ARISE -- Adaptive Refinement and Iterative Scenario Engineering
Konstantin Poddubnyy, Igor Vozniak, Ivan Burmistrov, Nils Lipp, Davit Hovhannisyan, Christian Mueller, Philipp Slusallek

TL;DR
ARISE is a multi-stage, LLM-guided system that iteratively refines natural language prompts into executable traffic scenarios, improving diversity, accuracy, and robustness for collision-free trajectory training.
Contribution
It introduces an iterative refinement process that enhances scenario generation from natural language, overcoming limitations of static and single-pass methods.
Findings
ARISE produces more accurate and executable scenarios.
The iterative process reduces manual intervention.
It outperforms baseline methods in scenario quality.
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…
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Taxonomy
TopicsAutonomous Vehicle Technology and Safety · Traffic control and management · Formal Methods in Verification
