Doe-1: Closed-Loop Autonomous Driving with Large World Model
Journal:
arXiv
Published Date:
Dec 12, 2024
Abstract
End-to-end autonomous driving has received increasing attention due to its
potential to learn from large amounts of data. However, most existing methods
are still open-loop and suffer from weak scalability, lack of high-order
interactions, and inefficient decision-making. In this paper, we explore a
closed-loop framework for autonomous driving and propose a large Driving wOrld
modEl (Doe-1) for unified perception, prediction, and planning. We formulate
autonomous driving as a next-token generation problem and use multi-modal
tokens to accomplish different tasks. Specifically, we use free-form texts
(i.e., scene descriptions) for perception and generate future predictions
directly in the RGB space with image tokens. For planning, we employ a
position-aware tokenizer to effectively encode action into discrete tokens. We
train a multi-modal transformer to autoregressively generate perception,
prediction, and planning tokens in an end-to-end and unified manner.
Experiments on the widely used nuScenes dataset demonstrate the effectiveness
of Doe-1 in various tasks including visual question-answering,
action-conditioned video generation, and motion planning. Code:
https://github.com/wzzheng/Doe.
