ML-SceGen: A Multi-level Scenario Generation Framework

TL;DR

ML-SceGen is a three-stage framework that leverages large language models and logical programming to generate controllable, comprehensive, and dangerous intersection scenarios for scientific research.

Contribution

It introduces a novel multi-level scenario generation framework combining LLMs and ASP to improve controllability and complexity of generated scenarios.

Findings

Enables generation of complex intersection scenarios with danger factors.

Provides controllability over scenario attributes through multi-stage process.

Integrates LLMs and ASP for comprehensive scenario creation.

Abstract

Current scientific research witnesses various attempts at applying Large Language Models for scenario generation but is inclined only to comprehensive or dangerous scenarios. In this paper, we seek to build a three-stage framework that not only lets users regain controllability over the generated scenarios but also generates comprehensive scenarios containing danger factors in uncontrolled intersection settings. In the first stage, LLM agents will contribute to translating the key components of the description of the expected scenarios into Functional Scenarios. For the second stage, we use Answer Set Programming (ASP) solver Clingo to help us generate comprehensive logical traffic within intersections. During the last stage, we use LLM to update relevant parameters to increase the critical level of the concrete scenario.