EvoWorld: Evolving Gaussians for 4D Occupancy Forecasting and Motion Planning

Future 3D semantic occupancy forecasting and motion planning are central to autonomous driving, as they require models to reason about how surrounding scenes evolve and how the ego vehicle should act. Vision-centric world models must infer this evolution from camera observations, yet many existing approaches discretize scenes into latent embeddings, volumetric features, or quantized tokens and forecast future states through fixed-step autoregressive generation. This limits temporal flexibility and makes the predicted scene evolution difficult to interpret. We propose EvoWorld, a vision-centric, non-autoregressive occupancy world model that predicts a compact set of 4D semantic Gaussian primitives from multi-view camera histories to represent the entire forecast horizon. For each primitive, EvoWorld directly predicts conditional spatial geometry, temporal support, and planar velocity, specifying its shape, motion, and temporal contribution at any queried time. Conditioning and splatting these primitives yields semantic occupancy at arbitrary timestamps, with independent temporal queries that can be decoded in parallel. Primitive velocity is further decomposed into shared ego-induced scene motion and object-level dynamic motion, and the same Gaussian representation supports open-loop motion planning. Extensive experiments on Occ3D-nuScenes and Occ3D-Waymo demonstrate improved vision-centric future occupancy forecasting and strong motion planning performance, with further analyses showing accurate primitive motion and consistent object association.