# Chat2Scenario: Scenario Extraction From Dataset Through Utilization of Large Language Model

arXiv: https://arxiv.org/abs/2404.16147 · HTML: https://mlsanity.com/paper/2404.16147/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2024.

## Built on by (most cited first, 14 of 14)

- [Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis](https://mlsanity.com/paper/2506.11526/) (2025)
- [AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework](https://mlsanity.com/paper/2507.13729/) (2025)
- [Survey of GenAI for Automotive Software Development: From Requirements to Executable Code](https://mlsanity.com/paper/2507.15025/) (2025)
- [Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles](https://mlsanity.com/paper/2508.14527/) (2025)
- [HAD-Gen: Human-like and Diverse Driving Behavior Modeling for Controllable Scenario Generation](https://mlsanity.com/paper/2503.15049/) (2025)
- [TrafficGamer: Reliable and Flexible Traffic Simulation for Safety-Critical Scenarios with Game-Theoretic Oracles](https://mlsanity.com/paper/2408.15538/) (2024)
- [Towards Benchmarking and Assessing the Safety and Robustness of Autonomous Driving on Safety-critical Scenarios](https://mlsanity.com/paper/2503.23708/) (2025)
- [Why Braking? Scenario Extraction and Reasoning Utilizing LLM](https://mlsanity.com/paper/2507.15874/) (2025)
- [VLM as Strategist: Adaptive Generation of Safety-critical Testing Scenarios via Guided Diffusion](https://mlsanity.com/paper/2512.02844/) (2025)
- [Efficient Cross-Country Data Acquisition Strategy for ADAS via Street-View Imagery](https://mlsanity.com/paper/2602.01836/) (2026)
- [From Driving Videos to Simulatable Scenarios](https://mlsanity.com/paper/2606.21993/) (2026)
- [A Large-Language-Model Supported Personalized Driving Framework for Lane Change in Highway Scenarios](https://mlsanity.com/paper/2606.31483/) (2026)
- [End-to-end Conditional Diffusion for Realistic and Controllable Visual Traffic Scenario Generation](https://mlsanity.com/paper/2607.18637/) (2026)
- [Think Inside the Chunk: RegulaRAG for Regulation-Compliant Scenario Generation using LLMs: A Case Study of UN Regulation No. 152](https://mlsanity.com/paper/2608.16394/) (2026)

## Abstract (arXiv)

The advent of Large Language Models (LLM) provides new insights to validate Automated Driving Systems (ADS). In the herein-introduced work, a novel approach to extracting scenarios from naturalistic driving datasets is presented. A framework called Chat2Scenario is proposed leveraging the advanced Natural Language Processing (NLP) capabilities of LLM to understand and identify different driving scenarios. By inputting descriptive texts of driving conditions and specifying the criticality metric thresholds, the framework efficiently searches for desired scenarios and converts them into ASAM OpenSCENARIO and IPG CarMaker text files. This methodology streamlines the scenario extraction process and enhances efficiency. Simulations are executed to validate the efficiency of the approach. The framework is presented based on a user-friendly web app and is accessible via the following link: https://github.com/ftgTUGraz/Chat2Scenario.