Reason--Imagine--Act: Closed-Loop LLM Decision Making with World Models for Autonomous Driving
arXiv:2605.24004v1 Announce Type: cross Abstract: Large language models (LLMs) are promising for autonomous driving, but semantics-only decision policies can yield physically unsafe behavior in dynamic traffic. Existing methods either perform online language reasoning without explicit dynamics verification or use world models mainly in offline pipelines, leaving a gap between semantic intent and physical feasibility at decision time. We propose Reason--Imagine--Act (RIA), a closed-loop framework that couples an LLM reasoner with an action-conditioned world model for online safety verification.
The rapid advancement of large language models (LLMs) and the increasing demand for robust autonomous driving solutions are converging, driving innovation in safety and decision-making frameworks.
This development addresses a critical safety gap in LLM-driven autonomous systems by integrating real-time physical verification, making their deployment in high-stakes applications more feasible and trustworthy.
Autonomous driving systems can now integrate semantic reasoning with explicit physical safety verification in a closed-loop online setting, reducing the risk of unsafe behaviors.
- · Autonomous Vehicle Developers
- · AI Safety Researchers
- · Automotive Industry
- · Software Companies
- · Developers of Purely Semantic LLM Driving Systems
- · Legacy Autonomous Driving Software
The adoption of LLMs in safety-critical applications like autonomous driving will accelerate.
Reduced accident rates and improved public trust could lead to faster regulatory approval and wider deployment of autonomous vehicles.
This integrated approach could become a standard for AI decision-making in other real-world, high-consequence domains beyond driving, such as robotics or industrial automation.
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Read at arXiv cs.LG