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Motion planning is a challenging task to generate safe and feasible trajectories in highly dynamic and complex environments, forming a core capability for autonomous vehicles.
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Fan, H., Zhu, F., Liu, C., Zhang, L., Zhuang, L., Li, D., Zhu, W., Hu, J., Li, H., Kong, Q.: Baidu Apollo EM Motion Planner. arXiv (jul 2018)
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He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 16000–16009 (2022), https://openaccess.thecvf.com/content/CVPR2022/papers/He˙Masked˙Autoencoders˙Are˙Scalable˙Vision˙Learners˙CVPR˙2022˙paper.pdf
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Sun, S., Liu, Z., Yin, H., Ang, M.H.: Fiss: A trajectory planning framework using fast iterative search and sampling strategy for autonomous driving. IEEE Robotics and Automation Letters 7(4), 9985–9992 (2022)
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Chitta, K., Prakash, A., Jaeger, B., Yu, Z., Renz, K., Geiger, A.: Transfuser: Imitation with transformer-based sensor fusion for autonomous driving. Pattern Analysis and Machine Intelligence (PAMI) (2023)
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Sun, S., Chen, J., Sun, J., Yuan, C., Li, Y., Zhang, T., Ang, M.H.: Fiss+: Efficient and focused trajectory generation and refinement using fast iterative search and sampling strategy. In: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 10527–10534. IEEE (2023)
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Zhou, Z., Wang, J., Li, Y.H., Huang, Y.K.: Query-centric trajectory prediction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 17863–17873 (2023)
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Dauner, D., Hallgarten, M., Geiger, A., Chitta, K.: Parting with misconceptions about learning-based vehicle motion planning. In: Conference on Robot Learning. pp. 1268–1281. PMLR (2023)
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Hu, Y., Yang, J., Chen, L., Li, K., Sima, C., Zhu, X., Chai, S., Du, S., Lin, T., Wang, W.: Planning-oriented autonomous driving. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 17853–17862 (2023)
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