“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
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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

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图 1 “自动+人工”混合驾驶程度

Figure 1. Degree of "automatic+manual" driving

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图 2 文献筛选过程

Figure 2. Literature screening process

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图 3 1997—2020年发文量

Figure 3. Number of papers published in1997—2020

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图 4 研究机构关系图谱

Figure 4. Relationship of research institutions

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图 5 自动驾驶研究突现词

Figure 5. Emergent words in automatic driving research

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图 6 关键词共现网络图谱

Figure 6. Co-occurrence network of keywords

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图 7 结合车路协同的目标检测

Figure 7. Target detection combined with vehicle-road cooperation

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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

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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

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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

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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。

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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

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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

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