GeoWorldAD: Geometry World Action Model for Autonomous Driving

arXiv 2026 25.8 method, application

TLDR

A geometry world action model using present and future 3D geometry for safe autonomous driving planning, achieving SOTA on NAVSIM.

Reasoning

Strengths include explicit geometric grounding and future geometry prediction to balance safety and efficiency. Weaknesses are evaluation only on simulated benchmarks and limited domain scope without real-world validation.

Read-first score

Read-first score 25.8, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 18.

Recency 6%

100

Uses a gentle age decay so recent papers surface without erasing older foundations. 2026

Methodology quality 18%

Screens visible abstract and analysis fields for experiment, dataset, baseline, metric, and limitation evidence. markers=experiment,metric

Reproducibility 18%

Screens links and visible text for paper, code, dataset, artifact, and repository signals. pdf=True; code=False; dataset=False; markers=none

Topical relevance 29%

25.7

Uses existing LLM keyword relevance scores normalized to 0-100. world model,world simulator,generative world model,interactive world model,video world model,world dynamics prediction,model-based reinforcement learning world model

Citation impact 18%

Uses OpenAlex-shaped citation metadata as a bibliometric attention signal, separate from paper quality. cited_by_count=0

Citation velocity 12%

Citation velocity estimates citations per publication-year to reduce old-paper bias. velocity=0.00

Field roles

Frontier

Rank sensitivity

Stability: volatile; rank range: 64.

Keyword Scores

world dynamics prediction

world model

model-based reinforcement learning world model

generative world model

video world model

world simulator

interactive world model

Resources

Paper