VectorWorld: Efficient Streaming World Model via Diffusion Flow on Vector Graphs
This addresses the need for efficient and stable simulation in autonomous driving, though it appears incremental as it builds on existing world model concepts with specific optimizations.
The paper tackled the problem of closed-loop evaluation for autonomous-driving policies by proposing VectorWorld, a streaming world model that generates ego-centric vector-graph tiles during rollout, resulting in improved map-structure fidelity and initialization validity, and enabling stable, real-time 1km+ closed-loop rollouts on Waymo open motion and nuPlan datasets.
Closed-loop evaluation of autonomous-driving policies requires interactive simulation beyond log replay. However, existing generative world models often degrade in closed loop due to (i) history-free initialization that mismatches policy inputs, (ii) multi-step sampling latency that violates real-time budgets, and (iii) compounding kinematic infeasibility over long horizons. We propose VectorWorld, a streaming world model that incrementally generates ego-centric $64 \mathrm{m}\times 64\mathrm{m}$ lane--agent vector-graph tiles during rollout. VectorWorld aligns initialization with history-conditioned policies by producing a policy-compatible interaction state via a motion-aware gated VAE. It enables real-time outpainting via solver-free one-step masked completion with an edge-gated relational DiT trained with interval-conditioned MeanFlow and JVP-based large-step supervision. To stabilize long-horizon rollouts, we introduce $Î$Sim, a physics-aligned non-ego (NPC) policy with hybrid discrete--continuous actions and differentiable kinematic logit shaping. On Waymo open motion and nuPlan, VectorWorld improves map-structure fidelity and initialization validity, and supports stable, real-time $1\mathrm{km}+$ closed-loop rollouts (\href{https://github.com/jiangchaokang/VectorWorld}{code}).