This paper proposes an intelligent car testing and evaluation method based on digital twins, which is crucial for ensuring the proper functioning of autonomous driving systems. This method utilizes digital twin testing technology to effectively map and integrate real vehicles in real-world testing scenarios with virtual test environments. By enriching the testing and validation environment for smart cars, this approach improves testing efficiency and reduces costs. This study connects real test vehicles with simulation software testing toolchains to build a digital twin autonomous driving testing platform. This platform facilitates the validation, testing, and evaluation of functional algorithms, and case study is conducted through testing and validation of an emergency collision avoidance system. By rapidly applying digital twin testing and evaluation techniques for intelligent cars, this approach accelerates the development and deployment of autonomous vehicles.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Digital Twin Test Method for Autonomous Vehicles Based on PanoSim


    Additional title:

    Sae Technical Papers


    Contributors:
    Jianyu, Duan (author) / Ding, Juan (author) / Deng, Weiwen (author) / Wang, Ying (author)

    Conference:

    SAE 2023 Intelligent and Connected Vehicles Symposium ; 2023



    Publication date :

    2023-12-20




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    Demo:Research on test method of autonomous driving based on digital twin

    Ge, Yuming / Wang, Yang / Yu, Rundong et al. | IEEE | 2019


    Digital twin for an autonomous vehicle

    European Patent Office | 2021

    Free access

    DIGITAL TWIN FOR AN AUTONOMOUS VEHICLE

    RIDER CONRAD / HAMILTON KELVIN | European Patent Office | 2023

    Free access

    Digital twin for an autonomous vehicle

    CONRAD RIDER / KELVIN HAMILTON | European Patent Office | 2023

    Free access

    Twin Scenarios Establishment for Autonomous Vehicle Digital Twin Empowered SOTIF Assessment

    Hou, Zhonglin / Wang, Shouwei / Liu, Hong et al. | IEEE | 2024