WorldOcc: A Real-Time Occupancy Simulator Towards Closed-Loop Evaluation with Diverse and Learnable Traffic Behaviors

Closed-loop simulation for autonomous driving has garnered widespread attention in recent years, yet, to our knowledge, no occupancy simulator is at once real-time, high-fidelity, long-horizon, and able to exercise diverse, learnable traffic. This shortfall stems largely from the prevailing practice of building occupancy simulators on a learned world model, which entangles scene evolution with the decision making of traffic participants (i.e., actors). We therefore rethink whether an occupancy simulator must rely on a learned world model, and propose WorldOcc, an occupancy simulator that is fast, faithful, and sustained over long rollouts, without a learned world model. At its core is a policy-scene disentangled architecture that models the scene transition and the actor policy independently, so each serves its own end, the transition for fidelity and the policy for diversity and flexibility. For high fidelity over a long horizon, we design a lossless rigid-operator transition, Ego Warp-Recenter and Actor Erase-Print, that advances the occupancy state with bounded errors. For diverse traffic behaviors, we tailor a plug-and-play actor policy network that learns and deploys behaviors, from normal to adversarial, effectively inside the simulator. WorldOcc simulates at about 1270 FPS on an RTX 4090 GPU while holding IoU ≥ 70% over 16 s. Beyond simulation, we introduce WorldOcc-Bench, to our knowledge the first closed-loop benchmark for occupancy planners with diverse traffic, benchmarking six recent planner families. WorldOcc not only reveals the vulnerability of occupancy planners under closed-loop conditions with diverse traffic, but also shows potential as an effective post-training environment.