Autonomous vehicles (AVs) have the potential to significantly revolutionize society by providing a secure and efficient mode of transportation. Recent years have witnessed notable advancements in autonomous driving perception and prediction, but the challenge of validating the performance of AVs remains largely unresolved. Data-driven microscopic traffic simulation has become an important tool for autonomous driving testing due to 1) availability of high-fidelity traffic data; 2) its advantages of enabling large-scale testing and scenario reproducibility; and 3) its potential in reactive and realistic traffic simulation. However, a comprehensive review of this topic is currently lacking. This paper aims to fill this gap by summarizing relevant studies. The primary objective of this paper is to review current research efforts and provide a futuristic perspective that will benefit future developments in the field. It introduces the general issues of data-driven traffic simulation and outlines key concepts and terms. After overviewing traffic simulation, various datasets and evaluation metrics commonly used are reviewed. The paper then offers a comprehensive evaluation of imitation learning, reinforcement learning, deep generative and deep learning methods, summarizing each and analyzing their advantages and disadvantages in detail. Moreover, it evaluates the state-of-the-art, existing challenges, and future research directions.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Data-Driven Traffic Simulation: A Comprehensive Review


    Contributors:
    Chen, Di (author) / Zhu, Meixin (author) / Yang, Hao (author) / Wang, Xuesong (author) / Wang, Yinhai (author)

    Published in:

    Publication date :

    2024-04-01


    Size :

    5886192 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Traffic simulation evaluation system driven by multi-source data

    LI YI / CHEN JIANJUN / LUO LING et al. | European Patent Office | 2021

    Free access

    Real‐time data‐driven traffic simulation for performance measure estimation

    Henclewood, Dwayne / Suh, Wonho / Guin, Angshuman et al. | Wiley | 2016

    Free access

    Real-time data-driven traffic simulation for performance measure estimation

    Henclewood, Dwayne / Suh, Wonho / Guin, Angshuman et al. | IET | 2016

    Free access

    A Data-driven Model for Lane-changing in Traffic Simulation

    Bi, Huikun / Mao, Tianlu / Wang, Zhaoqi et al. | DataCite | 2016