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arXiv · 2602.02002

UniDriveDreamer: A Single-Stage Multimodal World Model for Autonomous Driving

Guosheng Zhao·Yaozeng Wang·Xiaofeng Wang·Zheng Zhu·Tingdong Yu·Guan Huang·Yongchen Zai·Ji Jiao·Changliang Xue·Xiaole Wang·Zhen Yang·Futang Zhu·Xingang Wang
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Abstract

World models have demonstrated significant promise for data synthesis in autonomous driving. However, existing methods predominantly concentrate on single-modality generation, typically focusing on either multi-camera video or LiDAR sequence synthesis. In this paper, we propose UniDriveDreamer, a single-stage unified multimodal world model for autonomous driving, which directly generates multimodal future observations without relying on intermediate representations or cascaded modules. Our framework introduces a LiDAR-specific variational autoencoder (VAE) designed to encode input LiDAR sequences, alongside a video VAE for multi-camera images. To ensure cross-modal compatibility and training stability, we propose Unified Latent Anchoring (ULA), which explicitly aligns the latent distributions of the two modalities. The aligned features are fused and processed by a diffusion transformer that jointly models their geometric correspondence and temporal evolution. Additionally, structured scene layout information is projected per modality as a conditioning signal to guide the synthesis. Extensive experiments demonstrate that UniDriveDreamer outperforms previous state-of-the-art methods in both video and LiDAR generation, while also yielding measurable improvements in downstream

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Guosheng Zhao, Yaozeng Wang, Xiaofeng Wang, Zheng Zhu, Tingdong Yu, Guan Huang, Yongchen Zai, Ji Jiao, Changliang Xue, Xiaole Wang, Zhen Yang, Futang Zhu, Xingang Wang. 2026-02-02. UniDriveDreamer: A Single-Stage Multimodal World Model for Autonomous Driving. https://arxiv.org/abs/2602.02002

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