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DTDNet: Dynamic Target Driven Network for pedestrian trajectory prediction

文献类型：期刊论文

作者Liu, Shaohua2; Sun, Jingkai1,2; Yao, Pengfei1,3; Zhu, Yinglong1,2; Mao, Tianlu1; Wang, Zhaoqi1
刊名FRONTIERS IN NEUROSCIENCE
出版日期2024-04-30
卷号18页码:11
关键词multimodal trajectory prediction pedestrian intention prediction multi-precision motion prediction multi-task neural network trajectory endpoint prediction
DOI10.3389/fnins.2024.1346374
英文摘要Predicting the trajectories of pedestrians is an important and difficult task for many applications, such as robot navigation and autonomous driving. Most of the existing methods believe that an accurate prediction of the pedestrian intention can improve the prediction quality. These works tend to predict a fixed destination coordinate as the agent intention and predict the future trajectory accordingly. However, in the process of moving, the intention of a pedestrian could be a definite location or a general direction and area, and may change dynamically with the changes of surrounding. Thus, regarding the agent intention as a fixed 2-d coordinate is insufficient to improve the future trajectory prediction. To address this problem, we propose Dynamic Target Driven Network for pedestrian trajectory prediction (DTDNet), which employs a multi-precision pedestrian intention analysis module to capture this dynamic. To ensure that this extracted feature contains comprehensive intention information, we design three sub-tasks: predicting coarse-precision endpoint coordinate, predicting fine-precision endpoint coordinate and scoring scene sub-regions. In addition, we propose a original multi-precision trajectory data extraction method to achieve multi-resolution representation of future intention and make it easier to extract local scene information. We compare our model with previous methods on two publicly available datasets (ETH-UCY and Stanford Drone Dataset). The experimental results show that our DTDNet achieves better trajectory prediction performance, and conducts better pedestrian intention feature representation.
资助项目Major Program of National Natural Science Foundation of China[91938301] ; National Key Research and Development Program of China[2020YFB1710400] ; Youth Program of National Natural Science Foundation of China[62002345] ; Innovation Program of Institute of Computing Technology Chinese Academy of Sciences[E261070]
WOS研究方向Neurosciences & Neurology
语种英语
WOS记录号WOS:001220613800001
出版者FRONTIERS MEDIA SA
源URL[http://119.78.100.204/handle/2XEOYT63/38968]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Mao, Tianlu
作者单位1.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing, Peoples R China
2.Beijing Univ Posts & Telecommun, Sch Elect Engn, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Shaohua,Sun, Jingkai,Yao, Pengfei,et al. DTDNet: Dynamic Target Driven Network for pedestrian trajectory prediction[J]. FRONTIERS IN NEUROSCIENCE,2024,18:11.
APA Liu, Shaohua,Sun, Jingkai,Yao, Pengfei,Zhu, Yinglong,Mao, Tianlu,&Wang, Zhaoqi.(2024).DTDNet: Dynamic Target Driven Network for pedestrian trajectory prediction.FRONTIERS IN NEUROSCIENCE,18,11.
MLA Liu, Shaohua,et al."DTDNet: Dynamic Target Driven Network for pedestrian trajectory prediction".FRONTIERS IN NEUROSCIENCE 18(2024):11.

入库方式： OAI收割

来源：计算技术研究所

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