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With the fast development of autonomous driving technologies, there is an increasing demand for high-definition (HD) maps, which provide reliable and robust prior information about the static part of the traffic environments.
A. Joshi and M. R. James, “Generation of accurate lane-level maps from coarse prior maps and lidar,” IEEE Intelligent Transportation Systems Magazine , vol. 7, no. 1, pp. 19–29, 2015
2015
Earlier work this paper cites.
F. Bastani, S. He, S. Abbar, M. Alizadeh, H. Balakrishnan, S. Chawla, S. Madden, and D. DeWitt, “Roadtracer: Automatic extraction of road networks from aerial images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 4720–4728
2018
Earlier work this paper cites.
N. Homayounfar, W.-C. Ma, S. Kowshika Lakshmikanth, and R. Urtasun, “Hierarchical recurrent attention networks for structured online maps,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 3417–3426
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
N. Homayounfar, W.-C. Ma, J. Liang, X. Wu, J. Fan, and R. Urtasun, “Dagmapper: Learning to map by discovering lane topology,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 2911–2920
2019
Earlier work this paper cites.
J. Liang, N. Homayounfar, W.-C. Ma, S. Wang, and R. Urtasun, “Convolutional recurrent network for road boundary extraction,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 9512–9521
2019
Earlier work this paper cites.
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
Earlier work this paper cites.
M. Liang, B. Yang, R. Hu, Y. Chen, R. Liao, S. Feng, and R. Urtasun, “Learning lane graph representations for motion forecasting,” in European Conference on Computer Vision . Springer, 2020, pp. 541–556
2020
Earlier work this paper cites.
B. Pan, J. Sun, H. Y. T. Leung, A. Andonian, and B. Zhou, “Cross-view semantic segmentation for sensing surroundings,” IEEE Robotics and Automation Letters , vol. 5, no. 3, pp. 4867–4873, 2020
2020
Earlier work this paper cites.
J. Philion and S. Fidler, “Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d,” in European Conference on Computer Vision . Springer, 2020, pp. 194–210
2020
Earlier work this paper cites.
N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in European Conference on Computer Vision . Springer, 2020, pp. 213–229
2020
Cited alongside, same era.
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
Cited alongside, same era.
M. Elhousni, Y. Lyu, Z. Zhang, and X. Huang, “Automatic building and labeling of hd maps with deep learning,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 08, 2020, pp. 13 255–13 260
2020
Cited alongside, same era.
S. He, F. Bastani, S. Jagwani, M. Alizadeh, H. Balakrishnan, S. Chawla, M. M. Elshrif, S. Madden, and M. A. Sadeghi, “Sat2graph: road graph extraction through graph-tensor encoding,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIV 16 . Springer, 2020, pp. 51–67
Z. Xu, Y. Sun, L. Wang, and M. Liu, “Cp-loss: Connectivity-preserving loss for road curb detection in autonomous driving with aerial images,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 1117–1123
2021
Later among the works it cites.
T. Liu, Q. hai Liao, L. Gan, F. Ma, J. Cheng, X. Xie, Z. Wang, Y. Chen, Y. Zhu, S. Zhang et al. , “The role of the hercules autonomous vehicle during the covid-19 pandemic: An autonomous logistic vehicle for contactless goods transportation,” IEEE Robotics & Automation Magazine , vol. 28, no. 1, pp. 48–58, 2021
2021
Later among the works it cites.
Y. Zhou, Y. Takeda, M. Tomizuka, and W. Zhan, “Automatic construction of lane-level hd maps for urban scenes,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 6649–6656
2021
Later among the works it cites.
H. Christensen, D. Paz, H. Zhang, D. Meyer, H. Xiang, Y. Han, Y. Liu, A. Liang, Z. Zhong, and S. Tang, “Autonomous vehicles for micro-mobility,” Autonomous Intelligent Systems , vol. 1, no. 1, pp. 1–35, 2021
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2020
Cited alongside, same era.
Q. Li, Y. Wang, Y. Wang, and H. Zhao, “Hdmapnet: A local semantic map learning and evaluation framework,” 2021
2021
Cited alongside, same era.
Y. B. Can, A. Liniger, D. P. Paudel, and L. Van Gool, “Structured bird’s-eye-view traffic scene understanding from onboard images,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 15 661–15 670
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Z. Xu, Y. Sun, and M. Liu, “Topo-boundary: A benchmark dataset on topological road-boundary detection using aerial images for autonomous driving,” IEEE Robotics and Automation Letters , vol. 6, no. 4, pp. 7248–7255, 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Z. Xu, Y. Sun, and M. Liu, “icurb: Imitation learning-based detection of road curbs using aerial images for autonomous driving,” IEEE Robotics and Automation Letters , vol. 6, no. 2, pp. 1097–1104, 2021
2021
Cited alongside, same era.
2021
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2022
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2022
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2022
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2022
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S. He and H. Balakrishnan, “Lane-level street map extraction from aerial imagery,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2022, pp. 2080–2089
2089
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