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Demystifying the interactions among multiple agents from their past trajectories is fundamental to precise and interpretable trajectory prediction.
Discrete choice models of pedestrian walking behavior
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Unsupervised activity perception in crowded and complicated scenes using hierarchical bayesian models
Xiaogang Wang, Xiaoxu Ma, and W Eric L Grimson · 2008
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Learning trajectory patterns by clustering: Experimental studies and comparative evaluation
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Stefano Pellegrini, Andreas Ess, Konrad Schindler, and Luc Van Gool · 2009
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Trajectory analysis and semantic region modeling using nonparametric hierarchical bayesian models
Xiaogang Wang, Keng Teck Ma, Gee-Wah Ng, and W Eric L Grimson · 2011
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Adam: A method for stochastic optimization
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Sequence to sequence learning with neural networks
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Learning structured output representation using deep conditional generative models
Kihyuk Sohn, Honglak Lee, and Xinchen Yan · 2015
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Alexandre Alahi, Kratarth Goel, Vignesh Ramanathan, Alexandre Robicquet, Li Fei-Fei, and Silvio Savarese · 2016
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The concrete distribution: A continuous relaxation of discrete random variables
Chris J Maddison, Andriy Mnih, and Yee Whye Teh · 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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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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Chauffeurnet: Learning to drive by imitating the best and synthesizing the worst
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Predicting motion of vulnerable road users using high-definition maps and efficient convnets
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Convolutional social pooling for vehicle trajectory prediction
Nachiket Deo and Mohan M Trivedi · 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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Dynamic neural relational inference
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Collaborative motion prediction via neural motion message passing
Yue Hu, Siheng Chen, Ya Zhang, and Xiao Gu · 2020
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Evolvegraph: Multi-agent trajectory prediction with dynamic relational reasoning
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Dynamic multiscale graph neural networks for 3d skeleton based human motion prediction
Maosen Li, Siheng Chen, Yangheng Zhao, Ya Zhang, Yanfeng Wang, and Qi Tian · 2020
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It is not the journey but the destination: Endpoint conditioned trajectory prediction
Karttikeya Mangalam, Harshayu Girase, Shreyas Agarwal, Kuan-Hui Lee, Ehsan Adeli, Jitendra Malik, and Adrien Gaidon · 2020
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Anirudh Vemula, Katharina Muelling, and Jean Oh · 2018
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Non-local neural networks
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Encoding crowd interaction with deep neural network for pedestrian trajectory prediction
Yanyu Xu, Zhixin Piao, and Shenghua Gao · 2018
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Conditional flow variational autoencoders for structured sequence prediction
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Graph u-nets
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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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Connecting the dots: Multivariate time series forecasting with graph neural networks
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Spatio-temporal graph transformer networks for pedestrian trajectory prediction
Cunjun Yu, Xiao Ma, Jiawei Ren, Haiyu Zhao, and Shuai Yi · 2020
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Spectral temporal graph neural network for multivariate time-series forecasting
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Transformer networks for trajectory forecasting
Francesco Giuliari, Irtiza Hasan, Marco Cristani, and Fabio Galasso · 2021
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Rain: Reinforced hybrid attention inference network for motion forecasting
Jiachen Li, Fan Yang, Hengbo Ma, Srikanth Malla, Masayoshi Tomizuka, and Chiho Choi · 2021
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Online multi-agent forecasting with interpretable collaborative graph neural network
Maosen Li, Siheng Chen, Yanning Shen, Genjia Liu, Ivor W Tsang, and Ya Zhang · 2021
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Collaborative uncertainty in multi-agent trajectory forecasting
Bohan Tang, Yiqi Zhong, Ulrich Neumann, Gang Wang, Ya Zhang, and Siheng Chen · 2021
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Agentformer: Agent-aware transformers for socio-temporal multi-agent forecasting
Ye Yuan, Xinshuo Weng, Yanglan Ou, and Kris M Kitani · 2021
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