超视界路况下网联汽车的辅助驾驶方法

doi:10.3969/j.issn.1004-132X.2020.14.004

中国机械工程 ›› 2020, Vol. 31 ›› Issue (14): 1666-1671.DOI: 10.3969/j.issn.1004-132X.2020.14.004

超视界路况下网联汽车的辅助驾驶方法

张洪昌^1,2, 郭军^1,2, 赵伟^1,2, 密兴林^1,2, 曾娟^1,2

1. 现代汽车零部件技术湖北省重点实验室(武汉理工大学), 武汉, 430070;
2. 汽车零部件技术湖北省协同创新中心, 武汉, 430070

收稿日期:2019-06-17 出版日期:2020-07-25 发布日期:2020-08-26
通讯作者: 曾娟(通信作者),女,1973年生,副教授。研究方向为汽车主/被动安全技术。E-mail:zhc112@126.com。
作者简介:张洪昌,男,1980年生,副教授。研究方向为汽车电控系统研发、车联网技术应用等。E-mail:zhanghc@whut.edu.cn。
基金资助:
中央高校基本科研业务费专项资金资助项目（191007013）

Assisted Driving Method of Network-connected Vehicle in Out-of-sight Roads

ZHANG Hongchang^1,2, GUO Jun^1,2, ZHAO Wei^1,2, MI Xinglin^1,2, ZENG Juan^1,2

1. Hubei Key Laboratory of Advanced Technology for Automotive Components(Wuhan University of Technology), Wuhan, 430070;
2. Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan, 430070

Received:2019-06-17 Online:2020-07-25 Published:2020-08-26

摘要/Abstract

摘要： 针对车辆前方超视界路况不易预测、驾驶员易因路况不明而错误操作车辆导致失稳等危险事故的情况，提出了一种超视界路况下的网联汽车辅助驾驶方法。设计了基于车联网实时通信技术的网联汽车辅助驾驶系统，它主要由云端服务、共享驿站和车载单元组成；研究了行驶车辆的路况信息识别方法，包括路面附着系数识别、道路曲率计算和路面坡度测量；研究了基于多车辆测量数据的路况信息融合与校正方法，建立了基于三自由度车辆的刚体力学的弯道安全车速计算模型。实车实验结果表明，所提方法能够准确测量车辆前方超视界的路况信息，并可使驾驶员提前、准确地获知超视界路况信息，特别是弯道路况信息。

关键词: 超视界道路, 路况, 网联汽车, 辅助驾驶

Abstract: A assisted driving method of network-connected vehicle was proposed for solving the problems that out-of-sight road conditions in front of vehicles which were not easy to predict, and the drivers were prone to dangerous accidents such as misoperation of vehicles due to unclear road conditions. A assisted driving system of network-connected vehicle was designed based on real-time communication technology of vehicle networking. The system was mainly composed of cloud service, shared stations and vehicle-mounted units. The methods of road condition information identification was studied based on moving vehicles, including road adhesion coefficient identification, road curvature calculation and pavement slope measurement. Then the fusion and correction method of road condition information was studied based on multi-vehicle measurement data, and a calculation model of the safe driving speed on a bend was established based on three-degree-of-freedom vehicle model. The real vehicleing test results show that the proposed method may accurately measure the road condition information of the roads out of sight, and may enable the driver to obtain the out-of-sight road traffic informations in advance and accurately, especially the road condition information of the bend.

Key words: out-of-sight road, road condition, network-connected vehicle, assisted driving

中图分类号:

U471.1

张洪昌, 郭军, 赵伟, 密兴林, 曾娟. 超视界路况下网联汽车的辅助驾驶方法[J]. 中国机械工程, 2020, 31(14): 1666-1671.

ZHANG Hongchang, GUO Jun, ZHAO Wei, MI Xinglin, ZENG Juan. Assisted Driving Method of Network-connected Vehicle in Out-of-sight Roads[J]. China Mechanical Engineering, 2020, 31(14): 1666-1671.

参考文献

[1] 公安部交通管理局. 中华人民共和国道路交通事故统计年报(2018)[R]. 北京:公安部交通管理科学研究所, 2019. The Ministry of Public Security Traffic Management Bureau. Annual Report of Road Traffic Accidents Statistics of the People's Republic of China(2018)[R]. Beijing:Traffic Management Research Institute of the Ministry of Public Security, 2019.
[2] 靳立强, 石冠男, 孔德隽,等. 基于零力矩点指标和侧翻时间算法的车辆侧翻预警[J]. 汽车工程, 2017, 39(3):281-287. JIN Liqiang, SHI Guannan, KONG Dejun, et al. Vehicle Rollover Warning Based on Zero-moment Point Indicator and Time-to-rollover Algorithm[J]. Automotive Engineering, 2017, 39(3):281-287.
[3] MCCANN K, FONTAINE M D. Assessing Driver Speed Choice in Fog with the Use of Visibility Data from Road Weather Information Systems[J]. Transportation Research Record, 2016, 2551:90-99.
[4] 朱冰, 朴奇, 赵健,等. 基于路面附着系数估计的汽车纵向碰撞预警策略[J]. 汽车工程, 2016, 38(4):446-452. ZHU Bing, PARK Qi, ZHAO Jian, et al. Vehicle Longitudinal Collision Warning Strategy Based on Road Adhesive Coefficient Estimation[J]. Automotive Engineering, 2016, 38(4):446-452.
[5] KIM W. Adaptive Variable Weights Tuning in an Integrated Chassis Control for Lateral Stability Enhancement[J]. Transactions of the Korean Society of Mechanical Engineers, 2016, 40(1):103-111.
[6] WANG Y, CAI Y, CHEN L. Design of Intelligent and Connected Vehicle Path Tracking Controller Based on Model Predictive Control[J]. Journal of Mechanical Engineering, 2019, 55(8):136-144.
[7] 李克强, 戴一凡, 李升波,等. 智能网联汽车(ICV)技术的发展现状及趋势[J]. 汽车安全与节能学报,2017, 8(1):1-14. LI Keqiang, DAI Yifan, LI Shengbo, et al. State-of-the-art and Technical Trends of Intelligent and Connected Vehicles[J]. Journal of Automotive Safety and Energy, 2017, 8(1):1-14.
[8] 陈杰, 李亮, 宋健. 基于LTV-MPC的车辆稳定性控制研究[J]. 汽车工程, 2016, 38(3):308-316. CHEN Jie, LI Liang, SONG Jian. A Study on Vehicle Stability Control Based on LTV-MPC[J]. Automotive Engineering, 2016, 38(3):308-316.
[9] ACOSTA M, KANARACHOS S, BLUNDELL M. Road Friction Virtual Sensing:a Review of Estimation Techniques with Emphasis on Low Excitation Approaches[J]. Applied Sciences-Basel, 2017, 7(12):1-47.
[10] KHALEGHIAN S, EMAMI A, TAHERI S. A Technical Survey on Tire-road Friction Estimation[J]. Friction, 2017, 5(2):123-146.
[11] HAN K, LEE E, CHOI M, et al. Adaptive Scheme for the Real-time Estimation of Tire-road Friction Coefficient and Vehicle Velocity[J]. IEEE ASME Transactions on Mechatronics, 2017, 22(4):1508-1518.
[12] MORRIS C M, DONNELL E T. Passenger Car and Truck Operating Speed Models on Multilane Highways with Combinations of Horizontal Curves and Steep Grades[J]. Journal of Transportation Engineering, 2014, 140(1):1-11.
[13] CVITANIC D, MALJKOVIC B. Operating Speed Models of Two-lane Rural State Roads Developed on Continuous Speed Data[J]. Tehnicki Vjesnik-Technical Gazette, 2017, 24(6):1915-1921.
[14] 孙川, 吴超仲, 褚端峰,等. 弯道安全车速计算改进模型研究[J]. 中国公路学报, 2015, 28(8):101-108. SUN Chuan, WU Chaozhong, CHU Duanfeng, et al. Improved Model Study of Safety Speed Calculation in Curves[J]. China Journal of Highway and Transport, 2015, 28(8):101-108.
[15] JEONG C H, KIM J Y, JUNG D H. Research on Vehicle Stability Technology Based on Wheel Force[J]. International Journal of Automotive Technology, 2015, 16(3):435-445.

超视界路况下网联汽车的辅助驾驶方法

Assisted Driving Method of Network-connected Vehicle in Out-of-sight Roads

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 6

编辑推荐

Metrics

[1]	李茂月;吕虹毓;王飞;贾冬开. 基于机器视觉的智能车辆鲁棒车道线识别方法[J]. 中国机械工程, 2021, 32(02): 242-251.
[2]	冯谢星1;张新钰2;张颖麟1;曹立波1，3. 针对打车软件的分心驾驶研究[J]. 中国机械工程, 2019, 30(15): 1782-1789.
[3]	张伟伟1;糜泽阳1;肖凌云2;钱宇彬1. 基于深度卷积网络和在线学习跟踪的驾驶员打哈欠检测[J]. 中国机械工程, 2019, 30(08): 902-911.
[4]	凤鹏飞1，2;金会庆1，2;王慧然3;刘法勇4. 考虑路面附着和自适应时间系数影响的车道保持控制[J]. 中国机械工程, 2019, 30(07): 804-811.
[5]	黄锡昌1,2;宗志坚2. 基于改进预瞄跟随算法的电动车智能转向控制[J]. 中国机械工程, 2014, 25(14): 1984-1987.
[6]	沈峘, 凌锐, 李舜酩. 基于预瞄最优曲率模型的大曲率转向控制方法[J]. 中国机械工程, 2012, 23(17): 2111-2116.