GeoWorldAD: Geometry World Action Model for Autonomous Driving

• URL: http://arxiv.org/abs/2607.17521v2
• Date: Thu, 23 Jul 2026 04:08:20 GMT
• Title: GeoWorldAD: Geometry World Action Model for Autonomous Driving
• Abstract summary: We propose GeoWorldAD, a geometry world action model that grounds trajectory planning in ego-aligned 3D space.<n>Present geometry provides essential spatial constraints for safe planning, while future geometry reveals how surrounding agents and ego-centric free space may evolve.<n>Experiments on NAVSIM v1 and v2 demonstrate state-of-the-art performance, highlighting the effectiveness of explicit 3D geometry grounding.
• Score: 45.0114703918243
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Autonomous driving requires both safe and efficient planning decisions in dynamic 3D environments. Although recent Vision/Video-Action models learn policies directly from visual observations and scale well with advances in vision transformers and large-scale training data, they often lack explicit geometric grounding and future-aware spatial guidance, limiting their ability to balance collision avoidance and driving progress. In this work, we propose GeoWorldAD, a geometry world action model that grounds trajectory planning in ego-aligned 3D space and anticipates short-horizon scene evolution with latent future geometry tokens. Present geometry provides essential spatial constraints for safe planning, while future geometry reveals how surrounding agents and ego-centric free space may evolve, reducing overly conservative decisions without sacrificing safety. To efficiently exploit these geometric cues, GeoWorldAD progressively aggregates multi-scale present geometry and latent future geometry through iterative trajectory refinement. Experiments on NAVSIM v1 and v2 demonstrate state-of-the-art performance, highlighting the effectiveness of explicit 3D geometry grounding and future geometry world modeling for safe and efficient autonomous driving.

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