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BEVWorld: A Multimodal World Simulator for Autonomous Driving via Scene-Level BEV Latents
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World models have attracted increasing attention in autonomous driving for their ability to forecast potential future scenarios. In this paper, we propose BEVWorld, a novel framework that transforms multimodal sensor inputs into a unified and compact Bird's Eye View (BEV) latent space for holistic environment modeling. The proposed world model consists of two main components: a multi-modal tokenizer and a latent BEV sequence diffusion model. The multi-modal tokenizer first encodes heterogeneous sensory data, and its decoder reconstructs the latent BEV tokens into LiDAR and surround-view image observations via ray-casting rendering in a self-supervised manner. This enables joint modeling and bidirectional encoding-decoding of panoramic imagery and point cloud data within a shared spatial representation. On top of this, the latent BEV sequence diffusion model performs temporally consistent forecasting of future scenes, conditioned on high-level action tokens, enabling scene-level reasoning over time. Extensive experiments demonstrate the effectiveness of BEVWorld on autonomous driving benchmarks, showcasing its capability in realistic future scene generation and its benefits for downstream tasks such as perception and motion prediction.
Forward citations
Cited by 8 Pith papers
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MoRAL: Sensor-Grounded BEV Reasoning for Compact VLMs toward Edge-Oriented Autonomous Driving
A 2B VLM fine-tuned in two stages on a physics-encoded bird's-eye view image outperforms a zero-shot 8B VLM on eight driving-reasoning question types and raises emergency-braking recall from 10.8% to 47.8%.
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Genesis: Multimodal Driving Scene Generation with Spatio-Temporal and Cross-Modal Consistency
A joint video and LiDAR generation framework for driving scenes, conditioned on shared scene layouts, VLM captions, and BEV features, achieves SOTA generation and downstream perception gains on nuScenes.
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GeoDrive: 3D Geometry-Informed Driving World Model with Precise Action Control
GeoDrive conditions a frozen video diffusion model on a 3D-rendered version of the requested ego trajectory, cutting trajectory-following error by 42% versus Vista while using 99.7% less training data.
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A Definition and Roadmap for World Models
A perspective article defining world models as finite-resource compression of physical state transitions and outlining a roadmap toward physical AGI via unified representations and interactive simulators.
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UniDrive-WM: Unified Understanding, Planning and Generation World Model for Autonomous Driving
A unified VLM for autonomous driving that couples trajectory planning with future-frame image generation improves open- and closed-loop planning metrics on Bench2Drive and nuScenes.
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OpenWorldLib: A Unified Codebase and Definition of Advanced World Models
OpenWorldLib offers a standardized codebase and definition for world models that combine perception, interaction, and memory to understand and predict the world.
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Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities
Foundation-model perception for autonomous driving is surveyed through four capability lenses: generalized knowledge, spatial understanding, multi-sensor robustness, and temporal understanding.
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Generative AI for Autonomous Driving: A Review
A review of generative models (VAEs, GANs, diffusion, transformers, LLMs) applied to map generation, scenario generation, trajectory prediction, and motion planning for autonomous driving.
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