  The well-established modular autonomous driving system is decoupled into
different standalone tasks, e.g. perception, prediction and planning, suffering
from information loss and error accumulation across modules. In contrast,
end-to-end paradigms unify multi-tasks into a fully differentiable framework,
allowing for optimization in a planning-oriented spirit. Despite the great
potential of end-to-end paradigms, both the performance and efficiency of
existing methods are not satisfactory, particularly in terms of planning
safety. We attribute this to the computationally expensive BEV (bird's eye
view) features and the straightforward design for prediction and planning. To
this end, we explore the sparse representation and review the task design for
end-to-end autonomous driving, proposing a new paradigm named SparseDrive.
Concretely, SparseDrive consists of a symmetric sparse perception module and a
parallel motion planner. The sparse perception module unifies detection,
tracking and online mapping with a symmetric model architecture, learning a
fully sparse representation of the driving scene. For motion prediction and
planning, we review the great similarity between these two tasks, leading to a
parallel design for motion planner. Based on this parallel design, which models
planning as a multi-modal problem, we propose a hierarchical planning selection
strategy , which incorporates a collision-aware rescore module, to select a
rational and safe trajectory as the final planning output. With such effective
designs, SparseDrive surpasses previous state-of-the-arts by a large margin in
performance of all tasks, while achieving much higher training and inference
efficiency. Code will be avaliable at https://github.com/swc-17/SparseDrive for
facilitating future research.
