Collaborative Uncertainty Benefits Multi-Agent Multi-Modal Trajectory Forecasting-Reference-Cited by-同舟云学术

Collaborative Uncertainty Benefits Multi-Agent Multi-Modal Trajectory Forecasting

Author:

Tang Bohan¹^ORCID,Zhong Yiqi²^ORCID,Xu Chenxin³^ORCID,Wu Wei-Tao⁴,Neumann Ulrich²^ORCID,Zhang Ya⁵^ORCID,Chen Siheng⁵^ORCID,Wang Yanfeng⁵

Affiliation:

1. Department of Engineering Science, University of Oxford, Oxford, U.K.

2. Computer Graphics and Immersive Technologies (CGIT) Laboratory, University of Southern California, Los Angeles, CA, USA

3. Cooperative Medianet Innovation Center, Shanghai Jiao Tong University, Shanghai, China

4. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China

5. Shanghai Jiao Tong University and Shanghai AI Laboratory, Shanghai, China

Funder

National Natural Science Foundation of China

Science and Technology Commission of Shanghai Municipal

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Link

Reference74 articles.

5. EvolveGraph: Multi-agent trajectory prediction with dynamic relational reasoning;li;Proc Neural Inf Process Syst,2020

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