Fetching the paper…
Reading the bibliography…
Autonomous driving systems require the ability to fully understand and predict the surrounding environment to make informed decisions in complex scenarios.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “Carla: An open urban driving simulator,” in Conference on robot learning . PMLR, 2017, pp. 1–16
2017
Earlier work this paper cites.
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” IEEE transactions on pattern analysis and machine intelligence , vol. 40, no. 4, pp. 834–848, 2017
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
N. Rhinehart, R. McAllister, K. Kitani, and S. Levine, “Precog: Prediction conditioned on goals in visual multi-agent settings,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 2821–2830
2019
Earlier work this paper cites.
W. Zeng, W. Luo, S. Suo, A. Sadat, B. Yang, S. Casas, and R. Urtasun, “End-to-end interpretable neural motion planner,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 8660–8669
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 , vol. 23, no. 1, pp. 33–47, 2020
2020
Earlier work this paper cites.
P. Hang, C. Lv, C. Huang, J. Cai, Z. Hu, and Y. Xing, “An integrated framework of decision making and motion planning for autonomous vehicles considering social behaviors,” IEEE transactions on vehicular technology , vol. 69, no. 12, pp. 14 458–14 469, 2020
2020
Earlier work this paper cites.
M. Liang, B. Yang, W. Zeng, Y. Chen, R. Hu, S. Casas, and R. Urtasun, “Pnpnet: End-to-end perception and prediction with tracking in the loop,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 553–11 562
2020
Earlier work this paper cites.
M. Bhardwaj, B. Boots, and M. Mukadam, “Differentiable gaussian process motion planning,” in 2020 IEEE international conference on robotics and automation (ICRA) . IEEE, 2020, pp. 10 598–10 604
2020
Earlier work this paper cites.
H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 621–11 631
2020
Earlier work this paper cites.
D. Kim, S. Woo, J.-Y. Lee, and I. S. Kweon, “Video panoptic segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 9859–9868
2020
Earlier work this paper cites.
A. Kumar, A. Zhou, G. Tucker, and S. Levine, “Conservative q-learning for offline reinforcement learning,” Advances in Neural Information Processing Systems , vol. 33, pp. 1179–1191, 2020
2020
Earlier work this paper cites.
2021
Earlier work this paper cites.
S. Casas, A. Sadat, and R. Urtasun, “Mp3: A unified model to map, perceive, predict and plan,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 14 403–14 412
2021
Earlier work this paper cites.
Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 10 012–10 022
2021
Earlier work this paper cites.
S. Ettinger, S. Cheng, B. Caine, C. Liu, H. Zhao, S. Pradhan, Y. Chai, B. Sapp, C. R. Qi, Y. Zhou et al. , “Large scale interactive motion forecasting for autonomous driving: The waymo open motion dataset,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 9710–9719
2021
Earlier work this paper cites.
P. Hu, A. Huang, J. Dolan, D. Held, and D. Ramanan, “Safe local motion planning with self-supervised freespace forecasting,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 12 732–12 741
2021
Earlier work this paper cites.
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
Earlier work this paper cites.
A. Hu, Z. Murez, N. Mohan, S. Dudas, J. Hawke, V. Badrinarayanan, R. Cipolla, and A. Kendall, “Fiery: Future instance prediction in bird’s-eye view from surround monocular cameras,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 15 273–15 282
2021
Earlier work this paper cites.
Z. Zhang, A. Liniger, D. Dai, F. Yu, and L. Van Gool, “End-to-end urban driving by imitating a reinforcement learning coach,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 15 222–15 232
2021
Earlier work this paper cites.
L. Chen, Y. Li, C. Huang, B. Li, Y. Xing, D. Tian, L. Li, Z. Hu, X. Na, Z. Li et al. , “Milestones in autonomous driving and intelligent vehicles: Survey of surveys,” IEEE Transactions on Intelligent Vehicles , 2022
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
S. Hu, L. Chen, P. Wu, H. Li, J. Yan, and D. Tao, “St-p3: End-to-end vision-based autonomous driving via spatial-temporal feature learning,” in European Conference on Computer Vision . Springer, 2022, pp. 533–549
2022
Cited alongside, same era.
X. Mo, Z. Huang, Y. Xing, and C. Lv, “Multi-agent trajectory prediction with heterogeneous edge-enhanced graph attention network,” IEEE Transactions on Intelligent Transportation Systems , 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Z. Huang, X. Mo, and C. Lv, “Multi-modal motion prediction with transformer-based neural network for autonomous driving,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 2605–2611
2022
Cited alongside, same era.
2023
Later among the works it cites.
S. Pini, C. S. Perone, A. Ahuja, A. S. R. Ferreira, M. Niendorf, and S. Zagoruyko, “Safe real-world autonomous driving by learning to predict and plan with a mixture of experts,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 10 069–10 075
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
S. Shi, L. Jiang, D. Dai, and B. Schiele, “Motion transformer with global intention localization and local movement refinement,” Advances in Neural Information Processing Systems , 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
A. Kamenev, L. Wang, O. B. Bohan, I. Kulkarni, B. Kartal, A. Molchanov, S. Birchfield, D. Nistér, and N. Smolyanskiy, “Predictionnet: Real-time joint probabilistic traffic prediction for planning, control, and simulation,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 8936–8942
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
J. L. V. Espinoza, A. Liniger, W. Schwarting, D. Rus, and L. Van Gool, “Deep interactive motion prediction and planning: Playing games with motion prediction models,” in Learning for Dynamics and Control Conference . PMLR, 2022, pp. 1006–1019
2022
Cited alongside, same era.
C. Burger, J. Fischer, F. Bieder, Ö. Ş. Taş, and C. Stiller, “Interaction-aware game-theoretic motion planning for automated vehicles using bi-level optimization,” in 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2022, pp. 3978–3985
2022
Cited alongside, same era.
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
X. Jia, P. Wu, L. Chen, Y. Liu, H. Li, and J. Yan, “Hdgt: Heterogeneous driving graph transformer for multi-agent trajectory prediction via scene encoding,” IEEE transactions on pattern analysis and machine intelligence , 2023
2023
Later among the works it cites.
X. Mo, Y. Xing, H. Liu, and C. Lv, “Map-adaptive multimodal trajectory prediction using hierarchical graph neural networks,” IEEE Robotics and Automation Letters , 2023
2023
Later among the works it cites.
Z. Huang, H. Liu, J. Wu, and C. Lv, “Conditional predictive behavior planning with inverse reinforcement learning for human-like autonomous driving,” IEEE Transactions on Intelligent Transportation Systems , 2023
2023
Later among the works it cites.
D. Xu, Y. Chen, B. Ivanovic, and M. Pavone, “Bits: Bi-level imitation for traffic simulation,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 2929–2936
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
P. Karkus, B. Ivanovic, S. Mannor, and M. Pavone, “Diffstack: A differentiable and modular control stack for autonomous vehicles,” in Conference on Robot Learning . PMLR, 2023, pp. 2170–2180
2023
Later among the works it cites.
H. Li, C. Sima, J. Dai, W. Wang, L. Lu, H. Wang, J. Zeng, Z. Li, J. Yang, H. Deng et al. , “Delving into the devils of bird’s-eye-view perception: A review, evaluation and recipe,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023
2023
Later among the works it cites.
X. Jia, Y. Gao, L. Chen, J. Yan, P. L. Liu, and H. Li, “Driveadapter: Breaking the coupling barrier of perception and planning in end-to-end autonomous driving,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 7953–7963
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
H. Liu, Z. Huang, and C. Lv, “Multi-modal hierarchical transformer for occupancy flow field prediction in autonomous driving,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 1449–1455
2023
Later among the works it cites.
W. Tong, C. Sima, T. Wang, L. Chen, S. Wu, H. Deng, Y. Gu, L. Lu, P. Luo, D. Lin et al. , “Scene as occupancy,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 8406–8415
2023
Later among the works it cites.
2023
Later among the works it cites.
J. Gu, C. Hu, T. Zhang, X. Chen, Y. Wang, Y. Wang, and H. Zhao, “Vip3d: End-to-end visual trajectory prediction via 3d agent queries,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 5496–5506
2023
Later among the works it cites.