DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving

Chen Min; Dawei Zhao; Liang Xiao; Jian Zhao; Xinli Xu; Zheng Zhu; Lei Jin; Jianshu Li; Yulan Guo; Junliang Xing; Liping Jing; Yiming Nie; Bin Dai

arXiv · 2405.04390

DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving

Chen Min·Dawei Zhao·Liang Xiao·Jian Zhao·Xinli Xu·Zheng Zhu·Lei Jin·Jianshu Li·Yulan Guo·Junliang Xing·Liping Jing·Yiming Nie·Bin Dai

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Abstract

Vision-centric autonomous driving has recently raised wide attention due to its lower cost. Pre-training is essential for extracting a universal representation. However, current vision-centric pre-training typically relies on either 2D or 3D pre-text tasks, overlooking the temporal characteristics of autonomous driving as a 4D scene understanding task. In this paper, we address this challenge by introducing a world model-based autonomous driving 4D representation learning framework, dubbed \emph{DriveWorld}, which is capable of pre-training from multi-camera driving videos in a spatio-temporal fashion. Specifically, we propose a Memory State-Space Model for spatio-temporal modelling, which consists of a Dynamic Memory Bank module for learning temporal-aware latent dynamics to predict future changes and a Static Scene Propagation module for learning spatial-aware latent statics to offer comprehensive scene contexts. We additionally introduce a Task Prompt to decouple task-aware features for various downstream tasks. The experiments demonstrate that DriveWorld delivers promising results on various autonomous driving tasks. When pre-trained with the OpenScene dataset, DriveWorld achieves a 7.5% increase in mAP for 3D object detection, a 3.0% increase in IoU for online mapping, a 5.0% increase in AMOTA for multi-object tracking, a 0.1m decrease in minADE for motion forecasting, a 3.0% increase in IoU for occupancy prediction, and a 0.34m reduction in average L2 error for planning.

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Chen Min, Dawei Zhao, Liang Xiao, Jian Zhao, Xinli Xu, Zheng Zhu, Lei Jin, Jianshu Li, Yulan Guo, Junliang Xing, Liping Jing, Yiming Nie, Bin Dai. 2024-05-07. DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving. https://arxiv.org/abs/2405.04390

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