Fetching the paper…
Reading the bibliography…
Predicting the behaviors of other agents on the road is critical for autonomous driving to ensure safety and efficiency.
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.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems , 2017, pp. 5998–6008
2017
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 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 2255–2264
2018
Earlier work this paper cites.
L. A. Thiede and P. P. Brahma, “Analyzing the variety loss in the context of probabilistic trajectory prediction,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 9954–9963
2019
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.
S. Mozaffari, O. Y. Al-Jarrah, M. Dianati, P. Jennings, and A. Mouzakitis, “Deep learning-based vehicle behavior prediction for autonomous driving applications: A review,” IEEE Transactions on Intelligent Transportation Systems , 2020
2020
Earlier work this paper cites.
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
Earlier work this paper cites.
H. Zhao, J. Gao, T. Lan, C. Sun, B. Sapp, B. Varadarajan, Y. Shen, Y. Shen, Y. Chai, C. Schmid, et al. , “Tnt: Target-driven trajectory prediction,” in Conference on Robot Learning (CoRL) , 2020
2020
Earlier work this paper 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
Cited alongside, same era.
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 , 2020, pp. 541–556
2020
Cited alongside, same era.
B. Ivanovic, K. Leung, E. Schmerling, and M. Pavone, “Multimodal deep generative models for trajectory prediction: A conditional variational autoencoder approach,” IEEE Robotics and Automation Letters , vol. 6, no. 2, pp. 295–302, 2020
2020
Cited alongside, same era.
Y. Chai, B. Sapp, M. Bansal, and D. Anguelov, “Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction,” in Conference on Robot Learning , 2020
2020
Z. Huang, J. Wu, and C. Lv, “Driving behavior modeling using naturalistic human driving data with inverse reinforcement learning,” IEEE Transactions on Intelligent Transportation Systems , 2021
2021
Closest in time.
B. Dong, H. Liu, Y. Bai, J. Lin, Z. Xu, X. Xu, and Q. Kong, “Multi-modal trajectory prediction for autonomous driving with semantic map and dynamic graph attention network,” in Machine Learning for Autonomous Driving Workshop at the 34th Conference on Neural Information Processing Systems , 2021
2021
Closest in time.
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
Closest in time.
2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
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
Cited alongside, same era.
L. Zhang, P.-H. Su, J. Hoang, G. C. Haynes, and M. Marchetti-Bowick, “Map-adaptive goal-based trajectory prediction,” in Conference on Robot Learning , 2020
2020
Cited alongside, same era.
L. Fang, Q. Jiang, J. Shi, and B. Zhou, “Tpnet: Trajectory proposal network for motion prediction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 6797–6806
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.
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
Closest in time.
2021
Closest in time.
2021
Closest in time.
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 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 2090–2096
2096
Closest in time.