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Motion forecasting for autonomous driving is a challenging task because complex driving scenarios result in a heterogeneous mix of static and dynamic inputs.
Attention augmented convolutional networks
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Argoverse: 3d tracking and forecasting with rich maps
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Trajectron++: Multi-agent generative trajectory forecasting with heterogeneous data for control
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Language models are few-shot learners
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One thousand and one hours: Self-driving motion prediction dataset
J. Houston, G. Zuidhof, L. Bergamini, Y. Ye, A. Jain, S. Omari, V. Iglovikov, and P. Ondruska · 2006
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Soda: Multi-object tracking with soft data association
W. Hung, H. Kretzschmar, T. Lin, Y. Chai, R. Yu, M. Yang, and D. Anguelov · 2008
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Social lstm: Human trajectory prediction in crowded spaces
A. Alahi, K. Goel, V. Ramanathan, A. Robicquet, L. Fei-Fei, and S. Savarese · 2016
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DESIRE: distant future prediction in dynamic scenes with interacting agents
N. Lee, W. Choi, P. Vernaza, C. B. Choy, P. H. S. Torr, and M. K. Chandraker · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
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R2P2: A reparameterized pushforward policy for diverse, precise generative path forecasting
N. Rhinehart, K. Kitani, and P. Vernaza · 2018
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Intentnet: Learning to predict intention from raw sensor data
S. Casas, W. Luo, and R. Urtasun · 2018
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Social gan: Socially acceptable trajectories with generative adversarial networks
A. Gupta, J. Johnson, L. Fei-Fei, S. Savarese, and A. Alahi · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2018
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Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction
Y. Chai, B. Sapp, M. Bansal, and D. Anguelov · 2019
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Multimodal trajectory predictions for autonomous driving using deep convolutional networks
H. Cui, V. Radosavljevic, F.-C. Chou, T.-H. Lin, T. Nguyen, T.-K. Huang, J. Schneider, and N. Djuric · 2019
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Multiple futures prediction
C. Tang and R. R. Salakhutdinov · 2019
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End-to-end interpretable neural motion planner
W. Zeng, W. Luo, S. Suo, A. Sadat, B. Yang, S. Casas, and R. Urtasun · 2019
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Axial attention in multidimensional transformers
J. Ho, N. Kalchbrenner, D. Weissenborn, and T. Salimans · 2019
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Set transformer: A framework for attention-based permutation-invariant neural networks
J. Lee, Y. Lee, J. Kim, A. R. Kosiorek, S. Choi, and Y. W. Teh · 2019
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Decoupled weight decay regularization
I. Loshchilov and F. Hutter · 2019
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Precog: Prediction conditioned on goals in visual multi-agent settings
N. Rhinehart, R. McAllister, K. Kitani, and S. Levine · 2019
End-to-end object detection with transformers
N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko · 2020
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Efficient transformers: A survey
Y. Tay, M. Dehghani, D. Bahri, and D. Metzler · 2020
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Multipath++: Efficient information fusion and trajectory aggregation for behavior prediction
B. Varadarajan, A. S. Hefny, A. Srivastava, K. S. Refaat, N. Nayakanti, A. Cornman, K. Chen, B. Douillard, C. P. Lam, D. Anguelov, and B. Sapp · 2021
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Latent variable nested set transformers & autobots
R. Girgis, F. Golemo, F. Codevilla, J. A. D’Souza, S. E. Kahou, F. Heide, and C. J. Pal · 2021
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Scene transformer: A unified multi-task model for behavior prediction and planning
J. Ngiam, B. Caine, V. Vasudevan, Z. Zhang, H. L. Chiang, J. Ling, R. Roelofs, A. Bewley, C. Liu, A. Venugopal, D. Weiss, B. Sapp, Z. Chen, and J. Shlens · 2021
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Social-bigat: Multimodal trajectory forecasting using bicycle-gan and graph attention networks
V. Kosaraju, A. Sadeghian, R. Martín-Martín, I. D. Reid, S. H. Rezatofighi, and S. Savarese · 2019
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Stgat: Modeling spatial-temporal interactions for human trajectory prediction
Y. Huang, H. Bi, Z. Li, T. Mao, and Z. qi Wang · 2019
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The garden of forking paths: Towards multi-future trajectory prediction
J. Liang, L. Jiang, K. Murphy, T. Yu, and A. Hauptmann · 2020
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Multi-head attention for multi-modal joint vehicle motion forecasting
J. P. Mercat, T. Gilles, N. E. Zoghby, G. Sandou, D. Beauvois, and G. P. Gil · 2020
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Spatio-temporal graph transformer networks for pedestrian trajectory prediction
C. Yu, X. Ma, J. Ren, H. Zhao, and S. Yi · 2020
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Scaling laws for neural language models
J. Kaplan, S. McCandlish, T. J. Henighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, and D. Amodei · 2020
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Agentformer: Agent-aware transformers for socio-temporal multi-agent forecasting
Y. Yuan, X. Weng, Y. Ou, and K. Kitani · 2021
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Multimodal motion prediction with stacked transformers
Y. Liu, J. Zhang, L. Fang, Q. Jiang, and B. Zhou · 2021
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Vivit: A video vision transformer
A. Arnab, M. Dehghani, G. Heigold, C. Sun, M. Lučić, and C. Schmid · 2021
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Perceiver: General perception with iterative attention
A. Jaegle, F. Gimeno, A. Brock, A. Zisserman, O. Vinyals, and J. Carreira · 2021
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Densetnt: End-to-end trajectory prediction from dense goal sets
J. Gu, C. Sun, and H. Zhao · 2021
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Lanercnn: Distributed representations for graph-centric motion forecasting
W. Zeng, M. Liang, R. Liao, and R. Urtasun · 2021
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Large scale interactive motion forecasting for autonomous driving : The waymo open motion dataset
S. Ettinger, S. Cheng, B. Caine, C. Liu, H. Zhao, S. Pradhan, Y. Chai, B. Sapp, C. R. Qi, Y. Zhou, Z. Yang, A. Chouard, P. Sun, J. Ngiam, V. Vasudevan, A. McCauley, J. Shlens, and D. Anguelov · 2021
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Bottleneck transformers for visual recognition
A. Srinivas, T. Lin, N. Parmar, J. Shlens, P. Abbeel, and A. Vaswani · 2021
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Perceiver io: A general architecture for structured inputs & outputs
A. Jaegle, S. Borgeaud, J.-B. Alayrac, C. Doersch, C. Ionescu, D. Ding, S. Koppula, A. Brock, E. Shelhamer, O. J. H’enaff, M. M. Botvinick, A. Zisserman, O. Vinyals, and J. Carreira · 2021
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Long range arena: A benchmark for efficient transformers
Y. Tay, M. Dehghani, S. Abnar, Y. Shen, D. Bahri, P. Pham, J. Rao, L. Yang, S. Ruder, and D. Metzler · 2021
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Dcms: Motion forecasting with dual consistency and multi-pseudo-target supervision, 2022
M. Ye, J. Xu, X. Xu, T. Cao, and Q. Chen · 2022
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