Reason--Imagine--Act: Closed-Loop LLM Decision Making with World Models for Autonomous Driving
TLDR
A closed-loop LLM framework with a world model for online safety verification in autonomous driving, outperforming baselines in CARLA.
Reasoning
The paper presents a novel integration of LLM reasoning with world model rollouts for real-time safety, supported by strong empirical results in simulation. However, it lacks real-world validation and the world model is not generative or video-based, limiting the scope of some keyword scores.
Read-first score
Read-first score 60.3, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 36.
Field roles
Rank sensitivity
Stability: volatile; rank range: 463.
Keyword Scores
Deep Analysis
Innovations
- Closed-loop framework coupling LLM reasoner with action-conditioned world model for online safety verification
- Action template and candidate sub-actions proposed by LLM, with short-horizon rollouts by world model and safety scorer selection
- Feedback from safety scorer to next reasoning step, enabling dynamic verification of physical feasibility
Methodology
The Reason-Imagine-Act (RIA) framework integrates an LLM reasoner with an action-conditioned world model. At each decision step, the LLM generates an action template and candidate sub-actions; the world model performs short-horizon rollouts to predict outcomes; a safety scorer selects the safest executable action and provides feedback to the LLM for subsequent reasoning. The framework is evaluated under a unified CARLA point-goal protocol with 1000 episodes, comparing against training-free baselines CARLA TM and MADA on closed-loop metrics.
Key Results
RIA achieves 80.05% route completion, 51.10% arrival rate, and 0.20% collision rate, consistently outperforming training-free baselines under the same closed-loop interface.