Interactive scenarios are of great significance for the testing of high-level automated vehicles. Based on the structure of Generative Adversarial Network (GAN), the Interactive Trajectories GAN model (ITGAN) is proposed in this paper. This research comprehensively considers both of the dynamic interactions between agents and the responses of agents to the static road environment. ITGAN consists of a specially designed pair of generator and discriminator, which are trained iteratively in an adversarial manner. As a data-driven agent model, ITGAN has the ability to generate agent interactive trajectories just like those from the real world. In order to verify the utility of ITGAN, experiments are conducted with the co-simulation framework of CARLA and SUMO, where ITGAN is implemented as the external agent control model to help generate interactive driving scenarios on various types of urban roads. By the qualitative and quantitative analysis of the experiment results, it shows that ITGAN can produce more effective interactive scenarios compared with the traditional agent model and has great potential to supplement the deficiencies of existing testing methods for high-level automated vehicles.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    ITGAN: An Interactive Trajectories Generative Adversarial Network Model for Automated Driving Scenario Generation


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Liao, Zeguang (author) / Cheng, Han (author) / Wang, Xuan (author) / Tao, Xin (author) / Zhang, Yihuan (author) / Dai, Yifan (author) / Li, Keqiang (author)

    Conference:

    Society of Automotive Engineers (SAE)-China Congress ; 2022 ; Shanghai, China November 22, 2022 - November 24, 2022



    Publication date :

    2023-04-29


    Size :

    13 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





    Generation of Driving Scenario Trajectories with Generative Adversarial Networks

    Demetriou, Andreas / Allsvag, Henrik / Rahrovani, Sadegh et al. | IEEE | 2020


    Driving Scenario Trajectories

    Demetriou, Andreas | DataCite | 2024


    Generative adversarial network enriched driving simulation

    SONG HAO / PENG JUN / DENG NENGXIU et al. | European Patent Office | 2022

    Free access

    Generative adversarial network enriched driving simulation

    SONG HAO / PENG JUN / DENG NENGXIU et al. | European Patent Office | 2023

    Free access