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基于激光雷达的无人驾驶3D多目标跟踪

熊珍凯 程晓强 吴幼冬 左志强 刘家胜

熊珍凯, 程晓强, 吴幼冬, 左志强, 刘家胜. 基于激光雷达的无人驾驶3D多目标跟踪. 自动化学报, 2020, 46(13): 1−11 doi: 10.16383/j.aas.c210783
引用本文: 熊珍凯, 程晓强, 吴幼冬, 左志强, 刘家胜. 基于激光雷达的无人驾驶3D多目标跟踪. 自动化学报, 2020, 46(13): 1−11 doi: 10.16383/j.aas.c210783
Xiong Zhen-Kai, Cheng Xiao-Qiang, Wu You-Dong, Zuo Zhi-Qiang, Liu Jia-Sheng. Lidar-based 3d multi-object tracking for unmanned vehicles. Acta Automatica Sinica, 2020, 46(13): 1−11 doi: 10.16383/j.aas.c210783
Citation: Xiong Zhen-Kai, Cheng Xiao-Qiang, Wu You-Dong, Zuo Zhi-Qiang, Liu Jia-Sheng. Lidar-based 3d multi-object tracking for unmanned vehicles. Acta Automatica Sinica, 2020, 46(13): 1−11 doi: 10.16383/j.aas.c210783

基于激光雷达的无人驾驶3D多目标跟踪

doi: 10.16383/j.aas.c210783
基金项目: 国家自然科学基金(62036008, 62173243, 61933014), 中国船舶集团自立科技研发专项(202118J)资助
详细信息
    作者简介:

    熊珍凯:中国船舶集团有限公司第七一三研究所研究员, 安徽理工大学新能源与智能网联汽车学院教授. 2012年获得哈尔滨工程大学博士学位.主要研究方向为特种车辆和无人系统. E-mail: zhkxiong@sina.com

    程晓强:驭势科技(北京)有限公司工程师. 2021年获得天津大学电气自动化与信息工程学院硕士学位.主要研究方向为3D目标检测和多目标跟踪. E-mail: chengxq@tju.edu.cn

    吴幼冬:中国船舶集团有限公司第七一三研究所研究员.主要研究方向为智能控制, 特种车辆和无人系统. E-mail: wyd@sina.com

    左志强:天津大学电气自动化与信息工程学院教授. 2004年获得北京大学博士学位.主要研究方向为自动驾驶和多智能体系统.本文通讯作者. E-mail: zqzuo@tju.edu.cn

    刘家胜:中国船舶集团有限公司第七一三研究所研究员.主要研究方向为特种车辆和无人系统. E-mail: ljs@sina.com

LiDAR-based 3D Multi-Object Tracking for Unmanned Vehicles

Funds: Supported by National Natural Science Foundation of China (62036008, 62173243, 61933014), Science and Technology Research Project of China State Shipbuilding Corporation Limited (202118J)
More Information
    Author Bio:

    XIONG Zhen-Kai Professor at 713 Research Institute of China State Shipbuilding Corporation Limited, and College of New Energy and Intelligent Connected Vehicle, Anhui University of Science &Technology. He received his Ph.D. degree from Harbin Engineering University in 2012. His research interest covers special vehicle and unmanned systems

    CHENG Xiao-Qiang Engineer at the UISEE Technology (Beijing) Co., Ltd. He received his master degree from School of Electrical and Information Engineering, Tianjin University in 2021. His research interest covers 3D object detection and multi-object tracking

    WU You-Dong Professor at 713 Research Institute of China State Shipbuilding Corporation Limited. His research interest covers intelligent control, special vehicle and unmanned systems

    ZUO Zhi-Qiang Professor at School of Electrical and Information Engineering, Tianjin University. He received his Ph.D. degree from Peking University in 2004. His research interest covers autonomous vehicles and multi-agent systems. Corresponding author of this paper

    LIU Jia-Sheng Professor at 713 Research Institute of China State Shipbuilding Corporation Limited. His research interest covers special vehicle and unmanned systems

  • 摘要: 无人驾驶汽车行驶是连续时空的三维运动, 汽车周围的目标不可能突然消失或者出现, 因此, 对于感知层而言, 稳定可靠的多目标跟踪(Multi-object tracking, MOT)意义重大. 针对传统的目标关联和固定生存周期管理的不足, 提出了基于边界交并比(Border intersection over union, BIoU)度量的目标关联和自适应生存周期管理策略. BIoU综合了欧氏距离和交并比(Intersection over union, IoU)的优点, 提高了目标关联的精度. 自适应生存周期管理将目标轨迹置信度与生存周期相联系, 显著减少了目标丢失和误检. 在KITTI多目标跟踪数据集上的实验验证了该方法的有效性.
  • 图  1  IoU度量和欧氏距离度量失效情况

    Fig.  1  Invalid cases about IoU metrics and Euclidean distance metrics

    图  2  基于BIoU和自适应生存周期的3D多目标跟踪

    Fig.  2  3D multi-object tracking based on BIoU and adaptive birth and death memory

    图  3  边界交并比示意图

    Fig.  3  Schematic diagram of BIoU

    图  4  自适应生存周期

    Fig.  4  Adaptive birth and death memory

    图  5  基于激光雷达的3D多目标跟踪整体流程

    Fig.  5  Overall pipeline for LiDAR-based 3D multi-object tracking

    图  6  改进方法与基准方法的跟踪比较 (误检)

    Fig.  6  Tracking comparison between our improved method and baseline (false detection)

    图  7  改进方法与基准方法的跟踪比较 (漏检)

    Fig.  7  Tracking comparison between our improved method and baseline (missed detection)

    表  1  模型参数

    Table  1  Model parameters

    参数 说明
    $ \gamma $ 0.05 BIoU惩罚因子
    $ \alpha $ 0.5 生存周期尺度系数
    $ \beta $ 4 生存周期偏移系数
    $ F_{\max} $ 3 (Car)
    5 (Others)
    最大生存周期
    对Car目标该值为3
    其他类别目标为5
    $ F_{\min} $ 3 目标轨迹的最小跟踪周期
    该值与AB3DMOT相同
    $ {\rm{BIoU}}_{\rm{thres}} $ $ -0.01 $ BIoU阈值
    小于阈值认为匹配失败
    下载: 导出CSV

    表  2  KITTI数据集上对3类目标 (汽车, 行人, 自行车) 跟踪性能对比

    Table  2  Tracking performance comparison about three kinds of objects (Car, Pedestrian, Bycicle) on KITTI dataset

    类别 方法 MOTA$ \uparrow $1 MOTP$ \uparrow $ MT$ \uparrow $ ML$ \downarrow $2 IDS$ \downarrow $ FRAG$ \downarrow $
    Car FANTrack[21] 76.52 84.81 73.14 9.25 1 54
    DiTNet[22] 81.08 87.83 79.35 4.21 20 120
    AB3DMOT[6] 85.70 86.99 75.68 3.78 2 24
    Ours 85.69 86.96 76.22 3.78 2 24
    Pedestrian AB3DMOT[6] 59.76 67.27 40.14 20.42 52 371
    Ours 59.93 67.22 42.25 20.42 52 377
    Bycicle AB3DMOT[6] 74.75 79.89 62.42 14.02 54 403
    Ours 76.43 79.89 64.49 11.63 54 409
    1$ \uparrow $表示指标数值越大性能越好
    2$ \downarrow $表示指标数值越小性能越好
    下载: 导出CSV

    表  3  消融实验

    Table  3  Ablation experiments

    类别 BIoU $ F_{\rm{Amax}} $ MOTA$ \uparrow $ MOTP$ \uparrow $ MT$ \uparrow $ ML$ \downarrow $ IDS$ \downarrow $ FRAG$ \downarrow $
    Car 81.55 86.72 79.46 4.32 2 21
    $ \checkmark $ 81.69 86.69 80.00 4.32 3 22
    $ \checkmark $ 84.09 86.98 75.68 4.32 2 24
    $ \checkmark $ $ \checkmark $ 84.31 86.96 76.22 4.32 2 24
    Pedestrian 57.54 67.19 42.96 21.13 99 411
    $ \checkmark $ 57.73 67.18 46.48 20.42 99 417
    $ \checkmark $ 59.59 67.24 40.14 21.13 59 372
    $ \checkmark $ $ \checkmark $ 59.77 67.21 44.37 16.90 61 393
    Bycicle 73.44 85.40 75.00 14.29 0 5
    $ \checkmark $ 77.82 85.61 75.00 14.29 0 5
    $ \checkmark $ 77.97 85.41 71.43 17.86 0 7
    $ \checkmark $ $ \checkmark $ 82.94 85.51 75.00 10.71 0 7
    下载: 导出CSV
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  • 收稿日期:  2021-08-17
  • 录用日期:  2022-05-25
  • 网络出版日期:  2022-07-18

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