Cooperative Lane Change Decision-Making Model of Bottleneck Emergency Section in Weaving Area Based on Social Force

doi:10.12141/j.issn.1000-565X.210787

Abstract

Abstract:

To describe the lane-changing decision mechanism of vehicles in the bottleneck section of weaving area and provide a lane-changing decision model in the emergency environment, this paper constructed a collaborative lane-changing decision model of vehicles facing bottleneck section based on the micro-trajectory information of vehicles and the human traffic flow model of social force. This model can provide a lane-changing decision method for the sudden bottleneck environment of intelligent network connection. Firstly, based on the characteristics of lane-changing decision of vehicles in the sudden bottleneck section, the vehicle equivalent mass model was constructed to improve the social force model by considering the types of vehicles and drivers. On this basis, the factors driving vehicle lane-changing were described as automatic driving force, repulsive force among vehicles and repulsive force of obstacles, and the collaborative lane-changing decision model was constructed. Then, 832 microscopic trajectory data of lane change decisions were selected and divided into calibration set and verification set. The model was calibrated using genetic algorithm with acceleration as index and Manhattan distance as objective function. The validity of the calibration method was verified based on simulated data and measured data. Finally, this model was compared with the active lane changing decision model in lane changing direction identification, lane changing intention intensity and model prediction error. The results show that the success rate of lane change direction recognition of the proposed model is 92.6%, the output lane change intention intensity is basically consistent with the measured data, and the predicted RMSPE value decreases by 0.825 on average and RE value decreases by 1.379 on average, which are significantly better than those of the active lane change decision model. The research results can provide a theoretical basis for the identification of vehicle lane change intention in the bottleneck section of intelligent network environment and traffic management and control under emergencies.

Key words: weaving area, emergency, lane changing decision, social force, genetic algorithm, micro-trajectory data, lane change intention recognition

CLC Number:

U491

QIN Yaqin, QIAN Zhengfu, XIE Jiming, et al. Cooperative Lane Change Decision-Making Model of Bottleneck Emergency Section in Weaving Area Based on Social Force[J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(7): 66-75.

Figures/Tables 12

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Table 1

Vehicle position parameters in two scenarios"

车辆空间位置分布	场景1	场景2
$i$	（0，0）	（0，0）
$i - 1$	（-9，0）	（-9，0）
$n$	（4，4）	（4，-4）
$n - 1$	（-5，4）	（-5，-4）
A	（7，0）	（7，0）

Table 1

Table 2

Fig.7

Fig.8

Fig.9

Table 3

References

1	ZHAO J， LIU P， XU C，et al ．Understand the impact of traffic states on crash risk in the vicinities of type A weaving segments：a deep learning approach［J］．Accident Analysis & Prevention，2021，159：106293.
2	REN J， CHEN Y， XIN L，et al ．Detecting and locating of traffic incidents in a road segment based on lane-changing characteristics［J］．Transportmetrica A：Transport Science，2017，13（10）：853–873.
3	YANG D， WU Y， SUN F，et al ．Freeway accident detection and classification based on the multi-vehicle trajectory data and deep learning model［J］．Transportation Research Part C：Emerging Technologies，2021，130：103303.
4	SUN C， PEI X， HAO J，et al ．Role of road network features in the evaluation of incident impacts on urban traffic mobility［J］．Transportation Research Part B：Methodological，2018，117：101-116.
5	CAO D， WU J， DONG X，et al ．Quantification of the impact of traffic incidents on speed reduction：a causal inference based approach［J］．Accident Analysis & Prevention，2021，157：106163/1-14.
6	DAS A， KHAN M N， AHMED M M ．Detecting lane change maneuvers using SHRP2 naturalistic driving data：a comparative study machine learning techniques［J/OL］．Accident Analysis & Prevention，2020，142：105578.
7	WANG Z， SHI X， ZHAO X，et al ．Modeling decentralized mandatory lane change for connected and autonomous vehicles：an analytical method［J］．Transportation Research Part C：Emerging Technologies，2021，133：103441.
8	王殿海，陶鹏飞，金盛，等．跟驰模型参数标定及验证方法［J］．吉林大学学报（工学版），2011，41（S1）：59-65.
8	WANG Dian-hai， TAO Peng-fei， JIN Sheng，et al ．Method of calibrating and validating car-following model［J］．Journal of Jilin University （Engineering and Technology Edition），2011，41（S1）：59-65.
9	UO D， MA J， CHANG R ．Lane-changing-decision characteristics and the allocation of visual attention of drivers with an angry driving style［J］．Transportation Research Part F：Traffic Psychology and Behaviour，2020，71：62-75.
10	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.
10	CHEN Hui， WANG Jie-xin ．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.
11	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.
11	PENG Bo， WENG Yu-ting， XIE Ji-ming，et al ．Multi-stage lane changing decision model of urban trunk road’s short weaving area based on cellular automata［J］．Journal of Transportation Systems Engineering and Information Technology，2020，20（4）：41-48，70.
12	张兰芳，陈程，张佳妍，等．基于自然驾驶数据的高速公路出口换道决策模型［J］．同济大学学报（自然科学版），2018，46（3）：318-325，333.
12	ZHANG Lan-fang， CHEN Cheng， ZHANG Jia-yan，et al ．Modeling lane-changing behavior in freeway off-ramp areas using naturalistic driving data［J］．Journal of Tongji University（Natural Science），2018，46（3）：318-325，333.
13	张青周，李振龙，曹政，等．高速公路浓雾环境下换道决策规则提取及决策算法［J］.科学技术与工程，2019，19（21）：303-308.
13	ZHANG Qing-zhou， LI Zhen-long， CAO Zheng，et al ．Decision-making rule extraction and decision-making algorithm for lane change in dense fog environment［J］．Science Technology and Engineering，2019，19（21）：303-308.
14	李立，徐志刚，赵祥模，等．智能网联汽车运动规划方法研究综述［J］．中国公路学报，2019，32（6）：20-33.
14	LI Li， XU Zhi-gang， ZHAO Xiang-mo，et al ．Review of motion planning methods of intelligent connected vehicles［J］．China Journal of Highway and Transport，2019，32（6）：20-33.
15	WANG H， HUANG Y， KHAJEPOUR A，et al ．Local path planning for autonomous vehicles： crash mitigation［C］∥ Proceeding of 2018 IEEE Intelligent Vehicles Symposium （IV）．Changshu：IEEE，1602-1606.
16	FENG Z， FU M， SONG W，et al ．Decision making and local trajectory planning for autonomous driving in off-road environment［C］∥ Proceeding of 2020 3rd International Conference on Unmanned Systems （ICUS）．Harbin：IEEE，1180-1186.
17	FENG Z， SONG W， FU M，et al ．Decision-making and path planning for highway autonomous driving based on spatio-temporal lane-change gaps［J］．IEEE Systems Journal，2021：1-11.
18	KUEFLER A， MORTON J， WHEELER T，et al ．Imitating driver behavior with generative adversarial networks［C/OL］∥ Proceedings of 2017 IEEE Intelligent Vehicles Symposium （IV）．Redondo Beach：IEEE，2017：204-211.
19	陈大飞．基于社会力的城市道路混合交通流建模与分析［D］．长沙：中南大学，2011.
20	DENG J-H， FENG H-H ．A multilane cellular automaton multi-attribute lane-changing decision model［J］．Physica A：Statistical Mechanics and its Applications，2019，529：121545.
21	杨达，苏刚，吴丹红．基于社会力模型的无车道划分异质交通流研究［J］．交通运输系统工程与信息，2018，18（3）：94-100.
21	YANG Da， SU Gang， WU Dan-hong ．Non-lane-based heterogeneous traffic flow research based on social force model［J］．Journal of Transportation Systems Engineering and Information Technology，2018，18（3）：94-100.
22	邱小平，孙若晓，马丽娜，等．基于社会力的信号交叉口施工区交通流建模［J］．交通运输系统工程与信息，2016，16（1）：99-104.
22	QIU Xiao-ping， SUN Ruo-xiao， MA Li-na，et al ．Modeling and analyzing of traffic flow on the work zone of urban signalized intersection based on social force［J］．Journal of Transportation Systems Engineering and Information Technology，2016，16（1）：99-104.
23	YANG X， YANG X， LI Y，et al ．Obstacle avoidance in the improved social force model based on ant colony optimization during pedestrian evacuation［J］．Physica A：Statistical Mechanics and Its Applications，2021，583：126256/1-15.
24	LI G， MA J， YANG Z ．Characteristics of heavy vehicle discretionary lane changing based on trajectory Data［J］?．Transportation Research Record，2022，2676（3），258-275.
25	ZHU M， WANG X， TARKO A，et al ．Modeling car-following behavior on urban expressways in Shanghai：a naturalistic driving study［J］．Transportation Research Part C：Emerging Technologies，2018，93：425-445.
26	YANG D， ZHOU X， SU G，et al ．Model and simulation of the heterogeneous traffic flow of the urban signalized intersection with an island work zone［J］．IEEE Transactions on Intelligent Transportation Systems，2019，20（5）：1719-1727.
27	LIU S， WANG X， HASSANIN O，et al ．Calibration and evaluation of responsibility-sensitive safety （RSS） in automated vehicle performance during cut-in scenarios［J］．Transportation Research Part C：Emerging Technologies，2021，125：103037.
28	余朝军，江驹，徐海燕，等．基于改进遗传算法的航班-登机口分配多目标优化［J］．交通运输工程学报，2020，20（2）：121-130.
28	YU Chao-jun， JIANG Ju， XU Hai-yan，et al ．Multi-objective optimization of flight-gate assignment based on improved genetic algorithm ［J］．Journal of Traffic and Transportation Engineering，2020，20（2）：121-130.
29	禚保玲．基于社会力的同伴群走行模型研究［D］．北京：北京交通大学． 2014.
30	王建强，吴剑，李洋．基于人-车-路协同的行车风险场概念、原理及建模［J］．中国公路学报，2016，29（1）：105-114.
30	WANG Jian-qiang， WU Jian， LI Yang ．Concept，principle and modeling of driving risk field based on driver-vehicle-road interaction［J］．China Journal of Highway and Transport，2016，29（1）：105-114.
31	SAIFUZZAMAN M， ZHENG Z， MAZHARUL HAQUE M，et al ．Revisiting the task-capability interface model for incorporating human factors into car-following models［J/OL］．Transportation Research Part B：Methodological，2015，82：1-19.
32	LI L X， WANG F Y ．The automated lane-changing model of intelligent vehicle highway systems［C］∥ Proceedings of the IEEE 5th International Conference on Intelligent Transportation Systems．Singapore：IEEE，2002.
33	LI A， JIANG H， LI Z，et al ．Human-like trajectory planning on curved road：learning from human drivers［J］．IEEE Transactions on Intelligent Transportation Systems，2020，21（8）：3388-3397.
34	孙秦豫，付锐，王畅，等．人机协作系统中车辆轨迹规划与轨迹跟踪控制研究［J］．中国公路学报，2021，34（9）：146-160.
34	SUN Qin-yu， FU Rui， WANG Chang，et al ．Vehicle trajectory-planning and trajectory-tracking control in human-autonomous collaboration system ［J］．China Journal of Highway and Transport，2021，34（9）：146-160.

参数	参数描述	参数范围	平均值	标准差	5% 分位数	95% 分位数
α	期望速度修正系数	［-10.09，12.13］	1.612	10.892	2.918	3.465
β	从众系数	［-13.52，24.84］	1.279	9.502	2.194	4.974
χ	对后车的排斥强度	［0.17，38.42］	6.658	8.069	0.766	22.179
δ	对后车排斥力的作用范围	［2.63，76.76］	18.558	17.128	5.538	45.270
ε	对相邻车道前后车辆的排斥强度	［1.02，66.22］	13.339	12.228	5.093	36.456
?	受相邻车道车辆排斥力的作用范围	［7.77，97.43］	26.356	22.517	11.623	68.838
φ	对事故车辆（障碍物）的排斥强度	［4.13，99.51］	27.020	19.334	12.653	71.623
γ	受事故车辆（障碍物）排斥力的作用范围	［3.15，100］	31.462	26.190	12.344	85.709

指标	协同换道决策模型标定结果		协同换道决策模型预测结果		主动换道决策模型预测结果
指标	RMSPE	RE	RMSPE	RE	RMSPE	RE
误差范围	［0，0.372］	［0，1.563］	［0.003，0.592］	［0.004，2.030］	［0.094，4.841］	［0.397，4.340］
误差平均值	0.018	0.067	0.121	0.331	0.946	1.710
误差标准差	0.069	0.290	0.147	0.481	0.925	0.918

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