Highlights A game theory-based mandatory lane-changing model in a connected environment. The model was calibrated using data from traditional and connected environments. The model was rigorously tested using a variety of performance indicators. The model outperformed the existing game theory-based lane-changing models. Behavioural soundness and consistency of the model was confirmed.

    Abstract The connected environment provides real-time information about surrounding traffic; such information can be helpful in complex driving manoeuvres, such as lane-changing, that require information about surrounding vehicles. Lane-changing modelling in the connected environment has so far received little attention. This is due to the novelty of connected environment, and the consequent scarcity of data. A behaviourally sound lane-changing model is not even available for the traditional environment; that is, an environment without driving aids. To address this need, this study develops a game theory-based mandatory lane-changing model (AZHW model) for the traditional environment and extends it for the connected environment. The CARRS-Q advanced driving simulator is used to collect high-quality vehicle trajectory data for the connected environment. The developed models (for traditional environment and connected environment) are calibrated using NGSIM and simulator data in a bi-level calibration framework. The performance of the models has been rigorously evaluated using various performance indicators. These include the true positive, false positive, detection rate, false alarm rate, time prediction error, and location prediction error. Results consistently show that the developed game theory-based models can effectively capture mandatory lane-changing decisions with a high degree of accuracy. Furthermore, the performance of the developed AZHW models is compared with representative game theory-based lane-changing models in the literature. The comparative analysis reveals that the AZHW models developed in this study outperform existing models.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A game theory-based approach for modelling mandatory lane-changing behaviour in a connected environment


    Beteiligte:
    Ali, Yasir (Autor:in) / Zheng, Zuduo (Autor:in) / Haque, Md. Mazharul (Autor:in) / Wang, Meng (Autor:in)


    Erscheinungsdatum :

    2019-07-12


    Format / Umfang :

    23 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Efficient Mandatory Lane Changing of Connected and Autonomous Vehicles

    Lin, Shang-Chien / Kung, Chia-Chu / Lin, Lee et al. | IEEE | 2021


    Modeling lane-changing behavior in a connected environment: A game theory approach

    Talebpour, Alireza / Mahmassani, Hani S. / Hamdar, Samer H. | Elsevier | 2015



    Understanding the discretionary lane-changing behaviour in the connected environment

    Ali, Yasir / Zheng, Zuduo / Mazharul Haque, Md. et al. | Elsevier | 2020


    Deep Reinforcement Learning Approach for Automated Vehicle Mandatory Lane Changing

    Ammourah, Rami / Talebpour, Alireza | Transportation Research Record | 2022