Fetching the paper…
Reading the bibliography…
We introduce a motion forecasting (behavior prediction) method that meets the latency requirements for autonomous driving in dense urban environments without sacrificing accuracy.
S. Thrun and A. Bücken, “Integrating grid-based and topological maps for mobile robot navigation,” in Proceedings of the National Conference on Artificial Intelligence , 1996, pp. 944–951
1996
Earlier work this paper cites.
R. Pascanu, T. Mikolov, and Y. Bengio, “On the difficulty of training recurrent neural networks,” in ICML , 2013, pp. 1310–1318
2013
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” ICLR , 2015
2015
Earlier work this paper cites.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in CVPR , 2017, pp. 652–660
2017
Earlier work this paper cites.
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” TPAMI , vol. 40, no. 4, pp. 834–848, 2017
2017
Earlier work this paper cites.
G. Máttyus, W. Luo, and R. Urtasun, “Deeproadmapper: Extracting road topology from aerial images,” in ICCV , 2017, pp. 3438–3446
2017
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 CVPR , 2017, pp. 336–345
2017
Earlier work this paper cites.
S. Casas, W. Luo, and R. Urtasun, “Intentnet: Learning to predict intention from raw sensor data,” in CoRL , 2018, pp. 947–956
2018
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 ECCV , 2018, pp. 772–788
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 CVPR , 2019
2019
Earlier work this paper cites.
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 CVPR , 2019, pp. 8454–8462
2019
Earlier work this paper cites.
Y. Chai, B. Sapp, M. Bansal, and D. Anguelov, “Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction,” CoRL , 2019
2019
Cited alongside, same era.
M. Bansal, A. Krizhevsky, and A. Ogale, “Chauffeurnet: Learning to drive by imitating the best and synthesizing the worst,” RSS , 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 CVPR , 2019, pp. 12 697–12 705
2019
Cited alongside, same era.
C. Tang and R. R. Salakhutdinov, “Multiple futures prediction,” in NeurIPS , 2019, pp. 15 424–15 434
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 CVPR , 2019, pp. 12 126–12 134
2019
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 ICRA , 2020, pp. 9638–9644
2020
Later among the works it cites.
N. Sriram, B. Liu, F. Pittaluga, and M. Chandraker, “Smart: Simultaneous multi-agent recurrent trajectory prediction,” in ECCV . Springer, 2020, pp. 463–479
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 CVPR , 2020, pp. 14 074–14 083
2020
Later among the works it cites.
Y. Biktairov, M. Stebelev, I. Rudenko, O. Shliazhko, and B. Yangel, “Prank: motion prediction based on ranking,” NeurIPS , vol. 33, 2020
2020
Later among the works it cites.
T. Buhet, E. Wirbel, and X. Perrotton, “Plop: Probabilistic polynomial objects trajectory planning for autonomous driving,” CoRL , 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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,” in CVPR , 2020, pp. 11 621–11 631
2020
Cited alongside, same era.
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,” https://level5.lyft.com/dataset/ , 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 CVPR , 2020, pp. 11 525–11 533
2020
Cited alongside, same era.
H. Zhao, J. Gao, T. Lan, C. Sun, B. Sapp, B. Varadarajan, Y. Shen, Y. Shen, Y. Chai, C. Schmid, C. Li, and D. Anguelov, “Tnt: Target-driven trajectory prediction,” CoRL , 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
A. Rudenko, L. Palmieri, M. Herman, K. M. Kitani, D. M. Gavrila, and K. O. Arras, “Human motion trajectory prediction: A survey,” The International Journal of Robotics Research , vol. 39, no. 8, pp. 895–935, 2020
2020
Cited alongside, same era.
2020
Later among the works it cites.
A. Jain, S. Casas, R. Liao, Y. Xiong, S. Feng, S. Segal, and R. Urtasun, “Discrete residual flow for probabilistic pedestrian behavior prediction,” in CoRL . PMLR, 2020, pp. 407–419
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, et al. , “Multixnet: Multiclass multistage multimodal motion prediction,” IV , 2020
2020
Later among the works it cites.
J. Strohbeck, V. Belagiannis, J. Müller, M. Schreiber, M. Herrmann, D. Wolf, and M. Buchholz, “Multiple trajectory prediction with deep temporal and spatial convolutional neural networks,” 2020
2020
Later among the works it cites.
E. Tolstaya, R. Mahjourian, C. Downey, B. Vadarajan, B. Sapp, and D. Anguelov, “Identifying driver interactions via conditional behavior prediction,” ICRA , 2021
2021
Later among the works it cites.
S. Casas, A. Sadat, and R. Urtasun, “Mp3: A unified model to map, perceive, predict and plan,” CVPR , 2021
2021
Later among the works it cites.
S. Ettinger, S. Cheng, B. Caine, C. Liu, H. Zhao, S. Pradhan, Y. Chai, B. Sapp, C. Qi, Y. Zhou, et al. , “Large scale interactive motion forecasting for autonomous driving: The waymo open motion dataset,” ICCV , 2021
2021
Later among the works it cites.