From Proxy Learning to Driving Decisions: A Transfer-Based Framework for Evaluating Future-Aware Autonomous Driving Planners

arXiv 2026 18.5 method, application

TLDR

Proposes Proxy-to-Decision Transfer framework to evaluate whether future-aware world models improve autonomous driving planning; using NAVSIM-v1, finds most improvements fail reliability gates.

Reasoning

The paper contributes a rigorous evaluation framework with decomposition and reliability constraints, and demonstrates its use with real-world driving benchmark data. Its main weakness is that it focuses on a single representative planner and largely reports negative or indeterminate transfer results, limiting broader conclusions.

Read-first score

Read-first score 18.5, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 6.

Recency 5%

100

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

Methodology quality 15%

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

Reproducibility 15%

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

Topical relevance 25%

8.6

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

Graph prestige 15%

5.7

PageRank-style prestige over the local citation graph built from available OpenAlex references. local_pagerank=0.0016

Citation impact 15%

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

Citation velocity 10%

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

Field roles

Frontier

Rank sensitivity

Stability: volatile; rank range: 43.

Keyword Scores

world model

world simulator

generative world model

interactive world model

video world model

world dynamics prediction

model-based reinforcement learning world model

Resources

Paper