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行人轨迹预测条件端点局部目的地池化网络

毛琳 解云娇 杨大伟 张汝波

毛琳, 解云娇, 杨大伟, 张汝波. 行人轨迹预测条件端点局部目的地池化网络[J]. 电子与信息学报, 2022, 44(10): 3465-3475. doi: 10.11999/JEIT210716
引用本文: 毛琳, 解云娇, 杨大伟, 张汝波. 行人轨迹预测条件端点局部目的地池化网络[J]. 电子与信息学报, 2022, 44(10): 3465-3475. doi: 10.11999/JEIT210716
MAO Lin, XIE Yunjiao, YANG Dawei, ZHANG Rubo. Local Destination Pooling Network for Pedestrian Trajectory Prediction of Condition Endpoint[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3465-3475. doi: 10.11999/JEIT210716
Citation: MAO Lin, XIE Yunjiao, YANG Dawei, ZHANG Rubo. Local Destination Pooling Network for Pedestrian Trajectory Prediction of Condition Endpoint[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3465-3475. doi: 10.11999/JEIT210716

行人轨迹预测条件端点局部目的地池化网络

doi: 10.11999/JEIT210716
基金项目: 国家自然科学基金(61673084),辽宁省自然科学基金(20180550866)
详细信息
    作者简介:

    毛琳:女,副教授,研究方向为目标跟踪与多传感器信息融合

    解云娇:女,硕士生,研究方向为目标跟踪与轨迹预测

    杨大伟:男,副教授,研究方向为计算机视觉处理技术

    张汝波:男,教授,研究方向为智能机器人技术及智能信息处理技术

    通讯作者:

    解云娇 xiexiaohaiisbest@163.com

  • 中图分类号: TP391.4

Local Destination Pooling Network for Pedestrian Trajectory Prediction of Condition Endpoint

Funds: The National Natural Science Foundation of China (61673084), The Natural Science Foundation of Liaoning Province (20180550866)
  • 摘要: 轨迹预测是自动驾驶系统中的核心任务之一。现阶段基于深度学习的轨迹预测算法,涉及目标的信息表示、环境感知和运动推理。针对现有轨迹预测模型在运动推理过程中对行人社交动机考虑不足,无法有效预知场景中行人在不同社交条件下局部目的地的问题,该文提出一种条件端点局部目的地池化网络(CEPNET)。该网络通过条件变分自编码器推理潜在轨迹分布空间来学习历史观测轨迹在特定场景中的概率分布,构建条件端点局部特征推理算法,将条件端点作为局部目的地特征进行相似性特征编码,利用社交池化网络过滤掉场景中的干扰信号,融入自注意力社交掩码来增强行人的自我注意力。为验证算法各模块的可靠性,使用公开的行人鸟瞰数据集(BIWI)和塞浦路斯大学多人轨迹数据集(UCY)对CEPNET进行消融实验,并与平凡长短时记忆网络(Vanilla)、社交池化生成对抗网络(SGAN)和图注意力生成对抗网络(S-BiGAT)等先进轨迹预测算法进行对比分析。在Trajnet++基准上的实验结果表明,CEPNET算法性能优于现有先进算法,并且与基准算法Vanilla相比,平均位移误差(ADE)降低22.52%,最终位移误差(FDE)降低20%,预测碰撞率Col-I降低9.75%,真值碰撞率Col-II降低9.15%。
  • 图  1  CEPNET逻辑框架

    图  2  条件变分自编码器

    图  3  条件端点局部特征推理算法框架

    图  4  自注意力社交池化网络

    图  5  自注意力社交关系掩码

    图  6  条件端点局部目的地池化网络整体框架

    图  7  各个算法训练框架示意图

    图  8  不同模型训练和验证损失折线图

    图  9  不同场景中5种算法轨迹路径预测值可视化

    图  10  不同交互类型轨迹路径预测值可视化

    表  1  模型迭代学习率配置

    迭代次数1~910~1819~25
    学习率10-310-410-5
    下载: 导出CSV

    表  2  各算法模型迭代平均运行时间(min)

    SGANS-BiGATVanillaCENET-I(本文)CEPNET(本文)
    平均运行时间38.38151.682.9766.1859.09
    下载: 导出CSV

    表  3  CEPTNET与其他算法在ETH和UCY数据集上的定量结果

    数据集SGANS-BiGATVanilla(Baseline)CENET-I(本文)CEPNET(本文)
    ADE/FDECol-ICol-IIADE/FDECol-ICol-IIADE/FDECol-ICol-IIADE/FDECol-ICol-IIADE/FDECol-ICol-II
    ETH0.66/1.307.408.980.96/1.797.3111.620.99/1.8912.0612.591.22/2.479.7710.920.66/1.349.428.45
    Hotel0.44/0.845.665.660.84/1.523.775.660.85/1.607.551.890.85/1.613.773.770.51/1.025.663.77
    Univ0.69/1.505.335.330.61/1.362.464.510.63/1.442.052.870.69/1.642.462.870.60/1.392.462.05
    Zara10.43/0.904.208.390.46/0.980.711.890.42/0.988.398.390.41/0.887.699.790.39/0.846.997.69
    Zara20.53/1.1614.2514.250.50/1.128.3916.680.48/1.1014.5615.250.47/1.0515.4115.20.45/1.0214.6715.30
    均值0.55/1.147.378.520.67/1.364.5310.070.68/1.408.928.200.72/1.497.829.110.52/1.128.057.45
    注:Datasets属性下的粗体为未参与训练的测试集名称;红色为最低误差值,蓝色为第2低误差值。
    下载: 导出CSV

    表  4  不同场景下4种交互类别的预测值评估结果

    类型模型场景序号ADE (m)FDE (m)Col-I (%)Col-II(%)
    ISGAN1020.200.4111.766.68
    IS-BiGAT1020.220.478.8210.78
    IVallina(Baseline)1020.210.4616.679.80
    ICENET-I(本文)1020.220.5012.7512.75
    ICEPNET(本文)1020.13(↓38.1%)0.30(↓34.8%)6.86(↓59.9%)6.68(↓31.8%)
    IISGAN7790.400.8011.8111.42
    IIS-BiGAT7790.460.917.7511.68
    IIVallina(Baseline)7790.460.9111.813.22
    IICENET-I(本文)7790.531.1112.0710.14
    IICEPNET(本文)7790.32(↓30.4%)0.69(↓24.2%)11.17(↓5.6%)9.50(↓28.1%)
    IIISGAN17340.611.2814.2413.67
    IIIS-BiGAT17340.721.499.6315.63
    IIIVallina(Baseline)17340.741.5415.9216.03
    IIICENET-I(本文)17340.831.7715.5116.03
    IIICEPNET(本文)17340.61(↓17.6%)1.31(↓14.9%)15.4(↓3.27%)15.5(↓3.30%)
    IVSGAN6600.711.504.855.91
    IVS-BiGAT6600.861.783.186.36
    IVVallina(Baseline)6600.821.745.767.27
    IVCENET-I(本文)6600.841.795.007.42
    IVCEPNET(本文)6600.66(19.5%)1.44(↓17.2%)3.48(↓39.6%)6.36(↓12.5%)
    注:红色为最低误差值,蓝色为第2低误差值。
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-07-15
  • 修回日期:  2021-12-17
  • 录用日期:  2021-12-29
  • 网络出版日期:  2022-01-13
  • 刊出日期:  2022-10-19

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