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A typical trajectory planner of autonomous driving commonly relies on predicting the future behavior of surrounding obstacles.
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2020
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2020
Z. Cao, S. Xu, H. Peng, D. Yang, and R. Zidek, “Confidence-aware reinforcement learning for self-driving cars,” IEEE Transactions on Intelligent Transportation Systems , 2021
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2021
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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 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 525–11 533
2020
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2021
Cited alongside, same era.
2021
Cited alongside, same era.
O. Makansi, Ö. Cicek, Y. Marrakchi, and T. Brox, “On exposing the challenging long tail in future prediction of traffic actors,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 13 147–13 157
2021
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E. Hüllermeier and W. Waegeman, “Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods,” Machine Learning , vol. 110, no. 3, pp. 457–506, 2021
2021
Cited alongside, same era.
B. Mersch, T. Höllen, K. Zhao, C. Stachniss, and R. Roscher, “Maneuver-based trajectory prediction for self-driving cars using spatio-temporal convolutional networks,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 4888–4895
2021
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S. Xu, R. Zidek, Z. Cao, P. Lu, X. Wang, B. Li, and H. Peng, “System and experiments of model-driven motion planning and control for autonomous vehicles,” IEEE Transactions on Systems, Man, and Cybernetics: Systems , 2021
2021
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M. Ganaie, M. Hu et al. , “Ensemble deep learning: A review,” arXiv preprint arXiv:2104.02395 , 2021
2021
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Z. Cao, S. Xu, X. Jiao, H. Peng, and D. Yang, “Trustworthy safety improvement for autonomous driving using reinforcement learning,” Transportation research part C: emerging technologies , vol. 138, p. 103656, 2022
2022
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