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

TL;DR

Chat2Scenario leverages large language models to extract and convert driving scenarios from datasets into standardized formats, streamlining validation of Automated Driving Systems with an efficient, NLP-based approach.

Contribution

The paper introduces Chat2Scenario, a novel NLP framework utilizing LLMs to extract and convert driving scenarios from datasets into standardized simulation formats.

Findings

Efficient scenario extraction from datasets using LLMs.

Conversion of scenarios into ASAM OpenSCENARIO and IPG CarMaker formats.

Validated approach through simulations.

Abstract

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.