OWMDrive: Causality-Aware End-to-End Autonomous Driving via 4D Occupancy World Model

Understand

Autonomous driving systems are steadily moving toward end-to-end paradigms to mitigate the limited adaptability of rule-based pipelines in complex traffic environments.

• However, most existing learning-based methods still make decisions from static representations of the current scene, without explicit future rollouts or modeling of the temporal causal dynamics in traffic interactions.
• This limitation often results in unstable or overly conservative planning under high-uncertainty conditions, such as occlusions and unexpected events.
• To overcome these challenges, we introduce OWMDrive, a generative end-to-end driving framework built upon an Occupancy World Model for multi-step 3D occupancy forecasting, which serves as a conditional prior to guide diffusion-based planning.