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面向自动驾驶的车路协同感知点云融合模式时延影响分析

叶青 赵聪 朱逸凡 俞山川

叶青, 赵聪, 朱逸凡, 俞山川. 面向自动驾驶的车路协同感知点云融合模式时延影响分析[J]. 交通信息与安全, 2023, 41(4): 72-79. doi: 10.3963/j.jssn.1674-4861.2023.04.008
引用本文: 叶青, 赵聪, 朱逸凡, 俞山川. 面向自动驾驶的车路协同感知点云融合模式时延影响分析[J]. 交通信息与安全, 2023, 41(4): 72-79. doi: 10.3963/j.jssn.1674-4861.2023.04.008
YE Qing, ZHAO Cong, ZHU Yifan, YU Shanchuan. An Analysis of the Impact of Time Delay of Fusion Modes for Point Clouds from Cooperative Road Vehicle Systems on Autonomous Driving[J]. Journal of Transport Information and Safety, 2023, 41(4): 72-79. doi: 10.3963/j.jssn.1674-4861.2023.04.008
Citation: YE Qing, ZHAO Cong, ZHU Yifan, YU Shanchuan. An Analysis of the Impact of Time Delay of Fusion Modes for Point Clouds from Cooperative Road Vehicle Systems on Autonomous Driving[J]. Journal of Transport Information and Safety, 2023, 41(4): 72-79. doi: 10.3963/j.jssn.1674-4861.2023.04.008

面向自动驾驶的车路协同感知点云融合模式时延影响分析

doi: 10.3963/j.jssn.1674-4861.2023.04.008
基金项目: 

国家重点研发计划项目 2021YFB1600403

重庆市技术创新与应用发展专项重点项目 cstc2021jscx-gksbX0057

详细信息
    作者简介:

    叶青(1994—),博士研究生. 研究方向:智能交通系统、车路协同与自动驾驶. E-mail:yeqing2@cmhk.com

    通讯作者:

    赵聪(1992—),博士,副研究员. 研究方向:车路协同数字感知与智能决策. E-mail: zhc@tongji.edu.cn

  • 中图分类号: U491.5

An Analysis of the Impact of Time Delay of Fusion Modes for Point Clouds from Cooperative Road Vehicle Systems on Autonomous Driving

  • 摘要: 新一代通信技术的快速发展为车路协同感知提供了基础,可进一步提升自动驾驶车辆在复杂场景中的感知能力,现有研究对不同的协同感知信息融合模式进行了探索,但忽略了对感知精度与通信时延平衡性的分析。针对自动驾驶协同感知中点云融合模式的时延特征,本文以前融合、特征融合、后融合3种模式为研究对象,提出了基于模拟仿真的时延影响分析框架。考虑通信时延引起的协同感知结果时滞性,利用扩展卡尔曼滤波算法对存在时延的协同感知结果进行预测性补偿,创新提出了滞后补偿误差和等效时延评价指标,用以综合评价不同融合模式对协同感知结果的影响;针对不同点云融合模式的感知结果,构建了平均感知精度与平移误差分布关系模型,依据目标检测平移误差的分布特征生成带有感知误差的仿真轨迹,进而对协同感知效果进行评估。结合TrajNet++行人轨迹数据集,以不同时延参数及点云融合模式,对1 200条轨迹进行了180 000次数值仿真。结果显示,感知目标的已知轨迹长度越短、速度越高,时延对协同感知精度的影响越大,以100 ms时延下后融合为基准,当特征融合时延在500 ms以内、前融合时延在700 ms以内时,可以达到相同或更高的协同感知精度。针对目标易突然出现且速度快的复杂场景,宜采用低时延、低精度的后融合模式,反之,宜采用具有高时延、高精度的特征融合或前融合模式。本研究可为自动驾驶协同感知的点云融合模式选择提供依据。

     

  • 图  1  车路点云示意图

    Figure  1.  Schematic diagram of point cloud of vehicle and road

    图  2  点云前融合流程

    Figure  2.  Process of pre-fusion modefor point cloud

    图  3  点云特征融合流程

    Figure  3.  Process of feature-fusion mode for point cloud

    图  4  点云后融合流程

    Figure  4.  Process of post-fusion mode for point cloud

    图  5  自动驾驶协同感知流程

    Figure  5.  Cooperative perception process for autonomous driving

    图  6  时延影响分析框架

    Figure  6.  The framework of latency impact analysis

    图  7  基于点云的目标检测平均精度与平均平移误差关系

    Figure  7.  Relationship between mean average precision and mean average translation error of object detection based on point cloud

    图  8  不同融合模式和已知轨迹时长下的滞后补偿误差

    Figure  8.  LCE under different fusion modes and known trajectory duration

    图  9  不同融合模式和目标速度时长下的滞后补偿误差

    Figure  9.  LCE under different fusion modes and object velocity

    图  10  不同条件下的等效时延

    Figure  10.  Equivalent latency under different conditions

    表  1  点云目标检测模型的平均精度和平均平移误差

    Table  1.   mean average precision and mean average translation error of object detection models based on point cloud

    序号 平均精度LmAP 平均平移误差LmATE
    1 0.723 9 0.236 6
    2 0.697 2 0.237 0
    3 0.674 0 0.254 6
    4 0.681 5 0.255 8
    5 0.671 5 0.248 8
    6 0.668 0 0.253 1
    7 0.668 0 0.253 5
    8 0.662 8 0.240 3
    9 0.666 9 0.254 2
    10 0.665 9 0.272 6
    下载: 导出CSV

    表  2  点云融合模式评价指标

    Table  2.   Evaluationindexes of point cloud fusion modes

    点云融合模式 时延/ms 平均精度LmAP
    前融合 高(100~1 000) 0.762 9
    特征融合 中(50~200) 0.635 1
    后融合 低(0~100) 0.489 9
    下载: 导出CSV

    表  3  仿真实验参数

    Table  3.   Simulation experiment parameters 单位: ms

    参数 取值 取值步长
    时延 100~1 000 100
    点云融合模式 前融合、特征融合、后融合
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-06-07
  • 网络出版日期:  2023-11-23

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