吉林大学学报(工学版) ›› 2024, Vol. 54 ›› Issue (5): 1302-1310.doi: 10.13229/j.cnki.jdxbgxb.20220850

• 交通运输工程·土木工程 • 上一篇    

载货汽车弯道侧翻路侧预测方法

许清津1(),付锐1,2(),郭应时1,2,吴付威1,2   

  1. 1.长安大学 汽车学院，西安 710064
    2.长安大学 汽车运输安全保障技术交通行业重点实验室，西安 710064
  • 收稿日期:2022-07-04 出版日期:2024-05-01 发布日期:2024-06-11
  • 通讯作者: 付锐 E-mail:qingjinxu@outlook.com;furui@chd.edu.cn
  • 作者简介:许清津（1995-），男，博士研究生. 研究方向：智能交通. E-mail： qingjinxu@outlook.com
  • 基金资助:
    国家重点研发计划项目(2019YFB1600500);陕西省重点研发计划项目(2022GY-303)

Roadside prediction method for truck rollover on the curve

Qing-jin XU1(),Rui FU1,2(),Ying-shi GUO1,2,Fu-wei WU1,2   

  1. 1.School of Automobile，Chang'an University，Xi'an 710064，China
    2.Key Laboratory of Automobile Transportation Safety Technology，Ministry of Transport，Chang'an University，Xi′an 710064，China
  • Received:2022-07-04 Online:2024-05-01 Published:2024-06-11
  • Contact: Rui FU E-mail:qingjinxu@outlook.com;furui@chd.edu.cn

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摘要：

载货汽车重心高的特点极易因操作不当引发侧翻，为此，依靠现有路侧测量设备提出一种可快速投入使用的车辆弯道侧翻预测方法。首先，提出基于改进KNN的车辆重心高度回归预测模型实现车辆重心高度路侧动态估计；然后，建立基于横向载荷转移率的车辆侧倾估计模型实现侧倾程度路侧估计；最后，提出在线ARIMA模型在线预测车辆侧倾状态。MATLAB/Simulink与TruckSim联合仿真结果表明：本文提出的侧翻预测方法在弯道全程均能准确预测车辆LTR变化趋势和其极限值，从而为路侧设施在弯道发布针对性预警提供依据。

关键词: 交通工程, 侧翻预测, 在线ARIMA, 路侧感知设备, 重心高度估计, TruckSim仿真

Abstract:

Truck is easy to rollover due to improper operation because of its high center of gravity （c.g）. This paper proposes a vehicle rollover prediction method which could be put into use quickly based on existing roadside measurement equipment. First， a regression prediction model of vehicle c.g height based on improved KNN was proposed to realize the roadside dynamic estimation of vehicle c.g. Then， a roll estimation model based on lateral load transfer rate （LTR） was established to realize the roadside estimation of roll degree. Finally， an online ARIMA model was proposed to predict vehicle roll state online. The results of MATLAB/Simulink and TruckSim co-simulation showed that the proposed rollover prediction method could accurately predict the LTR trend and the LTR limit value in the whole process， thus providing a basis for roadside equipment to transmit specific warnings in advance on the curve.

Key words: transportation engineering, rollover prediction, online ARIMA, roadside sensing equipment, estimation of center of gravity height, TruckSim simulation

中图分类号: 

  • U461.1

图1

弯道车辆侧翻预测方法流程图"

图2

车辆横坡受力作用示意图"

图3

在线学习流程"

图4

不同回归模型的预测结果"

表1

不同回归模型的预测精度"

方法ETRFXGBGB改进KNN
R20.38680.77690.71700.69600.8582
MSE1.37690.08310.09350.09690.0662
MAE0.08010.06160.06630.07560.0492

表2

仿真车辆参数"

参数符号数值单位
质量m2532kg
轮距B1.900m
重心高度hg0.781m

图5

侧倾程度估计测试结果"

图6

侧倾程度预测测试结果"

1 Chen B C, Peng H. A real-time rollover threat index for sports utility vehicles[C]∥Proceedings of the American Control Conference, California, USA, 1999: 1233-1237.
2 Yu H, Güvenç L, Özgüner Ü. Heavy duty vehicle rollover detection and active roll control[J]. Vehicle System Dynamics, 2008, 46(6): 451-470.
3 Preston T J, Woodrooffe J H F. Feasibility study of a rollover warning device for heavy trucks[R]. Technical Report TR-VDL-005, Ottawa, Canada: Institute of Mechanical Engineering, National Research Council of Canada, 1990.
4 Qu G X, He Y L, Sun X D, et al. Modeling of lateral stability of tractor-semitrailer on combined alignments of freeway[J]. Discrete Dynamics in Nature and Society, 2018(1445): 1-17.
5 Zhang X J, Yang Y, Guo K H, et al. Contour line of load transfer ratio for vehicle rollover prediction[J]. Vehicle System Dynamics, 2017, 55(11): 1748-1763.
6 Dong Z, Wu Y W, Pei M T, et al. Vehicle type classification using a semi-supervised convolutional neural network[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(4): 2247-2256.
7 李立,吴晓强,杨文臣,等. 基于路侧毫米波雷达的群体车辆目标识别与跟踪[J/OL]. [2024-03-08].
Li Li, Wu Xiao-qiang, Yang Wen-chen, et al. Target recognition and tracking of group vehicles based on roadside millimeter-wave radar [J/OL]. [2024-03-08].
8 Zhou Y, Pei Y L, Li Z W, et al. Vehicle weight identification system for spatiotemporal load distribution on bridges based on non-contact machine vision technology and deep learning algorithms[J]. Measurement, 2020, 159: 107801.
9 Feng Q Y, Wu Z Z, Wang D D, et al. Curved roads dpeed sdvisory strategy under connected vehicle environment[C]∥The 17th COTA International Conference of Transportation Professionals, Shanghai, China, 2017: 4922-4931.
10 Xu Q J, Fu R, Wu F W, et al. Roadside estimation of a vehicle's center of gravity height based on an improved single-stage detection algorithm and regression prediction technology[J]. IEEE Sensors Journal, 2021, 21(21): 24520-24530.
11 卢凯, 吴蔚, 林观荣, 等. 基于KNN回归的客运枢纽聚集人数组合预测方法[J]. 吉林大学学报: 工学版, 2021, 51(4): 1241-1250.
Lu Kai, Wu Wei, Lin Guan-rong, et al. Combination forecasting model for number of assembling passengers at transportation terminal based on KNN regression algorithm[J]. Journal of Jilin University (Engineering and Technology Edition), 2021, 51(4):1241-1250.
12 Liu C H, Hoi S C H, Zhao P L, et al. Online ARIMA algorithms for time series prediction[C]∥Proceedings of the AAAI Conference on Artificial Intelligence, Phoenix, USA, 2016: 1867-1873.
13 Hazan E, Agarwal A, Kale S. Logarithmic regret algorithms for online convex optimization[J]. Machine Learning, 2007, 69(2/3): 169-192.
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