Geometry-Aware Spatio-Temporal Context Modeling for 4D Occupancy Forecasting

4D occupancy forecasting models the spatio-temporal evo-lution of 3D scenes and is crucial for autonomous driving, especially forcorner-case simulation. Existing methods often rely on discrete tokeniza-tion followed by autoregressive prediction, yet struggle with geometricdistortion in static structures and inconsistent temporal coherence overthe forecasting horizon. In this work, we propose a Geometry-AwareSpatio-Temporal context modeling method (GAST) for 4D occupancyforecasting, built upon progressive explicit-implicit generation and dual-path spatio-temporal modeling. Specifically, the generation module pro-duces per-frame occupancy with high geometric fidelity and semanticplausibility through pose-driven warping, motion-aware feature modula-tion, and attention-based feature refinement. Subsequently, the spatio-temporal module enhances spatial consistency through global contextaggregation while capturing scene evolution through temporal dynam-ics extraction. This unified design enables joint optimization of histori-cal reconstruction and future forecasting in an end-to-end manner. Ex-tensive experiments on Occ3D-nuScenes demonstrate the superiority ofour method, outperforming the state-of-the-art by 7.67% in mIoU and6.44% in IoU with a 2.84× speedup, while maintaining strong perfor-mance in long-term forecasting. Our source code is publicly available athttps://github.com/chenst27/GAST.