基于安全势场理论的高速公路交通事故下协同换道引导策略

doi:10.16265/j.cnki.issn1003-3033.2024.07.0069

摘要/Abstract

摘要：

为缓解交通事故对道路通行效率的影响,提高事故路段的通过能力,利用车路协同系统中车与车、车与路的实时信息交互,提出一种高速公路突发交通事故下基于安全势场理论的车辆协同换道引导策略。首先,针对单向双车道和多车道等不同车道下发生交通事故的多种场景,将协同换道的引导区域划分为事故保护区、引导过渡区、协同换道引导区和自由换道区4个区域,并确定换道引导阈值,以便更新车辆状态;然后,构建交通事故的安全势场,针对不同场景提出相应的车辆协同换道引导策略,并给出引导过程中车辆换道安全距离和最晚换道位置的计算方法;最后,基于城市交通仿真 (SUMO)软件进行多种场景下的仿真验证。结果表明:在单向双车道场景下,当车辆协同引导率在75%时优化效果最明显;在多车道场景下,引导率在50%时的优化效果最明显;此外,通过对比分析发现,采用换道引导策略后,车辆通过事故路段的平均速度最高提升了6.3%,车辆延误最高减少了14.6%。

关键词: 安全势场理论, 高速公路, 交通事故, 协同换道, 引导策略

Abstract:

In order to mitigate the impact of traffic accidents on the operation efficiency of freeways, and improve the throughput capacity of the accident area, based on real-time information via vehicle-to-vehicle and vehicle-to-road, a cooperative lane change guidance strategy in freeway traffic accident areas was proposed with the safety potential field theory. Firstly, in view of various traffic accidents in different lanes in two-lane or multi-lane traffic of one-way, the guidance area of collaborative lane change was divided into four areas: accident protection area, guidance transition area, collaborative lane change guidance area and free lane change area. The guidance threshold of lane change was determined to update vehicle status. Furthermore, the safety potential field of traffic accidents was established, and the corresponding guidance strategies of vehicle cooperative lane change were proposed according to different scenarios. The calculation methods of the safe distance for vehicle lane change and the latest lane change position were given. Finally, based on simulation of urban mobility (SUMO) software, the simulation results were verified in various scenarios. The results show that in the two-lane of one-way scenario, the optimization effect is most obvious when the vehicle cooperative guidance rate is at 75%. In the multi-lane of one-way scenario, the optimization effect is most obvious when the guidance rate is at 50%. Meanwhile, through comparative analysis, it is found that the average speed of vehicles passing through the accident section is increased by up to 6.3% and the maximum vehicle delay is reduced by 14.6% after adopting the lane change guidance strategy.

Key words: safety potential field theory, freeway, traffic accident, collaborative lane change, guidance strategy

中图分类号:

姚佼, 杨承逸, 杨媛媛, 李俊杰, 朱笑笑. 基于安全势场理论的高速公路交通事故下协同换道引导策略[J]. 中国安全科学学报, 2024, 34(7): 71-82.

YAO Jiao, YANG Chengyi, YANG Yuanyuan, LI Junjie, ZHU Xiaoxiao. Cooperative lane change guidance strategy in freeway traffic accident area based on safety potential field theory[J]. China Safety Science Journal, 2024, 34(7): 71-82.

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参考文献 19

[1]	MATSUBARA T, IGARASHI S, AKIYAMA T. Vehicle driving assistance system: U.S.A. 9,718,46B2 [P].2017-08-01.
[2]	RAMYAR S, HOMAIFAR A, SALAKEN S M, et al. A personalized highway driving assistance system[C]. IEEE Intelligent Vehicles Symposium, 2017: 1596-1601.
[3]	陈慧, 王洁新. 基于驾驶人不满度的高速公路自动驾驶换道决策[J]. 中国公路学报, 2019, 32(12):1-9,45. doi: 10.19721/j.cnki.1001-7372.2019.12.001
	CHEN Hui, WANG Jiexin. A decision-making method for lane changes of automated vehicles on freeways based on drivers' dissatisfaction[J]. China Journal of Highway and Transport, 2019, 32(12):1-9,45. doi: 10.19721/j.cnki.1001-7372.2019.12.001
[4]	龚思远, 赵璐, 武亚龙, 等. 考虑车车与车路通讯并存的路侧单元优化布设方法[J]. 中国公路学报, 2022, 35(10):226-243. doi: 10.19721/j.cnki.1001-7372.2022.10.020
	GONG Siyuan, ZHAO Lu, WU Yalong, et al. Road-side unit optimal layout scheme by considering coexisting of V2I and V2V communication[J]. China Journal of Highway and Transport, 2022, 35(10):226-243. doi: 10.19721/j.cnki.1001-7372.2022.10.020
[5]	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. doi: 10.19721/j.cnki.1001-7372.2021.07.007
	JIAO Pengpeng, YANG Ziyu, HONG Weiqi, 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. doi: 10.19721/j.cnki.1001-7372.2021.07.007
[6]	潘兵宏, 王烨. 基于双曲正切函数的小客车换道轨迹模型[J]. 江苏大学学报:自然科学版, 2020, 41(4):419-425.
	PAN Binghong, WANG Ye. Car lane changing trajectory model based on hyperbolic tangent function[J]. Journal of Jiangsu University: Natural Science Edition, 2020, 41(4):419-425.
[7]	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):12150-12 157.
	ZHANG Xinfeng, CHEN Jianwei, ZUO Si. Trajectory planning for intelligent commercial vehicle obstacle avoidance based on quartic B'ezier curve[J]. Science Technology and Engineering, 2020, 20(29):12150-12 157.
[8]	李珣, 马文哲, 赵征凡, 等. 车路协同下基于行车指引的改进STCA双车道换道模型[J]. 东南大学学报:自然科学版, 2020, 50(6):1134-1142.
	LI Xun, MA Wenzhe, ZHAO Zhengfan, et al. Improved STCA lane changing model for two-ane road based on driving guidance under CVIS[J]. Journal of Southeast University: Natural Science Edition, 2020, 50(6):1134-1142.
[9]	曲大义, 黑凯先, 郭海兵, 等. 车联网环境下车辆换道博弈行为及模型[J]. 吉林大学学报:工学版, 2022, 52(1):101-109.
	QU Dayi, HEI Kaixian, GUO Haibing, et al. Game behavior and model of lane-changing on the internet of vehicles environment[J]. Journal of Jilin University: Engineering and Technology Edition, 2022, 52(1):101-109.
[10]	NIE Jianqiang, ZHANG Jian, DING Wanting, et al. Decentralized cooperative lane-changing decision-making for connected autonomous vehicles[J]. IEEE Access, 2016, 4:9413-9420.
[11]	LOMBARD A, ABBAS-TURKI A, EL-MOUDNI A. V2V-based memetic optimization for improving traffic efficiency on multilane roads[J]. IEEE Intelligent Transportation Systems Magazine, 2020, 12(1): 35-46.
[12]	冯嵩, 钱宇彬, 曲现国, 等. 基于安全距离的雾天车辆换道轨迹模型[J]. 中国安全科学学报, 2022, 32(7):165-171. doi: 10.16265/j.cnki.issn1003-3033.2022.07.1182
	FENG Song, QIAN Yubin, QU Xianguo, et al. Vehicle lane-changing trajectory model in foggy weather based on safetydistance[J]. China Safety Science Journal, 2022, 32(7):165-171. doi: 10.16265/j.cnki.issn1003-3033.2022.07.1182
[13]	王长帅, 徐铖铖, 邵永成, 等. 基于生存分析的自动驾驶接管时间分析[J]. 中国安全科学学报, 2023, 33(8):142-148. doi: 10.16265/j.cnki.issn1003-3033.2023.08.1474
	WANG Changshuai, XU Chengcheng, SHAO Yongcheng, et al. Analysis of takeover time of automated driving based on survival analysis[J]. China Safety Science Journal, 2023, 33(8):142-148. doi: 10.16265/j.cnki.issn1003-3033.2023.08.1474
[14]	BYRNE S, NAEEM W, FERGUSON S. Improved APF strategies for dual-arm local motion planning[J]. Transactions of the Institute of Measurement & Control, 2015, 37(1):73-90.
[15]	WOLF M T, BURDICK J W. Artificial potential functions for highway driving with collision avoid-ance[C]. IEEE International Conference on Robotics and Automation, 2008:3731-3736.
[16]	MA Yanli, ZHANG Peng, HU Baoyu. Active lane-changing model of vehicle in B-type weaving region based on potential energy field theory[J]. Physica A: Statistical Mechanics and its Applications, 2019, 535:1-11.
[17]	王树凤, 张钧鑫, 张俊友. 基于人工势场和虚拟领航者的智能车辆编队控制[J]. 上海交通大学学报, 2020, 54(3):305-311.
	WANG Shufeng, ZHANG Junxin, ZHANG Junyou. Intelligent vehicles formation control based on artificial potential field and virtual leader[J]. Journal of Shanghai Jiaotong University, 2020, 54(3):305-311.
[18]	LI Chenggang, JIANG Xiaobei, WANG Wuhong, et al. A simplified car following model based on the artificial potential field[J]. Procedia Engineering, 2016, 137: 13-20.
[19]	张小丽, 傅永腾, 喻言, 等. 基于安全势场的超车模型研究[J]. 交通节能与环保, 2022, 18(6):49-54.
	ZHANG Xiaoli, FU Yongteng, YU Yan, et al. Research on overtaking model based on safety potential field[J]. Transport Energy Conservation & Environmental Protection, 2022, 18(6):49-54.

引导率/ %	单向2车道
	事故发生在内侧车道		事故发生在外侧车道
	速度/(m·s^-1)	延误/s	速度/(m·s^-1)	延误/s
0	13.97(↑0.0%)	3.54(↓0.0%)	13.87(↑0.0%)	3.76(↓0.0%)
25	13.95(↓1.4%)	3.62(↑2.3%)	13.75(↓0.8%)	3.82(↑1.6%)
50	13.62(↓2.4%)	3.52(↓2.8%)	13.91(↑1.2%)	3.73(↓2.4%)
75	14.21(↑4.3%)	3.21(↓8.8%)	14.32(↑2.9%)	3.32(↓11.0%)
100	14.52(↑2.2%)	3.02(↓5.9%)	14.37(↑0.3%)	3.28(↓1.2%)

引导率/ %	单向多车道
	事故发生在外侧车道		事故发生在中间车道		事故发生在内侧车道
	速度/(m·s^-1)	延误/s	速度/(m·s^-1)	延误/s	速度/(m·s^-1)	延误/s
0	13.80(↑0.0%)	3.7(↓0.0%)	13.94(↑0.0%)	3.6(↓0.0%)	13.95(↑0.0%)	3.4(↓0.0%)
25	13.65(↓1.1%)	3.82(↑0.1%)	14.11(↑1.2%)	3.59(↓1.6%)	14.09(↑0.4%)	3.52(↑2.9%)
50	14.16(↑3.7%)	3.56(↓6.8%)	14.18(↑0.5%)	3.51(↓2.2%)	14.41(↑2.3%)	3.30(↓6.2%)
75	14.48(↑2.3%)	3.45(↓3.0%)	14.46(↑2.0%)	3.34(↓4.8%)	14.43(↑0.1%)	3.21(↓2.7%)
100	14.56(↑0.5%)	3.38(↓2.0%)	14.52(↑0.4%)	3.29(↓1.5%)	14.51(↑0.5%)	3.18(↓0.9%)

车辆状态	事故发生在内侧车道		事故发生在外侧车道
车辆状态	未采用换道策略	采用换道策略	未采用换道策略	采用换道策略
开始引导的位置/m	—	120	—	120
引导车辆的平均速度/(m·s^-1)	13.55	14.40	13.81	14.35
行程时间/s	80	71	85	73
延误/s	3.54	3.02	3.76	3.23

车辆状态	事故发生在外侧车道		事故发生在中间车道		事故发生在内侧车道
车辆状态	未采用换道策略	采用换道策略	未采用换道策略	采用换道策略	未采用换道策略	采用换道策略
开始引导的位置/m	—	150	—	150	—	150
引导车辆的平均速度/(m·s^-1)	13.80	14.48	13.94	14.46	13.95	14.36
行程时间/s	78	70	76	72	75	72
延误/s	3.78	3.45	3.65	3.34	3.42	3.11