A decision model for in-field lane-changing at highway traffic risk in combination of flat and longitudinal sections

doi:10.16265/j.cnki.issn1003-3033.2026.08.0657

Abstract

Abstract:

In order to characterize the driving risks faced by vehicles during lane changing in the combination of longitudinal and flat road sections, and to ensure safe lane-changing maneuvers, this study firstly establishes a unified field model of driving risks and defines the influence factors of road conditions R_i(gradient and curvature of the round curve) to characterize the amplification of the dynamic risk field of the vehicle by such road conditions. Secondly, the influence factors of road conditions are introduced into the model of minimum safe lane change distance, and the level of road safety ξ is proposed to reflect the different influences of different combinations of longitudinal and flat road sections on the minimum safe lane change distance. Finally, a Minimum Safe-Distance Lane-Change (MLC) model for combined road sections was established. The minimum safe lane-changing distance required by vehicles at different speeds, different accelerations, and different combinations of longitudinal and flat lines is validated through real-vehicle experiments. The results show that the speed and acceleration of the vehicle synergistically affect the minimum safe lane-changing distance; the larger the influence factor of the road conditions is, the longer the minimum safe lane-changing distance is; the correlation coefficient between the simulation data and the real-vehicle test data reaches 0.996, which means that the model has high accuracy. Compared with the ellipsoid-based lane-changing model, the F₁ score of the lane-changing model proposed in this study reaches 89.45% and 90.99%, which are 7.49% and 8.10% higher, respectively. Compared with the safety potential field lane-changing model, it improved by 4.57% and 4.32%.

Key words: highway, flat-longitudinal combined road sections, travelling risk, lane-changing decision model, minimum safe lane-changing distance, road condition factors

CLC Number:

X951交通运输安全

Hu Liwei, Ma Siyue, Chen Jiale, Pan Jiangxiong, Yang Can, Gong Qi. A decision model for in-field lane-changing at highway traffic risk in combination of flat and longitudinal sections[J]. China Safety Science Journal, 2026, 36(8): 74-83.

Figures/Tables 15

Table 1

Fig.1

Fig.2

Fig.3

Table 2

Table 3

Fig.4

Fig.5

Fig.6

Table 4

Fig.7

Fig.8

Fig.9

Table 5

Table 6

References 15

[1]	郑淑欣, 李振龙, 赵晓华. 雾天高速公路换道行为特性研究[J]. 交通信息与安全, 2020, 38(1):35-42, 51.
	Zheng Shuxin, Li Zhenlong, Zhao Xiaohua. Behavioral characteristics of lane-changing on highway in foggy weather[J]. Journal of Transport Information and Safety, 2020, 38(1):35-42, 51.
[2]	Zhang Qingyu, Langari R, Tseng H E, et al. A game theoretic model predictive controller with aggressiveness estimation for mandatory lane change[J]. IEEE Transactions on Intelligent Vehicles, 2019, 5(1): 75-89. doi: 10.1109/TIV
[3]	Wang Yugang, Lyu Nengchao, Wen Jianghui. Game theory-based mandatory lane change model in intelligent connected vehicles environment[J]. Applied Mathematical Modelling, 2024, 132: 146-165. doi: 10.1016/j.apm.2024.04.047
[4]	Li Guofa, Yang Yifan, Li Shen, et al. Decision making of autonomous vehicles in lane change scenarios: deep reinforcement learning approaches with risk awareness[J]. Transportation Research Part C: Emerging Technologies, 2022, 134: DOI:10.1016/j.trc.2021.103452.
[5]	Huang Heye, Wang Jianqiang, Fei Cong, et al. A probabilistic risk assessment framework considering lane-changing behavior interaction[J]. Science China Information Sciences, 2020, 63(9): DOI:10.1007/s11432-019-2983-0.
[6]	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 safety distance[J]. China Safety Science Journal, 2022, 32(7):165-171. doi: 10.16265/j.cnki.issn1003-3033.2022.07.1182
[7]	姚佼, 杨承逸, 杨媛媛, 等. 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. doi: 10.16265/j.cnki.issn1003-3033.2024.07.0069
	Yao Jiao, Yang Chengyi, Yang Yuanyuan, et al. 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. doi: 10.16265/j.cnki.issn1003-3033.2024.07.0069
[8]	Woo H, Ji Y, Kono H, et al. Lane-change detection based on vehicle-trajectory prediction[J]. IEEE Robotics & Automation Letters, 2017, 2(2):1109-1116.
[9]	Wang Jianqiang, Wu Jian, Zheng Xunjia, et al. Driving safety field theory modeling and its application in pre-collision warning system[J]. Transportation Research Part C: Emerging Technologies, 2016, 72:306-324. doi: 10.1016/j.trc.2016.10.003
[10]	李林恒, 甘婧, 曲栩, 等. 智能网联环境下基于安全势场理论的车辆跟驰模型[J]. 中国公路学报, 2019, 32(12):76-87. doi: 10.19721/j.cnki.1001-7372.2019.12.008
	Li Linheng, Gan Jing, Qu Xu, et al. Car-following model based on safety potential field theory under connected and automated vehicle environment[J]. China Journal of Highway and Transport, 2019, 32(12):76-87. doi: 10.19721/j.cnki.1001-7372.2019.12.008
[11]	张驰, 王茜茹, 向宇杰, 等. 基于平纵组合的山区高速公路连续下坡路段行车安全评价方法[J]. 重庆交通大学学报(自然科学版), 2024, 43(5):78-90.
	Zhang Chi, Wang Xiru, Xiang Yujie, et al. Driving safety evaluation method for continuous downhill section of mountainous expressway based on longitudinal-transverse combination[J]. Journal of Chongqing Jiaotong University (Natural Science), 2024, 43(5):78-90.
[12]	马聪, 张生瑞, 马壮林, 等. 高速公路交通事故非线性负二项预测模型[J]. 中国公路学报, 2018, 31(11):176-185.
	Ma Cong, Zhang Shengrui, Ma Zhuanglin, et al. Nonlinear negative binomial regression model of expressway traffic accident frequency prediction[J]. China Journal of Highway and Transport, 2018, 31(11):176-185.
[13]	Ahmed M, Huang Helai, Abdel-Aty M, et al. Exploring a Bayesian hierarchical approach for developing safety performance functions for a mountainous freeway[J]. Accident Analysis Prevention, 2011, 43(4):1581-1589. doi: 10.1016/j.aap.2011.03.021
[14]	许伦辉, 胡三根, 罗强, 等. 基于驾驶员类型的车辆换道模型[J]. 华南理工大学学报 (自然科学版), 2014, 42(8): 104-111.
	Xu Lunhui, Hu San'gen, Luo Qiang, et al. Lane-changing model based on different types of drivers[J]. Journal of South China University of Technology (Natural Science Edition), 2014, 42(8): 104-111.
[15]	李林恒, 甘婧, 曲栩, 等. 智能网联环境下基于安全势场理论的车辆换道模型[J]. 中国公路学报, 2021, 34(6):184-195. doi: 10.19721/j.cnki.1001-7372.2021.06.018
	Li Linheng, Gan Jing, Qu Xu, et al. Lane-changing model based on safety potential field theory under the connected and automated vehicles environment[J]. China Journal of Highway and Transport, 2021, 34(6):184-195. doi: 10.19721/j.cnki.1001-7372.2021.06.018

路段	最高限速/(km/h)	桩号范围/km	车道数	路线长度/km	平纵组合路段占比/%
A	100	22+0.024 ~ 30+0.094	双向4车道	8.07	37.4
B	120	94+0.172 ~ 105+0.852	双向4车道	11.68	42.3
C	120	28+0.478 ~ 37+0.268	双向4车道	8.79	44.1

参数	取值
k₀	1.997
β	-0.183
σ	0.021
α	2.538
λ	3.869

模型	RMSE/(m/s²)	MAPE
安全势场跟驰模型	3.002	0.178
所构跟驰模型	2.496	0.144

路段	圆曲线最小半径/km	圆曲线最大半径/km	最大纵坡/%	最大坡长/m
A	0.83	4.64	3	650
B	1.49	3.89	3	730
C	1.16	5.27	3.5	680

试验类型	仿真试验	实车试验
仿真试验	1	0.996
实车试验	0.996	1