A Diffusion Transformer world model in V-JEPA2 latent space predicts action-conditioned future scenes on nuScenes, outperforming regression on KID/FID while preserving steering controllability and adding a jump model for motion magnitude.
Driveworld: 4d pre-trained scene understanding via world models for autonomous driving
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LAW introduces a self-supervised prediction task on latent scene features that boosts end-to-end driving performance on nuScenes, NAVSIM, and CARLA benchmarks.
citing papers explorer
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Diffusion Transformer World-Action Model for AV Scene Prediction
A Diffusion Transformer world model in V-JEPA2 latent space predicts action-conditioned future scenes on nuScenes, outperforming regression on KID/FID while preserving steering controllability and adding a jump model for motion magnitude.
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Enhancing End-to-End Autonomous Driving with Latent World Model
LAW introduces a self-supervised prediction task on latent scene features that boosts end-to-end driving performance on nuScenes, NAVSIM, and CARLA benchmarks.
- VISA: VLM-Guided Instance Semantic Auditing for 3D Occupancy World Models