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Reasoning about the future behavior of other agents is critical to safe robot navigation.
R. E. Kalman, “A new approach to linear filtering and prediction problems,” ASME Journal of Basic Engineering , vol. 82, pp. 35–45, 1960
1960
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation , 1997
1997
Earlier work this paper cites.
W. A. Wright, “Bayesian approach to neural-network modeling with input uncertainty,” IEEE Transactions on Neural Networks , vol. 10, no. 6, pp. 1261–1270, 1999
1999
Earlier work this paper cites.
S. Thrun, W. Burgard, and D. Fox, “The extended Kalman filter,” in Probabilistic Robotics . MIT Press, 2005, pp. 54–64
2005
Earlier work this paper cites.
S. M. LaValle, “Better unicycle models,” in Planning Algorithms . Cambridge Univ. Press, 2006, pp. 743–743
2006
Earlier work this paper cites.
R. F. Astudillo and J. P. S. Neto, “Propagation of uncertainty through multilayer perceptrons for robust automatic speech recognition,” in Conf. of the Int. Speech Communication Association , 2011
2011
Earlier work this paper cites.
A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the kitti vision benchmark suite,” in Conference on Computer Vision and Pattern Recognition (CVPR) , 2012
2012
Earlier work this paper cites.
S. Lefèvre, D. Vasquez, and C. Laugier, “A survey on motion prediction and risk assessment for intelligent vehicles,” ROBOMECH journal , vol. 1, no. 1, pp. 1–14, 2014
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Conf. on Neural Information Processing Systems , 2014
2014
Earlier work this paper cites.
K. Cho, B. van Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using rnn encoder-decoder for statistical machine translation,” in Proc. of Conf. on Empirical Methods in Natural Language Processing , 2014, pp. 1724–1734
2014
Earlier work this paper cites.
K. Sohn, H. Lee, and X. Yan, “Learning structured output representation using deep conditional generative models,” in Conf. on Neural Information Processing Systems , 2015
2015
Earlier work this paper cites.
D. Hallac, J. Leskovec, and S. Boyd, “Network lasso: Clustering and optimization in large graphs,” in ACM Int. Conf. on Knowledge Discovery and Data Mining , 2015
2015
Earlier work this paper cites.
S. R. Bowman, L. Vilnis, O. Vinyals, A. M. Dai, R. Jozefowicz, and S. Bengio, “Generating sentences from a continuous space,” in Proc. Annual Meeting of the Association for Computational Linguistics , 2015
2015
Earlier work this paper cites.
H. Wang, X. Shi, and D.-Y. Yeung, “Natural-parameter networks: A class of probabilistic neural networks,” in Conf. on Neural Information Processing Systems , 2016
2016
Earlier work this paper cites.
P. W. Battaglia, R. Pascanu, M. Lai, D. Rezende, and K. Kavukcuoglu, “Interaction networks for learning about objects, relations and physics,” in Conf. on Neural Information Processing Systems , 2016
2016
Earlier work this paper cites.
A. Jain, A. R. Zamir, S. Savarese, and A. Saxena, “Structural-RNN: Deep learning on spatio-temporal graphs,” in IEEE Conf. on Computer Vision and Pattern Recognition , 2016
2016
Earlier work this paper cites.
B. Paden, M. Čáp, S. Z. Yong, D. Yershov, and E. Frazzoli, “A survey of motion planning and control techniques for self-driving urban vehicles,” IEEE Transactions on Intelligent Vehicles , vol. 1, no. 1, pp. 33–55, 2016
2016
Cited alongside, same era.
R. McAllister, Y. Gal, A. Kendall, M. Van Der Wilk, A. Shah, R. Cipolla, and A. V. Weller, “Concrete problems for autonomous vehicle safety: advantages of Bayesian deep learning,” in International Joint Conferences on Artificial Intelligence , 2017
2017
Cited alongside, same era.
C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger, “On calibration of modern neural networks,” in Int. Conf. on Machine Learning , 2017
2017
Cited alongside, same era.
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in PyTorch,” in Conf. on Neural Information Processing Systems - Autodiff Workshop , 2017
2017
Cited alongside, same era.
B. Ivanovic and M. Pavone, “The Trajectron: Probabilistic multi-agent trajectory modeling with dynamic spatiotemporal graphs,” in IEEE Int. Conf. on Computer Vision , 2019
2019
Later among the works it cites.
D. Roy, T. Ishizaka, C. K. Mohan, and A. Fukuda, “Vehicle trajectory prediction at intersections using interaction based generative adversarial networks,” in IEEE Intelligent Transportation Systems Conference (ITSC) , 2019, pp. 2318–2323
2019
Later among the works it cites.
N. Rhinehart, R. McAllister, K. Kitani, and S. Levine, “PRECOG: Prediction conditioned on goals in visual multi-agent settings,” in International Conference on Computer Vision (ICCV) , October 2019
2019
Later among the works it cites.
S. Zhao, J. Song, and S. Ermon, “InfoVAE: Balancing learning and inference in variational autoencoders,” in Proc. AAAI Conf. on Artificial Intelligence , 2019
2019
Later among the works it cites.
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E. Jang, S. Gu, and B. Poole, “Categorial reparameterization with gumbel-softmax,” in Int. Conf. on Learning Representations , 2017
2017
Cited alongside, same era.
W. Schwarting, J. Alonso-Mora, and D. Rus, “Planning and decision-making for autonomous vehicles,” Annual Review of Control, Robotics, and Autonomous Systems , 2018
2018
Cited alongside, same era.
N. Rhinehart, K. M. Kitani, and P. Vernaza, “R2p2: A reparameterized pushforward policy for diverse, precise generative path forecasting,” in The European Conference on Computer Vision (ECCV) , September 2018
2018
Cited alongside, same era.
E. Schmerling, K. Leung, W. Vollprecht, and M. Pavone, “Multimodal probabilistic model-based planning for human-robot interaction,” in Proc. IEEE Conf. on Robotics and Automation , 2018
2018
Cited alongside, same era.
B. Ivanovic, E. Schmerling, K. Leung, and M. Pavone, “Generative modeling of multimodal multi-human behavior,” in IEEE/RSJ Int. Conf. on Intelligent Robots & Systems , 2018
2018
Cited alongside, same era.
A. Gupta, J. Johnson, F. Li, S. Savarese, and A. Alahi, “Social GAN: Socially acceptable trajectories with generative adversarial networks,” in IEEE Conf. on Computer Vision and Pattern Recognition , 2018
2018
Cited alongside, same era.
W. Luo, B. Yang, and R. Urtasun, “Fast and furious: Real time end-to-end 3d detection, tracking and motion forecasting with a single convolutional net,” in Computer Vision and Pattern Recognition (CVPR) , 2018, pp. 3569–3577
2018
Cited alongside, same era.
S. Casas, W. Luo, and R. Urtasun, “Intentnet: Learning to predict intention from raw sensor data,” in Conference on Robot Learning (CoRL) . PMLR, 2018, pp. 947–956
2018
Cited alongside, same era.
H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuScenes: A multimodal dataset for autonomous driving,” 2019
2019
Later among the works it cites.
A. Rudenko, L. Palmieri, M. Herman, K. M. Kitani, D. M. Gavrila, and K. O. Arras, “Human motion trajectory prediction: A survey,” Int. Journal of Robotics Research , vol. 39, no. 8, pp. 895–935, 2020
2020
Later among the works it cites.
D. Bhatt, D. Bansal, G. Gupta, H. Lee, K. M. Jatavallabhula, and L. Paull, “Probabilistic object detection: Strengths, weaknesses, opportunities,” in Workshop on AI for Autonomous Driving at the International Conference on Machine Learning (ICML) , 2020
2020
Later among the works it cites.
T. Salzmann, B. Ivanovic, P. Chakravarty, and M. Pavone, “Trajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data,” in European Conf. on Computer Vision , 2020
2020
Later among the works it cites.
J. Houston, G. Zuidhof, L. Bergamini, Y. Ye, A. Jain, S. Omari, V. Iglovikov, and P. Ondruska, “One thousand and one hours: Self-driving motion prediction dataset,” in Conf. on Robot Learning , 2020
2020
Later among the works it cites.
S. Casas, C. Gulino, R. Liao, and R. Urtasun, “SpAGNN: Spatially-aware graph neural networks for relational behavior forecasting from sensor data,” in International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 9491–9497
2020
Later among the works it cites.
S. Casas, C. Gulino, S. Suo, K. Luo, R. Liao, and R. Urtasun, “Implicit latent variable model for scene-consistent motion forecasting,” in European Conference on Computer Vision (ECCV) , 2020
2020
Later among the works it cites.
M. Itkina, B. Ivanovic, R. Senanayake, M. J. Kochenderfer, and M. Pavone, “Evidential sparsification of multimodal latent spaces in conditional variational autoencoders,” in Conf. on Neural Information Processing Systems , 2020
2020
Later among the works it cites.
B. Ivanovic, A. Elhafsi, G. Rosman, A. Gaidon, and M. Pavone, “MATS: An interpretable trajectory forecasting representation for planning and control,” in Conf. on Robot Learning , 2020
2020
Later among the works it cites.
C. Schöller, V. Aravantinos, F. Lay, and A. Knoll, “What the constant velocity model can teach us about pedestrian motion prediction,” IEEE Robotics and Automation Letters , 2020
2020
Later among the works it cites.
N. Djuric, H. Cui, Z. Su, S. Wu, H. Wang, F.-C. Chou, L. S. Martin, S. Feng, R. Hu, Y. Xu, A. Dayan, S. Zhang, B. C. Becker, G. P. Meyer, C. Vallespi-Gonzalez, and C. K. Wellington, “MultiXNet: Multiclass multistage multimodal motion prediction,” in IEEE Intelligent Vehicles Symposium (IV) , 2021
2021
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