“自动+人工”混合驾驶环境下交通管理研究综述

裴玉龙; 迟佰强; 吕景亮; 岳志坤

doi:10.3963/j.jssn.1674-4861.2021.05.001

“自动+人工”混合驾驶环境下交通管理研究综述

doi: 10.3963/j.jssn.1674-4861.2021.05.001

东北林业大学交通学院哈尔滨 150040

基金项目:

国家自然科学基金项目 71771047

国家自然科学基金项目 51638004

详细信息

通讯作者:
裴玉龙（1961—），博士，教授.研究方向：道路交通安全，交通规划，交通管理. E-mail：peiyulong@nefu.edu.cn

中图分类号: U491.3
计量
- 文章访问数: 4410
- HTML全文浏览量: 1423
- PDF下载量: 491
- 被引次数: 0
出版历程
- 收稿日期: 2021-05-23

An Overview of Traffic Management in "Automatic+Manual" Driving Environment

School of Traffic and Transportation, Northeast Forestry University, Harbin 150040, China

摘要

摘要:
为了解混合驾驶环境下交通管理的研究现状和发展趋势，以“自动驾驶汽车”发展现状为基础，分析自动驾驶汽车在混合驾驶环境下存在的问题，基于Citespace文献计量工具，CNKI核心数据库近24年（1997—2020年）有关自动驾驶研究的文献为研究数据源，从发文年代、期刊来源、研究机构、关键词等进行文献计量和可视化分析，并生成各研究机构间的关系网络图谱及关键词共现网络图谱。结果表明：国内近5年自动驾驶发文量呈上升趋势；《中国公路学报》为发文量最高的期刊；自动驾驶汽车研究的方向主要包括：①目标检测及场景感知研究；②决策与控制；③交通事故责任划定研究。在未来混合驾驶环境下交通管理应结合车路协同、高精度地图技术，从标志标线设计、信号配时优化、路权归属、交通事故责任划定等方面进行研究，使道路运输更安全、高效、便捷。
- 智能交通 /
- 交通管理 /
- 自动驾驶 /
- 路径规划 /
- 场景感知 /
- 责任认定 /
- 混合驾驶
Abstract:
Based on the development status of "automatic driving vehicle", the problems existing in mixed driving environment of autopilot cars are analyzed to understand the current situations and development trends of traffic management in the mixed driving environment. In terms of the Citespace bibliometric tool, the CNKI core database in the past 24 years(1997—2020)is taken as the data source. The bibliometric and visual analysis are performed from publication year, journal source, research institution, and keywords, and network maps of relationships between research institutions and keyword co-occurrence is generated. The results show that the number of automatic driving documents has been increasing in China in recent 5 years. The journal with the most of related papers is China Journal of Highway and Transport. Its research directions of the automatic driving vehicles include: ①Research on target detection and scene perception. ②Research on decision making and control. ③Research on responsibility delineation of traffic accidents. In the future, for mixed driving environment, traffic management should combine vehicle-road coordination and high-precision map technology, study from the design of signs and markings, signal timing optimization, ownership of road rights, and the delineation of traffic accident responsibilities, thus making road transportation safe, efficient and convenient.
- intelligent transportation system /
- traffic management /
- autonomous driving /
- route planning /
- scene awareness /
- confirmation of responsibility /
- hybrid driving

HTML全文

图 1 “自动+人工”混合驾驶程度

Figure 1. Degree of "automatic+manual" driving

下载: 全尺寸图片幻灯片

图 2 文献筛选过程

Figure 2. Literature screening process

下载: 全尺寸图片幻灯片

图 3 1997—2020年发文量

Figure 3. Number of papers published in1997—2020

下载: 全尺寸图片幻灯片

图 4 研究机构关系图谱

Figure 4. Relationship of research institutions

下载: 全尺寸图片幻灯片

图 5 自动驾驶研究突现词

Figure 5. Emergent words in automatic driving research

下载: 全尺寸图片幻灯片

图 6 关键词共现网络图谱

Figure 6. Co-occurrence network of keywords

下载: 全尺寸图片幻灯片

图 7 结合车路协同的目标检测

Figure 7. Target detection combined with vehicle-road cooperation

下载: 全尺寸图片幻灯片

图 8 高精度地图+车路协同系统的路径规划技术

Figure 8. Path planning technologies of high-precision map + collaborative vehicle-road system

下载: 全尺寸图片幻灯片

图 9 混合驾驶环境下交通管理问题分析

Figure 9. Analysis of traffic management problems in the mixed driving environment

下载: 全尺寸图片幻灯片

表 1 各期刊来源分布

Table 1. Source distribution of Journals

期刊来源	发文数量/篇	占比/%
SCI期刊	4	0.76
EI期刊	145	27.67
CSCD/CSSCI期刊	281	53.63
北大核心期刊	94	17.94
合计	524	100.00

下载: 导出CSV

表 2 自动驾驶文献EI期刊发文统计

Table 2. Statistics of papers published in EI journals of automatic driving literature

期刊	发文量/篇	占比/%	复合影响因子
《中国公路学报》	27	5.15	2.438
《汽车工程》	13	2.48	1.752
《交通运输系统工程与信息》	9	1.72	1.827
《吉林大学学报(工学版)》	8	1.53	1.386
《自动化学报》	6	1.15	4.466
注：“占比” 为占全部文献的比例。

下载: 导出CSV

表 3 自动驾驶文献CSCD/CSSCI期刊发文统计

Table 3. Statistics of papers published in CSCD/CSSCI Journals

期刊	发文量/篇	占比/%	复合影响因子
《汽车技术》	20	3.82	1.406
《汽车安全与节能学报》	16	3.05	2.018
《交通信息与安全》	8	1.53	1.265
《测绘通报》	7	1.34	1.806
《计算机工程与应用》	7	1.34	1.748
《重庆交通大学学报（自然科学版)》	7	1.34	1.048
《系统仿真学报》	7	1.34	0.990
《计算机应用》	6	1.15	2.063
《法学》	5	0.95	6.283
《科技管理研究》	5	0.95	1.952

下载: 导出CSV

表 4 关键词共现（频次大于4）

Table 4. Co-occurrence of keywords (frequency > 4)

序号	频次	中心度	关键词	序号	频次	中心度	关键词
1	511	0.46	自动驾驶汽车	16	8	0.06	自动驾驶系统
2	83	0.30	人工智能	17	7	0.13	轨迹规划
3	72	0.04	目标检测	18	7	0.09	注意义务
4	59	0.11	深度学习	19	7	0.06	轨迹跟踪
5	55	0.11	卷积神经网络	20	6	0.08	智能车辆
6	54	0.20	产品责任	21	6	0.01	强化学习
7	47	0.25	模型预测控制	22	5	0.08	交通肇事
8	46	0.25	自适应	23	4	0.10	遗传算法
9	45	0.23	计算机视觉	24	4	0.07	驾驶行为
10	16	0.13	交通工程	25	4	0.06	责任分配
11	15	0.07	智能交通系统	26	4	0.04	路径跟踪
12	12	0.09	刑事责任	27	4	0.03	车路协同
13	10	0.23	车辆工程	28	4	0.02	场景理解
14	10	0.04	路径规划	29	4	0.01	车辆检测
15	9	0.11	模糊控制	30	4	0.00	汽车产业

下载: 导出CSV

参考文献(41)

[1]	MONTANARO U, DIXIT S, FALLAH S, et al. Towards connect-ed autonomous driving: review of use-cases[J]. Vehicle System Dynamics, 2019, 57 (6): 779-814. doi: 10.1080/00423114.2018.1492142
[2]	KALRA N, PADDOCK S M. Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability? [J]. Transportation Research Part A: Policy and Practice, 2016 (94) : 182-193.
[3]	张新钰, 高洪波, 赵建辉, 等. 基于深度学习的自动驾驶技术综述[J]. 清华大学学报(自然科学版), 2018, 58 (4): 438-444. https://www.cnki.com.cn/Article/CJFDTOTAL-QHXB201804017.htm ZHANG Xinyu, GAO Hong, ZHAO Janhui, et al. Overview of deep learning intelligent driving methods[J]. Journal of Tsinghua University(Science and Technology), 2018, 58(4): 438-444. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-QHXB201804017.htm
[4]	朱冰, 张培兴, 赵健, 等. 基于场景的自动驾驶汽车虚拟测试研究进展[J]. 中国公路学报, 2019, 32 (6): 1-19. ZHU Bing, ZHANG Peixing, ZHAO Jian, et al. Review of scenario-based virtual validation methods for automated vehicles[J]. China Journal of Highway and Transport, 2019, 32(6): 1-19. (in Chinese)
[5]	徐筱秦, 冯忠祥, 李靖宇. 驾驶员接管自动驾驶车辆研究进展[J]. 交通信息与安全, 2019, 37 (5): 1-8. doi: 10.3963/j.issn.1674-4861.2019.05.001 XU Xiaoqin, FENG Zhongxiang, LI Jingyu. A review of progresses on drivers'takeover behaviors of automatic vehicles[J]. Journal of Transport Information and Safety, 2019, 37(5): 1-8. (in Chinese) doi: 10.3963/j.issn.1674-4861.2019.05.001
[6]	CAO Jingwei, SONG Chuanxue, XIAO Feng, et al. Lane detection algorithm for intelligent vehicles in complex road conditions anddynamicenvironment[R/OL](. 2019-12)[2021-04-01]. https://www.webofscience.com/wos/alldb/full-record/WOS:000479160300133.
[7]	王海, 王宽, 蔡英凤, 等. 基于改进级联卷积神经网络的交通标志识别[J]. 汽车工程, 2020, 42 (9): 1256-1262+1269. https://www.cnki.com.cn/Article/CJFDTOTAL-QCGC202009016.htm WANG Hai, WANG Kuan, CAI Yingfeng, et al. Traffic sign recognition based on improved cascade convolution neural network[J]. Automotive Engineering, 2020, 42(9): 1256-1262 + 1269. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-QCGC202009016.htm
[8]	YASSMINA S, ALI B. An overview of traffic sign detection and classification methods[J]. International Journal of Multimedia Information Retrieval, 2017, 6 (3): 193-210. doi: 10.1007/s13735-017-0129-8
[9]	王瑞军, 李斌, 应世杰, 等. 智能公路磁道钉编码差错控制技术研究[J]. 公路交通科技, 2008 (10): 134-139. doi: 10.3969/j.issn.1002-0268.2008.10.027 WANG Ruijun, LI Bin, YING Shijie, et al. Research on magnetic markers coding mistake control technology for intelligent highway system[J]. Journal of Highway and Transportation Research and Development, 2008 (10): 134-139. (in Chinese) doi: 10.3969/j.issn.1002-0268.2008.10.027
[10]	钱基德, 陈斌, 钱基业, 等. 基于感兴趣区域模型的车道线快速检测算法[J]. 电子科技大学学报, 2018, 47(3): 356-361. https://www.cnki.com.cn/Article/CJFDTOTAL-DKDX201803006.htm QIAN Jide, CHEN Bin, QIAN Jiye, et al. Fast lane detection algorithm based on region of interest model[J]. Journal of University of Electronic Science and Technology of China, 2020, 47 (3): 356-361. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DKDX201803006.htm
[11]	钟泽滨. 一种用于车道线识别的图像灰度化方法[J]. 同济大学学报(自然科学版), 2019, 47 (增刊1): 178-182. https://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ2019S1033.htm ZHONG Zebin. An image graying method for lane detection[J]. Journal of Tongji University(Natural Science), 2019, 47 (S1): 178-182. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ2019S1033.htm
[12]	MAMMERI A, BOUKERCHE A, TANG Z Z. A real-time lane marking localization, tracking and communication system[J]. Computer Communications, 2015 (73) : 132-143.
[13]	OBRADOVIC D, KONJOVIC Z, PAP E, et al. Linear fuzzy space based road lane model and detection[J]. Knowledge-Based Systems, 2013, 38 (4): 37-47.
[14]	PARK H. Lane detection algorithm based on Hough transform for high-speed self driving vehicles[J]. International Journal of Web and Grid Services, 2019, 15 (3) : 240-250. doi: 10.1504/IJWGS.2019.10022421
[15]	PALAFOX P R, BETZ J, NOBIS F, et al. Semantic depth: fusing semantic segmentation and monocular depth estimation for enabling autonomous driving in roads without lane lines[R/OL].(2019-12)[2021-04-01]. https://www.webofscience.com/wos/alldb/full-record/WOS:000479160300191
[16]	LI Xi, MA Huimin, WANG Xiang, et al. Traffic light recognition for complex scene with fusion detections[J]. IEEE Transactions On Intelligent Transportation Systems, 2018, 19(1): 199-208. doi: 10.1109/TITS.2017.2749971
[17]	HAN Bing, WANG Yunhao, YANG Zheng, et al. Small-scale pedestrian detection based on deep neural network[J]. IEEE Transactions On Intelligent Transportation Systems, 2020, 21 (7): 3046-3055. doi: 10.1109/TITS.2019.2923752
[18]	WANG Xue. Moving vehicle detection and tracking based on video sequences[J]. Traitement Du Signal, 2020, 37(2): 325-331. doi: 10.18280/ts.370219
[19]	张丞, 何坚, 王伟东. 空间上下文与时序特征融合的交警指挥手势识别技术[J]. 电子学报, 2020, 48 (5): 966-974. doi: 10.3969/j.issn.0372-2112.2020.05.018 ZHANG Cheng, HE Jian, WANG Weidong. Visual recognition of chinese traffic police gestures based on spatial context and temporal features[J]. Acta Electronica Sinica, 2020, 48(5): 966-974. (in Chinese) doi: 10.3969/j.issn.0372-2112.2020.05.018
[20]	GUO Fan, TANG Jin, WANG Xile. Gesture recognition of traffic police based on static and dynamic descriptor fusion[J]. Pattern Analysis and Applications, 2017, 76 (6): 8915-8936.
[21]	HE Jian, ZHANG Cheng, HE Xinlin, et al. Visual recognition of traffic police gestures with convolutional pose machine and handcrafted features[J]. Neurocomputing, 2020 (390) : 248-259.
[22]	SUKUVAARA T, NURMI P. Wireless traffic service platform for combined vehicle-to-vehicle and vehicle-to-infrastructure communications[J]. IEEE Wireless Communications, 2009, 16 (6): 54-61. doi: 10.1109/MWC.2009.5361179
[23]	LIU Changhao, ZHANG Yixiao, ZHANG Tingting, et al. High throughput vehicle coordination strategies at road intersections[J]. IEEE Transactions on Vehicular Technology, 2020, 69 (12): 14341-14354. doi: 10.1109/TVT.2020.3029933
[24]	任永利, 董航瑞. 车路协同+自动驾驶助力郑州智慧岛交通强国示范[J]. 科技导报, 2020, 38 (9): 82-88. https://www.cnki.com.cn/Article/CJFDTOTAL-KJDB202009013.htm REN Yongli, DONG Hangrui. To build a powerful transportation demonstration of China: Zhengzhou smart island with the help of vehicle-road coordination and self-driving[J]. Science & Technology Review, 2020, 38 (9): 82-88. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-KJDB202009013.htm
[25]	张家旭, 张振兆, 赵健, 等. 采用极点配置的自动驾驶汽车换道路径规划与跟踪控制[J]. 西安交通大学学报, 2020, 54 (10): 160-167. https://www.cnki.com.cn/Article/CJFDTOTAL-XAJT202010020.htm ZHANG Jiaxu, ZHANG Zhenzhao, ZHAO Jian, et al. A path planning and tracking control method for lane changing of autonomous vehicle using pole assignment[J]. Journal of Xi'an Jiaotong University, 2020, 54 (10): 160-167. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XAJT202010020.htm
[26]	WANG Lukun, ZHAO Xiaoying, SU Hao, et al. Lane changing trajectory planning and tracking control for intelligent vehicle on curved road[J]. Journal of Xi'an Jiaotong University, 2016, 5 (1): 1-18.
[27]	LI Xingyu, TANG Bo, Ball J, et al. Rollover-free path planning for off-road autonomous driving[J]. Electronics, 2019, 8 (6): 614. doi: 10.3390/electronics8060614
[28]	王鑫鹏, 陈志军, 吴超仲, 等. 考虑驾驶风格的智能车自主驾驶决策方法[J]. 交通信息与安全, 2020, 38 (2): 37-46. https://www.cnki.com.cn/Article/CJFDTOTAL-JTJS202002008.htm WANG Xinpeng, CHEN Zhijun, WU Chaozhong, et al. A method of automatic driving decision for smart car considering driving style[J]. Journal of Transport Information and Safety, 2020, 38 (2): 37-46. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JTJS202002008.htm
[29]	WANG Yulei, SHAO Qian, ZHOU Jian, et al. Longitudinal and lateral control of autonomous vehicles in multi-vehicle driving environments[J]. IET Intelligent Transport Systems, 2020, 14 (8): 924-935. doi: 10.1049/iet-its.2019.0846
[30]	周维, 过学迅, 裴晓飞, 等. 基于RRT与MPC的智能车辆路径规划与跟踪控制研究[J]. 汽车工程, 2020, 42(9): 1151-1158. https://www.cnki.com.cn/Article/CJFDTOTAL-QCGC202009002.htm ZHOU We, GUO Xuexun, PEI Xiaofei, et al. Study on path planning and tracking control for intelligent vehicle based on RRT and MPC[J]. Automotive Engineering, 2020, 42(9): 1151-1158. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-QCGC202009002.htm
[31]	TAHIR Z, QURESHI AH, AYAZ Y, et al. Using artificial potential field theory for a cooperative control model in a connected and automated vehicles environment[J]. Transportation Research record, 2020, 2674 (9): 1005-1018. doi: 10.1177/0361198120933271
[32]	PRIMATESTA S, GUGLIERI G, RIZZO A. A risk-aware path planning strategy for uavs in urban environments[J]. Journal of Intelligent & Robotic systems, 2019, 95 (2): 629-643.
[33]	BAHAR MRB, GHIASI A R, BAHAR H B. Grid roadmap based ANN corridor search for collision free, path planning[J]. Scientia Iranica, 2012, 19 (6): 1850-1855. doi: 10.1016/j.scient.2012.02.028
[34]	LI Junjun, XU Bowei, YANG Yongsheng, et al. Quantum ant colony optimization algorithm for AGVs path planning based on Bloch coordinates of pheromones[J]. Natural Computing, 2020, 19 (4): 673-682. doi: 10.1007/s11047-018-9711-0
[35]	ZHAO Dongming, YU Huimin, FANG Xiang, et al. A path planning method based on multi objective cauchy mutation cat swarm optimization algorithm for navigation system of intelligent patrol car[J]. IEEE Access, 2020 (8) : 151788-151803.
[36]	JIANG Kun, YANG Diange, LIU Chaoran, et al. A flexible multi-layer map model designed for lane-level routeplanning in autonomous vehicles[J]. IEEE Transactions on knowledge and Data Engineering, 2019 (2): 305-318.
[37]	胡钊政, 孙莹妹, 李祎承. 路面路标高精度地图构建与多尺度车辆定位[J]. 哈尔滨工业大学学报, 2019, 51 (9): 149-156. https://www.cnki.com.cn/Article/CJFDTOTAL-HEBX201909023.htm HU Zhaozheng, SUN Yingmei, LI Weicheng. High definition map construction from pavement landmarks for multiscale vehicle localization[J]. Journal of Harbin Institute of Technology, 2019, 51 (9): 149-156. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HEBX201909023.htm
[38]	陈志军, 许开立. 生产安全事故侵权责任的归责原则研究[J]. 中国安全科学学报, 2016, 26 (9): 13-18. https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK201609003.htm CHEN Zhijun, XU Kaili. Study on liability principles for work safety accident tort liability[J]. China Safety Science Journal, 2016, 26 (9): 13-18. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK201609003.htm
[39]	CUER R, PIETRAC L, NIEL E. A formal framework for the safe design of the autonomous driving supervision[J]. Reliability Engineering & System Safety, 2018, (174) : 29-40.
[40]	牛彬彬. 我国高度自动驾驶汽车侵权责任体系之建构[J]. 西北民族大学学报(哲学社会科学版), 2019 (3): 177-188. doi: 10.3969/j.issn.1001-5140.2019.03.023 NIU Binbin. Construction of highly autonomous vehicles' tort liability system in China[J]. Journal of Northwest Minzu University(Philosophy and Social Sciences), 2019(3): 177-188. (in Chinese) doi: 10.3969/j.issn.1001-5140.2019.03.023
[41]	张龙. 自动驾驶型道路交通事故责任主体认定研究[J]. 苏州大学学报(哲学社会科学版), 2018, 39 (5): 79-80. https://www.cnki.com.cn/Article/CJFDTOTAL-SZDX201805010.htm ZHANG Long. Research on the identification of the responsible subject of automatic driving road traffic accidents[J]. Journal of Soochow University(Philosophy & Social Science Edition), 2018, 39 (5): 79-80. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-SZDX201805010.htm