基于信息分形的行人轨迹预测方法

杨田; 王钢; 赖健; 汪洋

doi:10.11999/JEIT230726

基于信息分形的行人轨迹预测方法

doi: 10.11999/JEIT230726

杨田¹,
王钢¹,
赖健²,
汪洋^{2, 3, ,}

1.
哈尔滨工业大学电子与信息工程学院哈尔滨 150001
2.
哈尔滨工业大学电子与信息工程学院深圳 518055
3.
哈尔滨工业大学智能海洋工程研究院深圳 518055

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
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- 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: 2024-02-29

Pedestrian Trajectory Prediction Method Based on Information Fractals

YANG Tian¹,
WANG Gang¹,
LAI Jian²,
WANG Yang^{2, 3
, ,}

1.
School of Electronic and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
2.
School of Electronics and Information Engineering, Harbin Institute of Technology, Shenzhen 518055, China
3.
Intelligent Ocean Engineering Research Institute, Harbin Institute of Technology, Shenzhen 518055, China

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

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%。
- 行人轨迹预测 /
- 不确定信息处理 /
- 信息分形 /
- 生成对抗网络
Abstract: Pedestrian trajectory prediction has been widely used in several fields, such as autonomous driving and robot navigation. In trajectory prediction, some uncertain information, such as the uncertainty of trajectory information discrimination in the discriminator and complex interactive information, bring challenges to the trajectory prediction task. In the field of uncertain information processing, information fractals can effectively deal with the uncertainty and complexity of uncertain information. Inspired by this, a trajectory prediction method based on the information fractal is proposed to fully deal with the uncertainty of trajectory information discrimination in the discriminator and improve the prediction accuracy. First, the scene and historical trajectory information are extracted by the feature extraction module. Subsequently, the scene-pedestrian interaction and pedestrian-pedestrian interaction information are obtained through the attention module. Finally, reasonable trajectories are generated using generative adversarial networks and information fractals. Experiments on the two public datasets ETH and UCY reveal that the proposed method can effectively deal with the uncertainty of trajectory information and improve the accuracy of trajectory prediction. For example, the trajectories of sudden turns, overtaking, avoidance, and other behaviors can be effectively predicted. Moreover, the Average Displacement Error (ADE) and Final Displacement Error (FDE) are reduced by an average of 11.11% and 23.48%, respectively compared with the benchmark model error.
- Pedestrian trajectory prediction /
- Uncertain information processing /
- Information fractal /
- Generative adversarial networks

HTML全文

图 1 基于信息分形的轨迹预测方法模型图

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图 2 场景特征子模块提取的场景特征的图例

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图 3 eth场景以及相应的轨迹预测结果

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图 4 hotel场景以及相应的轨迹预测结果

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图 5 zara01场景以及相应的轨迹预测结果

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图 6 zara02 场景以及相应的轨迹预测结果

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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
VAE_psp	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

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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
VAE_psp	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

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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	0.75/1.40	0.78/1.47	0.66/1.27	0.37/0.76	0.35/0.74	0.58/1.13
完整模型	0.76/1.26	0.48/0.87	0.45/0.86	0.35/0.71	0.34/0.69	0.48/0.88

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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)
Sophie	242.924	108193.49	137.43
本方法	136.948	68595.78	132.30

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