DrivingWorld: Constructing World Model for Autonomous Driving via Video GPT

arXiv 24.12 2024 48.3 method, application

TLDR

DrivingWorld is a GPT-style world model for autonomous driving that generates long-duration, high-fidelity video sequences using spatial-temporal fusion.

Reasoning

Strengths include novel spatial-temporal fusion mechanisms and achieving longer video generation than prior work. Weaknesses are limited quantitative results and lack of detail on evaluation metrics beyond duration.

Read-first score

Read-first score 48.3, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 45.

Reproducibility 18%

Screens links and visible text for paper, code, dataset, artifact, and repository signals. pdf=True; code=True; dataset=False; markers=code,github

Recency 6%

75.1

Uses a gentle age decay so recent papers surface without erasing older foundations. 2024

Topical relevance 29%

64.3

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

Methodology quality 18%

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

Citation impact 18%

0.3

Uses OpenAlex-shaped citation metadata as a bibliometric attention signal, separate from paper quality. citation_normalized_percentile=0.0028463

Citation velocity 12%

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

Field roles

Reproducibility anchor

Rank sensitivity

Stability: volatile; rank range: 628.

Keyword Scores

world model

video world model

generative world model

world dynamics prediction

world simulator

interactive world model

model-based reinforcement learning world model

Deep Analysis

Innovations

Spatial-temporal fusion mechanisms for GPT-style world model
Next-state prediction strategy to model temporal coherence between consecutive frames
Next-token prediction strategy to capture spatial information within each frame
Novel masking strategy and reweighting strategy for token prediction to mitigate long-term drifting and enable precise control

Methodology

DrivingWorld is a GPT-style world model for autonomous driving that integrates spatial-temporal fusion mechanisms. It uses a next-state prediction strategy to model temporal coherence between consecutive frames and a next-token prediction strategy to capture spatial information within each frame. Additionally, a novel masking strategy and reweighting strategy are applied to token prediction to mitigate long-term drifting and enable precise control.

Key Results

The model generates high-fidelity and consistent video clips of over 40 seconds in duration, which is over 2 times longer than state-of-the-art driving world models, achieving superior visual quality and significantly more accurate controllable future video generation.

Resources

Paper Code