  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.
