网联自动驾驶车辆道路交通安全研究综述

郭延永; 刘佩; 袁泉; 刘攀; 徐进; 张晖

doi:10.19818/j.cnki.1671-1637.2023.05.002

网联自动驾驶车辆道路交通安全研究综述

doi: 10.19818/j.cnki.1671-1637.2023.05.002

郭延永^1,,
刘佩¹,
袁泉²,
刘攀^1, ,,
徐进³,
张晖⁴

1.
东南大学交通学院，江苏南京 211189
2.
清华大学车辆与运载学院，北京 100084
3.
重庆交通大学交通运输学院，重庆 400074
4.
武汉理工大学智能交通系统研究中心，湖北武汉 430063

基金项目:

国家自然科学基金项目 52272343

国家自然科学基金项目 51925801

国家自然科学基金项目 52232012

详细信息

作者简介:
郭延永(1985-)，男，河北邢台人，东南大学教授，工学博士，从事交通冲突技术与自动驾驶安全理论研究

通讯作者:
刘攀(1979-)，男，江苏扬州人，东南大学教授，工学博士

中图分类号: U491.3
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出版历程
- 收稿日期: 2023-03-21
- 网络出版日期: 2023-11-17
- 刊出日期: 2023-10-25

Review on research of road traffic safety of connected and automated vehicles

GUO Yan-yong^1
,,
LIU Pei¹,
YUAN Quan²,
LIU Pan^{1
, ,},
XU Jin³,
ZHANG Hui⁴

1.
School of Transportation, Southeast University, Nanjing 211189, Jiangsu, China
2.
School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
3.
School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
4.
Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, Hubei, China

Funds:

National Nature Science Foundation of China 52272343

National Nature Science Foundation of China 51925801

National Nature Science Foundation of China 52232012

More Information

Author Bio:
GUO Yan-yong(1985-), male, professor, PhD, guoyanyong@seu.edu.cn

LIU Pan(1979-), male, professor, PhD, liupan@seu.edu.cn

摘要

摘要: 为全面了解网联自动驾驶交通安全领域的研究进展，利用文献计量方法通过Web of Science核心数据库对Connected and Automated (Autonomous) Vehicles、Connected (Autonomous) Vehicles、Traffic Safety (Accident, Crash, Collision, Conflict)等关键词进行检索，共获取2010至2021年2 130篇相关文献，涵盖5 474位作者和7 017个关键词；利用科学知识图谱对网联自动驾驶道路交通安全研究发展历程、研究归属地、研究主题与内容、研究热点等进行分析总结和可视化解析；通过研究主题和热点的分析指出未来研究方向。研究结果表明：网联自动驾驶道路交通安全研究经历了起步阶段、缓慢增长阶段和快速发展阶段；美国和中国是当今世界对网联自动驾驶道路交通安全领域贡献最大的2个研究主体；研究主题主要围绕宏微观交通流、交通系统影响(交通出行、交通环境、交通安全)、车辆安全避障与路径规划、交通安全评价等展开，研究热点重点围绕网联自动驾驶交通控制与系统优化、新型混合交通流交通安全分析、微观行为建模与仿真安全评估等；未来研究需重视由单车安全转向交通流事故风险传播研究，突破智能网联车队群体决策与编队控制技术，构建虚拟现实下智能网联数据化仿真环境与深度测试平台，挖掘网联自动驾驶人机共驾情境下驾驶人接管绩效评价体系，从而进行精细化的事故风险致因分析、交通安全建模与评估以及事故风险防控策略与算法研究。
- 智能交通 /
- 道路交通安全 /
- 网联自动驾驶车辆 /
- 文献计量统计 /
- 科学知识图谱 /
- 可视化分析
Abstract: To comprehensively understand the research progress in the field of traffic safety concerning connected and automated vehicles (CAVs), a bibliometric method was used to retrieve key words, such as connected and automated (autonomous) vehicles, connected (autonomous) vehicles, and traffic safety (accident, crash, collision, conflict) through the Web of Science core database. A total of 2 130 relevant publications spanning from 2010 to 2021 were retrieved. These publications encompassed contributions from 5 474 authors and addressed 7 017 key words. A map of scientific knowledge was employed to analyze, summarize, and visual elucidate the developmental trajectory, research affiliations, research themes and contents, and research focal points of investigations in the realm of road traffic safety for CAVs. By scrutinizing the research themes and focal points, forthcoming research directions were delineated. Analysis results show that the study of road traffic safety for CAVs has undergone stages of inception, gradual progression, and rapid expansion. Presently, the United States and China are the foremost contributors to research on the road traffic safety for CAVs. Research themes primarily focus on the macro and micro traffic flow, traffic system impacts (travel, traffic environment, and traffic safety), vehicle safety and obstacle avoidance, route planning, and traffic safety assessment. Research focal points encompass the CAV traffic control, system optimization, new hybrid traffic flow safety analysis, micro-behavior modeling, and simulation-based safety evaluation. Future research should pivot towards a transition from the single-vehicle safety to the traffic flow accident risk propagation. This shift calls for breakthroughs in intelligent connected fleet decision-making and formation control technology. Additionally, there is a need to establish a virtual reality-based intelligent connected data-driven simulation environment, coupled with an in-depth testing platform. These advancements will facilitate an assessment system of driver takeover performance in the context of CAV man-machine co-driving scenarios. Ultimately, this will enable a refined analysis on the accident risk causation, traffic safety modeling and assessment, as well as the development of strategies and algorithms for the accident risk prevention and control.
- intelligent transportation /
- road traffic safety /
- connected and automated vehicle /
- bibliometric statistic /
- map of scientific knowledge /
- visual analysis

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