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DriveVA: Video Action Models are Zero-Shot Drivers
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DriveVA: Video Action Models are Zero-Shot Drivers
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Generalization is a central challenge in autonomous driving, as real-world deployment requires robust performance under unseen scenarios, sensor domains, and environmental conditions. Recent world-model-based planning methods have shown strong capabilities in scene understanding and multi-modal future prediction, yet their generalization across datasets and sensor configurations remains limited. In addition, their loosely coupled planning paradigm often leads to poor video-trajectory consistency during visual imagination. To overcome these limitations, we propose DriveVA, a novel autonomous driving world model that jointly decodes future visual forecasts and action sequences in a shared latent generative process. DriveVA inherits rich priors on motion dynamics and physical plausibility from well-pretrained large-scale video generation models to capture continuous spatiotemporal evolution and 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 evolution. 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 benchmark. Extensive experiments also demonstrate the zero-shot capability and cross-domain generalization of DriveVA, which reduces average L2 error 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 the state-of-the-art world-model-based planner.
Forward citations
Cited by 9 Pith papers
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MemoBench: Benchmarking World Modeling in Dynamically Changing Environments
MemoBench is a new diagnostic benchmark with 360 synthetic and real clips plus VQA evaluation that tests memory consistency in video models under the disappear-and-reappear paradigm in dynamically changing environments.
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ReWorld: Learning Better Representations for World Action Models
ReWorld applies future-predictive, cross-modal, and hard-negative supervision directly to intermediate representations in Video and Action DiTs for WAMs, reporting 23.9% FVD improvement and PDMS rise from 89.1 to 90.4...
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Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models
Applies differentiable search over prompt fusion schemes (concatenation, addition, affine, cross-attention) per ViT layer to improve visual prompt tuning, reporting gains across 34 datasets.
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LVDrive: Latent Visual Representation Enhanced Vision-Language-Action Autonomous Driving Model
LVDrive improves closed-loop driving on Bench2Drive by adding latent future scene prediction to VLA models via unified embedding space processing and two-stage trajectory decoding.
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Temporal and Cross-Modal Alignment for Enhanced Audiovisual Video Captioning
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World Action Models: A Survey
A survey that clarifies boundaries and organizes World Action Models by generation requirements and predictive substrates, identifying a trend toward generating less of the future.
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