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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

郭延永, 刘佩, 袁泉, 刘攀, 徐进, 张晖. 网联自动驾驶车辆道路交通安全研究综述[J]. 交通运输工程学报, 2023, 23(5): 19-38. doi: 10.19818/j.cnki.1671-1637.2023.05.002
引用本文: 郭延永, 刘佩, 袁泉, 刘攀, 徐进, 张晖. 网联自动驾驶车辆道路交通安全研究综述[J]. 交通运输工程学报, 2023, 23(5): 19-38. doi: 10.19818/j.cnki.1671-1637.2023.05.002
GUO Yan-yong, LIU Pei, YUAN Quan, LIU Pan, XU Jin, ZHANG Hui. Review on research of road traffic safety of connected and automated vehicles[J]. Journal of Traffic and Transportation Engineering, 2023, 23(5): 19-38. doi: 10.19818/j.cnki.1671-1637.2023.05.002
Citation: GUO Yan-yong, LIU Pei, YUAN Quan, LIU Pan, XU Jin, ZHANG Hui. Review on research of road traffic safety of connected and automated vehicles[J]. Journal of Traffic and Transportation Engineering, 2023, 23(5): 19-38. 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
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

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

Funds: 

National Nature Science Foundation of China 52272343

National Nature Science Foundation of China 51925801

National Nature Science Foundation of China 52232012

More Information
  • 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。

     

  • 图  1  网联自动驾驶车辆车路协同系统

    Figure  1.  Cooperative system for CAV and road infrastructure

    图  2  研究方法流程

    Figure  2.  Flow of research methodology

    图  3  网联自动驾驶车辆道路交通安全研究发展趋势

    Figure  3.  Development trend of road traffic safety research of CAV

    图  4  新型混合交通流道路安全问题

    Figure  4.  Road safety issues in new hybrid traffic flow

    图  5  网联自动驾驶车辆道路交通安全研究发展阶段

    Figure  5.  Development stages in research on road traffic safety of CAV

    图  6  网联自动驾驶车辆道路交通安全研究文献地理分布

    Figure  6.  Geographical distribution of road traffic safety research literatures in CAV

    图  7  网联自动驾驶车辆道路交通安全研究文献共被引分析

    Figure  7.  Co-citation analysis of road traffic safety research literature on CAV

    图  8  网联自动驾驶道路交通安全研究主题

    Figure  8.  Research themes in road traffic safety for CAV

    图  9  道路交通安全研究文献关键词共现分析

    Figure  9.  Co-occurrence analysis of key words in road traffic safety research literature

    图  10  网联自动驾驶车辆道路交通安全研究热点

    Figure  10.  Road traffic safety research hotspots for CAV

    图  11  道路交通安全仿真环境与深度测试发展进程

    Figure  11.  Development process of road traffic safety simulation environment and in-depth testing

    表  1  基于网联自动驾驶车辆微观交通流的研究主题总结

    Table  1.   Summary of research topics based on micro traffic flow of CAV

    应用场景 相关文献 微观交通流研究内容 研究方法
    微观跟驰 [54] 传统微观单车道模型、驾驶人辅助系统 智能驾驶模型
    纵向控制 [55]、[56] 驾驶人辅助系统及其优化改进 自适应巡航控制系统协同自适应巡航控制
    [11] 研究不同ACC策略对交通流特性的影响 强化智能驾驶模型
    [57]、[58] 研究CACC系统对智能网联交通流特性的影响 微观交通仿真模型
    横向控制 [59] 构建考虑相邻车辆运动状态的网联自动驾驶车道变换轨迹生成方法,应比较多种非线性变换曲线以评估选择最佳换道模型 交会引导技术
    [60] 考虑网联自动驾驶车辆横纵向运动之间的耦合效应,确定换道行为的最佳控制序列和碰撞规避及动态安全约束 非线性单轨车辆动力学模型多段变道过程模型
    编队换道 [61] 研究网联自动驾驶车队在拥挤车流中保证编队稳定性的同时提高变道成功率 协同自适应巡航车辆编队变道控制器
    [62] 提出能够仿真具有不同通信能力车辆安全跟驰行为的技术框架以解决车辆联通性和自动化区分不足的问题 多模型融合
    [63] 开发计算自动驾驶车辆和常规车辆混合交通流通行能力的通用公式以根据需求确定跨车道的自动驾驶车辆分布 多目标优化
    下载: 导出CSV

    表  2  基于网联自动驾驶车辆对交通系统影响的研究主题总结

    Table  2.   Summary of research topics based on impact of CAV on transportation systems

    应用领域 相关文献 对交通系统影响的相关研究内容 研究方法
    交通环境交通安全 [64] 验证网联自动驾驶可解决汽车共享障碍,降低车辆排放对环境影响,保障共享车使用者安全出行,达到整体利益最大化 智能体模型
    交通出行交通环境交通安全 [21] 探讨网联自动驾驶在一阶(交通、出行成本和出行选择),二阶(车辆所有权、地点选择和土地使用以及交通基础设施)和三阶(能源消耗、空气污染、交通安全、社会公平、经济和公共健康)对社会政策的潜在影响 连锁反应概念
    公众态度 [65] 研究公众对网联自动驾驶的态度,确定用户接受程度和购买意愿,评估自动驾驶技术推广与个人变量的相关性 网络问卷调查
    用户偏好 [66] 探究选择拥有和使用网联自动驾驶车辆的个人动机,开发网联自动驾驶车辆长期选择决策模型 陈述偏好问卷调查
    下载: 导出CSV

    表  3  基于网联自动驾驶车辆安全避障与交通安全评价的研究主题总结

    Table  3.   Summary of research topics based on safety obstacle avoidance and traffic safety evaluation of CAV

    应用领域 相关文献 主要研究内容 研究方法
    运动规划、反馈控制 [67] 比较多种典型运动规划和反馈控制算法,分析优势和局限性 并列比较
    安全避障 [68] 对决策过程分步骤进行算法复杂性和性能准确性的评估 批判性评估
    轨迹规划 [16] 回顾智能驾驶车辆运动规划技术,指出研究目标应集中优化复杂驾驶环境下的运动轨迹规划,设置具有避障功能的导航系统 综合性分析
    安全替代指标评估 [69] 系统总结安全替代指标在网联自动驾驶安全建模和评价中的应用,回顾不同安全替代指标的有效性和适用性 系统性总结
    [70] 提取碰撞时间TTC和制动次数BTN作为安全替代指标,基于极值理论估计事故频率以评估自动驾驶交通安全 极值模型
    下载: 导出CSV

    表  4  网联自动驾驶车辆交通控制优化与运动规划研究热点总结

    Table  4.   Summary of research hotspots on traffic control optimization and motion planning of CAV

    应用场景 相关文献 主要研究内容
    高速公路 [77] 利用IDM模型评估不同渗透率条件下自动驾驶编队的纵向安全性
    [75] 构建自动驾驶时空波动率曲线以识别交通网络中的潜在危险并做出积极的驾驶决策
    [76] 通过优化安全变道次数最大限度地减少混合交通流中断以提高吞吐量并减少拥堵
    信号交叉口 [10] 利用网联自动驾驶车辆作交叉口控制媒介通过预测微观模拟算法响应即时车辆需求
    [78]、[79] 基于启发式算法识别网联自动驾驶车辆轨迹以产生最佳安全性能的交通控制
    [8] 使用交叉口周边交通状态数据进行实时自适应信号相位分配
    [80] 开发适用于城市交通走廊的协作式信号控制算法
    [81] 提出考虑车辆随机到达的两级控制模型,用于不同交通需求下多个网联自动驾驶车辆的交通信号配时设计和轨迹规划
    [82] 构建根据不同交通状态指定合适信号控制方法的概念框架
    车道编队 [47] 基于行车环境势场和车辆动力学建模,结合模型预测控制中的优化算法完成编队车辆轨迹规划和控制,实现动态避障
    [83] 介绍Demo 2000协同驾驶系统中的自动驾驶和车间通信技术并提出多车道编队概念
    [84] 基于协作式自适应巡航控制车辆跟驰算法,添加分层控制和信号交叉口优化控制模块,准确估计车辆状态、监控潜在碰撞风险进而优化车辆轨迹、调整信号配时
    下载: 导出CSV

    表  5  网联自动驾驶车辆新型混合交通流交通安全分析研究热点

    Table  5.   Research hotspots on traffic safety analysis of new hybrid traffic flow for CAV

    应用领域 相关文献 主要研究内容
    混合交通流运行效率 [91] 基于传统车辆与网联自动驾驶车辆的异质交通流模型分析不同渗透率下网联自动驾驶车辆对交通流量的影响
    [92] 通过驾驶模拟器研究网联自动驾驶车辆对常规车辆驾驶人交互行为影响
    [93] 设计基于模糊规则的网联自动驾驶车辆运动控制系统促进与常规车辆的博弈合作
    [94] 开发网联自动驾驶车辆情景感知安全控制模块解决城市交通网络中自动驾驶车辆和常规车辆交互冲突问题
    混合交通流事故分析 [95] 利用加利福尼亚州实车试验数据集分析网联自动驾驶车辆主要事故类型
    [96] 利用加利福尼亚州实车试验数据集探究影响自主驾驶模式脱离的影响因素
    其他方面安全管理问题 [41]、[45] 信息、性能、政策等安全管理挑战,包括法律保障、软件防护、隐私安全、城市发展
    [97]、[98]
    下载: 导出CSV

    表  6  网联自动驾驶车辆微观行为建模与仿真安全评估研究热点

    Table  6.   Research hotspots on micro-behavior modeling and simulation safety evaluation of CAV

    应用场景 相关文献 主要研究内容
    车辆交互 [99] 使用MATLAB构建仿真环境利用IDM模型模拟常规车辆跟驰行为、基于CACC跟驰机制确定自动驾驶车辆纵向决策操作,提出混合交通跟驰策略以识别整体机动性、安全性最佳的车队配置
    [100] 使用VISSIM构建微观仿真环境利用Wiedemann模型控制常规车辆跟车行为,基于规则控制算法对网联驾驶车辆行为部署,使用替代安全评估模块SSAM评估不同网联自动驾驶车辆渗透率对交通性能的影响
    [101] 建立兼顾自动驾驶车辆平均车头时距和电子油门角度差的扩展跟驰模型,稳定自动驾驶车辆交通流、减少因交通流紊乱造成的车间冲突
    [102] 综合考虑网联自动驾驶车辆轨迹控制、常规车辆跟驰和变道操作,添加自由变道约束设计基于变道感知轨迹优化的网联自动驾驶车辆跟驰模型,感知常规车辆轨迹变化以采取强制变道让步策略
    仿真测试 [4]、[5]、[15] [103]、[104] 基于仿真场景中车辆的微观驾驶行为和反馈信息对网联自动驾驶车辆进行自适应巡航控制、自动转向技术、避障轨迹规划、风险行为检测等安全系统设计
    安全评估 [12]、[105]~[110] 虚拟测试和仿真模拟、数学建模与数字孪生、场景搭建和行为分析、驾驶模拟与试点测试等
    人机共驾 [111]~[117] 人机共驾车辆控制权切换安全、接管能力评价指标选择、驾驶人接管能力影响因素、接管能力提升途径等
    下载: 导出CSV
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