高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于信息分形的行人轨迹预测方法

杨田 王钢 赖健 汪洋

杨田, 王钢, 赖健, 汪洋. 基于信息分形的行人轨迹预测方法[J]. 电子与信息学报, 2024, 46(2): 527-537. doi: 10.11999/JEIT230726
引用本文: 杨田, 王钢, 赖健, 汪洋. 基于信息分形的行人轨迹预测方法[J]. 电子与信息学报, 2024, 46(2): 527-537. doi: 10.11999/JEIT230726
YANG Tian, WANG Gang, LAI Jian, WANG Yang. Pedestrian Trajectory Prediction Method Based on Information Fractals[J]. Journal of Electronics & Information Technology, 2024, 46(2): 527-537. doi: 10.11999/JEIT230726
Citation: YANG Tian, WANG Gang, LAI Jian, WANG Yang. Pedestrian Trajectory Prediction Method Based on Information Fractals[J]. Journal of Electronics & Information Technology, 2024, 46(2): 527-537. doi: 10.11999/JEIT230726

基于信息分形的行人轨迹预测方法

doi: 10.11999/JEIT230726
基金项目: 国家自然科学基金(62071146),广东省海洋经济发展项目(GDNRC [2020]014),深圳市科技计划项目(JCYJ20200109113424990)
详细信息
    作者简介:

    杨田:女,博士生,研究方向为行人轨迹预测

    王钢:男,教授,研究方向为数据通信、物理层网络编码、通信网理论与技术

    赖健:男,硕士,研究方向为基于多源信息融合的行人轨迹预测

    汪洋:男,副教授,研究方向为5G前沿理论与技术、无人驾驶理论与技术、卫星通信理论与技术、智慧家庭技术

    通讯作者:

    汪洋 21b905068@stu.hit.edu.cn; gwang51@hit.edu.cn; 20S152098@stu.hit.edu.cn; yangw@hit.edu.cn

  • 中图分类号: TN911.73;TP391

Pedestrian Trajectory Prediction Method Based on Information Fractals

Funds: The National Natural Science Foundation of China (62071146), Marine Economy Development Project of Guangdong Province (GDNRC [2020]014), Science and Technology Project of Shenzhen (JCYJ20200109113424990)
  • 摘要: 行人轨迹预测应用十分广泛,比如自动驾驶、机器人导航等。在轨迹预测中,一些不确定信息给轨迹预测任务带来了挑战,比如判别器中对轨迹信息判别的不确定,复杂的交互信息。在不确定信息处理科学领域,信息分形能有效处理不确定信息的不确定性和复杂性。受此启发,为了充分处理判别器中轨迹信息判别的不确定性,提升预测精度,该文提出了基于信息分形的轨迹预测方法。首先,场景信息和历史轨迹信息被特征提取模块提取。然后,通过注意力模块获取到场景-行人之间的交互信息与行人-行人之间的交互信息。最后基于生成对抗网络和信息分形生成合理的轨迹。在两个公共数据集ETH/UCY上实验表明,该方法能有效处理轨迹信息的不确定性,提高轨迹预测的精度。比如突然转弯、从后方超越前人、避让等行为的轨迹都能有效预测。在平均位移误差(ADE)和终点位移误差(FDE)上相比基准模型误差平均降低了11.11%和23.48%。
  • 图  1  基于信息分形的轨迹预测方法模型图

    图  2  场景特征子模块提取的场景特征的图例

    图  3  eth场景以及相应的轨迹预测结果

    图  4  hotel场景以及相应的轨迹预测结果

    图  5  zara01场景以及相应的轨迹预测结果

    图  6  zara02 场景以及相应的轨迹预测结果

    表  1  轨迹预测的平均位移误差ADE(m)

    基准模型\数据集 ETH主楼
    eth
    酒店入口hotel 校园路口univ 购物街zara01 购物街zara02 平均误差AVG
    BR-GAN 0.73 0.55 0.53 0.35 0.35 0.50
    SRAI-LSTM 0.59 0.29 0.55 0.37 0.43 0.45
    VAEpsp 0.75 0.60 0.53 0.30 0.34 0.50
    SCAN 0.79 0.37 0.58 0.37 0.31 0.48
    S-GAN 0.81 0.72 0.60 0.34 0.42 0.58
    Sophie 0.70 0.76 0.54 0.30 0.38 0.54
    本方法 0.76 0.48 0.45 0.35 0.34 0.48
    下载: 导出CSV

    表  2  轨迹预测的终点位移误差FDE(m)

    基准模型\数据集 ETH主楼eth 酒店入口hotel 校园路口univ 购物街zara01 购物街zara02 平均误差AVG
    BR-GAN 1.37 1.13 1.07 0.71 0.72 1.00
    SRAI-LSTM 1.16 0.56 1.19 0.82 0.93 0.93
    VAEpsp 1.62 1.28 1.35 0.79 0.91 1.19
    SCAN 1.49 0.74 1.23 0.78 0.66 0.98
    S-GAN 1.52 1.61 1.26 0.69 0.84 1.18
    Sophie 1.43 1.67 1.24 0.63 0.78 1.15
    本方法 1.26 0.87 0.86 0.71 0.69 0.88
    下载: 导出CSV

    表  3  消融模型及完整模型轨迹预测的平均位移误差ADE(m)/终点位移误差FDE(m)

    基准模型\数据集ETH主楼
    eth
    酒店入口hotel校园路口univ购物街zara01购物街zara02平均误差AVG
    消融模型0.75/1.400.78/1.470.66/1.270.37/0.760.35/0.740.58/1.13
    完整模型0.76/1.260.48/0.870.45/0.860.35/0.710.34/0.690.48/0.88
    下载: 导出CSV

    表  4  模型参数表

    模型\性能预测耗时(s)训练耗时(s)参数总数(MB)
    Sophie242.924108193.49137.43
    本方法136.94868595.78132.30
    下载: 导出CSV
  • [1] PANG Shumin, CAO Jinxin, JIAN Meiying, et al. BR-GAN: A pedestrian trajectory prediction model combined with behavior recognition[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(12): 24609–24620. doi: 10.1109/TITS.2022.3193442.
    [2] 孔玮, 刘云, 李辉, 等. 基于深度学习的行人轨迹预测方法综述[J]. 控制与决策, 2021, 36(12): 2841–2850. doi: 10.13195/j.kzyjc.2020.1841.

    KONG Wei, LIU Yun, LI Hui, et al. Survey of pedestrian trajectory prediction methods based on deep learning[J]. Control and Decision, 2021, 36(12): 2841–2850. doi: 10.13195/j.kzyjc.2020.1841.
    [3] WANG Meiming and REN Jing. Neither too much nor too little: Leveraging moderate data in pedestrian trajectory prediction[C]. Proceedings of 2020 International Conference on Artificial Intelligence and Computer Engineering, Beijing, China, 2020: 444–448. doi: 10.1109/ICAICE51518.2020.00093.
    [4] CHEN Kai, SONG Xiao, and REN Xiaoxiang. Pedestrian trajectory prediction in heterogeneous traffic using pose keypoints-based convolutional encoder-decoder network[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2021, 31(5): 1764–1775. doi: 10.1109/TCSVT.2020.3013254.
    [5] CAI Yingfeng, DAI Lei, WANG Hai, et al. Pedestrian motion trajectory prediction in intelligent driving from far shot first-person perspective video[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(6): 5298–5313. doi: 10.1109/TITS.2021.3052908.
    [6] ZHU Q. Hidden Markov model for dynamic obstacle avoidance of mobile robot navigation[J]. IEEE Transactions on Robotics and Automation, 1991, 7(3): 390–397. doi: 10.1109/70.88149.
    [7] HELBING D and MOLNAR P. Social force model for pedestrian dynamics[J]. Physical Review E, 1995, 51(5): 4282–4286. doi: 10.1103/PhysRevE.51.4282.
    [8] WANG Chuhua, WANG Chuhua, XU Mingze, et al. Stepwise goal-driven networks for trajectory prediction[J]. IEEE Robotics and Automation Letters, 2022, 7(2): 2716–2723. doi: 10.1109/LRA.2022.3145090.
    [9] SADEGHIAN A, KOSARAJU V, SADEGHIAN A, et al. SoPhie: An attentive GAN for predicting paths compliant to social and physical constraints[C]. Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019: 1349–1358. doi: 10.1109/CVPR.2019.00144.
    [10] GUPTA A, JOHNSON J, FEI-FEI L, et al. Social GAN: Socially acceptable trajectories with generative adversarial networks[C]. Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 2255–2264. doi: 10.1109/CVPR.2018.00240.
    [11] 李琳辉, 周彬, 连静, 等. 基于社会注意力机制的行人轨迹预测方法研究[J]. 通信学报, 2020, 41(6): 175–183. doi: 10.11959/j.issn.1000-436x.2020100.

    LI Linhui, ZHOU Bin, and LIAN Jing, et al. Research on pedestrian trajectory prediction method based on social attention mechanism[J]. Journal on Communications, 2020, 41(6): 175–183. doi: 10.11959/j.issn.1000-436x.2020100.
    [12] LIAN Jing, REN Weiwei, LI Linhui, et al. PTP-STGCN: Pedestrian trajectory prediction based on a spatio-temporal graph convolutional neural network[J]. Applied Intelligence, 2023, 53(3): 2862–2878. doi: 10.1007/s10489-022-03524-1.
    [13] SYED A and MORRIS B T. SSeg-LSTM: Semantic scene segmentation for trajectory prediction[C]. Proceedings of 2019 IEEE Intelligent Vehicles Symposium, Paris, France, 2019: 2504–2509. doi: 10.1109/IVS.2019.8813801.
    [14] HU Hongyu, WANG Qi, DU Laigang, et al. Vehicle trajectory prediction considering aleatoric uncertainty[J]. Knowledge-Based Systems, 2022, 255: 109617. doi: 10.1016/j.knosys.2022.109617.
    [15] XIAO Fuyuan and PEDRYCZ W. Negation of the quantum mass function for multisource quantum information fusion with its application to pattern classification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(2): 2054–2070. doi: 10.1109/TPAMI.2022.3167045.
    [16] XIAO Fuyuan, CAO Zehong, and LIN C T. A complex weighted discounting multisource information fusion with its application in pattern classification[J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(8): 7609–7623. doi: 10.1109/TKDE.2022.3206871.
    [17] XIAO Fuyuan. CEQD: A complex mass function to predict interference effects[J]. IEEE Transactions on Cybernetics, 2022, 52(8): 7402–7414. doi: 10.1109/TCYB.2020.3040770.
    [18] DENG Yong. Information volume of mass function[J]. International Journal of Computers Communications & Control, 2020, 15(6): 3983.
    [19] DEMPSTER A P. Upper and lower probabilities induced by a multivalued mapping[J]. The Annals of Mathematical Statistics, 1967, 38(2): 325–339. doi: 10.1214/aoms/1177698950.
    [20] DENG Yong. Random permutation set[J]. International Journal of Computers Communications & Control, 2022, 17(1): 4542. doi: 10.15837/ijccc.2022.1.4542.
    [21] QIANG Chenhui, DENG Yong, and CHEONG K H. Information fractal dimension of mass function[J]. Fractals, 2022, 30(6): 2250110. doi: 10.1142/S0218348X22501109.
    [22] CASTILLO O and MELIN P. Forecasting of COVID-19 time series for countries in the world based on a hybrid approach combining the fractal dimension and fuzzy logic[J]. Chaos, Solitons & Fractals, 2020, 140: 110242. doi: 10.1016/j.chaos.2020.110242.
    [23] CASTILLO O and MELIN P. A new approach for plant monitoring using type-2 fuzzy logic and fractal theory[J]. International Journal of General Systems, 2004, 33(2/3): 305–319. doi: 10.1080/03081070310001633617.
    [24] 刘云, 薛盼盼, 李辉, 等. 基于深度学习的关节点行为识别综述[J]. 电子与信息学报, 2021, 43(6): 1789–1802. doi: 10.11999/JEIT 200267.

    LIU Yun, XUE Panpan, LI Hui, et al. A review of action recognition using joints based on deep learning[J]. Journal of Electronics & Information Technology, 2021, 43(6): 1789–1802. doi: 10.11999/JEIT200267.
    [25] DENG Yong. Deng entropy[J]. Chaos, Solitons & Fractals, 2016, 91: 549–553. doi: 10.1016/j.chaos.2016.07.014.
    [26] PENG Yusheng, ZHANG Gaofeng, SHI Jun, et al. SRAI-LSTM: A social relation attention-based interaction-aware LSTM for human trajectory prediction[J]. Neurocomputing, 2022, 490: 258–268. doi: 10.1016/j.neucom.2021.11.089.
    [27] Syed A and Morris B T. Semantic scene upgrades for trajectory prediction[J]. Machine Vision and Applications, 2023, 34(2): 23. doi: 10.1007/s00138-022-01357-z.
    [28] SEKHON J and FLEMING C. SCAN: A spatial context attentive network for joint multi-agent intent prediction[C]. Proceedings of the 35th AAAI Conference on Artificial Intelligence, Palo Alto, USA, 2021: 6119–6127. doi: 10.1609/aaai.v35i7.16762.
  • 加载中
图(6) / 表(4)
计量
  • 文章访问数:  261
  • HTML全文浏览量:  166
  • PDF下载量:  62
  • 被引次数: 0
出版历程
  • 收稿日期:  2023-07-19
  • 修回日期:  2023-10-25
  • 网络出版日期:  2023-10-27
  • 刊出日期:  2024-02-10

目录

    /

    返回文章
    返回