2020

Multi-Head Attention based Probabilistic Vehicle Trajectory Prediction

Kim, Hayoung, Kim, Dongchan, Kim, Gihoon et al.

Understand

This paper presents online-capable deep learning model for probabilistic vehicle trajectory prediction.

We propose a simple encoder-decoder architecture based on multi-head attention.
The proposed model generates the distribution of the predicted trajectories for multiple vehicles in parallel.
Our approach to model the interactions can learn to attend to a few influential vehicles in an unsupervised manner, which can improve the interpretability of the network.

Built on

S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
A. Polychronopoulos, M. Tsogas, A. J. Amditis, and L. Andreone, “Sensor fusion for predicting vehicles’ path for collision avoidance systems,” IEEE Transactions on Intelligent Transportation Systems , vol. 8, no. 3, pp. 549–562, 2007
2007
Earlier work this paper cites.
J. Colyar and J. Halkias, “US highway 101 dataset,” Federal Highway Administration (FHWA), Tech. Rep. FHWA-HRT-07-030 , 2007
2007
Earlier work this paper cites.
A. Berthelot, A. Tamke, T. Dang, and G. Breuel, “Handling uncertainties in criticality assessment,” in 2011 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2011, pp. 571–576
2011
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 . IEEE, 2012, pp. 141–146
2012
Earlier work this paper cites.
I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in Advances in neural information processing systems , 2014, pp. 3104–3112
2014
Earlier work this paper cites.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” The journal of machine learning research , vol. 15, no. 1, pp. 1929–1958, 2014
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014
Original
2014
Earlier work this paper cites.

Similar

K. Sohn, H. Lee, and X. Yan, “Learning structured output representation using deep conditional generative models,” in Advances in Neural Information Processing Systems 28 , C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, Eds. Curran Associates, Inc., 2015, pp. 3483–3491
2015
Cited alongside, same era.
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
Cited alongside, same era.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” arXiv preprint arXiv:1609.02907 , 2016
Original
2016
Cited alongside, same era.
J. L. Ba, J. R. Kiros, and G. E. Hinton, “Layer normalization,” arXiv preprint arXiv:1607.06450 , 2016
Original
2016
Cited alongside, same era.
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, et al. , “Tensorflow: A system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16) , 2016, pp. 265–283
2016
Cited alongside, same era.
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
Cited alongside, same era.
G. Xie, H. Gao, L. Qian, B. Huang, K. Li, and J. Wang, “Vehicle trajectory prediction by integrating physics-and maneuver-based approaches using interactive multiple models,” IEEE Transactions on Industrial Electronics , vol. 65, no. 7, pp. 5999–6008, 2017
2017
Cited alongside, same era.
F. Altché and A. de La Fortelle, “An lstm network for highway trajectory prediction,” in 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2017, pp. 353–359
2017
Cited alongside, same era.

Then

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
Later among the works it cites.
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
Later among the works it cites.
R. Krajewski, J. Bock, L. Kloeker, and L. Eckstein, “The highD dataset: A drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems,” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC) , 2018, pp. 2118–2125
2018
Later among the works it cites.
X. Feng, Z. Cen, J. Hu, and Y. Zhang, “Vehicle trajectory prediction using intention-based conditional variational autoencoder,” in 2019 IEEE Intelligent Transportation Systems Conference (ITSC) . IEEE, 2019, pp. 3514–3519
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. Scheel, N. S. Nagaraja, L. Schwarz, N. Navab, and F. Tombari, “Attention-based lane change prediction,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 8655–8661
2019
Later among the works it cites.
C. Dong, Y. Chen, and J. M. Dolan, “Interactive trajectory prediction for autonomous driving via recurrent meta induction neural network,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 1212–1217
2019
Later among the works it cites.

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