual obstruction between vehicle and vulnerable road users(VRU). Firstly, the traffic state model of the vehicle and VRU is established to analyze the critical distance of braking to avoid a collision. Then the collision avoidance scenarios are divided into three types with different braking deceleration speeds adopted. The autonomous emergency collision-avoidance control strategy is proposed based on the critical distance and the collision time ratio between the vehicle and VRU in this scenario. Finally, the strategy and the traditional TTC braking algorithm are analyzed by the Euro NCAP test scenario. The results show that the vehicle can avoid collisions at a higher speed under ideal circumstances using this strategy in the Euro NCAP CPNC test scenario. Compared with the traditional TTC algorithm, it can reduce the collision speed more effectively in the high-speed driving condition where collisions cannot be avoided and reduce the risk of serious injury and death to improve the safety of vehicles.
- traverse /
- VRU /
- visual obstruction /
- AEB /
- collision avoidance strategy

HTML全文

图 1 “鬼探头”场景示意图

Figure 1. Obscured VRU-vehicle crashes

下载: 全尺寸图片幻灯片

图 2 “鬼探头”场景示意图

Figure 2. Obscured VRU-vehicle crashes

下载: 全尺寸图片幻灯片

图 3 制动避撞的控制逻辑

Figure 3. Control logic of collision avoidance by braking

下载: 全尺寸图片幻灯片

图 4 CPNC行人测试场景

Figure 4. CPNC pedestrian test scenario

下载: 全尺寸图片幻灯片

图 5 纵向避撞距离

Figure 5. Required longitudinal distance for collision avoidance

下载: 全尺寸图片幻灯片

图 6 不同制动算法下的碰撞速度

Figure 6. Impact speeds with different braking algorithms

下载: 全尺寸图片幻灯片

图 7 不同制动算法下的重伤风险曲线

Figure 7. Severe injury-risk curves with different braking algorithms

下载: 全尺寸图片幻灯片

图 8 不同制动算法下的死亡风险曲线

Figure 8. Fatal injury-risk curves with different braking algorithms

下载: 全尺寸图片幻灯片

表 1 不同路面状况摩擦系数取值

Table 1. Values of friction coefficients under different road conditions

路面情况	摩擦系数取值
干燥	0.7~0.8
潮湿	0.65~0.7
冰面	0.2~0.25
积雪	0.3~0.35

下载: 导出CSV

参考文献(23)

[1]	WHO. Global status report on road safety: Time for action[R]. Geneva: World Health Organization, 2015.
[2]	CHEN Q, CHEN Y, BOSTROM O, et al. A comparison study of car-to-pedestrian and car-to-E-bike accidents: Data source: the China in-depth accident study(CIDAS)[C]. SAE 2014World Congress & amp; Exhibition, Detroit, Michigan, USA: SAE, 2014.
[3]	TREAT J R, TUMBAS N S, MCDONALD S T, et al. Tri-level study of the causes of traffic accidents: Final report. Executive summary[R]. Bloomington: Indiana University, Institute for Research in Public Safety, 1979.
[4]	EURO NCAP. Test protocol-AEB systems Version 1.1[EB/OL]. (2015-7-6)[2021-03-04]. https://www.euroncap.com/en/forengineers/protocols/safety-assist/.
[5]	何仁, 冯海鹏. 自动紧急制动(AEB)技术的研究与进展[J]. 汽车安全与节能学报, 2019, 10(1): 1-15. doi: 10.3969/j.issn.1674-8484.2019.01.001 HE Ren, FENG Haipeng. Research and development of autonomous emergency brake(AEB)technology[J]. Journal of Automotive Safety and Energy, 2019, 10(1): 1-15. (in Chinese). doi: 10.3969/j.issn.1674-8484.2019.01.001
[6]	LEE K, PENG H. Evaluation of automotive forward collision warning and collision avoidance algorithms[J]. Vehicle System Dynamics, 2005, 43(10): 735-751. doi: 10.1080/00423110412331282850
[7]	FANCHER P S, BAREKET Z, ERVIN R D, et al. Human-centered design of an Acc-with-braking and forward-crash-warning system[J]. Vehicle System Dynamics, 2001, 36(2/3): 203-223. http://www.onacademic.com/detail/journal_1000037062648010_f234.html
[8]	李霖, 贺锦鹏, 刘卫国, 等. 基于驾驶员紧急制动行为特征的危险估计算法[J]. 同济大学学报(自然科学版), 2014, 42(1): 109-114. https://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ201401019.htm LI Lin, HE Jinpeng, LIU Weiguo, et al. Threat assessment algorithm based on characteristic of driver emergency braking behavior[J]. Journal of Tongji University(Natural Science), 2014, 42(1): 109-114. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ201401019.htm
[9]	SAVINO G, BROWN J, RIZZI M, et al. Triggering algorithm based on inevitable collision states for autonomous emergency braking(AEB)in motorcycle-to-car crashes[C]. 26^thIEEE Intelligent Vehicles Symposium(Ⅳ), Seoul, South Korea: IEEE, 2015.
[10]	张立存, 苗强, 耿冬冬. 乘用车与两轮车事故特征分析和目标识别因素研究[J]. 汽车技术, 2020(3): 41-44. https://www.cnki.com.cn/Article/CJFDTOTAL-QCJS202003011.htm ZHANG Licun, MIAO Qiang, GENG Dongdong. Research on car and two-wheeler accident characteristic analysis and object detection factor[J]. Automobile Technology, 2020(3): 41-44. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-QCJS202003011.htm
[11]	SUI B, ZHOU S, ZHAO X, et al. An overview of car-to-twowheeler accidents in China: Guidance for AEB assessment[C]. 25^thInternational Technical Conference on the Enhanced Safety of Vehicles(ESV), Eindhoven, Netherlands: NHTSA, 2017.
[12]	CAO Y, XIAO L, DONG H, et al. Typical pre-crash scenarios reconstruction for two-wheelers and passenger vehicles and its application in parameter optimization of aeb system based on nais database[C]. 26^thInternational Technical Conference on the Enhanced Safety of Vehicles(ESV), Detroit, Michigan USA: NHTSA, 2019.
[13]	刘颖, 贺锦鹏, 刘卫国, 等. 自动紧急制动系统行人测试场景的研究[J]. 汽车技术, 2014(3): 35-39. doi: 10.3969/j.issn.1000-3703.2014.03.009 LIU Ying, HE Jinpeng, LIU Weiguo, et al. Research on test scenarios for AEB pedestrian system[J]. Automobile Technology, 2014(3): 35-39. (in Chinese). doi: 10.3969/j.issn.1000-3703.2014.03.009
[14]	SUI B, LUBBE N, BÄRGMAN J. A clustering approach to developing car-to-two-wheeler test scenarios for the assessment of automated emergency braking in China using in-depth Chinese crash data[J]. Accident Analysis & amp; Prevention, 2019(132): 105242. http://www.sciencedirect.com/science/article/pii/S0001457519303264
[15]	EURO NCAP. Test Protocol-AEB VRU systems Version 3.0. 3[EB/OL]. (2020-06)[2021-03-04]. https://cdn.euroncap.com/media/58226/euro-ncap-aeb-vru-test-protocol-v303.pdf.
[16]	SUN Ling, LI Yameng, GAO Jian. Architecture and application research of cooperative intelligent transport systems[J]. Procedia Engineering, 2016(137): 747-753. http://www.sciencedirect.com/science/article/pii/S1877705816003398/pdf?md5=3e174bd7813037962a9b293b6c7004b5&pid=1-s2.0-S1877705816003398-main.pdf
[17]	SERGEI KORJAGIN, PAVEL KLACHEK. Innovative development of intelligent transport systems based on biocybernetical vehicle control systems[J]. Transportation Research Procedia, 2017(20): 326-333. http://www.onacademic.com/detail/journal_1000039829808410_ee06.html
[18]	YAN Gongjun, DANDA B R. Vehicle-to-vehicle connectivity analysis for vehicular ad-hoc networks[J]. Ad Hoc Networks, 2017(58): 25-35. http://www.sciencedirect.com/science?_ob=ShoppingCartURL&_method=add&_eid=1-s2.0-S1570870516303274&originContentFamily=serial&_origin=article&_ts=1482496148&md5=6a81ac0854da3cd4e551a9e52581ec21
[19]	SHANE T, MARTIN G, EDWARD J, et al. Hybrid testbed for simulating in-vehicle automotive networks[J]. Simulation Modelling Practice and Theory, 2016(66): 193-211. http://www.onacademic.com/detail/journal_1000038935847810_86c3.html
[20]	李霖, 朱西产, 陈海林. 驾驶员制动和转向避撞极限[J]. 同济大学学报(自然科学版), 2016, 44(11): 1743-1748. doi: 10.11908/j.issn.0253-374x.2016.11.015 LI Lin, ZHU Xichan, CHEN Hailin. Drivers'collision avoidance limit by braking and steering[J]. Journal of Tongji University(Natural Science), 2016, 44(11): 1743-1748. (in Chinese). doi: 10.11908/j.issn.0253-374x.2016.11.015
[21]	MCLAUGHLIN SB. Analytic assessment of collision avoidance systems and driver dynamic performance in rear-end crashes and near-crashes[D]. Virginia: Virginia Polytechnic Institute and State University, 2008.
[22]	李霖, 朱西产, 董小飞, 等. 自主紧急制动系统避撞策略的研究[J]. 汽车工程, 2015, 37(2): 168-174. https://www.cnki.com.cn/Article/CJFDTOTAL-QCGC201502008.htm LI Lin, ZHU Xichan, DONG Xiaofei, et al. A research on the collision avoidance strategy for autonomous emergency braking system[J]. Automotive Engineering, 2015, 37(2): 168-174. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-QCGC201502008.htm
[23]	ROSÉN E, KAÄLLHAMMER J E, ERIKSSON D, et al. Pedestrian injury mitigation by autonomous braking[J]. Accident Analysis & amp; Prevention, 2010, 42(6): 1949-1957. http://www.sciencedirect.com/science?_ob=ShoppingCartURL&_method=add&_eid=1-s2.0-S0001457510001636&originContentFamily=serial&_origin=article&_ts=1477547159&md5=bf83c0465122e4d3a822857914b93ad9