[1]郝 威,张兆磊,吴其育,等.网联自动驾驶车辆下匝道换道决策模型[J].交通运输工程学报,2023,23(05):242-252.[doi:10.19818/j.cnki.1671-1637.2023.05.017]
　HAO Wei,ZHANG Zhao-lei,WU Qi-yu,et al.Lane-changing decision model of connected and automated vehicles driving off ramp[J].Journal of Traffic and Transportation Engineering,2023,23(05):242-252.[doi:10.19818/j.cnki.1671-1637.2023.05.017]
点击复制

网联自动驾驶车辆下匝道换道决策模型()
分享到：

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

卷:
第23卷
期数:
2023年05期
页码:
242-252
栏目:
交通信息工程及控制
出版日期:
2023-11-10

文章信息/Info

Title:
Lane-changing decision model of connected and automated vehicles driving off ramp
文章编号:
1671-1637(2023)05-0242-11
作者:
郝 威; 张兆磊; 吴其育; 易可夫
(长沙理工大学 智能道路与车路协同湖南省重点试验室,湖南 长沙 410114)
Author(s):
HAO Wei;  ZHANG Zhao-lei;  WU Qi-yu;  YI Ke-fu
(Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha University of Science and Technology, Changsha 410114, Hunan, China)
关键词:
网联自动驾驶车辆;  换道决策模型;  成本函数;  混合交通流;  数值仿真;  CAV渗透率
Keywords:
CAV;  lane-changing decision model;  cost function;  mixed traffic flow;  numerical simulation;  CAV penetration rate
分类号:
U491.2
DOI:
10.19818/j.cnki.1671-1637.2023.05.017
文献标志码:
A
摘要:
为宏观刻画自动驾驶专用车道上的网联自动驾驶车辆(CAV)下匝道的行为,提出了混合交通流下基于安全风险的CAV下匝道换道决策模型; 该模型将换道间隙选择过程抽象为成功换道或不成功换道的伯努利试验,并在此基础上建立了基于交通流理论的车辆换道成功率计算方法; 提出了耦合换道安全与效率的下匝道换道决策成本函数,其中安全与效率的权重参数根据不同的驾驶模式确定,从而确定CAV最优的换道意图生成点,为CAV换道提供指令。数值分析结果表明:CAV下匝道成功率由换道准备距离、交通需求和CAV渗透率共同决定,成本函数随着CAV渗透率的变化出现明显的拐点; 交通量为2 400 veh?h-1时,CAV的最佳换道意图生成点为距离下匝道入口1 km处; 当交通量增加至4 000 veh?h-1时,最佳换道意图生成点为距离下匝道入口2.5 km处; 当交通量大于6 400 veh?h-1时,需要提高CAV的侵略性才能高效驶出高速公路; 成本函数随着CAV渗透率的增大先下降再升高,若渗透率低于拐点渗透率,则增加换道准备距离可以降低成本函数,若渗透率高于拐点渗透率,则需通过减小换道准备距离降低成本函数。仿真结果表明:交通需求和渗透率对车辆下匝道的安全性影响显著,渗透率由30%提升至60%,碰撞时间最大降幅为76.23%。
Abstract:
A safety risk-based lane-changing decision model of connected and automated vehicles(CAVs)driving off the ramp in mixed traffic flow was proposed to macroscopically characterize the behaviors of CAVs driving off the ramp in the CAV lane. The model abstracted the lane-changing gap selection process into Bernoulli experiments of successful or unsuccessful lane-changing, and a lane-changing success rate formula was set up based on the traffic flow theory. A driving off ramp lane-changing decision cost function considering lane-changing safety and efficiency was proposed, in which the weight parameters of safety and efficiency were determined based on different driving modes, so as to determined the optimal lane-changing intention generation point for CAVs and provided instructions for CAV lane-changing. Numerical analysis results show that the success rate of CAV driving off the ramp is determined by the preparation distance of lane-changing, traffic demand, and CAV penetration rate. The cost function has an obvious inflection point with the change of CAV penetration rate. When the traffic volume is 2 400 veh?h-1, the optimal lane-changing intention generation point for CAVs is 1 km away from the entrance of the off-ramp. When the traffic volume increases to 4 000 veh?h-1, the optimal lane-changing intention generation point is 2.5 km away from the entrance of the off-ramp. When the traffic volume is greater than 6 400 veh?h-1, the CAV needs to increase aggressiveness to drive off the highway efficiently. The cost function first decreases and then increases as the CAV penetration rate increases. If the penetration rate is lower than the inflection point penetration rate, the cost function can be reduced by increasing the preparation distance of lane-changing. If the penetration rate is higher than the inflection point penetration rate, reducing the preparation distance of lane-changing is necessary to reduce the cost function. Simulated results show that the traffic safety of driving off the ramp significantly influenced by the traffic demand and CAV penetration rate. The time-to-collision reduces to 76.23%,with the penetration rate increasing from 30% to 60%. 2 tabs, 8 figs, 31 refs.

参考文献/References:

[1] 邱小平,马丽娜,周小霞,等.基于安全距离的手动—自动驾驶混合交通流研究[J].交通运输系统工程与信息,2016,16(4):101-108,124.
QIU Xiao-ping, MA Li-na, ZHOU Xiao-xia, et al. The mixed traffic flow of manual-automated driving based on safety distance[J]. Journal of Transportation Systems Engineering and Information Technology, 2016, 16(4): 101-108, 124.(in Chinese)
[2] MOHAMED A A, WU Y N, MOATZ S. Safety and operational impact of connected vehicles' lane configuration on freeway facilities with managed lanes[J]. Accident Analysis and Prevention, 2020, 144: 105616.
[3] CHANG Xin, LI Hai-jian, RONG Jian, et al. Analysis on traffic stability and capacity for mixed traffic flow with platoons of intelligent connected vehicles[J]. Physica A: Statistical Mechanics and Its Applications, 2020,557: 124829.
[4] 吴 兵,王文璇,李林波,等.多前车影响的智能网联车辆纵向控制模型[J].交通运输工程学报,2020,20(2):184-194.
WU Bing, WANG Wen-xuan, LI Lin-bo, et al. Longitudinal control model for connected autonomous vehicles influenced by multiple preceding vehicles[J]. Journal of Traffic and Transportation Engineering, 2020,20(2): 184-194.(in Chinese)
[5] 常 鑫,李海舰,荣 建,等.混有网联车队的高速公路通行能力分析[J].华南理工大学学报(自然科学版),2020,48(4):142-148.
CHANG Xin, LI Hai-jian, RONG Jian, et al. Analysis of capacity for mixed traffic flow with connected vehicle platoon on freeway[J]. Journal of South China University of Technology(Natural Science Edition), 2020, 48(4): 142-148.(in Chinese)
[6] VANDER L Z, SADABADI K F. Operational performance of a congested corridor with lanes dedicated to autonomous vehicle traffic [J]. International Journal of Transportation Science and Technology, 2017, 6(1): 42-52.
[7] XIAO L, WANG M, VAN AREM B. Traffic flow impacts of converting an HOV lane into a dedicated CACC lane on a freeway corridor[J]. IEEE Intelligent Transportation Systems Magazine, 2020, 12(1): 60-73.
[8] LI T, GUO F, KRISHNAN R, et al. Right-of-way reallocation for mixed flow of autonomous vehicles and human driven vehicles[J]. Transportation Research Part C: Emerging Technologies, 2020, 115: 102630.
[9] ZHONG Z J, LEE J. Dedicated lane for connected and automated vehicle: how much does a homogeneous traffic flow contribute?[J]. arXiv, https:∥doi.org/10.48550/arXiv.1907.00422.
[10] 魏修建,胡荣鑫,苏 航,等.双车道自动-手动驾驶汽车混合交通流博弈模型及其仿真[J].系统工程,2018,36(11):97-104.
WEI Xiu-jian, HU Rong-xin, SU Hang, et al. Mixed traffic flow game model and simulation of automatic and manual driving vehicle in two-lane condition[J]. Systems Engineering, 2018, 36(11): 97-104.(in Chinese)
[11] GHIASI A, HUSSAIN O, QIAN Z, et al. A mixed traffic capacity analysis and lane management model for connected automated vehicles: a Markov chain method[J]. Transportation Research Part B: Methodological, 2017, 106: 266-292.
[12] GHIASI A, HUSSAIN O, QIAN Z, et al. Lane management with variable lane width and model calibration for connected automated vehicles[J]. Journal of Transportation Engineering, Part A: Systems, 2020, 146(3): 04019075.
[13] YE L H, YAMAMOTO T. Impact of dedicated lanes for connected and autonomous vehicle on traffic flow throughput[J]. Physica A: Statistical Mechanics and Its Applications, 2018, 512: 588-597.
[14] MOHAJERPOOR R, RAMEZANI M. Mixed flow of autonomous and human-driven vehicles: analytical headway modeling and optimal lane management[J]. Transportation Research Part C: Emerging Technologies, 2019, 109: 194-210.
[15] MAHMASSANI H S. 50th anniversary invited article—autonomous vehicles and connected vehicle systems: flow and operations considerations[J]. Transportation Science, 2016, 50(4): 1140-1162.
[16] CHEN D J, AHN S, CHITTURI M, et al. Towards vehicle automation: roadway capacity formulation for traffic mixed with regular and automated vehicles[J]. Transportation Research Part B: Methodological, 2017, 100: 196-221.
[17] CHEN Z B, HE F, ZHANG L H, et al. Optimal deployment of autonomous vehicle lanes with endogenous market penetration[J]. Transportation Research Part C: Emerging Technologies, 2016, 72: 143-156.
[18] ZHANG J, WU K R, CHENG M, et al. Safety evaluation for connected and autonomous vehicles' exclusive lanes considering penetrate ratios and impact of trucks using surrogate safety measures[J]. Journal of Advanced Transportation, 2020, 2020: 1-16.
[19] ZHONG Z J, LEE J. The effectiveness of managed lane strategies for the near-term deployment of cooperative adaptive cruise control[J]. Transportation Research Part A: Policy and Practice, 2019, 129: 257-270.
[20] TALEBPOUR A, MAHMASSANI H S, ELFAR A. Investigating the effects of reserved lanes for autonomous vehicles on congestion and travel time reliability[J]. Transportation Research Record, 2017, 2622(1): 1-12.
[21] 赵 鑫.基于元胞自动机的联网车辆专用车道设置研究[D].哈尔滨:哈尔滨工业大学,2020.
ZHAO Xin. Research on dedicated lane setting of networked vehicles based on cellular automata[D]. Harbin: Harbin Institute of Technology, 2020.(in Chinese)
[22] 董长印,王 昊,王 炜,等.混入智能车的下匝道瓶颈路段交通流建模与仿真分析[J].物理学报,2018,67(14):144501.
DONG Chang-yin, WANG Hao, WANG Wei, et al. Hybrid traffic flow model for intelligent vehicles exiting to off-ramp[J]. Acta Physica Sinica, 2018, 67(14): 144501.(in Chinese)
[23] DONG C Y, WANG H, LI Y, et al. Route control strategies for autonomous vehicles exiting to off-ramps[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(7): 3104-3116.
[24] YANG D, JIA B, DAI L, et al. Optimization model for the freeway-exiting position decision problem of automated vehicles[J]. Transportation Research Part B: Methodological, 2022, 159: 24-48.
[25] MILANÉS V, SHLADOVER S E. Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data[J]. Transportation Research Part C: Emerging Technologies, 2014, 48: 285-300.
[26] VANDERWERF J, SHLADOVER S, KOURJANSKAIA N, et al. Modeling effects of driver control assistance systems on traffic[J]. Transportation Research Record, 2001, 1748(1): 167-174.
[27] 郑施雨.自动驾驶车辆换道过程建模与分析[D].成都:西南交通大学,2018.
ZHENG Shi-yu. Modeling analysis of lane-changing process of autonomous vehicles[D]. Chengdu: Southwest Jiaotong University, 2018.(in Chinese)
[28] 陈 娇.城市快速路车头时距特性分析[D].长春:吉林大学,2012.
CHEN Jiao. Analysis of headway characteristics of urban expressway[D]. Changchun: Jilin University, 2012.(in Chinese)
[29] 秦严严,王 昊,王 炜,等.混有CACC车辆和ACC车辆的异质交通流基本图模型[J].中国公路学报,2017,30(10):127-136.
QIN Yan-yan, WANG Hao, WANG Wei, et al. Fundamental diagram model of heterogeneous traffic flow mixed with cooperative adaptive cruise control vehicles and adaptive cruise control vehicles[J]. China Journal of Highway and Transport, 2017, 30(10): 127-136.(in Chinese)
[30] TREIBER M, HENNECKE A, HELBING D. Congested traffic states in empirical observations and microscopic simulations[J]. Physical Review E, 2000, 62(2): 1805-1824.
[31] ERDMANN J. SUMO's lane-changing model[C]∥BEHRISCH M, WEBER M. Modeling Mobility with Open Data. Berlin: Springer, 2015: 105-123.

备注/Memo

备注/Memo:
收稿日期:2023-05-14
基金项目:国家重点研发计划(2022YFC3803700); 国家自然科学基金项目(52172339,52002036); 湖南省科技创新计划项目(2023RC1059,2023SK2052,2022WZ1011); 湖南省自然科学基金项目(2021JJ40577); 湖南省研究生科研创新项目(CX20220852); 长沙市科技计划项目(kh2202002,kh2301004); 湖南省教育厅科学研究项目(20B009)
作者简介:郝 威(1983-),男
更新日期/Last Update: 2023-11-10