Fetching the paper…
Reading the bibliography…
Behavior prediction models have proliferated in recent years, especially in the popular real-world robotics application of autonomous driving, where representing the distribution over possible futures of moving agents is essential for safe and comfortable motion planning.
C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics) . Berlin, Heidelberg: Springer-Verlag, 2006
2006
Earlier work this paper cites.
G. Hinton, O. Vinyals, and J. Dean, “Distilling the knowledge in a neural network,” stat , vol. 1050, p. 9, 2015
2015
Earlier work this paper cites.
Y. Kim and A. M. Rush, “Sequence-level knowledge distillation,” in EMNLP , 2016
2016
Earlier work this paper cites.
A. ”Alahi, K. Goel, V. Ramanathan, A. Robicquet, L. Fei-Fei, and S. Savarese, “Social LSTM: Human Trajectory Prediction in Crowded Spaces,” in CVPR , 2016
2016
Earlier work this paper cites.
N. Lee, W. Choi, P. Vernaza, C. B. Choy, P. H. Torr, and M. Chandraker, “Desire: Distant future prediction in dynamic scenes with interacting agents,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 336–345
2017
Earlier work this paper cites.
N. Rhinehart, K. M. Kitani, and P. Vernaza, “R2p2: A reparameterized pushforward policy for diverse, precise generative path forecasting,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 772–788
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
S. Casas, W. Luo, and R. Urtasun, “Intentnet: Learning to predict intention from raw sensor data,” in Conference on Robot Learning . PMLR, 2018, pp. 947–956
2018
Earlier work this paper cites.
A. Gupta, J. Johnson, L. Fei-Fei, S. Savarese, and A. Alahi, “Social GAN: Socially acceptable trajectories with generative adversarial networks,” in CVPR , 2018
2018
Earlier work this paper cites.
M.-F. Chang, J. Lambert, P. Sangkloy, J. Singh, S. Bak, A. Hartnett, D. Wang, P. Carr, S. Lucey, D. Ramanan, et al. , “Argoverse: 3d tracking and forecasting with rich maps,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 8748–8757
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
C. Tang and R. R. Salakhutdinov, “Multiple futures prediction,” Advances in Neural Information Processing Systems , vol. 32, pp. 15 424–15 434, 2019
2019
Earlier work this paper cites.
J. Hong, B. Sapp, and J. Philbin, “Rules of the road: Predicting driving behavior with a convolutional model of semantic interactions,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 8454–8462
2019
Earlier work this paper cites.
C. Tang and R. R. Salakhutdinov, “Multiple futures prediction,” in NeurIPS , 2019
2019
Earlier work this paper cites.
N. Rhinehart, R. McAllister, K. Kitani, and S. Levine, “PRECOG: Prediction conditioned on goals in visual multi-agent settings,” in Intl. Conf. on Computer Vision , 2019
2019
Earlier work this paper cites.
Y. Chai, B. Sapp, M. Bansal, and D. Anguelov, “Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction,” in Conference on Robot Learning , 2019
2019
Cited alongside, same era.
Y. Yuan and K. M. Kitani, “Diverse trajectory forecasting with determinantal point processes,” in International Conference on Learning Representations , 2019
2019
Cited alongside, same era.
M. Phuong and C. Lampert, “Towards understanding knowledge distillation,” in International Conference on Machine Learning . PMLR, 2019, pp. 5142–5151
2019
Cited alongside, same era.
C. Tang and R. R. Salakhutdinov, “Multiple futures prediction,” in nips , 2019
2019
Cited alongside, same era.
T. Zhao, Y. Xu, M. Monfort, W. Choi, C. Baker, Y. Zhao, Y. Wang, and Y. N. Wu, “Multi-agent tensor fusion for contextual trajectory prediction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 12 126–12 134
2020
Later among the works it cites.
T. Phan-Minh, E. C. Grigore, F. A. Boulton, O. Beijbom, and E. M. Wolff, “Covernet: Multimodal behavior prediction using trajectory sets,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 14 074–14 083
2020
Later among the works it cites.
T. Buhet, E. Wirbel, A. Bursuc, and X. Perrotton, “Plop: Probabilistic polynomial objects trajectory planning for autonomous driving,” 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2019
Cited alongside, same era.
J. Hong, B. Sapp, and J. Philbin, “Rules of the road: Predicting driving behavior with a convolutional model of semantic interactions,” in CVPR , 2019
2019
Cited alongside, same era.
A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, “Pointpillars: Fast encoders for object detection from point clouds,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 12 697–12 705
2019
Cited alongside, same era.
M. Tan and Q. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” in International Conference on Machine Learning . PMLR, 2019, pp. 6105–6114
2019
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,” 2020
2020
Cited alongside, same era.
J. Gao, C. Sun, H. Zhao, Y. Shen, D. Anguelov, C. Li, and C. Schmid, “Vectornet: Encoding hd maps and agent dynamics from vectorized representation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 525–11 533
2020
Cited alongside, same era.
J. Mercat, T. Gilles, N. El Zoghby, G. Sandou, D. Beauvois, and G. P. Gil, “Multi-head attention for multi-modal joint vehicle motion forecasting,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 9638–9644
2020
Cited alongside, same era.
J. Mercat, T. Gilles, N. Zoghby, G. Sandou, D. Beauvois, and G. Gil, “Multi-head attention for joint multi-modal vehicle motion forecasting,” in IEEE Intl. Conf. on Robotics and Automation , 2020
2020
Cited alongside, same era.
X. Cheng, Z. Rao, Y. Chen, and Q. Zhang, “Explaining knowledge distillation by quantifying the knowledge,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 12 925–12 935
2020
Later among the works it cites.
T. Phan-Minh, E. C. Grigore, F. A. Boulton, O. Beijbom, and E. M. Wolff, “Covernet: Multimodal behavior prediction using trajectory sets,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 14 074–14 083
2020
Later among the works it cites.
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 Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XVIII 16 . Springer, 2020, pp. 683–700
2020
Later among the works it cites.
M. Liang, B. Yang, R. Hu, Y. Chen, R. Liao, S. Feng, and R. Urtasun, “Learning lane graph representations for motion forecasting,” in European Conference on Computer Vision . Springer, 2020, pp. 541–556
2020
Later among the works it cites.
S. Ettinger, S. Cheng, B. Caine, C. Liu, H. Zhao, S. Pradhan, Y. Chai, B. Sapp, C. R. Qi, Y. Zhou, et al. , “Large scale interactive motion forecasting for autonomous driving: The waymo open motion dataset,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 9710–9719
2021
Later among the works it cites.
2021
Later among the works it cites.
W. Zeng, M. Liang, R. Liao, and R. Urtasun, “Lanercnn: Distributed representations for graph-centric motion forecasting,” in IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems , 2021
2021
Later among the works it cites.
Y. Liu, J. Zhang, L. Fang, Q. Jiang, and B. Zhou, “Multimodal motion prediction with stacked transformers,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 7577–7586
2021
Later among the works it cites.
M. Ye, T. Cao, and Q. Chen, “Tpcn: Temporal point cloud networks for motion forecasting,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 11 318–11 327
2021
Later among the works it cites.