DrivingWorld: Constructing World Model for Autonomous Driving via Video GPT

[2412.19505] DrivingWorld: Constructing World Model for Autonomous Driving via Video GPT Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2412.19505 (cs) [Submitted on 27 Dec 2024 ( v1 ), last revised 16 Aug 2026 (this version, v3)] Title: DrivingWorld: Constructing World Model for Autonomous Driving via Video GPT Authors: Xiaotao Hu , Mingkai Jia , Xiaoyang Guo , Qian Zhang , Xiao-xiao Long , Wei Yin View a PDF of the paper titled DrivingWorld: Constructing World Model for Autonomous Driving via Video GPT, by Xiaotao Hu and 5 other authors View PDF HTML (experimental) Abstract: Recent successes in autoregressive (AR) generation models, such as the GPT series in natural language processing, have motivated efforts to replicate this success in visual tasks. Some works attempt to extend this approach to autonomous driving by building video-based world models capable of generating realistic future video sequences and predicting ego states. However, prior works tend to produce unsatisfactory results, as the classic GPT framework is designed to handle 1D contextual information, such as text, and lacks the inherent ability to model the spatial and temporal dynamics essential for video generation. In this paper, we present DrivingWorld, a GPT-style world model for autonomous driving, featuring several spatial-temporal fusion mechanisms. This design enables effective modeling of both spatial and temporal dynamics, facilitating high-fidelity, long-duration video generation. Specifically, we propose a next-state prediction strategy to model temporal coherence between consecutive frames and apply a next-token prediction strategy to capture spatial information within each frame. To further enhance generalization ability, we propose a novel masking strategy and reweighting strategy for token prediction to mitigate long-term drifting issues and enable precise control. Our work demonstrates the ability to produce high-fidelity and consistent video clips of over 40 seconds in duration, which is over 2 times longer than state-of-the-art driving world models. Experiments show that, in contrast to prior works, our method achieves superior visual quality and significantly more accurate controllable future video generation. Our code is available at this https URL . Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2412.19505 [cs.CV] (or arXiv:2412.19505v3 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2412.19505 Focus to learn more arXiv-issued DOI via DataCite Journal reference: ICPR 2026 Submission history From: Xiaotao Hu [ view email ] [v1] Fri, 27 Dec 2024 07:44:07 UTC (1,683 KB) [v2] Mon, 30 Dec 2024 09:08:12 UTC (1,683 KB) [v3] Sun, 16 Aug 2026 15:53:34 UTC (1,767 KB) Full-text links: Access Paper: View a PDF of the paper titled DrivingWorld: Constructing World Model for Autonomous Driving via Video GPT, by Xiaotao Hu and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2024-12 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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