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Multi-agent interacting systems are prevalent in the world, from pure physical systems to complicated social dynamic systems.
Social force model for pedestrian dynamics
Dirk Helbing and Peter Molnar · 1995
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Jiachen Li, Hengbo Ma, Zhihao Zhang, and Masayoshi Tomizuka · 2002
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Who are you with and where are you going?
Kota Yamaguchi, Alexander C Berg, Luis E Ortiz, and Tamara L Berg · 2011
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Object-oriented bayesian networks for detection of lane change maneuvers
Dietmar Kasper, Galia Weidl, Thao Dang, Gabi Breuel, Andreas Tamke, Andreas Wedel, and Wolfgang Rosenstiel · 2012
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Abnormal crowd behavior detection based on social attribute-aware force model
Yanhao Zhang, Lei Qin, Hongxun Yao, and Qingming Huang · 2012
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Understanding pedestrian behaviors from stationary crowd groups
Shuai Yi, Hongsheng Li, and Xiaogang Wang · 2015
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Social lstm: Human trajectory prediction in crowded spaces
Alexandre Alahi, Kratarth Goel, Vignesh Ramanathan, Alexandre Robicquet, Li Fei-Fei, and Silvio Savarese · 2016
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Permutation-equivariant neural networks applied to dynamics prediction
Nicholas Guttenberg, Nathaniel Virgo, Olaf Witkowski, Hidetoshi Aoki, and Ryota Kanai · 2016
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Structural-rnn: Deep learning on spatio-temporal graphs
Ashesh Jain, Amir R Zamir, Silvio Savarese, and Ashutosh Saxena · 2016
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Learning social etiquette: Human trajectory understanding in crowded scenes
Alexandre Robicquet, Amir Sadeghian, Alexandre Alahi, and Silvio Savarese · 2016
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Vain: Attentional multi-agent predictive modeling
Yedid Hoshen · 2017
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Desire: Distant future prediction in dynamic scenes with interacting agents
Namhoon Lee, Wongun Choi, Paul Vernaza, Christopher B Choy, Philip HS Torr, and Manmohan Chandraker · 2017
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The concrete distribution: A continuous relaxation of discrete random variables
C Maddison, A Mnih, and Y Teh · 2017
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A simple neural network module for relational reasoning
Adam Santoro, David Raposo, David G Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, and Timothy Lillicrap · 2017
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Social gan: Socially acceptable trajectories with generative adversarial networks
Agrim Gupta, Justin Johnson, Li Fei-Fei, Silvio Savarese, and Alexandre Alahi · 2018
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Mx-lstm: mixing tracklets and vislets to jointly forecast trajectories and head poses
Irtiza Hasan, Francesco Setti, Theodore Tsesmelis, Alessio Del Bue, Fabio Galasso, and Marco Cristani · 2018
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Neural relational inference for interacting systems
Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, and Richard Zemel · 2018
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Probabilistic prediction of interactive driving behavior via hierarchical inverse reinforcement learning
Liting Sun, Wei Zhan, and Masayoshi Tomizuka · 2018
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Relational neural expectation maximization: Unsupervised discovery of objects and their interactions
Sjoerd van Steenkiste, Michael Chang, Klaus Greff, and Jürgen Schmidhuber · 2018
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Social attention: Modeling attention in human crowds
Anirudh Vemula, Katharina Muelling, and Jean Oh · 2018
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Encoding crowd interaction with deep neural network for pedestrian trajectory prediction
The h3d dataset for full-surround 3d multi-object detection and tracking in crowded urban scenes
Abhishek Patil, Srikanth Malla, Haiming Gang, and Yi-Ting Chen · 2019
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Precog: Prediction conditioned on goals in visual multi-agent settings
Nicholas Rhinehart, Rowan McAllister, Kris Kitani, and Sergey Levine · 2019
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Sophie: An attentive gan for predicting paths compliant to social and physical constraints
Amir Sadeghian, Vineet Kosaraju, Ali Sadeghian, Noriaki Hirose, Hamid Rezatofighi, and Silvio Savarese · 2019
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Potential field: Interpretable and unified representation for trajectory prediction
Shan Su, Cheng Peng, Jianbo Shi, and Chiho Choi · 2019
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Sr-lstm: State refinement for lstm towards pedestrian trajectory prediction
Pu Zhang, Wanli Ouyang, Pengfei Zhang, Jianru Xue, and Nanning Zheng · 2019
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Yanyu Xu, Zhixin Piao, and Shenghua Gao · 2018
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Towards a fatality-aware benchmark of probabilistic reaction prediction in highly interactive driving scenarios
Wei Zhan, Liting Sun, Yeping Hu, Jiachen Li, and Masayoshi Tomizuka · 2018
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Neural relational inference with fast modular meta-learning
Ferran Alet, Erica Weng, Tomás Lozano-Pérez, and Leslie Pack Kaelbling · 2019
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Looking to relations for future trajectory forecast
Chiho Choi and Behzad Dariush · 2019
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Rules of the road: Predicting driving behavior with a convolutional model of semantic interactions
Joey Hong, Benjamin Sapp, and James Philbin · 2019
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Uncertainty-aware driver trajectory prediction at urban intersections
Xin Huang, Stephen G McGill, Brian C Williams, Luke Fletcher, and Guy Rosman · 2019
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Relational representation learning for dynamic (knowledge) graphs: A survey
Seyed Mehran Kazemi, Rishab Goel, Kshitij Jain, Ivan Kobyzev, Akshay Sethi, Peter Forsyth, and Pascal Poupart · 2019
Cited alongside, same era.
Multi-agent tensor fusion for contextual trajectory prediction
Tianyang Zhao, Yifei Xu, Mathew Monfort, Wongun Choi, Chris Baker, Yibiao Zhao, Yizhou Wang, and Ying Nian Wu · 2019
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Spectral temporal graph neural network formultivariate time-series forecasting
Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Conguri Huang, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, and Qi Zhang · 2020
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Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction
Yuning Chai, Benjamin Sapp, Mayank Bansal, and Dragomir Anguelov · 2020
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Vectornet: Encoding hd maps and agent dynamics from vectorized representation
Jiyang Gao, Chen Sun, Hang Zhao, Yi Shen, Dragomir Anguelov, Congcong Li, and Cordelia Schmid · 2020
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Titan: Future forecast using action priors
Srikanth Malla, Behzad Dariush, and Chiho Choi · 2020
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Social-stgcnn: A social spatio-temporal graph convolutional neural network for human trajectory prediction
Abduallah Mohamed, Kun Qian, Mohamed Elhoseiny, and Christian Claudel · 2020
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Evolvegcn: Evolving graph convolutional networks for dynamic graphs
Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Tao B Schardl, and Charles E Leiserson · 2020
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Trajectron++: Multi-agent generative trajectory forecasting with heterogeneous data for control
Tim Salzmann, Boris Ivanovic, Punarjay Chakravarty, and Marco Pavone · 2020
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Learning to simulate complex physics with graph networks
Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, and Peter W Battaglia · 2020
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Multivariate time-series anomaly detection via graph attention network
Hang Zhao, Yujing Wang, Juanyong Duan, Congrui Huang, Defu Cao, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, and Qi Zhang · 2020
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