Vision-Centric 4D Occupancy Forecasting and Planning via Implicit Residual World Models

Understand

End-to-end autonomous driving systems increasingly rely on vision-centric world models to understand and predict their environment.

• However, a common ineffectiveness in these models is the full reconstruction of future scenes, which expends significant capacity on redundantly modeling static backgrounds.
• To address this, we propose IR-WM, an Implicit Residual World Model that focuses on modeling the current state and evolution of the world.
• IR-WM first establishes a robust bird's-eye-view representation of the current state from the visual observation.