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 OpenSCENARIO1 and IPG CarMaker text files2. 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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Contributors:
    Zhao, Yongqi (author) / Xiao, Wenbo (author) / Mihalj, Tomislav (author) / Hu, Jia (author) / Eichberger, Arno (author)


    Publication date :

    2024-06-02


    Size :

    3444334 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Scenario Extraction from a Large Real-World Dataset for the Assessment of Automated Vehicles

    Guo, Detian / Sanchez, Manuel Munoz / de Gelder, Erwin et al. | IEEE | 2023


    SceNDD: A Scenario-based Naturalistic Driving Dataset

    Prabu, Avinash / Ranjan, Nitya / Li, Lingxi et al. | IEEE | 2022


    ScenarioQA: Evaluating Test Scenario Reasoning Capabilities of Large Language Models

    Sinha, Shreya / Paranjape, Ishaan / Whitehead, Jim | IEEE | 2024



    SUPS: A Simulated Underground Parking Scenario Dataset for Autonomous Driving

    Hou, Jiawei / Chen, Qi / Cheng, Yurong et al. | IEEE | 2022