May 8, 2024, 4:46 a.m. | 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

cs.CV updates on arXiv.org arxiv.org

arXiv:2405.04390v1 Announce Type: new
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 …

abstract arxiv attention autonomous autonomous driving cost cs.cv current driving however pre-training representation tasks temporal text training type understanding universal via vision world world models

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