ARISE -- Adaptive Refinement and Iterative Scenario Engineering

2026-01-21

AI Summary

Existing text-to-simulation methods for generating high-fidelity, executable rare traffic scenarios often suffer from semantic inaccuracies, insufficient iterative refinement, and limited robustness. This work proposes a multi-stage closed-loop framework that leverages large language models (LLMs) to progressively translate natural language descriptions into executable Scenic scripts. The generated scenarios are automatically validated in a simulation environment, which provides structured diagnostic feedback to guide iterative LLM refinement. This approach significantly enhances the semantic accuracy, executability, and diversity of the generated scenarios. Experimental results demonstrate superior performance over existing baselines in rare traffic scenario generation, highlighting improved reliability and automation capabilities.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: Generation
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

The effectiveness of collision-free trajectory planners depends on the quality and diversity of training data, especially for rare scenarios. A widely used approach to improve dataset diversity involves generating realistic synthetic traffic scenarios. However, producing such scenarios remains difficult due to the precision required when scripting them manually or generating them in a single pass. Natural language offers a flexible way to describe scenarios, but existing text-to-simulation pipelines often rely on static snippet retrieval, limited grammar, single-pass decoding, or lack robust executability checks. Moreover, they depend heavily on constrained LLM prompting with minimal post-processing. To address these limitations, we introduce ARISE - Adaptive Refinement and Iterative Scenario Engineering, a multi-stage tool that converts natural language prompts into executable Scenic scripts through iterative LLM-guided refinement. After each generation, ARISE tests script executability in simulation software, feeding structured diagnostics back to the LLM until both syntactic and functional requirements are met. This process significantly reduces the need for manual intervention. Through extensive evaluation, ARISE outperforms the baseline in generating semantically accurate and executable traffic scenarios with greater reliability and robustness.

Problem

• natural language to simulation
• executable scenario generation
• traffic scenario diversity
• rare scenario synthesis
• trajectory planning data

Innovation

• iterative refinement
• natural language to simulation
• executable scenario generation
• LLM-guided debugging
• adaptive feedback loop