DriveVA: Video Action Models are Zero-Shot Drivers

Generalization is a central challenge in autonomous driv-ing, as real-world deployment requires robust performance under unseenscenarios, sensor domains, and environmental conditions. Recent world-model-based planning methods have shown strong capabilities in sceneunderstanding and multi-modal future prediction, yet their generaliza-tion across datasets and sensor configurations remains limited. In addi-tion, their loosely coupled planning paradigm often leads to poor video-trajectory consistency during visual imagination. To overcome these limi-tations, we propose DriveVA, a novel autonomous driving world modelthat jointly decodes future visual forecasts and action sequences in ashared latent generative process. DriveVA inherits rich priors on mo-tion dynamics and physical plausibility from well-pretrained large-scalevideo generation models to capture continuous spatiotemporal evolutionand causal interaction patterns. To this end, DriveVA employs a DiT-based decoder to jointly predict future action sequences (trajectories)and videos, enabling tighter alignment between planning and scene evolu-tion. We also introduce a video continuation strategy to strengthen long-duration rollout consistency. DriveVA achieves an impressive PDM-based planning performance of 90.9 PDM score on the NAVSIM bench-mark. Extensive experiments also demonstrate the zero-shot capabilityand cross-domain generalization of DriveVA, which reduces average L2error and collision rate by 78.9% and 83.3% on nuScenes and 52.5%and 52.4% on the Bench2Drive built on CARLA v2 compared with thestate-of-the-art world-model-based planner.