OccSim: Multi-kilometer Driving Simulation with Long-horizon Occupancy World Models

Data-driven driving simulation relies on pre-recorded logs or spatial priors such as HD maps, so the environments it can produce are capped by the scale of existing annotated datasets. To break this bottleneck, we present OccSim, the first 3D occupancy world model driven simulator that requires neither HD maps nor log-derived layouts. OccSim obviates the requirement for long continuous logs or HD maps; it is conditioned only on a single initial frame and a sequence of future ego-actions. It can stably generate over 3000 continuous frames, an 80x improvement in stable generation length over previous SoTA occupancy world models, and fuses them into maps spanning over 9 kilometers. Further qualitative stress tests show that it can roll out beyond 110K frames. OccSim couples W-DiT, a static occupancy world model that builds rigid transformations into its architecture for ultra-long-horizon rollout, with a Layout Generator that populates the synthesized topology with reactive agents. With these designs, OccSim can synthesize massive, diverse simulation streams. Extensive experiments demonstrate its downstream utility: data collected directly from OccSim can pre-train 4D semantic occupancy forecasting models to achieve up to 67% zero-shot performance on unseen data, outperforming previous asset-based simulator by 11%. When the collected data is scaled up to 5x its original size, zero-shot performance increases to 74%.