Planning-oriented Autonomous Driving
CVPR• 2023
Abstract
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.