The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
ST-P3: End-to-End Vision-Based Autonomous Driving via Spatial-Temporal Feature Learning
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Cites
Advances in neural information processing systems 7
Cios, K., & Shields, M. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,059
nuScenes: A Multimodal Dataset for Autonomous Driving
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., & Beijbom, O. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01164 · Cited by 7,152
A Survey of Autonomous Driving: Common Practices and Emerging Technologies
Yurtsever, E., Lambert, J., Carballo, A., & Takeda, K. (2020) · IEEE Access · doi:10.1109/access.2020.2983149 · Cited by 1,889
Li, Z., Wang, W., Li, H., Xie, E., Sima, C., Lu, T., Yu, Q., & Dai, J. (2022) · arXiv · doi:10.48550/arxiv.2203.17270 · Cited by 1,419
Self-citationLift, Splat, Shoot: Encoding Images from Arbitrary Camera Rigs by Implicitly Unprojecting to 3D
Philion, J., & Fidler, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58568-6_12 · Cited by 1,271
ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst
Bansal, M., Krizhevsky, A., & Ogale, A. (2019) · Robotics: Science and Systems XV · doi:10.15607/rss.2019.xv.031 · Cited by 731
Luo, W., Yang, B., & Urtasun, R. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00376 · Cited by 644
Categorical Depth Distribution Network for Monocular 3D Object Detection
Reading, C., Harakeh, A., Chae, J., & Waslander, S. L. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00845 · Cited by 595
Multi-Modal Fusion Transformer for End-to-End Autonomous Driving
Prakash, A., Chitta, K., & Geiger, A. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00700 · Cited by 584
Exploring the Limitations of Behavior Cloning for Autonomous Driving
Codevilla, F., Santana, E., Lopez, A., & Gaidon, A. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00942 · Cited by 509
Cited by
Planning-oriented Autonomous Driving
Hu, Y., Yang, J., Chen, L., Li, K., Sima, C., Zhu, X., Chai, S., Du, S., Lin, T., Wang, W., et al. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01712 · Cited by 931
Self-citationAtakishiyev, S., Salameh, M., Yao, H., & Goebel, R. (2021) · arXiv · doi:10.48550/arxiv.2112.11561 · Cited by 336
Atakishiyev, S., Salameh, M., Yao, H., & Goebel, R. (2024) · IEEE Access · doi:10.1109/access.2024.3431437 · Cited by 323
Towards learning-based planning: The nuPlan benchmark for real-world autonomous driving
Karnchanachari, N., Geromichalos, D., Tan, K. S., Li, N., Eriksen, C., Yaghoubi, S., Mehdipour, N., Bernasconi, G., Fong, W. K., Guo, Y., et al. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra57147.2024.10610077 · Cited by 46
DriveDreamer4D: World Models Are Effective Data Machines for 4D Driving Scene Representation
Zhao, G., Ni, C., Wang, X., Zhu, Z., Zhang, X., Wang, Y., Huang, G., Chen, X., Wang, B., Zhang, Y., et al. (2024) · arXiv · doi:10.48550/arxiv.2410.13571 · Cited by 41
Metadata
- Authors
- Hu, S., Chen, L., Wu, P., Li, H., Yan, J., & Tao, D.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2022
- Pages
- 533-549
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4312396550
- Cited by
- 259
- References
- 42
- FWCI
- 55.56
- Type
- conference paper
- Language
- en
- Open access
- green
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- Hu, S., Chen, L., Wu, P., Li, H., Yan, J., & Tao, D. (2022). ST-P3: End-to-End Vision-Based Autonomous Driving via Spatial-Temporal Feature Learning. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-031-19839-7_31
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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