Switching Control Decision of Lane-changing Model in Interweaving Areas of Mixed Traffic Flow with Human-driving and Autonomous Vehicles
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摘要: 因交织区的强制换道存在紧迫性, 车辆换道行为在交织区后半段会出现因换道意愿强烈而产生的激进换道行为, 这种微观的换道行为将给交通流带来一定影响; 在人机混驾情形下, 不同类型换道切换控制模型同样可能影响交织区通行能力。在分析人机混驾交通流交织区换道行为特性的基础上, 将换道类型分为保守型换道和激进型换道; 在可接受安全间隙模型的基础上结合自动驾驶车辆间的协同行为, 构建自动驾驶车辆在保守状态下的协同换道模型; 以及在激进型状态下考虑目标车道后车类型影响下, 构建激进型换道模型。通过分析津保立交桥实地调研轨迹数据和NGSIM中US-101交织路段轨迹数据, 分别拟合了保守型、激进型换道模型切换点分布函数; 考虑不同车辆驾驶行为特性及其相互作用, 提出人机混驾条件下换道模型切换控制逻辑决策。以SUMO仿真软件搭建实验平台, 考虑人工驾驶车辆换道模型切换点分布特性, 以优化最大流率、交织区整体车辆运行速度、换道车辆速度等为目标, 确定不同自动驾驶车辆渗透率下自动驾驶车辆的最佳保守型-激进型换道模型切换点。仿真结果显示: 在交织区长度为250 m, 自动驾驶渗透率分别为0.2, 0.5, 0.8时, 自动驾驶换道模型切换点分别在180, 80, 50 m处达到最佳, 即随着自动驾驶渗透率的提高, 换道切换点最佳位置将向交织区入口处逐渐移动, 且在自动驾驶渗透率较低时这种换道切换点的变化较为明显; 在较高渗透率下, 由于协同换道出现频率增高, 自动驾驶强制性换道行为比例降低, 换道模型切换点对交织区通行能力的影响逐渐变小。本项研究对人机混驾条件下高速公路交织区自动驾驶车辆的换道控制提供决策依据Abstract: Due to the urgency of forced lane change in weaving areas, lane-changing behaviors occur in the second half of a weaving segment due to a strong desire to change lanes, which will have a certain impact on traffic flow. In a situation of mixed traffic flow with human-driving and autonomous vehicles, different lane change control models can affect the capacity of weaving areas. Based on analyze the characteristics of lane-changing behaviors in the weaving areas with the mixed traffic flow, they are divided into two types: conservative lane-changing and radical lane-changing. Based on an acceptable safety gap model and the cooperative behavior among autonomous vehicles, a cooperative lane changing model for autonomous vehicles in a conservative state is constructed; and the radical lane change model under the influence of the vehicle type behind the target lane in the radical state. By analyzing the track data from the field survey of Jinbao Interchange and the track data of the US-101 weaving area in NGSIM, the distribution functions of switching points of conservative and radical lane changing models are fitted, respectively; Considering the characteristics of different vehicle driving behaviors and their interactions, the logic decision of lane change model switching control under the condition of the mixed traffic flow is proposed. The SUMO simulation software is used to develop an experimental platform. Considering the distribution characteristics of the switching points of the lane-changing model of the manual vehicles, and aiming at optimizing the maximum flow rate, the overall vehicle running speed in the weaving area, and the speed of the lane-changing vehicles, the optimal conservative-aggressive lane changing model switching points of the autonomous vehicles under different penetration rates of the autonomous vehicles are determined. The simulation results show that when the length of the weaving area is 250 m and the penetration rate of autonomous vehicles is 0.2, 0.5, 0.8, the switching point of automatic lane-changing model reach the best at 180, 80, and 50 m respectively, with the increase of the penetration rate of autonomous vehicles, the best position of the lane change switching point will gradually move towards the entrance of the weaving segment, and the change of this lane change switching point is more obvious when the penetration rate of autonomous vehicles is low; At higher permeability, due to the increased frequency of cooperative lane-changing, the proportion of autonomous vehicle forced lane changing behavior decreases, and the impact of lane-changing model switching points on the capacity of weaving area gradually decreases. This study provides a basis for lane change control decisions of autonomous vehicles in freeway weaving areas under the condition of mixed traffic flow.
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图 7 不同渗透率下换道模型切换点与最大流率分布图
Figure 7. Distribution of starting points of radical lane change under different mixing rates
图 8 不同渗透率下换道模型切换点与平均速度关系图
Figure 8. Switching point and average velocity under different permeability distribution of lane changing model
图 9 不同渗透率下换道模型切换点与换道平均速度关系图
Figure 9. The relationship between the switching point of the lane-changing model and the average speed of the lane-changing under different permeability
表 1 高斯分布参数表
Table 1. Gauss distribution parameter
类型 津保立交桥 US-101 xm σm xm σm 主路-匝道保守 62.55 8.17 121.01 9.57 主路-匝道激进 79.52 14.93 147.56 18.59 匝道-主路保守 55.33 10.54 96.28 7.75 匝道-主路激进 67.68 5.51 108.36 8.82 
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表 2 高斯分布均值
Table 2. Gauss distribution mean
类型 xm 主路-匝道保守 0.93 × l - 76.65 主路-匝道激进 0.99 × l - 82.5 匝道-主路保守 0.65 × l - 42.2 匝道-主路激进 0.64 × l - 29.2 注：l为交织区长度。
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表 3 仿真参数表
Table 3. Simulation parameter
道路参数 车辆参数 参数 取值 参数 人工驾驶 自动驾驶 交织区长度/m 250 最大速度/(m/s) 33 33 车身长度/m 5 加速度/(m/s2) 1.4 2 交织流量比 0.2 减速度/(m/s2) 2 2 交织比 0.3 反应时间/s 1.5 1 主路车辆初始速度/(m/s) 28 最小停车间隙/m 3 2 匝道车辆初始速度/(m/s) 17 车头时距/s 1.5 1
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表 4 不同渗透率下自动驾驶最佳模型切换点
Table 4. optimal model switching points of automatic driving under different permeability
自动驾驶渗透率 最大流率/(m/s) 平均速度/(m/s) 换道平均速度/(m/s) 最佳模型切换点/m 0.1 200 210 210 210 0.2 190 180 180 180 0.3 150 130 140 140 0.4 130 120 120 120 0.5 90 80 80 80 0.6 80 70 70 70 0.7 80 60 60 60 0.8 60 50 50 50 0.9 50 50 50 50 1.0 30 20 30 30
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[1] BOSE A, IOANNOU P. Mixed manual/semi-automated traffic: Amacroscopic analysis[J]. Transportation Research Part C: Emerging Technologies, 2003, 11(6): 439-462. doi: 10.1016/j.trc.2002.04.001 [2] KANARIS A, KOSMATOPOULOS E B, LOANNOU P A. Strategies and spacing requirements for lane changing and merging in automated highway systems[J]. IEEE Transactions on Vehicular Technology, 2001, 50(6): 1568-1581. doi: 10.1109/25.966586 [3] 黄玲, 郭亨聪, 张荣辉, 等. 人机混驾环境下基于LSTM的无人驾驶车辆换道行为模型[J]. 中国公路学报, 2020, 33(7): 156-166. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202007016.htmHUANG L, GUO HC, ZHANG RH, et al. Lane changing behavior model of driverless vehicle based on LSTM in man-machine mixed driving environment[J]. China Journal of Highway and Transport, 2020, 33(7): 156-166. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202007016.htm [4] 孟鑫. 混合车流环境下城市快速路交织区仿真研究[D]. 长春: 吉林大学, 2019.MENG X. Simulation research on urban expressway weaving area under mixed traffic flow environment[D]. Changchun: Jilin University, 2019. (in Chinese) [5] LIU Y, GUO J, TAPLIN J, et al. Characteristic analysis of mixed traffic flow of regular and autonomous vehicles using cellular automata[J]. Journal of Advanced Transportation, 2017(2017): 8142074. [6] 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. Study on lane changing model of intelligent networked vehicles in mixed flow environment[D]. ChangChun: Jilin University, 2020. (in Chinese) [7] 刘有军, 曹珊. 基于元胞自动机的强制换道模型研究[J]. 交通信息与安全, 2009, 27(3): 78-80. doi: 10.3963/j.cn.42-1781.U.2009.03.020LIU Y J, CAO S. Study on forced lane change model based on cellular automata[J]. Journal of Transport Information and Safety, 2009, 27(3): 78-80. (in Chinese) doi: 10.3963/j.cn.42-1781.U.2009.03.020 [8] 彭博, 王玉婷, 谢济铭, 等. 城市干线短交织区元胞自动机多级换道决策模型[J]. 交通运输系统工程与信息, 2020, 20(4): 41-48+70. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202004007.htmPENG B, WANG Y T, XIE J M, et al. Cellular automata multi-level lane change decision model for urban trunk short weaving area[J]. Transportation System Engineering and Information, 2020, 20(4): 41-48+70. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202004007.htm [9] HAO W, ZHANG Z, GAO Z, et al. Research on mandatory lane-changing behavior in highway weaving sections[J]. Journal of Advanced Transportation, 2020(2020): 3754062. [10] 曹珊. 城市道路车辆换道模型及换道影响研究[D]. 武汉: 华中科技大学, 2009.CAO S. Research on vehicle lane changing model and lane change impact on urban roads[D]. Wuhan: Huazhong University of Science and Technology, 2009. (in Chinese) [11] 邓建华, 冯焕焕, 葛婷. 多车道元胞自动机换道决策模型的冲突处理策略[J]. 交通运输系统工程与信息, 2019, 19(4): 50-54. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201904008.htmDENG J H, FENG H H, GE T. Conflict handling strategy for multi-lane cellular automata lane changing decision model[J]. Transportation Systems Engineering and Information, 2019, 19(4): 50-54. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201904008.htm [12] GONG S, DU L. Optimal location of advance warning for mandatory lane change near a two-lane highway off-ramp[J]. Transportation Research Part B: Methodological, 2016(84): 1-30. [13] 钟异莹, 陈坚, 邵毅明, 等. 强制性换道的空间特征对分流区交通流的影响[J]. 交通运输系统工程与信息, 2020, 20(5): 114-120. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202005017.htmZHONG Y Y, CHEN J, SHAO Y M, et al. Impact of spatial characteristics of mandatory lane change on traffic flow in diversion area[J]. Transportation System Engineering and Information, 2020, 20(5): 114-120. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202005017.htm [14] 许伦辉, 倪艳明, 罗强, 等. 基于最小安全距离的车辆换道模型研究[J]. 广西师范大学学报(自然科学版), 2011, 29(4): 1-6. https://www.cnki.com.cn/Article/CJFDTOTAL-GXSF201104002.htmXU L H, NI Y M, LUO Q, et al. Research on vehicle lane changing model based on minimum safety distance[J]. Journal of Guangxi Normal University(Natural Science Edition), 2011, 29(4): 1-6. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GXSF201104002.htm [15] 聂建强. 高速公路车辆自主性换道行为建模研究[D]. 南京: 东南大学.NIE J Q. Modeling of autonomous lane changing behavior of expressway vehicles[D]. Nanjing: Southeast University. (in Chinese) [16] Al-JAMEEL H A E. Empirical features of weaving sections[C]. 10th International Postgraduate Research Conference(IPGRC), Manchester, UK: University of Salford, 2011. [17] MARCZAK F, DAAMEN W, BUISSON C. Empirical analysis of lane changing behavior at a freeway weaving section[C]. 93rd Annual Meeting of the Transportation Research Board, Washington, DC. 2014. [18] 崔居福, 胡本旭, 夏辉, 等. SUMO平台下多种车辆跟驰模型的仿真对比分析[J]. 重庆大学学报, 2021, 44(7): 43-54+98. https://www.cnki.com.cn/Article/CJFDTOTAL-FIVE202107005.htmCUI J F, HU B X, XIA H, et al, Simulation and comparative analysis of various vehicle following models under SUMO platform[J]. Journal of Chongqing University, 2021, 44(7): 43-54+98. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-FIVE202107005.htm [19] 邓红星, 胡翼, 王猛. 考虑前车加速度信息的改进IDM模型研究[J]. 重庆理工大学学报(自然科学), 2022, 36(5): 226-232. https://www.cnki.com.cn/Article/CJFDTOTAL-CGGL202205028.htmDENG H X, HU Y, WANG M. Research on an improved IDM model considering the acceleration information of the preceding vehicle[J]. Journal of Chongqing University of Technology(Natural Science), 2022, 36(5): 226-232. https://www.cnki.com.cn/Article/CJFDTOTAL-CGGL202205028.htm [20] 王雪松, 孙平, 张晓春, 等. 基于自然驾驶数据的高速公路跟驰模型参数标定[J]. 中国公路学报, 2020, 33(5): 132-142. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202005012.htmWANG X S, SUN P, ZHANG X C, et al. Parameter calibration of expressway car following model based on natural driving data[J]. China Journal of Highway and Transport, 2020, 33(5): 132-142. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202005012.htm -
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