考虑驾驶风格的闭环反馈车速引导方法研究

交通运输系统工程与信息 ›› 2021, Vol. 21 ›› Issue (3): 94-100.

所属专题：车路协同与智能化技术

• 智能交通系统与信息技术 • 上一篇下一篇

考虑驾驶风格的闭环反馈车速引导方法研究

李浩然1，褚端峰¹，梁栋才^{2, 3}，周涂强^{* 4}

1. 武汉理工大学，智能交通系统研究中心，武汉 430070；2. 中国科学院，武汉岩土力学研究所，岩土力学与工程国家重点试验室，武汉 430071；3. 中国科学院大学，北京 100000；4. 华东交通大学，交通运输与物流学院，南昌 330013

收稿日期:2021-02-18 修回日期:2021-05-13 出版日期:2021-06-25 发布日期:2021-06-25
作者简介:李浩然(1988- )，男，湖北十堰人，博士生。
基金资助:
国家自然科学基金/National Natural Science Foundation of China(U1764262，52062015)；江西省教育厅科学技术研究项目/Science and Technology Research Project of Department of Education, Jiangxi Province(GJJ200670)。

Closed-loop Feedback Speed Guidance Method Considering Driving Style

LI Hao-ran¹ , CHU Duan-feng¹ , LIANG Dong-cai^{2, 3} , ZHOU Tu-qiang^{* 4}

1. Intelligent Transportation Research Center, Wuhan University of Technology, Wuhan 430070, China; 2. State Key laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China; 3. University of Chinese Academy of Sciences, Beijing 100000, China; 4. School of Transportation and Logistics, East China Jiaotong University, Nanchang 330013, China

Received:2021-02-18 Revised:2021-05-13 Online:2021-06-25 Published:2021-06-25

摘要/Abstract

摘要：

信号交叉口的车速控制不当会降低车辆的燃油经济性甚至引起追尾碰撞事故，车路协同环境下的车速引导系统可以有效提高信号交叉口处的通行效率和燃油经济性。现有车速引导研究大多忽略了驾驶员风格的差异性，将导致驾驶员无法准确跟踪引导速度。针对该问题，建立考虑驾驶风格的闭环反馈车速引导模型。首先，分析不同风格驾驶员车辆最大纵向加速度的概率分布；其次，研究闭环反馈车速引导方法，使驾驶员更准确地跟踪引导车速；然后，基于机会约束规划方法优化闭环反馈车速引导模型，使模型更加符合驾驶员的不同风格；最后，在MATLAB/ Simulink环境中设计仿真场景，对激进型、适中型和保守型3种闭环反馈车速引导模型进行仿真分析。仿真结果表明：相较于传统车速引导模型，本文模型可使不同风格的驾驶员更容易跟踪引导车速，其中，激进型和适中型车速引导模型可以使车辆以更短的时间通过交叉路口，保守型车速引导模型可以提高车辆在绿灯相位通过交叉口的概率。本文方法可以有效地提高信号交叉口的通行效率。

关键词: 智能交通, 车速引导, 闭环反馈, 驾驶风格, 模拟驾驶, 随机机会

Abstract:

At signalized intersections, unreasonable speed control may increase the vehicle fuel consumption and may even cause a rear-end collision. Speed guidance systems can improve the efficiency of vehicle speed control. However, drivers cannot accurately follow the speed guidance during driving. This study proposes a closed-loop feedback vehicle speed guidance system by considering the driving styles of drivers. Firstly, the probability distribution of vehicle maximum acceleration with different driving styles is analyzed. The closed-loop feedback approachis then proposed for drivers to follow thespeed guidance more accurately. A chance constraint model is further developed to consider different driving styles. Finally, a simulation is conducted in MATLAB/Simulink to validate the proposed speed guidance models. The simulation results show that the model with different driving styles in this study is more effective and reliable compared with the traditional model. The aggressive and moderate speed guidance models can make vehicles get through signalized intersections efficiently, and the cautious speed guidance model can increase the probability of getting through signalized intersections in green light phases. This speed guidance model can improve the traffic efficiency at signalized intersections.

Key words: intelligent transportation, speed guidance, closed-loop feedback, driving style, simulated driving, random chance

中图分类号:

U268.6

李浩然，褚端峰，梁栋才，周涂强. 考虑驾驶风格的闭环反馈车速引导方法研究[J]. 交通运输系统工程与信息, 2021, 21(3): 94-100.

LI Hao-ran,CHU Duan-feng,LIANG Dong-cai,ZHOU Tu-qiang. Closed-loop Feedback Speed Guidance Method Considering Driving Style[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(3): 94-100.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.tseit.org.cn/CN/abstract/abstract30248.shtml

http://www.tseit.org.cn/CN/Y2021/V21/I3/94

参考文献

[1] 李经纬, 赵治国, 沈沛鸿, 等. 驾驶风格K-means聚类与识别方法研究[J]. 汽车技术, 2018, 519(12): 12-16. [LI J W, ZHAO Z G, SHEN P H, et al. Research on methods of K- means clustering and recognition for driving style [J]. Automobile Technology, 2018, 519(12): 12-16.]

[2] 王海星, 王翔宇, 王招贤, 等. 基于数据挖掘的危险货物运输风险驾驶行为聚类分析[J]. 交通运输系统工程与信息, 2020, 20(1): 183-189. [WANG H X, WANG X Y, WANG Z X, et al. Dangerous driving behavior clustering analysis for hazardous materials transportation based on data ming[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(1): 183-189.]

[3] ZHANG J, TANG T, LIU K. A speed guidance model accounting for the driver's bounded rationality at a signalized intersection[J]. Physica A: Statistical Mechanics and Its Applications, 2017, 473(1): 45-52.

[4] BUTAKOV V A, IOANNOU P. Personalized driver assistance for signalized intersections using V2I communication[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(7): 1910-1919.

[5] QI X, WANG P, WU G et al. Connected cooperative ecodriving system considering human driver error[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(8): 2721-2733.

[6] 余铖铨. 考虑驾驶风格的混合动力汽车队列速度优化研究[D]. 南京: 江苏大学, 2019. [YU C Q. Research on speed optimization of hybrid electric vehicle queue considering the driving style[D]. Nanjing: Jiangsu University, 2019.]

[7] DENG Z, CHU D F, WU C Z, et al. Curve safe speed model considering driving style based on driver behaviour questionnaire[J]. Transportation Research Part F: Psychology and Behaviour, 2019, 65: 536-547.

[8] DO M S. Drivers' route choice and learning behavior under bounded information environments[J]. Journal of the Korean Society of Civil Engineers, 2005, 25(2D): 257-263.

[9] 张鹏, 汪鹏飞, 孙超, 等. 基于交叉口双站台的BRT 优先控制研究[J]. 交通运输系统工程与信息, 2020, 20 (3): 87-92. [ZHANG P, WANG P F, SUN C, et al. BRT priority control method based on two stations at intersection[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(3): 87-92.]

[10] XU L, HU J. Establishing style-oriented driver models by imitating human driving behaviors[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(5): 2522- 2530.

[11] 宋天佳, 陈劭, 崔永伟, 等. 汽车与行人交通冲突中驾驶员操作特性研究[J]. 世界科技研究与发展, 2010, 32 (2): 219- 221. [SONG T J, CHEN S, CUI Y W, et al. Analysis of driver's characteristic of operation in vehicle to pedestrian conflict[J]. World SCI-TECH R & D, 2010, 32(2): 219-221.]

[12] 项学海. 基于车联网的十字路口节能与安全车速引导技术研究[D]. 南京: 东南大学, 2015. [XIANG X H. Research on eco-safe speed advisory technology at intersections based on vehicle network[D]. Nanjing: Southeast University, 2015.]

[1]	郭烈, 胥林立, 秦增科, 王旭. 自动驾驶接管影响因素分析与研究进展[J]. 交通运输系统工程与信息, 2022, 22(2): 72-90.
[2]	徐猛, 刘涛, 钟绍鹏, 姜宇. 城市智慧公交研究综述与展望[J]. 交通运输系统工程与信息, 2022, 22(2): 91-108.
[3]	程君, 张立炎, 陈启宏. 一种基于地图辅助的自动驾驶视-惯融合定位方法[J]. 交通运输系统工程与信息, 2022, 22(2): 117-126.
[4]	王国栋, 刘立, 孟宇, 马智萍, 郑淏清, 顾青, 白国星. 一体式车辆避撞轨迹规划与跟踪控制[J]. 交通运输系统工程与信息, 2022, 22(2): 127-136.
[5]	马东方, 陈曦, 吴晓东, 金盛. 基于强化学习的干线信号混合协同优化方法[J]. 交通运输系统工程与信息, 2022, 22(2): 145-153.
[6]	许波桅, 王玲玲, 李军军. 自动化码头多级作业超网络延误传导特性研究[J]. 交通运输系统工程与信息, 2022, 22(2): 280-289.
[7]	田会娟, 刘嘉伟, 翟佳豪, 邓琳琳. 基于多入侵线的视频车速检测方法[J]. 交通运输系统工程与信息, 2022, 22(1): 49-56.
[8]	贾彦峰, 曲大义, 赵梓旭, 王韬, 宋慧. 基于安全势场的网联自主车辆跟驰行为决策及模型[J]. 交通运输系统工程与信息, 2022, 22(1): 85-97.
[9]	刘文, 汪文博. 基于秩最小化矩阵去噪的船舶轨迹重构方法[J]. 交通运输系统工程与信息, 2022, 22(1): 106-114.
[10]	赵建东, 陈溱, 焦彦利, 张凯丽, 韩明敏. 重点营运车辆的异常驾驶行为识别研究[J]. 交通运输系统工程与信息, 2022, 22(1): 282-291.
[11]	徐东伟, 彭航, 商学天, 魏臣臣, 杨艳芳. 基于图自编码-生成对抗网络的路网数据修复[J]. 交通运输系统工程与信息, 2021, 21(6): 33-41.
[12]	游峰, 梁健中, 曹水金, 肖智豪, 吴镇江, 王海玮. 面向多目标跟踪的密集行人群轨迹提取和运动语义感知[J]. 交通运输系统工程与信息, 2021, 21(6): 42-54.
[13]	熊坚, 施锦浩, 万华森. 人车路综合风险场模型构建及驾驶风格评估[J]. 交通运输系统工程与信息, 2021, 21(6): 105-114.
[14]	王福建, 俞佳浩, 赵锦焕, 梅振宇. 基于站点实时关联度的短时公交客流预测方法[J]. 交通运输系统工程与信息, 2021, 21(6): 131-144.
[15]	张毅, 姚丹亚, 李力, 裴华鑫, 晏松, 葛经纬. 智能车路协同系统关键技术与应用[J]. 交通运输系统工程与信息, 2021, 21(5): 40-51.

考虑驾驶风格的闭环反馈车速引导方法研究

Closed-loop Feedback Speed Guidance Method Considering Driving Style

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics