IDOL: Inverse-Dynamics-Guided Future Prediction for End-to-End Autonomous Driving

arXiv 2026 51.6 method, application

TLDR

IDOL uses inverse dynamics to bridge future prediction and trajectory optimization in a BEV world model for end-to-end autonomous driving.

Reasoning

The paper introduces a novel framework that integrates inverse dynamics with a world model to convert future scene predictions into actionable trajectory updates, addressing a key limitation in prior work. However, the abstract lacks explicit mention of real-world experiments or benchmarks, and the approach is confined to latent BEV space, which may limit generalizability.

Read-first score

Read-first score 51.6, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 32.

Recency 6%

100

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

Citation impact 18%

Uses OpenAlex-shaped citation metadata as a bibliometric attention signal, separate from paper quality. citation_normalized_percentile=0.75047491

Methodology quality 18%

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

Topical relevance 29%

45.7

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

Reproducibility 18%

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

Citation velocity 12%

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

Field roles

FoundationFrontierBridgeMethodology anchor

Rank sensitivity

Stability: volatile; rank range: 215.

Keyword Scores

world model

world dynamics prediction

generative world model

world simulator

model-based reinforcement learning world model

video world model

interactive world model

Deep Analysis

Innovations

Inverse-dynamics-guided future prediction framework for end-to-end autonomous driving in latent BEV space
Using inverse dynamics to decode transition-aware trajectory features from adjacent latent futures and recover planning-relevant motion deltas
Lightweight closed-loop refinement module that reuses the optimized trajectory for another round of future-aware reasoning to improve long-horizon consistency

Methodology

IDOL first predicts multiple future latent scene states using a BEV world model. An inverse dynamics model is then applied to adjacent latent futures to decode transition-aware trajectory features and recover motion deltas that explain how the latent world evolves. These inverse-dynamics-derived signals optimize the planned trajectory, and a closed-loop refinement module further improves long-horizon consistency by reusing the optimized trajectory for another round of future-aware reasoning.

Key Results

IDOL achieves state-of-the-art performance on the NAVSIM v1 and NAVSIM v2 benchmarks among comparable methods.

Resources

Paper