# ML-SceGen: A Multi-level Scenario Generation Framework

arXiv: https://arxiv.org/abs/2501.10782 · HTML: https://mlsanity.com/paper/2501.10782/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 28 references that are in the map.
- First posted on arXiv 18 Jan 2025.

## Builds on (most cited first, 17 of 17)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [UniSim: A Neural Closed-Loop Sensor Simulator](https://mlsanity.com/paper/2308.01898/) (2023)
- [LLM4Drive: A Survey of Large Language Models for Autonomous Driving](https://mlsanity.com/paper/2311.01043/) (2023)
- [ChatScene: Knowledge-Enabled Safety-Critical Scenario Generation for Autonomous Vehicles](https://mlsanity.com/paper/2405.14062/) (2024)
- [Time Efficiency in Optimization with a Bayesian-Evolutionary Algorithm](https://mlsanity.com/paper/2005.04166/) (2020)
- [Vision-Based Human Pose Estimation via Deep Learning: A Survey](https://mlsanity.com/paper/2308.13872/) (2023)
- [Class Binarization to NeuroEvolution for Multiclass Classification](https://mlsanity.com/paper/2308.13876/) (2023)
- [Traffic Scene Generation from Natural Language Description for Autonomous Vehicles with Large Language Model](https://mlsanity.com/paper/2409.09575/) (2024)
- [A Virtual Reality Training System for Automotive Engines Assembly and Disassembly](https://mlsanity.com/paper/2311.02108/) (2023)
- [The Key Artificial Intelligence Technologies in Early Childhood Education: A Review](https://mlsanity.com/paper/2401.05403/) (2023)
- [Simulation-based Validation for Autonomous Driving Systems](https://mlsanity.com/paper/2301.03941/) (2023)
- [SUSTechGAN: Image Generation for Object Detection in Adverse Conditions of Autonomous Driving](https://mlsanity.com/paper/2408.01430/) (2024)
- [Specification and Validation of Autonomous Driving Systems: A Multilevel Semantic Framework](https://mlsanity.com/paper/2109.06478/) (2021)
- [DIR-BHRNet: A Lightweight Network for Real-time Vision-based Multi-person Pose Estimation on Smartphones](https://mlsanity.com/paper/2407.13777/) (2024)

## Built on by (most cited first, 1 of 1)

- [SG-CADVLM: A Context-Aware Decoding Powered Vision Language Model for Safety-Critical Scenario Generation](https://mlsanity.com/paper/2601.18442/) (2026)

## Abstract (arXiv)

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.