  World models are receiving increasing attention in autonomous driving for
their ability to predict potential future scenarios. In this paper, we present
BEVWorld, a novel approach that tokenizes multimodal sensor inputs into a
unified and compact Bird's Eye View (BEV) latent space for environment
modeling. The world model consists of two parts: the multi-modal tokenizer and
the latent BEV sequence diffusion model. The multi-modal tokenizer first
encodes multi-modality information and the decoder is able to reconstruct the
latent BEV tokens into LiDAR and image observations by ray-casting rendering in
a self-supervised manner. Then the latent BEV sequence diffusion model predicts
future scenarios given action tokens as conditions. Experiments demonstrate the
effectiveness of BEVWorld in autonomous driving tasks, showcasing its
capability in generating future scenes and benefiting downstream tasks such as
perception and motion prediction. Code will be available at
https://github.com/zympsyche/BevWorld.
