Fetching the paper…
Reading the bibliography…
Trajectory prediction is crucial for autonomous vehicles.
R. E. Kalman, “A new approach to linear filtering and prediction problems,” 1960
1960
Earlier work this paper cites.
J. Wiest, M. Höffken, U. Kreßel, and K. Dietmayer, “Probabilistic trajectory prediction with gaussian mixture models,” in 2012 IEEE Intelligent Vehicles Symposium , 2012, pp. 141–146
2012
Earlier work this paper cites.
2014
Earlier work this paper cites.
2014
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 Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 961–971
2016
Earlier work this paper cites.
K. Greff, R. K. Srivastava, J. Koutník, B. R. Steunebrink, and J. Schmidhuber, “Lstm: A search space odyssey,” IEEE transactions on neural networks and learning systems , vol. 28, no. 10, pp. 2222–2232, 2016
2016
Earlier work this paper cites.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 2818–2826
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.
S. Casas, W. Luo, and R. Urtasun, “Intentnet: Learning to predict intention from raw sensor data,” in Conference on Robot Learning , 2018, pp. 947–956
2018
Earlier work this paper cites.
N. Deo and M. M. Trivedi, “Convolutional social pooling for vehicle trajectory prediction,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops , pp. 1549–15 498, 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
A. Gupta, J. Johnson, L. Fei-Fei, S. Savarese, and A. Alahi, “Social gan: Socially acceptable trajectories with generative adversarial networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 2255–2264
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 Proceedings of the IEEE conference on Computer Vision and Pattern Recognition , 2018, pp. 3569–3577
M. Huynh and G. Alaghband, “Trajectory prediction by coupling scene-lstm with human movement lstm,” in International Symposium on Visual Computing . Springer, 2019, pp. 244–259
2019
Later among the works it cites.
X. Li, X. Ying, and M. C. Chuah, “Grip: Graph-based interaction-aware trajectory prediction,” in 2019 IEEE Intelligent Transportation Systems Conference (ITSC) . IEEE, 2019, pp. 3960–3966
2019
Later among the works it cites.
O. Makansi, E. Ilg, Ö. Çiçek, and T. Brox, “Overcoming limitations of mixture density networks: A sampling and fitting framework for multimodal future prediction,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 7137–7146
2019
Later among the works it cites.
K. Messaoud, I. Yahiaoui, A. Verroust-Blondet, and F. Nashashibi, “Non-local social pooling for vehicle trajectory prediction,” in 2019 IEEE Intelligent Vehicles Symposium (IV) , 2019, pp. 975–980
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
S. H. Park, B. Kim, C. M. Kang, C. C. Chung, and J. W. Choi, “Sequence-to-sequence prediction of vehicle trajectory via lstm encoder-decoder architecture,” in 2018 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2018, pp. 1672–1678
2018
Cited alongside, same era.
2019
Cited alongside, same era.
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 Conference on Computer Vision and Pattern Recognition , 2019, pp. 8748–8757
2019
Cited alongside, same era.
S. B. Hong, J. and J. Philbin, “Rules of the road: Predicting driving behavior with a convolutional model of semantic interactions.” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 8454–8462
2019
Cited alongside, same era.
X. Huang, S. G. McGill, B. C. Williams, L. Fletcher, and G. Rosman, “Uncertainty-aware driver trajectory prediction at urban intersections,” 2019 International Conference on Robotics and Automation (ICRA) , pp. 9718–9724, 2019
2019
Cited alongside, same era.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, et al. , “Pytorch: An imperative style, high-performance deep learning library,” in Advances in neural information processing systems , 2019, pp. 8026–8037
2019
Later among the works it cites.
C. Tang and R. R. Salakhutdinov, “Multiple futures prediction,” in Advances in Neural Information Processing Systems , 2019, pp. 15 424–15 434
2019
Later among the works it cites.
2019
Later among the works it cites.
A. Zyner, S. Worrall, and E. Nebot, “Naturalistic driver intention and path prediction using recurrent neural networks,” IEEE Transactions on Intelligent Transportation Systems , vol. 21, no. 4, pp. 1584–1594, 2019
2019
Later among the works it cites.
H. Cui, V. Radosavljevic, F.-C. Chou, T.-H. Lin, T. Nguyen, T.-K. Huang, J. Schneider, and N. Djuric, “Multimodal trajectory predictions for autonomous driving using deep convolutional networks,” in International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 2090–2096
2096
Closest in time.