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

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 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.