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面向智能网联汽车的车路协同感知技术及发展趋势

张新钰 卢毅果 高鑫 黄雨宁 刘华平 李骏

张新钰, 卢毅果, 高鑫, 黄雨宁, 刘华平, 李骏. 面向智能网联汽车的车路协同感知技术及发展趋势. 自动化学报, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c230575
引用本文: 张新钰, 卢毅果, 高鑫, 黄雨宁, 刘华平, 李骏. 面向智能网联汽车的车路协同感知技术及发展趋势. 自动化学报, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c230575
Zhang Xin-Yu, Lu Yi-Guo, Gao Xin, Huang Yu-Ning, Liu Hua-Ping, Li Jun. Vehicle-road collaborative perception technology and development trend for intelligent connected vehicle. Acta Automatica Sinica, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c230575
Citation: Zhang Xin-Yu, Lu Yi-Guo, Gao Xin, Huang Yu-Ning, Liu Hua-Ping, Li Jun. Vehicle-road collaborative perception technology and development trend for intelligent connected vehicle. Acta Automatica Sinica, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c230575

面向智能网联汽车的车路协同感知技术及发展趋势

doi: 10.16383/j.aas.c230575
基金项目: 国家重点研发计划 (2018YFE0204300), 国家自然科学基金 (62273198, U1964203) 资助
详细信息
    作者简介:

    张新钰:清华大学车辆与运载学院研究员, 主要研究方向为智能驾驶和多模态信息融合. E-mail: xyzhang@tsinghua.edu.cn

    卢毅果:新疆大学软件学院硕士研究生. 主要研究方向为计算机视觉和语义分割. E-mail: yiguolu@stu.xju.edu.cn

    高鑫:中国矿业大学 (北京) 计算机科学与技术专业博士研究生. 主要研究方向为模式识别, 多模态融合, 图像处理. E-mail: bqt2000405024@student.cumtb.edu.cn

    黄雨宁:新疆大学软件学院硕士研究生. 主要研究方向为目标检测及其在计算机视觉中的应用. E-mail: 107552204759@stu.xju.edu.cn

    刘华平:清华大学计算机科学与技术系教授. 2004 年获清华大学计算机科学与技术博士学位. 主要研究方向为智能机器人感知, 智能机器人学习与控制. E-mail: hpliu@tsinghua.edu.cn

    李骏:中国工程院院士, 清华大学车辆与运载学院教授, 中国汽车工程学会理事长. 1989 年获吉林工业大学博士学位. 主要研究方向为智能网联汽车, 自动驾驶,发动机结构设计, 智能化参数设计. E-mail: junliqh@163.com

Vehicle-road Collaborative Perception Technology and Development Trend for Intelligent Connected Vehicle

Funds: Supported by National Key Research and Development Program of China (2018YFE0204300), National Natural Science Foundation of China (62273198, U1964203)
More Information
    Author Bio:

    ZHANG Xin-Yu Associate researcher at the School of Vehicle and Mobility at Tsinghua University, His research interests include intelligent driving and multimodal information fusion

    LU Yi-Guo Master student at School of Software, Xinjiang University. His main research interests are computer vision and semantic segmentation

    GAO Xin Ph.D. Candidate majoring in Computer Science and Technology in China University of Mining & Technology, Beijing, His research interests are pattern recognition, multimodal fusion, image processing

    HUANG Yu-Ning Master student at School of Software, Xinjiang University. Her research interests include deep learning, object detection and its applications in computer vision

    LIU Hua-Ping The professor with the Department of Computer Science and Technology at Tsinghua University. He received the Ph.D. degree in computer science and technology from Tsinghua University, in 2004. His main research interests include intelligent robot perception, intelligent robot learning and control

    LI Jun  Academician of Chinese Academy of Engineering, professor of School of Vehicle and Mobility, Tsinghua University, Chairman of China Society of Automotive Engineers. He received a Ph.D. degree in internal-combustion engineering at Jilin University of Technology, in 1989. His main research interests include intelligent connected vehicles, autonomous driving, engine structure design, intelligent parameter design

  • 摘要: 随着感知技术的不断发展以及智能交通基础设施的完善, 智能网联汽车应用在自动驾驶领域的地位逐渐提升, 自动驾驶感知从单车智能向车路协同迈进, 近年来涌现了一批新的协同感知技术与方法. 本文旨在全面阐述面向智能网联汽车的车路协同感知技术, 并总结相关可利用数据及该方向发展趋势. 首先对智能网联汽车的协同感知策略进行划分, 并总结了不同感知策略具备的优势与不足;其次, 对智能网联汽车协同感知的关键技术进行阐述, 包括车路协同感知过程中的感知技术与通信技术;然后对车路协同感知方法进行归纳, 总结了近年来解决协同感知中感知融合、感知信息选择与压缩等问题相关研究;最后对车路协同感知的大规模数据集进行了整理, 并对智能网联汽车协同感知的发展趋势进行了分析.
  • 图  1  车路协同示意图

    Fig.  1  Schematic diagram of vehicle-road collaboration

    图  2  协同感知策略对比图

    Fig.  2  Comparison chart of collaborative perception strategies

    图  3  基于点云数据的协同感知方法

    Fig.  3  Collaborative perception method based on point cloud data

    图  4  基于相机图像的协同感知方法

    Fig.  4  Collaborative perception method based on camera image

    图  5  通信交互示意图

    Fig.  5  Schematic diagram of communication interactions

    图  6  感知信息选择与压缩

    Fig.  6  Perceptual information selection and compression

    图  7  协同感知中的安全性问题

    Fig.  7  Security issues in collaborative perception

    表  1  不同协同策略传输性能分析

    Table  1  Analysis on transmission performance of different collaborative strategies

    策略/指标 带宽 (传输速率) 需求 精度/AP@50 算力评估
    早期协同20Mbps~60Mbps[19, 28]60.8[14]FPS2.63~3.45[19]
    GPUNvidia Quadro M4000
    MACs31.45G[14] on V2Xset
    中期协同10Mbps~20Mbps[20]V2VNet[22]57.8[14]FPS17.54~35.71[16]
    V2X-ViT[16]58.3[14]GPUTesla V100
    Where2comm[29]59.1[14]MACs60~200G[14] on V2Xset
    后期协同3Mbps~5Mbps[15]56.8[14]FPS2.56~3.23[20]
    GPUGeForce GTX 1080 Ti
    MACs31.34G[14] on V2Xset
    下载: 导出CSV

    表  2  智能网联汽车所具备的通信带宽

    Table  2  Communication bandwidth of intelligent connected vehicles

    通信方式/性能 车载通讯传输带宽 (速率) 通信延迟
    Wi-Fi 6Mbps~54Mbps[54]
    DSRC 3Mbps–27Mbps[54, 55] < 5ms[55]
    5G 290Mbps~350Mbps[56] 6ms~13ms[56]
    下载: 导出CSV

    表  3  车路协同感知方法汇总表

    Table  3  Summary table of vehicle-road cooperative perception methods

    方法年份感知/
    通信
    方法类型方法特点协同对象图像/点云/
    融合
    任务
    PFSCSP
    Cooper[17]2019感知稀疏点云检测V2V点云检测
    Who2com[85]2020通信低带宽需求, 无监督学习通信任务
    When2com[86]2020通信动态减少带宽需求, 无监督学习通信任务
    FRLCP[87]2021通信低带宽需求, 强化学习RB 分配, CPM 内容选择
    MMW-RCSF[49]2021通信传感器融合, 时空同步标定任务
    FPV-RCNN[24]2022感知损失优化, 基于关键点V2V传感器融合检测
    Coopernaut[59]2022感知端到端框架V2V点云控制决策
    CoBEVT[41]2022感知注意力机制V2V图像BEV分割
    V2XP-ASG[81]2022感知场景生成, 对抗攻击V2X点云检测
    V2X-ViT[16]2022感知位姿误差, 注意力机制, 自适应信息融合, 多尺度V2X点云检测
    MMVR[52]2022感知多尺度, 图神经网络, 注意力机制V2X传感器融合检测
    DAIR-V2X[15]2022感知时间补偿延迟融合, 时间异步鲁棒性V2X点云
    图像
    检测
    CO^3[35]2022感知无监督学习V2X点云检测
    RCP-MSF[53]2022感知鲁棒性增强, 低成本点云处理V2X传感器融合检测
    3D-Harmonic-Loss[88]2022感知损失函数优化, 点云稀疏检测V2X点云检测
    Where2comm[29]2022通信图神经网络, 低带宽需求, 特征压缩点云、图像检测
    PCP6G[89]2022通信新的数据传输类型, 特征压缩点云检测
    H2-FED[90]2022通信连接中断鲁棒性, 隐私保护计算, 联邦学习V2X通信任务
    CoPEM[91]2022通信感知错误建模V2X
    CAP-V2V[92]2022通信多车协同路径规划V2V点云路径规划
    ERCP[58]2022通信位姿误差鲁棒性, 基于迭代最近点, 基于最佳传输V2V
    PCG-SF[93]2022通信参数化协方差, 定位误差鲁棒性, 传感器融合定位任务
    VIMI[43]2023感知多尺度, 注意力机制, 特征压缩V2I图像检测
    FFNet[37]2023感知特征流预测, 延迟, 自监督学习V2I点云检测
    VICOD[50]2023感知低延迟感知, 减少通信成本V2I传感器融合检测
    LCCP[57]2023感知注意力机制, 不确定性感知, 有损通信下感知V2V点云检测
    UMC[94]2023感知多尺度, 图神经网络, 新的协同感知评价指标V2X点云检测
    DeepAccident[95]2023感知transformer 架构, 端到端框架V2X图像事故预测
    CoCa3D[42]2023感知仅相机协作V2X图像检测
    GevBEV[96]2023感知不确定性感知, 空间高斯点云BEV分割
    CCPAV[66]2023通信新的评分函数, BS 拥塞网络的优化方法V2XRB 分配
    SDVN-V2X[97]2023通信路侧设备中心化V2X通信任务
    Among Us[80]2023通信对抗攻击抵御点云检测
    下载: 导出CSV

    表  4  车路协同感知数据集汇总表

    Table  4  Summary of vehicle-road collaboration perception dataset

    数据集 年份 制作单位 场景 传感器 支持任务 数据量
    DAIR-V2X[15] 2022 清华大学智能产业研究院&北京市高级别自动驾驶示范区 城市道路、高速公路 (包含多种天气场景) 相机、雷达 检测、跟踪 71 254帧
    V2X-Sim[104] 2022 纽约大学AI4CE实验室&上海交通大学MediaBrain团队 交叉路口 相机、雷达 检测、跟踪、分割 47 200帧
    CoopInfo[19] 2022 英国华威大学华威制造集团智能汽车小组 T型路口 相机 检测 20 000帧
    CODD[32] 2021 英国华威大学华威制造集团智能汽车小组 路口场景、环岛场景 雷达 检测、跟踪 5 000帧
    IPS300+[101] 2023 清华大学&北京万集科技 交叉路口 相机、雷达 检测、跟踪 14 198帧
    OPV2V[23] 2022 加州大学洛杉矶分校移动实验室 (UCLA Mobility Lab) T型路口、交叉路口 相机、雷达 检测、跟踪、分割 11 464帧
    V2XSet[16] 2022 加州大学洛杉矶分校&德克萨斯大学奥斯汀分校&谷歌研究院&加州大学默塞德分校 十字路口、街区中段和入口坡道 雷达 检测 33 081帧
    DOLPHINS[105] 2022 清华大学电子工程系&北京交通大学电子信息工程学院 十字路口、T型路口、陡坡道、高速公路入口匝道和山路 (包含多种天气场景) 相机、雷达 检测、跟踪 42 376帧
    V2X-Seq[103] 2023 清华大学智能产业研究院&百度公司 城市道路、十字路口 相机、雷达 跟踪、轨迹预测 225 000帧
    V2V4Real[102] 2023 加州大学洛杉矶分校 高速公路、城市道路 相机、雷达 检测、跟踪、域自适应 60 000帧
    下载: 导出CSV
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  • 收稿日期:  2023-09-14
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