车路协同下避让紧急车辆协同换道策略

郝威; 梁聪; 张兆磊; 吕能超; 易可夫

doi:10.3963/j.jssn.1674-4861.2022.04.010

车路协同下避让紧急车辆协同换道策略

doi: 10.3963/j.jssn.1674-4861.2022.04.010

1.
长沙理工大学交通运输工程学院长沙 410114
2.
武汉理工大学智能交通系统研究中心武汉 430063
3.
长沙理工大学汽车与机械工程学院长沙 410114

基金项目:

国家自然科学基金项目 52172339

国家自然科学基金项目 52002036

湖南省科技厅重点研发计划项目 2020SK2098

湖南省科技创新团队项目 2020RC4048

湖南省教育厅重点项目 19A014

长沙理工大学研究生科研创新项目 CXCLY2022007

详细信息

通讯作者:
郝威（1983—），博士，研究员. 研究方向：交通信息工程及控制、交通安全等. E-mail: haowei@csust.edu.cn

中图分类号: U491.255
计量
- 文章访问数: 1134
- HTML全文浏览量: 424
- PDF下载量: 52
- 被引次数: 0
出版历程
- 收稿日期: 2022-05-22
- 网络出版日期: 2022-09-17

A Cooperative Lane Changing Strategy to Give Way to Emergency Vehicles with the Cooperative Vehicle Infrastructure System

1.
School of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, China
2.
Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China
3.
School of Automotive and Mechanical Engineering, Changsha University of Science & Technology, Changsha 410114, China

摘要

摘要: 为加快紧急车辆抵达事故现场的速度，同时减少紧急车辆优先权对其他车辆的影响，运用车路协同系统，提出避让紧急车辆协同换道策略，通过调整紧急车辆下游车辆位置，实现紧急车辆高效通过路段。以紧急车辆前车（DV）及其相邻目标车道车辆为控制对象，根据相邻车道车辆间距与车车通信范围，搜索DV可换道空间间隙集。以交通流整体恢复稳定时间最小为目标，确定DV换道轨迹和相邻车道协作车辆的速度变化，引导车辆完成协同合流，既能保障车辆安全换道，还能降低换道造成的速度振荡传递。同时，为快速恢复DV换道造成的目标车道车辆速度波动，对上游车辆（UV）采取先进先出规则的换道控制策略。所提协同避让紧急车辆的策略考虑了车辆协同换道对交通流的整体影响，并在原有换道策略的基础上提出了减少速度波动传递的控制方法。案例分析结果表明：采用上下游协同换道策略最短换道时间为6s，此时紧急车辆距前车78.66 m时发送避让信号。同时研究发现，恢复交通流速度稳定所需的时间为29 s，比未采用上下游协同换道策略降低了34%。
- 交通工程 /
- 紧急避让 /
- 协同换道策略 /
- 车路协同
Abstract: A cooperative lane-changing strategy is proposed to reduce the time loss when emergency vehicles approaching to accident scenes, and reduce the impacts of emergency vehicle priority on other vehicles with the cooperative vehicle infrastructure system (CVIS). By adjusting the position of the downstream vehicles (DV), the emergency vehicles can efficiently pass the road sections. The DV and vehicles in the adjacent target lane are taken as control objects. A space-gap set for lane-changing of DV is determined according to the spacing and communication range of vehicles in adjacent lanes. Further, aiming to minimize the overall recovery time of traffic flow, the lane-changing trajectory of DV and the speed change of cooperative vehicles in the adjacent lanes are determined to guide vehicles to complete cooperative merging. It can not only guarantee the safety of the lane-changing behaviors, but also reduce the transmission of speed fluctuations caused by the lane-changing behaviors. For upstream vehicles (UV), the rule of first-in-first-out (FIFO) is used to reduce the time for recover from the speed fluctuations caused by lane-changing behaviors of DV. Considering the impacts of lane-changing on the traffic flow, a control method to reduce the transmission of speed fluctuations is proposed based on a classic lane changing strategy. The results of a case study show that the shortest lane-changing time is 6 s when using the proposed cooperative lane-changing strategy, and the corresponding distance of sending emergency signal is 78.66 m. Meanwhile, the results show that the time required to restore stability of speed is 29 s, which is 34% shorter than that without the cooperative lane-changing strategy.
- traffic engineering /
- emergency avoidance /
- cooperative lane-changing strategy /
- cooperative vehicle infrastructure system

HTML全文

图 1 车辆避让紧急车辆的多车协同换道场景示意

Figure 1. Cooperative lane changing of vehicle avoiding emergency vehicle

下载: 全尺寸图片幻灯片

图 2 避让紧急车辆协同换道流程图

Figure 2. Flow chart of avoiding emergency vehicle coordinated lane changing

下载: 全尺寸图片幻灯片

图 3 车辆换道的安全距离示意

Figure 3. Safe distance of lane changing

下载: 全尺寸图片幻灯片

图 4 上游车辆行驶规则（FIFO）

Figure 4. Upstream vehicle driving rules(FIFO)

下载: 全尺寸图片幻灯片

图 5 下游车辆行驶规则（枚举法）

Figure 5. Downstream vehicle driving rules(enumeration method)

下载: 全尺寸图片幻灯片

图 6 不同策略车辆协同换道示意图

Figure 6. Diagram of vehicle cooperative lane changing under different strategies

下载: 全尺寸图片幻灯片

图 7 恢复稳定所需时间示意图

Figure 7. Diagram of the time requires to restore stability

下载: 全尺寸图片幻灯片

图 8 不同策略下平均速度-时间图

Figure 8. Average speed-time diagram under different strategies

下载: 全尺寸图片幻灯片

图 9 速度方差图

Figure 9. Velocity variance plot

下载: 全尺寸图片幻灯片

图 10 策略2车辆轨迹示意图

Figure 10. Vehicle trajectory diagram of strategy 2

下载: 全尺寸图片幻灯片

图 11 平均速度-时间示意图

Figure 11. Diagram of average speed-time

下载: 全尺寸图片幻灯片

表 1 不同策略下的参数信息

Table 1. Parameter information under different strategies

换道策略	换道时间/s	紧急车辆平均速度/(m/s)	恢复稳定所需时间/s	发送避让信号位置/m
策略1	12	16	36	108.66
策略2	6	16	29	78.66
策略3	16	16	30	128.66
策略4	28	16	28	188.66

下载: 导出CSV

参考文献(25)

[1]	黄秋阳. 基于多源数据的城市路网状态监测与交通事故风险评估方法研究[D]. 长春: 吉林大学, 2021. HUANG Q Y. Research on urban road-network condition monitoring and traffic accident risk inference method based on multi-source data[D]. Changchun: Jilin University, 2021. (in Chinese)
[2]	唐嘉佳, 韩印, 张俊, 等. 应急车辆信号优先控制策略研究[J]. 物流工程与管理, 2017, 39(1): 87-88+79. https://www.cnki.com.cn/Article/CJFDTOTAL-SPCY201701034.htm TANG J J, HAN Y, ZHANG J, et al. Study of the emergency traffic signal preemption[J]. Logistics Engineering and Management, 2017, 39(1): 87-88+79. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-SPCY201701034.htm
[3]	杨兆升, 孙晓梅, 孙鹏程. 非常态事件下应急车辆单点信号优先控制策略及实现方法[J]. 吉林大学学报(工学版), 2011, 41(3): 640-644. https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY201103008.htm YANG Z S, SUN X M, SUN P C. Signal priority control strategy and implementation for emergency vehicle at single intersection under traffic accidents[J]. Journal of Jilin University (Engineering and Technology Edition), 2011, 41(3): 640-644. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY201103008.htm
[4]	陈壮壮, 罗莉华. 网联自动驾驶车辆通过信号交叉口的速度轨迹优化[J]. 交通信息与安全, 2021, 39(4): 92-98+156. doi: 10.3963/j.jssn.1674-4861.2021.04.012 CHEN Z Z, LUO L H. Speed trajectory optimization of connected autonomous vehicles at signalized intersections[J]. Journal of Transport Information and Safety, 2021, 39(4): 92-98+156. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2021.04.012
[5]	HE Q, HEAD K L, DING J. Multi-modal traffic signal control with priority, signal actuation and coordination[J]. Transportation Research Part C: Emerging Technologies, 2014(46): 65-82.
[6]	席嘉鹏. 城市交通事故紧急救援车辆的路径决策与优先控制方法研究[D]. 广州: 华南理工大学, 2020. XI J P. Research on route decision and priority control of emergency vehicles in urban traffic accidents[D]. Guangzhou: South China University of Technology, 2020. (in Chinese)
[7]	霍云霄. 信号交叉口应急车辆优先通行协调控制研究[D]. 西安: 长安大学, 2020. HUO Y X. Research on coordinated control of emergency vehicle priority at signalized intersection[D]. Xi'an: Chang'an University, 2020. (in Chinese)
[8]	HAGHANI A, HU H, TIAN Q. An optimization model for real-time emergency vehicle dispatching and routing[J]. Transportation Research Record, 2004(1): 76-83.
[9]	宗传苓, 李相勇, 王英涛. 出行前路径选择的多目标规划模型[J]. 交通运输系统工程与信息, 2005(6): 58-61. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT200506016.htm ZONG C L, LI X Y, WANG Y T. A multi-objective programming model of route choice before travel[J]. Journal of Transportation Systems Engineering and Information Technology, 2005(6): 58-61. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT200506016.htm
[10]	张艺还. 车路协同环境下突发事件救援车辆路径规划算法研究[D]. 北京: 北京交通大学, 2019. ZHANG Y H. Research on route planning algorithm for emergency rescue vehicles under the environment of connected vehicle[D]. Bejing: Beijing Jiaotong University, 2019. (in Chinese)
[11]	WU J, KULCSÁR B, AHN S, et al. Emergency vehicle lane pre-clearing: From microscopic cooperation to routing decision making[J]. Transportation Research Part B: Methodological, 2020(141): 223-239.
[12]	田勇达. 混流环境下智能网联车辆换道模型研究[D]. 长春: 吉林大学, 2020. TIAN Y D. Lane changing model of connected autonomous vehicle in mixed flow environment[D]. Changchun: Jilin University, 2020. (in Chinese)
[13]	祁宏生, 应雨燕, 林俊山, 等. 混合自动驾驶场景多换道需求下的主动间隙适配和换道序列规划[J]. 交通运输工程与信息学报, 2021, 19(4): 36-51. https://www.cnki.com.cn/Article/CJFDTOTAL-JTGC202104004.htm QI H S, YING Y Y, LIN J S, et al. Proactive gap adaption and sequence planning for multiple lane-changing requests under mixed autonomous vehicle flow[J]. Journal of Traffic and Transportation Engineering, 2021, 19(4): 36-51. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JTGC202104004.htm
[14]	张家旭, 张振兆, 赵健, 等. 采用极点配置的自动驾驶汽车换道路径规划与跟踪控制[J]. 西安交通大学学报, 2020, 54 (10): 160-167. https://www.cnki.com.cn/Article/CJFDTOTAL-XAJT202010020.htm ZHANG J X, ZHANG Z Z, ZHAO J, et al. A path planning and tracking control method for lane changing of autonomous vehicle using pole assignment[J]. Journal of Xi'an Jiaotong University, 2020, 54(10): 160-167. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XAJT202010020.htm
[15]	KATZOURAKIS D, DE WINTER J C F, DE GROOT S, et al. Driving simulator parameterization using double-lane change steering metrics as recorded on five modern cars[J]. Simulation Modelling Practice and Theory, 2012 (26): 96-112.
[16]	ZHU S, AKSUN-GUVENC B. Trajectory planning of autonomous vehicles based on parameterized control optimization in dynamic on-road environments[J]. Journal of Intelligent & Robotic Systems, 2020, 100(4): 1055-1067.
[17]	张新锋, 陈建伟, 左思. 基于贝塞尔曲线的智能商用车换道避障轨迹规划[J]. 科学技术与工程, 2020, 20(29): 12 150-12157. https://www.cnki.com.cn/Article/CJFDTOTAL-KXJS202029047.htm ZHANG X F, CHEN J W, ZUO S. Trajectory planning for intelligent commercial vehicle obstacle avoidance based on Quartic B'ezier Curve[J]. Science Technology and Engineering, 2020, 20(29): 12150-12157. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-KXJS202029047.htm
[18]	XIE D F, FANG Z Z, JIA B, et al. A data-driven lane-changing model based on deep learning[J]. Transportation Research Part C: Emerging Technologies, 2019(106): 41-60.
[19]	XU H, ZHANG Y, CASSANDRAS C G, et al. A bi-level cooperative driving strategy allowing lane changes[J]. Transportation Research Part C: Emerging Technologies, 2020 (120): 102773.
[20]	焦朋朋, 杨紫煜, 洪玮琪, 等. 车路协同下车队避让紧急车辆的换道引导方法[J]. 中国公路学报, 2021, 34(7): 95-104. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202107008.htm JIAO P P, YANG Z Y, HONG W Q, et al. Lane changing guidance method for vehicle platoon to avoid emergency vehicles of CVIS[J]. China Journal of Highway and Transport, 2021, 34(7): 95-104. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202107008.htm
[21]	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): 172-183. 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): 172-183. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC202002014.htm
[22]	HAO W. Development of a safety prediction method for arterial roads based on big-data technology and stacked autoencoder-gated recurrent unit[R/OL]. (2022-05)[2022-08-10]. https://ieeexplore.ieee.org/abstract/document/9774974.
[23]	赵坡, 吴戈, 王翔, 等. 基于数据驱动的城市快速路段拥堵状态辨识及诱导对象选择方法[J]. 交通信息与安全, 2021, 39(6): 82-90. doi: 10.3963/j.jssn.1674-4861.2021.06.010 ZHAO P, WU G, WANG X, et al. A data-driven method for identifying congestion state and selecting guided vehicles for urban expressways[J]. Journal of Transport Information and Safety, 2021, 39(6): 82-90. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2021.06.010
[24]	YAO J, ZHANG K, YANG Y, et al. Emergency vehicle route oriented signal coordinated control model with two-level programming[J]. Soft Computing, 2018(22): 4283-4294.
[25]	张新锋, 夏八科, 张甫城. 基于V2V的高速换道轨迹多目标规划[J]. 江苏大学学报(自然科学版), 2020, 41(2): 131-137. https://www.cnki.com.cn/Article/CJFDTOTAL-JSLG202002002.htm ZHANG X F, XIA B K, ZHANG F C. Multi-objective Planning of highspeed lane changing trajectory based on V2V[J]. Journal of Jiangsu University(Natural Science Edition), 2020, 41(2): 131-137. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSLG202002002.htm