SparseAD: Sparse Query-Centric Paradigm for Efficient End-to-End
  Autonomous Driving
Diankun Zhang Guoan Wang Runwen Zhu Jianbo Zhao Xiwu Chen Siyu Zhang Jiahao Gong Qibin Zhou Wenyuan Zhang Ningzi Wang Feiyang Tan Hangning Zhou Ziyao Xu Haotian Yao Chi Zhang Xiaojun Liu Xiaoguang Di Bin Li
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.