The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Planning-oriented Autonomous Driving
Open accessAbstract
Modern autonomous driving system is characterized as modular tasks in sequential order, i.e., perception, prediction, and planning. In order to perform a wide diversity of tasks and achieve advanced-level intelligence, contemporary approaches either deploy standalone models for individual tasks, or design a multi-task paradigm with separate heads. However, they might suffer from accumulative errors or deficient task coordination. Instead, we argue that a favorable framework should be devised and optimized in pursuit of the ultimate goal, i.e., planning of the self-driving car. Oriented at this, we revisit the key components within perception and prediction, and prioritize the tasks such that all these tasks contribute to planning. We introduce Unified Autonomous Driving (UniAD), a comprehensive framework up-to-date that incorporates full-stack driving tasks in one network. It is exquisitely devised to leverage advantages of each module, and provide complementary feature abstractions for agent interaction from a global perspective. Tasks are communicated with unified query interfaces to facilitate each other toward planning. We instantiate UniAD on the challenging nuScenes benchmark. With extensive ablations, the effectiveness of using such a philosophy is proven by substantially outperforming previous state-of-the-arts in all aspects. Code and models are public.
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Citations
Cites
End-to-End Object Detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_13 · Cited by 18,285
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
Deformable DETR: Deformable Transformers for End-to-End Object Detection
Zhu, X., Su, W., Lu, L., Li, B., Wang, X., & Dai, J. (2020) · arXiv · doi:10.48550/arxiv.2010.04159 · Cited by 6,027
Self-citationCARLA: An open urban driving simulator
Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., & Koltun, V. (2017) · arXiv · doi:10.48550/arxiv.1711.03938 · Cited by 3,718
End-to-end Interpretable Neural Motion Planner
Zeng, W., Luo, W., Suo, S., Sadat, A., Yang, B., Casas, S., & Urtasun, R. (2021) · arXiv · doi:10.48550/arxiv.2101.06679 · Cited by 406
MP3: A Unified Model to Map, Perceive, Predict and Plan
Casas, S., Sadat, A., & Urtasun, R. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01417 · Cited by 228
Cited by
Atakishiyev, S., Salameh, M., Yao, H., & Goebel, R. (2021) · arXiv · doi:10.48550/arxiv.2112.11561 · Cited by 336
Understanding World or Predicting Future? A Comprehensive Survey of World Models
Ding, J., Zhang, Y., Shang, Y., Zhang, Y., Zong, Z., Feng, J., Yuan, Y., Su, H., Li, N., Sukiennik, N., et al. (2026) · ACM Computing Surveys · doi:10.1145/3746449 · Cited by 169
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
“With the recent success of deep learning, the learning based planning received considerable attention [35], [36], [37], [38], [39], [40], [20], [41].”
BackgroundLi, Q., Jia, X., Wang, S., & Yan, J. (2025) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-72995-9_9 · Cited by 42
Self-citationDriveDreamer4D: 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
OpenEMMA: Open-Source Multimodal Model for End-to-End Autonomous Driving
Xing, S., Qian, C., Wang, Y., Hua, H., Tian, K., Zhou, Y., & Tu, Z. (2025) · 2025 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw65960.2025.00113 · Cited by 37
GAIA-1: A Generative World Model for Autonomous Driving
Hu, A., Russell, L., Yeo, H., Murez, Z., Fedoseev, G., Kendall, A., Shotton, J., & Corrado, G. (2023) · arXiv · doi:10.48550/arxiv.2309.17080 · Cited by 28
“To achieve this, the development of a robust model of the world is imperative [1] and huge efforts have been made in the past to build such predictive world models for autonomous driving [2, 3, 4, 5, 6].”
BackgroundMagicDrive-V2: High-Resolution Long Video Generation for Autonomous Driving with Adaptive Control
Gao, R., Chen, K., Xiao, B., Hong, L., Li, Z., & Xu, Q. (2024) · arXiv · doi:10.48550/arxiv.2411.13807 · Cited by 16
A Novel Cognitively Inspired Deep Learning Approach to Detect Drivable Areas for Self-driving Cars
Jiang, F., Wang, Z., & Yue, G. (2024) · Cognitive Computation · doi:10.1007/s12559-023-10215-7 · Cited by 9
Yan, T., Tang, T., Gui, X., Li, Y., Zheng, J., Huang, W., Kong, L., Han, W., Zhou, X., Zhang, X., et al. (2026) · 2026 IEEE/CVF Conference on Computer Vision and Pattern Recognition- FINDINGS Track (CVPRF) · doi:10.1109/cvprf71909.2026.00109
Metadata
- Authors
- Hu, Y., Yang, J., Chen, L., Li, K., Sima, C., Zhu, X., Chai, S., Du, S., Lin, T., Wang, W., Lu, L., Jia, X., Liu, Q., Dai, J., Qiao, Y., & Li, H.
- Venue
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2023-06
- Pages
- 17853-17862
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4386076400
- Cited by
- 931
- References
- 6
- FWCI
- 304.30
- Type
- conference paper
- Language
- en
- Open access
- green
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