首页|The Group Interaction Field for Learning and Explaining Pedestrian Anticipation

The Group Interaction Field for Learning and Explaining Pedestrian Anticipation

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Anticipating others'actions is innate and essential in order for humans to navigate and interact well with others in dense crowds.This ability is urgently required for unmanned systems such as service robots and self-driving cars.However,existing solutions struggle to predict pedestrian anticipation accurately,because the influence of group-related social behaviors has not been well considered.While group rela-tionships and group interactions are ubiquitous and significantly influence pedestrian anticipation,their influence is diverse and subtle,making it difficult to explicitly quantify.Here,we propose the group inter-action field(GIF),a novel group-aware representation that quantifies pedestrian anticipation into a prob-ability field of pedestrians'future locations and attention orientations.An end-to-end neural network,GIFNet,is tailored to estimate the GIF from explicit multidimensional observations.GIFNet quantifies the influence of group behaviors by formulating a group interaction graph with propagation and graph attention that is adaptive to the group size and dynamic interaction states.The experimental results show that the GIF effectively represents the change in pedestrians'anticipation under the prominent impact of group behaviors and accurately predicts pedestrians'future states.Moreover,the GIF con-tributes to explaining various predictions of pedestrians'behavior in different social states.The proposed GIF will eventually be able to allow unmanned systems to work in a human-like manner and comply with social norms,thereby promoting harmonious human-machine relationships.

Human behavior modeling and predictionImplicit representation of pedestriananticipationGroup interactionGraph neural network

Xueyang Wang、Xuecheng Chen、Puhua Jiang、Haozhe Lin、Xiaoyun Yuan、Mengqi Ji、Yuchen Guo、Ruqi Huang、Lu Fang

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Sigma Laboratory,Department of Electronic Engineering,Tsinghua University,Beijing 100084,China

Beijing National Research Center for Information Science and Technology,Tsinghua University,Beijing 100084,China

Tsinghua Shenzhen International Graduate School,Shenzhen 518055,China

Department of Automation,Tsinghua University,Beijing 100084,China

Institute of Artificial Intelligence,Beihang University,Beijing 100191,China

Zhejiang Future Technology Institute,Yangtze Delta Region Institute of Tsinghua University,Zhejiang Jiaxing 314033,China

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National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaMinistry of Science and Technology of ChinaProvincial Key Research and Development Program of Zhejiang

NSFC6212510661860206003620881022021ZD01099012021C01016

2024

10.1016/j.eng.2023.05.020

工程(英文)

工程(英文)

CSTPCDEI
ISSN：2095-8099
年,卷(期)：2024.34(3)