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A novel framework that harmonizes semantic reasoning and predictive dynamics, achieving unprecedented performance in autonomous driving tasks.
Forget synthetic data – Unposed-to-3D learns to reconstruct realistic, simulation-ready 3D vehicles directly from real-world driving images.
MLLMs that ace simple traffic rules still struggle when multiple rules interact, especially when they conflict, revealing a critical gap in their ability to handle real-world driving complexity.
By explicitly encoding 3D geometry, GeoDrive achieves more realistic and controllable autonomous driving scene modeling, outperforming prior world models in action accuracy and spatial awareness.
