التفاصيل البيبلوغرافية
| العنوان: |
SparseAD: Sparse Query-Centric Paradigm for Efficient End-to-End Autonomous Driving |
| المؤلفون: |
Zhang, Diankun, Wang, Guoan, Zhu, Runwen, Zhao, Jianbo, Chen, Xiwu, Zhang, Siyu, Gong, Jiahao, Zhou, Qibin, Zhang, Wenyuan, Wang, Ningzi, Tan, Feiyang, Zhou, Hangning, Xu, Ziyao, Yao, Haotian, Zhang, Chi, Liu, Xiaojun, Di, Xiaoguang, Li, Bin |
| سنة النشر: |
2024 |
| المجموعة: |
Computer Science |
| مصطلحات موضوعية: |
Computer Science - Computer Vision and Pattern Recognition |
| الوصف: |
End-to-End paradigms use a unified framework to implement multi-tasks in an autonomous driving system. Despite simplicity and clarity, the performance of end-to-end autonomous driving methods on sub-tasks is still far behind the single-task methods. Meanwhile, the widely used dense BEV features in previous end-to-end methods make it costly to extend to more modalities or tasks. In this paper, we propose a Sparse query-centric paradigm for end-to-end Autonomous Driving (SparseAD), where the sparse queries completely represent the whole driving scenario across space, time and tasks without any dense BEV representation. Concretely, we design a unified sparse architecture for perception tasks including detection, tracking, and online mapping. Moreover, we revisit motion prediction and planning, and devise a more justifiable motion planner framework. On the challenging nuScenes dataset, SparseAD achieves SOTA full-task performance among end-to-end methods and significantly narrows the performance gap between end-to-end paradigms and single-task methods. Codes will be released soon. |
| نوع الوثيقة: |
Working Paper |
| URL الوصول: |
http://arxiv.org/abs/2404.06892 |
| رقم الانضمام: |
edsarx.2404.06892 |
| قاعدة البيانات: |
arXiv |