A journal of IEEE and CAA , publishes
high-quality papers in English on original
theoretical/experimental research
and development in all areas of automation
Volume 9
Issue 3
IEEE/CAA Journal of Automatica Sinica
| Citation: | J. J. Wang, Q. C. Zhang, and D. B. Zhao, “Highway lane change decision-making via attention-based deep reinforcement learning,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 3, pp. 567–569, Mar. 2022. doi: 10.1109/JAS.2021.1004395
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| [1] |
D. Zhao, K. Shao, Y. Zhu, D. Li, Y. Chen, H. Wang, D. Liu, T. Zhou, and C. Wang, “Review of deep reinforcement learning and discussions on the development of computer Go,” Control Theory &Applications, vol. 33, no. 6, pp. 701–717, 2016.
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| [2] |
D. Li, D. Zhao, and Q. Zhang, “Reinforcement learning based lane change decision-making with imaginary sampling,” in Proc. IEEE Symposium Series on Computational Intelligence (SSCI), 2019, pp. 16–21.
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| [3] |
J. Wang, Q. Zhang, D. Zhao, and Y. Chen, “Lane change decision-making through deep reinforcement learning with rule-based constraints,” in Proc. Int. Joint Conf. on Neural Networks (IJCNN), IEEE, 2019, pp. 1–6.
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| [4] |
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in Proc. Advances in Neural Information Processing Systems (NeurIPS), 2017, pp. 5998–6008.
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| [5] |
X. Li, Y. Liu, K. Wang, and F. Wang, “A recurrent attention and interaction model for pedestrian trajectory prediction,” IEEE/CAA Journal of Automatica Sinica, vol. 7, no. 5, pp. 1361–1370, 2020.
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| [6] |
X. Zhao, Y. Chen, J. Guo, and D. Zhao, “A spatial-temporal attention model for human trajectory prediction,” IEEE/CAA Journal of Automatica Sinica, vol. 7, no. 4, pp. 965–974, 2020. doi: 10.1109/JAS.2020.1003228
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| [7] |
X. Wang, R. Girshick, A. Gupta, and K. He, “Non-local neural networks,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2018, pp. 7794–7803.
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| [8] |
E. Leurent and J. Mercat, “Social attention for autonomous decision-making in dense traffic,” arXiv preprint arXiv: 1911.12250, 2019.
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| [9] |
K. Messaoud, N. Deo, M. M. Trivedi, and F. Nashashibi, “Trajectory prediction for autonomous driving based on multi-head attention with joint agent-map representation,” in Proc. IEEE Intelligent Vehicles Symposium (IV), 2021, pp. 165–170.
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| [10] |
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis, “Human-level control through deep reinforcement learning,” Nature, vol. 518, no. 7540, pp. 529–533, 2015. doi: 10.1038/nature14236
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| [11] |
R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction. Cambridge, USA: MIT Press, 2018.
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| [12] |
Z. Wang, T. Schaul, M. Hessel, H. Hasselt, M. Lanctot, and N. Freitas, “Dueling network architectures for deep reinforcement learning,” in Proc. Int. Conf. Machine Learning (ICML), PMLR, 2016, pp. 1995–2003.
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| [13] |
H. Van Hasselt, A. Guez, and D. Silver, “Deep reinforcement learning with double Q-learning,” in Proc. AAAI Conf. Artificial Intelligence (AAAI), vol. 30, no. 1, 2016.
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| [14] |
A. Vemula, K. Muelling, and J. Oh, “Social attention: Modeling attention in human crowds,” in Proc. IEEE international Conf. Robotics and Automation (ICRA), 2018, pp. 4601–4607.
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